Capital Investment Measures, Future Earnings and Future Returns: The UK Evidence

Transcription

1 Capial Invesmen Measures, Fuure Earnings and Fuure Reurns: The UK Evidence Nikola Perovic 1 School of Economics, Finance and Managemen, Universiy of Brisol Suar Manson Essex Business School, Universiy of Essex Jerry Coakley Essex Business School, Universiy of Essex This version: December 2010 Absrac: We examine he effec of various measures of capial invesmens used in previous lieraure on fuure sock reurns and profiabiliy using UK daa in period. Using porfolio and regression ess, we find ha all aggregae measures of capial invesmens are negaively relaed o fuure abnormal share reurns. The individual iems ha drive his relaion are mainly depreciaion and non-recurring iems. The similar paern is refleced in he profiabiliy ess. The observed relaion beween capial invesmens and fuure reurns and profiabiliy is consisen wih he US evidence. Depreciaion and non-recurring iems predic fuure reurns in crosssecion afer conrolling for employee growh, operaing profiabiliy, acquisiion, disposal and exernal financing aciviies and afer adjusing for indusry-median levels of capial invesmen. There are no differences in he srengh of he esed relaion across he levels of agency coss measured by he magniude of free cash flows, leverage, size and research and developmen expendiures. The underlying sources of he UK resuls are mosly consisen wih he mispricing of low reliabiliy and accouning measuremen error of capial invesmens and/or wih he pricing of operaing leverage proxied by depreciaion. The pure invesmen measures, such as capial expendiures, seem o add lile o he predicabiliy of sock reurns and profiabiliy. 1 Corresponding auhor. Tel: +44 (0) [email protected]. We are graeful o Nuno Soares for providing us wih LSPD daa o calculae delising reurns. We would like o hank o Collin Clubb, Hardy Thomas and paricipans a he 2009 BAA Conference, he 2010 Financial Reporing and Business Conference, Universiy of Brisol and Universiy of Swansea seminars for heir insighful commens and suggesions. Nikola graefully acknowledges he financial suppor provided by he UK Overseas Research Sudenship Award Scheme and he Essex Business School a he Universiy of Essex for a PhD scholarship. 1

2 1. Inroducion In his paper, we sudy he effec of capial invesmen on fuure profiabiliy and sock reurns in he UK marke. We use a range of measures sudied in previous accouning and finance lieraure and conrol for heir ineracions in order o shed he ligh on possible differen sources of predicabiliy of reurns. While our main findings are consisen wih he comparaive US evidence, our paper highlighs somewha differen channels, mainly operaing leverage and he effec of ransiory iems. The empirical evidence ha shows ha he amoun of companies inernal capial invesmen is inversely relaed o fuure sock reurns. 2 Raional explanaions argue ha an increase in capial invesmen is a response o he decreasing expeced discoun raes (e.g. Chen, Novy-Marx and Zhang, 2010). Behavioural explanaions are based on he invesors overreacion eiher o he managemen empire building (Timan, Wei and Xie, 2004) or o an effor of he managemen o exend he overvaluaion of company by signalling good prospecs by increased invesmen (Polk and Sapienza, 2009). From he accouning perspecive, invesors may overreac o he changes in curren performance caused by unreliable, ransiory changes in long-erm operaing asses (Richardson e al., 2005). In addiion, ceeris paribus, he evidence also shows ha he higher capial invesmen leads o lower fuure accouning rae of reurn, which could be jusified by he similar reasoning. 3 Ye, he empirical ess of invesmen relaions o fuure reurns and profiabiliy are join ess of he relaion iself and he hypohesis ha he invesmen measure is 2 See for example, Fairfield, Whisenan and Yohn (2003), Timan, Wei and Xie (2004), Lyandres, Sun and Zhang (2008), Zhang, Wu and Zhang (2008) and Anderson and Garcia-Feijoo (2006). See also Cochrane (1996), Lamon (2000), Li (2004), Fama and French (2006), Cooper, Gulen and Schill (2008), Wei and Xie (2008), Xing (2008) and Polk and Sapienza (2009). 3 See for example, Fairfield, Whisenan and Yohn (2003) and Richardson e al. (2005). 2

3 valid. Given ha he accouning iems are used o measure invesmens, finance-based sudies overlook poenial measuremen error in he underlying accouning consrucs, while accouning-based sudies ignore he underlying invesmen fundamenals (Dechow, Ge and Schrand, 2010). To address his issue, we employ a range of accouning-based invesmen measures used in he previous lieraure and assess various reasons, no only relaed o invesmen and accouning measuremen error, bu also o operaing leverage and persisence of ransiory iems, of why hese measures may predic fuure reurns. To link aggregae accouning-based measures of invesmen wih individual measures ypically used in he finance lieraure, we follow and build on he approach of Richardson e al. (2005) by gradually decomposing change in non-curren operaing asses as he broades indicaor of capial invesmen. These are: change in ne non-curren operaing asse, change in non-curren operaing asses and change in propery, plan and equipmen (PPE). We finally decompose change in PPE in capial expendiure, depreciaion and oher changes, and capial expendiure less depreciaion and oher changes. Exending he reliabiliy and correlaion wih invesmen assessmen of Richardson e al. (2005) and Wu, Zhang and Zhang (2010), we argue ha while all he aggregae and individual componens are expeced o have a high exen of measuremen error and are highly correlaed wih invesmen, his is no necessarily he case wih is individual componens. Depreciaion is no an invesmen measure and we validae hese assessmens by confirming ha depreciaion is he variable leas relaed o fuure shor- and long-erm changes in earnings and sales. Insead, depreciaion migh be priced as a manifesaion of operaing leverage. Similarly, oher changes in PPE conain some ransiory iems, such as impairmen of 3

4 PPE and book value of disposed asses, which may be inversely relaed o fuure reurns and profiabiliy. We perform our reurn and profiabiliy ess using UK daa for he accouning periods from 1990 o Invesors seem o misprice he consequences of aggregae measures of capial invesmen as well as changes in propery, plan and equipmen. In our univariae porfolio ess he rading sraegy based on buying (shoring) firms in he lowes (highes) quinile for hese measures earn beween 6 and 6.7% per year. Conrolling for he effec of oher relaed accouning variables and he reurn momenum and reversal effec in he regression ess does no change hese inferences. We find ha he profiabiliy one-year ahead is also inversely relaed o he aggregae measures of capial invesmens as well as o changes in propery, plan and equipmen. These resuls are consisen wih he exising evidence for he US marke. However, i appears ha his negaive relaion beween differen levels of capial invesmen and fuure profiabiliy and reurns is driven by he measure leas relaed o pure invesmen depreciaion. Porfolio ess show ha he abnormal reurn spread beween low and high depreciaion quinile is 6.7%, in line wih more aggregae measures. Is effec is higher han he reurns for oher changes in PPE (5.3%) and NEWINV (4.5%), while capial expendiures are no able o predic fuure reurns (in conras o US evidence). Tess from combined, non-overlap and condiional sraegies confirm his predicion pecking order as well as reurn regression ess. Resuls for fuure profiabiliy are consisen wih reurn ess. Overall, hese resuls indicae ha real invesmen per se is much weaker predicor han he informaion in capial invesmen measures relaed o reliabiliy, operaing leverage and persisence of ransiory iems. These conjecures are corroboraed by furher ess. The predicabiliy of reurns and profiabiliy of capial invesmen 4

5 measures does no vary across measures of agency coss of free cash flows (free cash flows and leverage) and agency coss relaed o he likelihood of caering and informaion asymmery (size and research and developmen expendiures), in conras o he US evidence. In addiion, employee growh, acquisiion, disposals and exernal financing do no subsanially reduce he predicive abiliy of he accouning-based invesmen measures casing doub on he growh explanaions. On he oher hand, when conrolling for variaions in capial invesmen measures across indusries, he sensiiviy of reurns o DD is reduced by more han 25%, while he oher coefficiens remain similar, consisen wih he inerpreaion ha DD may capure operaing flexibiliy due o various levels of capial inensiy ha varies across indusries and is relaed o fuure reurns. We conribue o he lieraure by highlighing wo oher possible channels from he decomposiion of aggregae accrual invesmen-relaed measures. Richardson e el. (2005) look a he reliabiliy issues down o change in ne curren operaing asses. Wu, Zhang and Zhang (2010) add he correlaion wih invesmen angle o he same decomposiion. We go furher and decompose change in ne curren operaing asses, and more revealingly, change in propery plan and equipmen, whose depreciaion and oher changes componen are linked o he informaion relaed o operaing flexibiliy and ransiory iems. While we are no able o precisely ease-ou and quanify he effec of each of provided explanaions for invesor s pricing of accouning measures of invesmen, our conribuion is o highligh ha various facors play a role and ha growh and/or accouning measuremen error are unlikely o be he only causes. In paricular, we cauion agains using changes in propery, plan and equipmen as he proxy for growh wihou considering oher informaion effecs i conains. 5

6 This is also one of he firs sudies o provide deailed evidence on he relaion beween depreciaion and reurns, hrough is link o operaing leverage. Thomas and Zhang (2002) provide some evidence in one of heir regressions, bu depreciaion is used only as a conrol variable. Novy-Marx (2010) and Gulen, Xing and Zhang (2008) find ha cross-secion of expeced reurns is associaed wih he degree of operaing leverage measured as he raio of cos of sales and general expenses-o-sales, hreeyear moving average of percenage change in operaing income before depreciaion relaive o percenage change in sales and raio of gross PPE o oal asses. However, hey do no consider he depreciaion as he indicaor of operaing leverage. While he lieraure focuses separaely on issues of operaing leverage and invesmen, hough he heories based on he q-heory of invesmen recognise ha here is a link beween hem (e.g. Li, Livdan and Zhang, 2009 and and Wu, Zhang and Zhang, 2010). Our paper shows ha accouning variables use as he real invesmen proxies are direcly relae o operaing leverage as well, beside he recognised link wih he accouning reliabiliy. Finally, o our knowledge, his is he firs sudy ha comprehensively explores he relaion beween various accouning-based capial invesmen measures and fuure reurns/profiabiliy in he UK conex. Previous sudies focused on he pricing of working capial accruals eiher in inernaional conex (e.g. Pincus, Rajgopal, and Venkaachalam, 2007) or by providing deailed evidence for he UK (Soares and Sark, 2009) bu none has looked a pricing and persisence of long-erm operaing accruals. The ou-of-sample ess should help he exernal validaion of resuls found in he US marke. The remainder of he paper proceeds as follows. In secion 2, we provide lieraure review and develop our hypoheses. Secion 3 discusses he daa and provides he 6

7 resuls of for he validaion ess of correlaion wih invesmens assessmen. Secion 4 presens he findings from empirical ess. Secion 5 concludes. 2. Lieraure review and hypoheses developmen 2.1 The link beween capial invesmens and fuure sock reurns There is growing empirical evidence ha he amoun of companies inernal capial invesmen is inversely relaed o fuure sock reurns. Finance sudies have explored he predicive abiliy of invesmen for fuure reurns eiher from risk-based or from behavioural-based perspecives. Risk-based sudies follow he q-heory of invesmen (Tobin, 1969; Hayashi, 1982) supplemened by he assumpion of a ime varying-discoun rae. The summary of he main argumen is given in Li, Livdan and Zhang (2009) and Wu, Zhang and Zhang (2010). The inverse relaion beween expeced reurn and invesmen is driven hrough wo possible channels: effec of decreasing reurns o scale (expeced cash flows channel) and capial adjusmen coss (discoun rae channel). In equilibrium, marginal cos of capial (discoun rae) should be equal o marginal reurns on capial. Firms will inves in new projecs unil he marginal reurn on capial equals discoun rae. If marginal reurns on capial are diminishing, he increase in invesmen will be associaed wih lower fuure discoun raes. In oher words, for a given discoun rae firms will inves in new projecs unil he presen value of expeced fuure cash flows equals iniial invesmen (zero NPV projec). In ha case, marginal q (he raio of he inrinsic value of capial o is replacemen cos) is equal o discoun rae. Consequenly, invesmen is inversely associaed wih expeced reurns. Even in he presence of consan reurns o scale, due o capial adjusmen coss invesmens respond negaively o changes in expeced discoun rae (Cochrane, 1991; 7

8 Bazdresch, Belo and Lin, 2009). Capial adjusmen coss are he deadweigh coss no relaed o value creaion. They include planning and insallaion coss, learning he use of new equipmen and he coss due o emporary inerrupion in producion (Bazdresch, Belo and Lin, 2009) and hese coss are usually assumed o be increasing and convex in he level of asses in place. Therefore, a fall in discoun raes has o compensae for he disproporionae increase in adjusmen cos as invesmen increase. Inuiively, given he expeced fuure cash flows adjusmen coss are a consrain for he invesmen under consan reurn o scale and for invesmen o be posiive ne presen value projec, discoun rae has o decrease. Cochrane (1991) esablished his resul and documened i in aggregae ime-series daa. Cochrane (1996) provides evidence ha he invesmen-based asse pricing model is able o explain he variaion in he cross-secion of reurns as well as he sandard CAPM. Li, Livdan and Zhang (2007) and Liu, Whied and Zhang (2007) use he insighs from Cochrane (1991) o explain he exernal financing anomaly, book-omarke anomaly and pos-earnings announcemen drif. Wu, Zhang and Zhang (2010) exend his line of argumen o explain he negaive associaion of working capial accruals ne of depreciaion and fuure reurns ( accrual anomaly ). 4 Behavioural explanaions of he inverse relaion beween invesmen and fuure sock reurns are based on he noion ha markes fail o accuraely accoun for he capial invesmen in negaive ne presen value projecs. If invesors rea hese invesmens as any oher opimal invesmen, hey will be subsequenly disappoined wih he firm s operaing performance which leads o negaive sock reurns. For example, markes may underreac o negaive presen value invesmens arising from 4 Beside he inverse invesmen/reurn relaion, insighs from he producion-based asse pricing are used o explain value premium. For he heoreical reamen see Berk, Green and Naik (1999), Gomes, Kogan and Zhang (2003), Carlson, Fisher and Giammarino (2004), Zhang (2005) and Cooper (2006). For he empirical ess see Anderson and Garcia-Feijoo (2006) and Xing (2008). 8

9 he empire building incenives of managers (Jensen, 1986), he explanaion advanced by Timan, Wei and Xie (2004) and Li (2004). Consisen wih his explanaion, hey find ha he negaive abnormal capial invesmen/fuure reurns relaion is sronges when he likelihood of invesmen in empire building projecs is highes for firms wih high cash flows and low leverage. Jensen (2005) and Polk and Sapienza (2009) argue ha he managers of firms wih ample cash or deb capaciy whose equiy is overvalued have incenives o inves even in he absence of invesmen opporuniies in order o prolong he overvaluaion. Similarly, hey may forego posiive ne presen value projecs in order o cu shor he perceived undervaluaion. These incenives are sronger for firms whose equiy holders have shor horizon and whose equiy is more difficul o value. Consisen wih his argumen, Polk and Sapienza (2009) find he inverse invesmen/fuure reurn effec is sronger for firms wih high research and developmen expendiure inensiy (proxy for uncerainy abou he rue equiy value) and for firms wih high share urnover (proxy for shor-erm invesors). In addiion o hese invesmen decision-based explanaions of he link beween capial invesmen, fuure profiabiliy and fuure reurns, Richardson e al. (2005) offer an explanaion based on he financial reporing choices of managemen. The empirical evidence on he capial invesmen-fuure sock reurns relaion is ypically based on some form of accouning iems (capial expendiure, changes in propery, plan and equipmen, changes in ne non-curren operaing asses). They argue ha he esimaes of non-curren operaing asses and propery, plan and equipmen as a dominan componen of non-curren operaing asses, in paricular, are no very reliable. Their esimaion involves various subjecive accouning judgemens abou wheher o recognise he cash oulay as an asse or o expense i in he income 9

10 saemen, how o deermine is useful life, depreciaion schedule, and esimaion of fuure of cash flows for he purpose of impairmen review once he asse has been recognised, ec. The resuling measuremen error, regardless of moivaion (deliberae or uninenional), leads o reversal in fuure earnings. In oher words, firms which repor high invesmen (disinvesmen) will have lower (higher) fuure earnings, conrolling for curren earnings. If invesors fail o undersand he consequences of curren reporing choices for fuure profiabiliy, heir expecaions of fuure profis will be biased upwards and will resul in a reversal of sock reurns when he magniude of he measuremen error is revealed. Finally, his capial invesmen effec may be closely relaed o accrual anomaly effec. The accrual anomaly appears o be driven mosly by changes in invenory (Thomas and Zhang, 2002; Zach, 2003; Chan e al., 2006; Gu and Jain, 2006). A build-up of invenory can be regarded as a form of invesmen so he wo effecs could happen simulaneously. However, he evidence also documens ha capial invesmen s abiliy o predic fuure reurns is incremenal o ha of he working capial accruals (Thomas and Zhang, 2002; Wei and Xie, 2008). By conras, Wu, Zhang and Zhang (2010) argue ha he accrual anomaly is jus a manifesaion of he invesmen effec. However, hey define invesmen as he sum of changes in invenories and propery, plan and equipmen hus mixing wo effecs ogeher. Irrespecive of he explanaions, he consisen evidence on he abiliy of capial invesmen o inversely predic fuure reurns emerges from he lieraure and which we es for he UK: H1: Capial invesmens are inversely relaed o fuure sock reurns. 10

11 2.2 The link beween capial invesmens and operaing profiabiliy In addiion, ceeris paribus, higher capial invesmen can lead o lower fuure accouning rae of reurn. 5 To he exen ha sock reurns are correlaed wih accouning earnings, boh risk-based and behavioural approaches also imply ha he increase in invesmen may be negaively relaed o fuure profis. Moreover, as he projec wih he highes inernal rae of reurn are usually underaken firs, diminishing marginal reurns o new invesmens should resul in he lower average fuure profiabiliy (Fairfield, Whisenan and Yohn, 2003 and Wu, Zhang and Zhang, 2010) However, he only empirical sudy ha ess for his relaion in he spiri of hese heories is Li (2004) who finds ha he negaive relaion beween curren capial invesmen and fuure operaing profiabiliy is sronger in firms wih high free cash flows and low deb, consisen wih he empire building explanaion. Richardson e al. (2005) find ha he changes in ne non-curren asses are negaively relaed o fuure profiabiliy, conrolling for curren profiabiliy, and ha he srengh of ha relaion is similar o he relaion of working capial accruals o fuure profiabiliy. We es ha hypohesis for he UK marke. H2: Capial invesmens are inversely relaed o fuure profiabiliy, conrolling for curren profiabiliy. 2.3 Informaion in he decomposed measures of aggregae invesmen and explanaions of he invesmens pricing Various accouning iems of differen level of aggregaion have been used in he previous lieraure as he proxy for capial invesmens. Capial expendiure and changes in propery plan and equipmen (PPE) are he mos common measure used in 5 See for example, Fairfield, Whisenan and Yohn (2003) and Richardson e al. (2005). 11

12 he finance lieraure. 6 Accouning researchers have used aggregae accrual measures of growh. Accruals refer o all he non-cash income and expense, valuaions and allocaions and cash inflows and ouflows deferred in he balance shee. Typically, his srand of research has disinguished beween growh in working capial and growh in non-curren operaing asses o examine heir differenial effec on fuure profis and reurns. This aggregae measure is usually refers o changes in ne non-curren operaing asses (Fairfield, Whisenan and Yohn, 2003; Li, 2004; Richardson e al., 2005), where depreciaion of propery, plan and equipmen is eiher included (Richardson e al., 2005) or no included (Fairfield, Whisenan and Yohn, 2003) in his measure of capial invesmen. To provide a comprehensive review and explore he links beween invesmen variables a various levels of aggregaion, we follow decomposiion as in Richardson e al. (2005). 7 We go one sep forward and also decompose changes in PPE. Various measures carry differen informaion and he decomposiion may reveal he acual sources of pricing and help in disinguishing beween various explanaions of he capial invesmen/reurn relaionship. The aggregae measure of invesmen is change in ne operaing asses (ΔNOA). I is calculaed as he difference beween operaing asses and operaing liabiliies where 6 Capial expendiure is used, for example, in Fazzari, Hubbard and Peersen (1988), Kaplan and Zingales (1997), Minon and Schrand (1999), Timan, Wei and Xie (2004), Anderson and Garcia- Feijoo (2006), Wei and Xie (2008), Xing (2008) and Polk and Sapienza (2009). Changes in PPE is used in Bazdresch, Belo and Lin, Thomas and Zhang (2002) and Li (2004). Richardson (2006) measures gross invesmen as he sum of capial expendiure, acquisiions and research and developmen expendiure less sales of propery, plan and equipmen ne of replacemen coss (sum of depreciaion and amorisaion. Wu, Zhang and Zhang (2010) and Lyandres, Sun and Zhang (2008) use he sum of changes in propery, plan and equipmen and changes in invenories. Some oher sudies resric heir measure o capial expendiure ne of depreciaion (e.g. Lang, Ofek and Sulz, 1996). 7 We do no include measures of research and developmen expendiure in invesmen given ha his iem is no an accrual iem and is inclusion would, herefore, blur one of he possible explanaions behind he relaion beween invesmen and fuure profiabiliy and reurns. Also, we do no include invenories in he capial invesmen measure. Previous sudies provide evidence ha he accrual anomaly is mosly driven by invenories (Thomas and Zhang, 2002; Zach, 2003; Chan e al., 2006; Gu and Jain, 2006). We focus on capial invesmen and long-erm accruals so he inclusion of invenories would also blur he effec we sudy. 12

13 operaing asses are oal asses ne of cash and cash equivalens and operaing liabiliies are oal asses less ordinary and preference shares, minoriy ineres and oal deb. 8 This measure includes long-erm and shor-erm componens as follows: Level 1 iniial decomposiion: ΔNOA = ΔNNCOA + ΔWC where ΔNNCOA is he change in ne non-curren operaing asses, and ΔWC is he change in working capial. ΔWC reflecs ne curren asses (working capial) less cash and cash equivalens plus shor erm deb. Given our focus on capial invesmen we decompose ΔNNCOA as follows: Level 2 exended decomposiion: ΔNNCOA = ΔNCOA ΔNCOL where ΔNCOA is he change in non-curren operaing asses and ΔNCOL is he change in non-curren operaing liabiliies. NCOL consis of long-erm payables and various provisions such as pension benefis and deferred axes. In he nex sep, we decompose ΔNCOA: Level 3 individual iems: ΔNCOA = ΔPPE + ΔINTANG + ΔONCOA where ΔPPE is he change in propery, plan and equipmen, ΔINTANG is he change in inangible asses and ΔONCOA is he change in oher non-curren operaing asses. ONCOA may include iems such as invesmen in equiies, long-erm receivables and long-erm advance paymens. Richardson e al. (2005) esed heir reliabiliy sory by using ΔNNCOA and ΔNCOA. Wu, Zhang and Zhang (2010) argue ha hese measures, alhough suffering 8 The definiion of operaing asses used in his paper is differen from he definiion in Richardson e al. (2005). They consider shor and long erm invesmen in shares and bonds as financial asses, whereas we include hem in operaing asses. Penman (2007) argues ha if he shares are held wih he goal of long-erm profi, i is more appropriae o rea hem as any oher operaing asses han financial asses given ha i is cosly o separae operaing from financing componen of he asses held. Invesmen in associaes could be he case in poin. Fairfield, Whisenan and Yohn (2003) include Compusa iem 69 Oher long erm asses in he operaing asses. 13

14 from measuremen error, are highly correlaed wih invesmens hemselves. To prove his poin, hey find he predicive abiliy of ΔNNCOA and ΔNCOA in respec wih fuure reurns is subsanially reduced when conrolling for heir invesmen facor. However, heir invesmen facor is consised of ΔPPE plus changes in invenories, so is mechanically relaed o more aggregae measures. I is difficul o disinguish he reliabiliy and invesmen explanaions a he level of ΔPPE and higher aggregaion because he measures share he same properies. The empirical ess of invesmen relaions o fuure reurns and profiabiliy are join ess of he relaion iself and he hypohesis ha he invesmen measure is valid (Dechow, Ge and Schrand, 2010). However, decomposiion of PPE may help in his regard. We decompose PPE as follows: Level 4 decomposiion of ΔPPE: ΔPPE = CAPEXP DD + O_ΔPPE = NEWINV + O_ΔPPE where CAPEXP is capial expendiure, DD is depreciaion and depleion and O_ΔPPE are oher changes in propery, plan and equipmen. To eliminae he porion of CAPEXP relaed o replacemen of exising asses, we follow Lang, Ofek and Sulz (1996) and calculae new invesmen (NEWINV) as he difference beween CAPEXP and DD. We assign he level of reliabiliy and correlaion wih invesmen o CAPEXP, DD and O_ΔPPE, similarly o Richardson e al. (2005) and Wu, Zhang and Zhang (2010), bu also add one more dimension relaed o he persisence of profiabiliy in respec o oher properies of hese variables. CAPEXP is a purchase of new PPE. As such i is highly correlaed wih invesmen. On he oher hand, i does no disinguish beween replacemen of asses and ruly new projecs. Correlaion assessmen wih invesmen is medium/high. However, here is considerable subjeciviy in capialising 14

15 PPE. The WorldCom debacle involved capialisaion of billions of dollars and capialisaion decisions are one of he mos popular earnings managemen echniques in regard wih he expense reducion (Jones, 2010 and Dechow and Schrand, 2004). Therefore CAPEXP have low reliabiliy. DD is subjec o esimaion errors abou useful life, depreciaion mehod and residual value so i is of a low reliabiliy. Low depreciaion usually means ha here will be fuure wrie-downs hrough resrucuring charges or losses on disposals of asses. Too high depreciaion resuls in larger gains from disposals (Penman, 2007). 9 Alhough porion of depreciaion is relaed o wrie-off of curren CAPEXP, mos of i is relaed o exising asses in place. Hence, he correlaion wih invesmen is medium. Finally, depreciaion conribues o operaing leverage, i.e. higher volailiy of earnings relaive o sales. I is in general fixed coss ha responds slowly o changes in demand and reflecs capial adjusmen coss (Lev, 1983). Lev (1983) and Baginski e al. (1999) find ha he firms wih high depreciaion-o-sales raio have indeed higher volailiy. This propery may conribue o he negaive associaion of depreciaion wih fuure profiabiliy conrolling for curren profiabiliy. In addiion, operaing leverage is associaed wih higher beas (Lev, 1974; Mandelker and Rhee, 1974) and herefore higher expeced reurns. More recenly, Novy-Marx (2010) and Gulen, Xing and Zhang (2008) find ha cross-secion of expeced reurns is associaed wih he degree of operaing leverage measured as he raio of cos of sales and general expenses-o-sales, hree-year moving average of percenage change in operaing income before depreciaion relaive o percenage change in sales and raio 9 Penman (2007) repors ha he exension of useful life and changes in he assumpions for esimaed residual values of car leases accouned for $790 million of profis which hen had o be wrien off in he early 1990s hrough resrucuring charges. 15

16 of gross PPE o oal asses. Noe ha high depreciaion acually reduces ΔPPE and oher invesmen measures of higher aggregaion, so i may conribue o he observed inverse relaion of hese measures and fuure reurns. As a combinaion of CAPEXP and DD, NEWINV is assigned low reliabiliy and medium/high correlaion wih capial invesmens and medium persisence. O_ΔPPE may include addiions o PPE hrough acquisiions, revaluaions, ne book value of disposed asses, impairmen charges and foreign currency ranslaion effecs. Acquisiions are a direc form of invesmen. Book value of disposals is highly correlaed wih asses sales. On he oher hand, impairmen represens changes in value of asses in place, no new invesmen. Consequenly, we assign a medium/high correlaion beween O_ΔPPE and invesmens. Impairmen is subjec o esimaion error abou fuure cash flows and discoun raes, if value in use is he benchmark value. Because of ha, we assign low reliabiliy o i. Finally, impairmen is supposed o recognise anicipaed losses from decreases in expeced cash flows from PPE. This iem is by is naure ransiory and is expeced o reverse in fuure earnings (Basu, 1997). Indeed, Dechow and Ge (2006) find ha he accruals ha include special iems (and impairmen is one of is mos common forms) are less persisen in fuure earnings han accruals which do no include special iems. Similarly, gains/losses on disposal may reveal he measuremen error from previous periods in under/overesimaion of book value of disposed asses. This iem is usually regarded as ransiory and non-recurring. If invesors fail o undersand heir non-recurring naure, his may lead o reversal in fuure reurns. Dechow and Ge (2006) find ha he higher reurns for low accruals firms are sronger if accompanied wih special iems. Reliabiliy, correlaion and persisence assessmen wih he relaed explanaions are summarised in Table 1. 16

17 [Table 1 around here] The associaion of characerisics of componens of ΔPPE wih he fuure profiabiliy and reurns lead o following hypoheses. Given ha all componens of ΔPPE are assigned wih low reliabiliy, i is difficul o disinguish beween reliabiliy and oher alernaive explanaions. However, i is possible o assess he imporance of invesmen explanaion relaive o oher economic characerisics associaed wih persisence by exploiing differences in he assigned correlaion wih invesmen. Measures more correlaed wih invesmens (medium/high and high) and wih medium effec on persisence should dominae hose ha are less correlaed wih invesmens (medium) bu have low persisence, and vice versa. Therefore, he following wo non-direcional hypoheses can be formulaed. H3a: Invesmen is sronger negaive predicor of fuure reurns/profiabiliy han operaing leverage. Therefore, CAPEXP and NEWINV are more srongly negaively relaed o fuure reurns/profiabiliy han DD. H3b: Operaing leverage is sronger predicors of fuure reurns/profiabiliy han invesmen. Therefore, DD is more srongly negaively relaed o fuure reurns/profiabiliy han CAPEXP and NEWINV. O_ΔPPE is assigned boh medium/high correlaion wih invesmen and low persisence. Moreover, he source of is correlaion wih invesmen is hrough acquisiion and is so of differen naure han organic growh refleced in CAPEXP and NEWINV. Through book value of disposals i direcly includes disinvesmen, absen from CAPEXP/NEWINV. Therefore, if O_ΔPPE pricing effec dominaes he pricing of CAPEXP/NEWINV or DD, i is difficul o ell if i is due o acquisiion or persisence effec. However, if i is subsumed by CAPEXP/NEWINV or DD, hen he following wo hypoheses can be esed: 17

18 H4a: Operaing leverage is sronger predicor of fuure reurns/profiabiliy han low persisence of ransiory iems. Therefore, DD is more srongly negaively relaed o fuure reurns/profiabiliy han O_ΔPPE. H4b: Organic growh is sronger predicor of fuure reurns/profiabiliy han he growh hrough acquisiion and/or disposals. Therefore, CAPEXP/NEWINV is more srongly negaively relaed o fuure reurns/profiabiliy han O_ΔPPE. 2.4 Cross-secional variaions and explanaions of he pricing of capial invesmens We explore some of he cross-secional implicaions of various explanaions of pricing of invesmens. Overinvesmen explanaions also posi ha he firms wih high cash flows and low deb have he highes incenives o overinves (Jensen, 1986) which should evenually ranslae in he highes spread in reurns beween high and low invesors. Consisen wih his explanaion, Timan and Xie (2004) find ha he negaive abnormal capial invesmen/fuure reurns relaion is sronges when he likelihood of invesmen in empire building projecs is highes for firms wih high cash flows and low leverage. We es his explanaion for he UK marke. H5: The negaive relaion beween capial invesmens and fuure sock reurns and profiabiliy is sronger in firms wih high free cash flows or low leverage. Caering hypohesis posi ha he overinvesmen and subsequen reurn reversal is sronger for firms wih more opaque informaion environmen. Smaller firms are deemed o be riskier o arbirage, less liquid, followed by smaller number of analyss and more opaque. Consequenly, hese firms should warran higher reurns. Similarly, larger firms are usually more sable and wih higher marke power which makes hem more profiable. Following Polk and Sapienza (2009) we also look a he research and developmen expendiures. Firms wih high research and developmen expendiures 18

19 have more uncerain fuure prospecs condiional on he oucome of he projecs. Consequenly, hey are more difficul o value, informaion asymmery is higher and incenives for caering higher. H6: The negaive relaion beween capial invesmens and fuure sock reurns and profiabiliy is sronger in smaller firms and firms wih high research and developmen expendiure. 3. Sample and variable measuremen 3.1 Sample The sample consiss of non-financial firm lised on he London Sock Exchange in he period. The financial saemen daa are obained from Worldscope daabase accessed hrough Daasream and reurns daa are obained from Daasream. To be included in he sample, firms need o have he available informaion on all capial invesmen measures we use in he analysis and oher componens of accruals. They also need o have available daa on marke value of equiy, book-o-marke raio and raw sock reurns needed o calculaed abnormal reurns in a one-year period afer porfolio formaion as well as he daa on free cash flows and leverage. Because of he naure of he subsequen es on indusry-adjused capial invesmen measures, we also require ha here are a leas eigh observaions for every indusry-year combinaion, where indusry is one of 32 secor levels from FTSE/Dow Jones Indusry Classificaion Benchmark. Daa on fuure accouning earnings are no required for our reurn ess bu hey are required for our profiabiliy ess, which resuls in a smaller sample in our profiabiliy ess relaive o reurn ess. We exclude daa on fuure profiabiliy for year 2005 as his was he year of ransiion o IFRS. We also exclude firms wih negaive book value as well as firms wih he financial year 19

20 longer han 380 days and shorer han 350 days. The final sample for mos of he reurn ess consiss of 12,340 firm-year observaions covering he financial year-ends periods from 1990 o The smalles number of firm-year observaions is 51 in 1990 and he highes one is 892 in 1994 and There are 1,833 differen firms in he sample. 3.2 Definiion of accouning variables Table 2 presens he definiions of he accouning variables used in his sudy. [Table 2 around here] The lieraure has used a range of differen deflaors o conrol for he size effec such as propery, plan and equipmen, oal asses, lagged marke value of firm (all ypically measured a he beginning of he year) or oal sales. To be consisen wih he exising accouning lieraure, all variables are scaled by average oal asses. In our regression ess, we use operaing income scaled by he average oal asses as he measure of profiabiliy (ROA). ΔWC, ΔNCOL, ΔINTANG, ΔONCOA, CAPEXP, DD, O_ΔPPE, NEWINV, NP and ROA are winsorized a +1 and -1. These winsorized variables are added ogeher o consruc he measures of higher aggregaion: ΔPPE, ΔNCOA, ΔNNCOA and ΔNOA. 3.3 Reurn measuremen The measuremen of sock reurns begins six monhs afer he financial year-end because six monhs is he period wihin which financial saemens are required o be published in he UK. Sock reurns are calculaed inclusive of dividends using he Daasream iem Reurn Index (RI), which is defined as he heoreical growh in he value of a share holding uni of an equiy or uni rus a he closing price applicable 20

21 on he ex-dividend dae. Raw equiy reurns (r i,j ) for monh j and each firm i are calculaed as follows: r i, j RI RI i, j1 i, j 1 For he calculaion of abnormal reurns, we use he approach based on he maching reurns o he benchmark porfolio based on size (marke value of equiy) and book-omarke raio. The reason for his is ha exan research repors ha size and book-omarke seem o be priced as characerisics raher han as he facor loadings in he spiri of Fama and French (1996) model in he UK marke (Lee, Liu and Srong, 2007; Michou, Mouselli and Sark, 2007; and Gregory, Tharyan and Huang, 2009). Following Hirshleifer e al. (2004) we calculae size and book-o-marke benchmark reurns on a monhly basis using sequenial soring ino 4x4 porfolios. The calculaion of benchmark reurns is based on hose firm-year observaions wih available reurns, marke value of equiy and posiive book value of equiy daa. 10 Each monh firms are sored ino four porfolios by size (marke value of equiy Daasream iem MV). Each porfolio is hen divided ino four porfolios sored by he book-o-marke raio. Book-o-marke raio is calculaed dividing he book value of equiy (Worldscope iem 03501) by he end-of-he monh marke value of equiy six monhs afer he financial year end. The book-o-marke raio does no change for he following 12 monhs or unil he nex financial saemens book-o-value of equiy becomes available if he financial year is shorer han 12 monhs. This procedure resuls in 16 porfolios in oal. The benchmark reurn is he equally weighed average reurn for each porfolio and each monh. 10 This procedure yields a sample of 22,804 firm-year observaions, higher han our final sample. Therefore, he calculaion of abnormal reurns is based on a universe of firms more represenaive of he enire marke. 21

22 The abnormal reurn (ABNRET) is he annual buy and hold reurn calculaed as he difference beween cumulaed raw reurn (RET) and equally weighed benchmark reurns of he porfolio o which a firm belongs each monh (E(r i,j )): ABNRET (1 r ) (1 E( r )) i,j i, j i, j j1 j1 If a firm deliss during he period, hen he delising reurn is assumed as he final reurn and he proceeds are reinvesed ino he benchmark porfolio. Following Liu e al. (2003) and Soares and Sark (2009), delising reurns for he valueless liquidaed companies and companies ha wen ino adminisraion is se o -1. For all he oher firms delising because of a merger, acquisiion, going privae, ec. he las available reurn index (RI) before delising is used o calculae delising reurns. 11 For he purposes of reurn regression ess, we also calculae reurns o conrol for shor-erm reversals in reurns, price momenum and long-run reversals following Hirshleifer e al. (2004). Shor erm reversal reurns (STREVRET) are calculaed for one monh prior o he beginning of cumulaing of fuure reurns. For example, if he year end is in 31 March, hen STREVRET is he sock reurn in Sepember. Price momenum (MOMRET) is calculaed as he reurn for he period saring one year before and ending one monh before he beginning of cumulaing of fuure reurns. Long erm reurn reversals (LTREVRET) are reurns for he period saring hree years before and ending one year before he beginning of cumulaing of fuure reurns. 3.4 Descripive saisics Descripive saisics is presened in Table 3. [Table 3 around here] 11 Daa on he ype of company s deah are colleced from 2006 London Share Price Daabse (LSPD). When he firm is coded 7, 14, 16, 20 or 21 i is assumed o be valueless or wen ino adminisraion. For all he oher codes, final RI before deah is used o calculae delising reurns. 22

23 Mean (median) aggregae invesmen (ΔNOA) is posiive a (0.025) meaning ha he average growh in ne operaing asses is jus over 3% of oal average asses. This growh comes mosly from he growh in long-erm ne operaing asses (ΔNNCOA) whose mean is Conversely, on average here are no changes in is working capial (ΔWC) componen. In addiion, he variabiliy of ΔNOA of seems o be driven by a more volaile non-curren componen ΔNNCOA (sandard deviaion of 0.162) raher han by working capial componen (ΔWC) whose sandard deviaion is Furher decomposiion shows ha in erms of variabiliy, ΔNNCOA is driven mosly by changes in is asse (ΔNCOA) raher han in is liabiliy componen (ΔNCOL). The average change in liabiliy componen is close o zero, and crosssecional variabiliy is small (sandard deviaion is 0.036). The saisics for hese aggregae measures is, in general, in line wih he summary saisics presened in Richardson e al. (2005) for he US marke. Conribuion o he average change in ΔNCOA of 3.6% of oal average asses is spli beween ΔPPE of 1.9% and ΔINTANG of 1.4% wih only around 0.3% coming from ΔONCOA. Similarly, mos of he variaion in ΔNCOA is aribuable o he variaion in ΔPPE and ΔINTANG. Changes in ne propery, plan and equipmen on average consis mosly of CAPEXP (mean of 0.064) and -DD (mean of ). However, he sandard deviaion of CAPEXP (0.067) is wice as large as he sandard deviaion of DD (0.030). The hird componen of he change in ne propery, plan and equipmen O_ΔPPE is he mos variable wih he sandard deviaion of Given ha his componen includes acquisiions, impairmen charges, disposals and revaluaions which are unusual and end o be large when hey occur in each direcion, i is no surprising o find ha i is small on average bu very volaile. 23

24 The correlaion marix in Table 4 is spli ino hree panels. In Panel A, he correlaion marix for he aggregae measures of capial invesmens (Level 1 and 2) is presened. In Panel B, correlaions of ΔNCOA wih Level 3 measures are given. In Panel C, correlaions of ΔPPE wih Level 4 measures are presened. [Table 4 around here] As expeced, ΔNNCOA and ΔWC are srongly posiively correlaed wih ΔNOA (around 0.9 and 0.5, respecively). ΔNNCOA and ΔWC end o grow ogeher bu heir correlaion is small in magniude. This may indicae ha ΔWC conains srong volaile componen no relaed o permanen growh in asses bu raher o flucuaions in cash paymens and receips from purchases and sales. Growh in ne curren operaing asses (ΔNNCOA) is driven by is asse componen (ΔNCOA). The correlaion beween ΔNCOA and ΔNCOL is negaive indicaing ha a leas par of he invesmen is financed by growh in long-erm operaing liabiliies (he increase in liabiliies which are negaive accruals is associaed wih growh in asses which are posiive accruals). The asses increasing accruals are posiively correlaed wih boh measures of profiabiliy, as expeced. In Panel B, as already inferred from he descripive saisics, ΔNCOA is srongly linearly correlaed wih ΔPPE and ΔINTANG, bu in erms of rank correlaion ΔPPE dominaes ΔINTANG. The componens of changes in non-curren operaing asses (ΔPPE, ΔINTANG and ΔONCOA) are weakly posiively correlaed wih each oher. O_ΔPPE is he sronges driver of changes in ne propery, plan and equipmen (Panel C). Pearson (Spearman) correlaion coefficien beween ΔPPE and O_ΔPPE is (0.638) in comparison wih he Pearson (Spearman) correlaion coefficien beween ΔPPE and NEWINV which is (0.637). CAPEXP and DD are negaively correlaed (Pearson and Spearman coefficiens of and ) an 24

25 increase in he depreciaion expense (decrease in ne asses) and an increase in he capial expendiure (increase in ne asse) end o move ogeher. O_ΔPPE has a small negaive correlaion wih CAPEXP, DD and NEWINV. NEWINV and CAPEXP are srongly posiively correlaed and (Pearson correlaion coefficien is and Spearman correlaion coefficien is 0.774). 3.5 Validaion of he correlaion wih invesmen assessmen To validae our reliabiliy, correlaion and persisence assessmen of he Level 4 measures of capial invesmen (CAPEXP, -DD, O_ΔPPE and NEWINV), we correlae hese measures wih fuure earnings and sales changes. Zhang (2007) argues ha if accruals capure firms fundamenal invesmen informaion and if invesmen is opimal, hen fuure earnings should increase wih he increase in accruals. Conversely, if accruals are conaminaed by he measuremen error, higher accruals should lead o lower fuure earnings. In our seing, his implies ha measures o which we assign high correlaion wih invesmen should be posiively correlaed wih fuure earnings. Similarly, he measures ha indicae more persisen earnings should be more posiively correlaed wih fuure earnings. Reliabiliy, on average does no play a role since all Level 4 measures are assigned wih low reliabiliy. If he assumpion on opimal invesmen is relaxed, and invesmen is inefficien i is sill expeced o increase fuure sales (if no earnings). Since CAPEXP and NEWINV are assigned wih higher correlaion wih invesmen and wih higher persisence han DD, which is relaed o fuure sales and earnings only hrough is correlaion wih CAPEXP. As for O_ΔPPE, he invesmen effec of acquisiions and disposals on he earnings and sales increase may dominae he impairmen effec ha is supposed o capure fuure drop in sales. Therefore, CAPEXP, NEWINV and O_ΔPPE are expeced o be more posiively correlaed wih 25

26 fuure earnings changes han DD while i is no sraighforward o CAPEXP, NEWINV and O_ΔPPE. To validae hese predicions, we use porfolio ess. Each year from 1990 o 2003 we sor firms ino quiniles by one of he Level 4 componens of capial invesmen (CAPEXP, -DD, O_ΔPPE and NEWINV). We repor he ime-series mean of he fuure shor-erm and long-erm changes in earnings and sales for each quinile, and he difference beween high and low quinile ogeher wih he associaed -saisics on he disribuion of annual means. Shor-erm changes in earnings (sales) are defined as he change in operaing profi (sales) from year o year +1, scaled by he oal asses in he curren year and winsorized a +1 and -1, for earnings, and a he 1% and 99% level, for sales. However, high invesmen or dis-invesmen aciviy is correlaed wih curren very good or bad performance. As exremely good or bad performance ends o reverse in he nex period, he invesmen may no be posiively relaed o nex year s earnings or sales changes (Zhang, 2007). Second, he curren invesmen in capaciy expansion may ake few years and resul in he acual increase in sales and profis only several years ino fuure. Therefore, we also repor long-erm rends. To eliminae noisiness of he shor erm changes, nex year s change in earnings (sales) is ignored, and following Zhang (2007), long-erm changes in earnings (sales) are defined as he difference beween average earnings (sales) from year +1 o year +3 and average earnings (sales) from year -3 o year -1, scaled by he average oal asses in from year -3 o year -1, and winsorized a +1 and -1, for earnings, and a he 1% and 99% level, for sales. Resuls are repored in Table 5, in Panel A for shor-erm changes and in Panel B for long-erm changes. [Table 5 around here] 26

27 Differences in O_ΔPPE are he bes indicaor of fuure changes in sales and earnings followed by NEWINV and CAPEXP. Difference beween shor-erm (longerm) sales growh beween O_ΔPPE exreme quiniles is 17.5 (88.7) percenage poins. This spread is abou 1.5 and 2.3 imes higher han he one produced by NEWINV and CAPEXP, respecively. Similar paern is refleced in long-erm earnings changes. As expeced, CAPEXP, NEWINV and O_ ΔPPE are far beer in explaining fuure growh in earnings and sales han DD. In fac, here is no significan spread in long-erm earnings or sales changes for DD. For example, he spread in long-erm earnings changes beween high and low O_ ΔPPE quinile is 6.6 percenage poins (-sas.=8.43) while he same spread is -1.2 percenage poins (sas.=-1.31) beween high and low O_ΔPPE quinile. The difference in magniude is even more pronounced for sales growh. As for shor-erm change in earnings, he sign is he opposie of wha is expeced or saisically insignifican, bu he magniude is low. Overall, he resuls are consisen wih our assigned levels of correlaion wih invesmen. 4. Relaion beween capial invesmen measures and fuure sock reurns In his secion, we presen he resuls of our ess on he hypohesis ha firms wih high capial invesmen earn lower subsequen abnormal reurns (H1) and have lower profiabiliy conrolling for curren profiabiliy (H2). We use univariae porfolio and mulivariae regression ess for his purpose. 4.1 Porfolio ess Uncondiional ess Resuls of our porfolio ess are presened in Table 6 27

28 [Table 6 around here] Every year from 1990 o 2004 we sor firms by he aggregae and individual measures of capial invesmen as well as by oher accruals which ogeher wih capial invesmen measures gradually add up o he mos aggregae capial invesmen measure (ΔNOA) ino deciles. We presen ime-series means of ABNRET and he ime-series average of he hedge abnormal reurns from buying he firms in he lowes invesmen and accrual quinile and shor-selling firms in he highes invesmen and accrual quinile ogeher wih associaed -saisics. The number of years wih posiive hedge abnormal reurns is also repored. In Panel A resuls for he aggregae componens of he measures of capial invesmen reveal ha he hedge reurn sraegy of invesing in socks wih low ΔNOA and shor-selling socks wih high ΔNOA yields 8.8%, significan a he 5% level and posiive in 12 ou of 15 sample years. These reurns seem o be driven by boh ΔNNCOA and ΔWC. These sraegies earn similar reurns of around 6.3% and 6.6% and are posiive in 13 and 12 years, respecively. The resuls for ΔNCOA are similar o ΔNNCOA hough he consisency of posiive reurns is weaker (10 years). Therefore, we verify ha he aggregae measures of capial invesmens are inversely relaed o fuure reurns, consisen wih he US evidence hough he profiabiliy of hese sraegies in he UK seems o be slighly lower Richardson e al. (2005) find ha ΔNNCOA based sraegy size-adjused reurns are 16.5% annually and ha ΔNCOA based sraegy size-adjused reurns are 16.1% annually in he period. Wu, Zhang and Zhang (2008) repor significan raw reurns on ΔNCOA based sraegy.fairfield, Whisenan and Yohn (2003) find ha growh in long erm asses based sraegy earns 7.5% annually. Li (2004) finds hedge reurns for ΔNCOA of 13.2% per year. Zhang, Wu and Zhang (2008) arepor significan raw reurns of 8.4% for ΔNNCOA based sraegy and 9.7% for for ΔNCOA based sraegy in period. Richardson e al. (2005) find srong predicabiliy for ΔNOA sraegy, while Cooper, Gulen and Schill (2008) find srong predicabiliy for he sraegy based on changes in oal asses. The magniude of reurns on ΔWC is consisen wih oher UK and US sudies (Sloan, 1996; Xie, 2001; Thomas and Zhang, 2002; Desai, Rajgopal and Venkaachalam, 2004; Richardson e al., 2005; Chan e al., 2006; LaFond, 2006, ec.). 28

29 In Panel B, he resuls for Level 3 measures of capial invesmens are presened. The hedge reurns based on ΔPPE sraegy are saisically and economically significan 6.1%, in line wih he resuls for more aggregae measures of capial invesmens. The hedge reurns are posiive in 11 years. By conras, ΔINTANG and ΔONCOA sraegies are no profiable. Consisen wih our findings, Thomas and Zhang (2002), Li (2004), Cooper, Gulen and Schill (2008) and Bazdresch, Belo and Lin (2009) find ha changes in propery, plan and equipmen are inversely relaed o fuure reurns in he US marke. Thomas and Zhang (2002) repor hedge size-adjused reurns from ΔPPE based sraegy of 7.5% per year and Li (2004) repors abnormal reurns of 12.6%. Wu, Zhang and Zhang (2010) find ha combined changes in propery, plan and equipmen and invenories are he srong predicor of fuure reurns. In Panel C, resuls for he decomposiion of ΔPPE are presened. This able reveals some ineresing resuls. -DD emerges as he srong predicor. DD rading sraegy yields 6.7% (very similar o oher measures of capial invesmens discussed previously), and is profiable in 13 years. Hedge reurns on O_ΔPPE are significan 5.3% in 11 years, and on NEWINV are significan 4.5% in 11 years. The cleanes invesmen measure CAPEXP has no predicive power a all. The resuls for CAPEXP are no consisen wih he resuls from numerous US-based sudies. 13 By conras, Thomas and Zhang (2002) are, o our knowledge, he only ones who use depreciaion as one of he explanaory variables of fuure size-adjused reurns. Conrolling for oher accrual measures, DD is a posiive predicor in 18 ou of Polk and Sapienza (2009), Xing (2008) and Wei and Xie (2008) find ha oal CAPEXP are inversely relaed o fuure reurns. In his porfolio ess, Xing (2008) repors ha CAPEXP based sraegy earns around 5.5% per year. Wei and Xie (2008) find even larger hedge reurns on CAPEXP sraegy of around 9% per year. Timan, Wei and Xie (2004) find he abnormal reurns from he sraegy based on he deviaion of curren capial expendiures-o-sales from he pas hree year average are around 3.7% per year. 29

30 years, wih he annual decile-based hedge reurns of 3.6%, significan a he 5% level. We are no aware of any comparable US resuls ha use NEWINV as a capial invesmen measure. The resuls also demonsrae ha he magniude of hedge reurns in various measures of capial invesmen is driven by boh long and shor componen. However, he long componen is more pronounced in DD versus shor componen (4.6% long versus -2.1% shor) and in NEWINV (3.4% and -1.1%), while he opposie is he case for O_ΔPPE (1.7% and -3.6%). Figure 1 presens he ime-series of he hedge reurns from capial invesmenbased sraegies. In Panel A, hedge reurns from Level 1, 2 and 3 measures seem o be more correlaed and more volaile in and period. The similar ime-series paern is consisen wih heir high correlaions. In Panel B, O_ΔPPE and NEWINV reurns have similar paerns o he more aggregae measures in Panel A. However, he correlaion of hese reurns wih reurns from DD seems o be small. ABNRET based on DD sraegy seem he leas volaile across he sample period. CAPEXP reurns are very volaile, and ofen negaive, consisen wih he saisical insignificance in Table 6. Overall, hese resuls are consisen wih invesmens predicing fuure reurns (H1) bu is doubful wheher his is really a resul of growh informaion. While aggregae measures of invesmen do predic reurns, he sronges individual predicor -DD is he componen leas relaed o real invesmen. NEWINV is a combinaion of CAPEXP and DD so i is no clear if i is is invesmen informaion ha is relaed o fuure reurns. O_ΔPPE, as demonsraed in Table 5, is clearly relaed o invesmen hrough exernal acquisiion and disposals bu i also conains oher ransiory componens ha could be responsible for he subsequen reurn reversal. 30

31 The cleanes invesmen measure does poorly. These findings are, herefore, consisen wih H3b ha operaing leverage is more imporan reurn predicor han invesmen. Combined, nonoverlap and condiional porfolios The uncondiional univariae porfolios canno direcly conrol for he correlaion beween level 4 variables, and he effec of O_ΔPPE versus DD. To explore he relaions beween Level 4 measures of capial invesmen and he correlaion of aggregae measures wih he decomposed measures we follow Desai, Rajgopal and Venkaachalam (2004) and Dechow, Richardson and Sloan (2008), and use combined, nonoverlap and condiional reurns from bivariae porfolio analysis. Combined hedge reurns are reurns from invesing in firms whose observaions lie in low quiniles of annual disribuion of boh variables and shor-selling firms whose observaions lie in high quiniles of boh variables. If he predicive effec of he variables under examinaion is independen, we expec o find higher reurns han under individual sraegies. However, he sraegies are considered joinly and from his es i is hard o see if one sraegy dominaes he oher. Under nonoverlap sraegy, hedge porfolio consiss of long posiion in low quinile and shor posiion in high quinile, afer eliminaing firms wih join long posiion and shor posiion of he conrolling variable. For example, o address H3a, nonoverlap sraegy consis of invesing in firms wihin low NEWINV quinile, afer eliminaing he observaions ha a he same ime belong o he low DD quinile, and shor selling firms wihin high NEWINV quinile, afer eliminaing he observaions ha a he same ime belong o he high DD quinile. This es shows if he CAPEXP sraegy survives afer eliminaing observaions wihin exreme quiniles of DD. 31

32 Condiional sraegy explores wheher he variaion of he variable under examinaion predics fuure reurns, afer conrolling for he level of he oher variable. Unlike nonoverlap ess, i does no focus on he uncondiional exreme rankings of he variables. Under his sraegy, firms are firs ranked ino quiniles according o he conrolling variable, and hen ranked ino sub-quiniles of he variable whose predicabiliy we examine. Condiional quiniles are he combined sub-quiniles of ha variable. Abnormal hedge reurns are abnormal reurns of porfolio from invesing in firms in low condiional quiniles and shor-selling firms in high condiional quinile. Because his sraegy direcly conrols for he level of conrolling variable, is resuls are likely o be similar o he resuls from he mulivariae regressions. Time-series means of he ABNRET from hese sraegies, along wih he associaed -saisics are repored in Table 7. Frequency of common observaions in he exreme quiniles for he main and conrolling variable is also repored. We do so because he correlaions repored in Table 4 use he enire sample, while he predicabiliy of reurns may be concenraed in he exreme observaions ha may be shared even if he overall correlaion is small. If he variables are no correlaed, by chance we would expec ha 20% of observaions are shared wihin he same quinile. [Table 7 around here] In Panel A, we examine wheher he aggregae measures of he capial invesmen derive is reurn predicabiliy from is decomposed measure, using Levels 1, 2 and 3 decomposiion. ΔNOA shares around 65% and 71% of observaions in low and high quinile wih ΔNCNOA. However, since ΔWC is also a significan predicor of reurns, combined, nonoverlap and condiional hedge abnormal reurns show ha ΔNOA predic reurns over and above ΔNNCOA. By conras, ΔNNCOA and 32

33 ΔNCOA do no survive conrol for heir immediae decomposed measures - ΔNCOA and ΔPPE. This is no surprising given ha, consisen wih correlaion resuls, hese combined measures share large number of observaions wihin exreme porfolios and ha variabiliy of ΔNNCOA is largely explained by he variabiliy in is asse componen, while he variabiliy of ΔNCOA is well explained by variabiliy of ΔPPE. Panel B shows if ΔPPE is able o predic fuure reurns afer conrolling for CAPEXP, -DD, O_ΔPPE and NEWINV, respecively. Consisen wih he correlaion resuls in Table 4, ΔPPE shares mos of he exreme observaions wih O_ΔPPE (around 69% in low quinile and 60% in high quinile), followed by NEWINV (50% and 65%). However, while combined ΔPPE and O_ΔPPE reurns are only abou 5% (lower han for he individual sraegies) boh nonoverlap and condiional ΔPPE reurns are significan, suggesing ha oher ΔPPE componens conribue o reurn predicabiliy. Combined ΔPPE and NEWINV reurns are higher han individual reurns and condiional ΔPPE reurns are significan, bu no overlap ΔPPE reurns. Though he share of common observaions wihin exreme quiniles is much smaller, he similar reurn paern is exhibied for ΔPPE and DD. Combined reurns are 13%, ΔPPE do no predic fuure reurns once he exreme DD common observaions are eliminaed, bu does yield significan 5.3% in he condiional form, slighly lower han uncondiional reurns (6.1%). As CAPEXP individually does no predic reurns, i is no surprising ha ΔPPE remains significan predicor afer conrolling for he level of CAPEXP. In Panel C, he relaions beween Level 4 measures of capial invesmens are explored. There is no srong share of he observaions wihin exreme quiniles, consisen wih correlaion resuls. Only DD and CAPEXP share very low number of common exreme observaions (5% in low quinile and 8% in high quinile), which is 33

34 a resul of heir negaive correlaion. The highes combined reurns are beween DD and O_ΔPPE and NEWINV and O_ΔPPE, 14.5% and 10.6%, respecively. DD sraegy survives nonoverlap and condiional ess for O_ΔPPE, while O_ΔPPE sraegy does no predic reurns in nonoverlap ess afer conrolling for DD bu do so in he condiional ess, wih he magniude of reurns similar o ha based on uncondiional ranking. Exacly he same paern is refleced in he relaion of NEWINV and DD sraegies. When NEWINV and O_ΔPPE sraegies are compared, O_ΔPPE predics reurns over and above NEWINV, while NEWINV is significan in condiional bu no in nonoverlap ess. DD and O_ΔPPE predic reurns conrolling for CAPEXP, while he opposie is no rue, excep for he condiional ess of CAPEXP conrolling for DD. This las resul can be inerpreed as he predicabiliy of NEWINV since he variabiliy of CAPEXP conrolling for he level DD exacly represens he levels of NEWINV. Overall, some mild pecking order emerges. DD is he sronges predicor surviving all hree ess irrespecive of he conrol variable. I is followed by O_ΔPPE and NEWINV which survive all condiional ess, bu no overlap ess when conrolling for DD. This suggess ha NEWINV and O_ΔPPE predicabiliy of reurns is no concenraed only in he exreme observaions. CAPEXP coninues o be insignifican. When looking a he abiliy of ΔPPE o predic reurns afer conrolling for one of is componens a he ime, he same paern is confirmed. DD dominaes he exreme porfolios, bu once moved o he condiional ess oher componens seem o play a role. These findings confirm o some exen ha operaing leverage is more imporan han invesmen (H3b), paricularly when measured by CAPEXP. However, he acquisiion/disposal/impairmen from O_ΔPPE and some invesmen informaion in NEWINV sill have some power in respec wih fuure reurns. 34

35 4.2 Regression ess for fuure sock reurns Porfolio ess are focused on he reurns from exreme deciles. They are also useful in allowing for a non-linear relaion beween reurns and he characerisic of ineres. However, o es for he hypohesized relaion beween fuure reurns and capial invesmens including full sample, we use regression ess. Also, while porfolio ess enable conrols for only one oher variable a he ime, mulivariae regressions can conrol for all oher accruals and oher reurn predicors ha can poenially eliminae predicive abiliy of our capial invesmen measures. We run he regressions of he following general form: 5 ABNRET α β CAPINV δ RET PRED ε (1) k _ k, 1 k 1 where CAPINV are individual capial invesmen measures a levels 1 o 4 of he gradual decomposiion of ΔNOA. RET_PRED are oher variables found o predic reurns. They include size, book-o-marke, shor-erm reurn reversals, momenum reurns and long-erm reurn reversals. All variables are defined in Table 2. The conrol for long-erm reversal is of paricular imporance given he evidence ha he firms wih high (low) working capial accruals have high (low) reurns wo and hree years prior o porfolio formaion (Kohari, Louskina and Nikolaev, 2006) and ha pas cumulaive reurns measured five years before unil one year before porfolio formaion subsume he predicive abiliy of ΔNOA for fuure reurns (Resuek, 2008). However, hese ess do no conrol for he correlaion beween capial invesmen measures a Level 4 and beween capial invesmen measures and oher accruals emerging from he decomposiion of ΔNOA. Therefore, he general form of he full regression is: 35

36 ABNRET n m 5 1 α0 βicapinv i, γ joth _ ACC j, δk RET _ i1 j1 k 1 PRED k, ε 1 (2) where OTH_ACC are oher accruals from he decomposiion of ΔNOA. The regressions are carried ou sequenially for he capial invesmen measures from Level 1 hrough 4, such ha n i1 CAPINV m i, OTH _ ACC j, i1 ΔNOA. For example, in he regression a Level 4 capial invesmen variables CAPEXP, -DD and O_ΔPPE are measures of CAPINV while OTH_ACC include ΔINTANG, ΔONCOA, -ΔNCOL and ΔWC so ha CAPINV and OTH_ACC add o ΔNOA. We do so o conrol for possible correlaion beween capial invesmen measures and oher accruals. The regression ha includes only ΔNOA is also run for comparaive purposes. We expec o find negaive β coefficiens, since he reurns are expeced o decrease wih an increase in CAPINV. The repored coefficiens are ime-series averages from he annual regressions and he -saisics are from he disribuion of he annual coefficien esimaes. Table 8 presens he resuls of hese ess. [Table 8 around here] Panel A shows ha he coefficien on he aggregae ΔNOA, ΔNNCOA and ΔNCOA are significan afer conrolling for oher accruals and reurn predicors. Similarly, in Panel B coefficien on ΔPPE is significan before and afer conrolling for oher accruals. These regression resuls are consisen wih he comparaive US sudies. 14 In Panel C, coefficiens on -DD and O_ΔPPE are significan a he 5% level in boh models (1) and (2). CAPEXP coninues o be insignifican, inconsisen wih 14 Richardson e al. (2005) find ha he reurns on ΔNNCOA and ΔNCOA remain significan in regressions when conrolling for he effec of oher accruals. In he US, he effec of PPE remains significan afer conrol for oher reurn predicors and working capial accruals (Thomas and Zhang, 2002; Cooper, Gulen and Schill, 2008; Bazdresch, Belo and Lin, 2009). 36

37 he US resuls. 15 Ineresingly, NEWINV is significan only a he 10% level in full model (2). When no conrolling for oher accruals ha add up o ΔNOA, NEWINV is no significan. To summarize, hese resuls confirm he predicive abiliy of aggregae measures of capial invesmen (consisen wih H1). Consisen wih porfolio ess, DD and O_ΔPPE are sill significan, while he abiliy of NEWINV is reduced when conrolling for oher reurn predicors. 4.3 Regression ess for fuure profiabiliy To es for he relaion beween capial invesmen measures and fuure profiabiliy (H2) we run he regressions of he following general form: ROA α β ROA ε (3) n ROA α β ROA γ CAPINV δ OTH ACC ε (4) i i, j _ j, 1 i1 j1 m Regressions are carried ou sequenially for he capial invesmen measures from Level 1 hrough 4, in a way described for regression equaion (2). ROA is operaing reurn on asse, operaing profi scaled by he average oal asses. All variables are defined in Table 2. For he profiabiliy ess, we do no run separae regressions for individual measures of CAPINV wihou conrolling for OTH_ACC. This is because he marginal effec of he capial invesmen variable is no confounded by is correlaion wih some oher accouning variable wih differen economic effec. Moreover, he coefficiens on he measures of capial invesmen and oher accruals can be inerpreed as he difference in he persisence of fuure earnings in he variables under consideraion relaive o free cash flows, such as he negaive 15 US sudies also find ha he CAPEXP effec remains significan afer conrolling for working capial accruals and oher variables ha predic reurns (Polk and Sapienza, 2009; Wei and Xie, 2008). 37

38 coefficien on he capial invesmens represen lower persisence in fuure profiabiliy relaive o free cash flows. 16 Given ha he measures of capial invesmen can be regarded as componens of accouning profi, he negaive coefficien in he regression shows ha he curren profis of firms wih a larger componen of capial invesmen will rever o he overall mean faser han he curren profis of firms wih smaller componen of capial invesmen. 17 The ime-series averages of coefficiens and associaed -saisics from 14 annual cross-secion regressions in spiri of Fama and MacBeh (1973) are presened in Table 9. [Table 9 around here] Benchmark coefficien on ROA from regression (3) is 0.820, wih he adjused R 2 of This coefficien slighly increases o when he capial invesmens along wih oher accruals are added. This reflecs he higher persisence of free cash flows relaive o he persisence of accrual componen. Coefficiens on he aggregae measures of ΔNOA, ΔNNCOA and ΔNCOA are all negaive around -0.2 and significan a he 5% level. The resuls for ΔNNCOA and ΔNCOA are consisen wih he US-based sudies of Fairfield, Whisenan and Yohn (2003) and Richardson e al. (2005) who presen resuls of similar regressions using operaing income afer 16 To see why, le us recall ha operaing profiabiliy can be expressed as a sum of free cash flows (FCF) and changes in ne operaing asses (ΔNOA). Regression ROA 1 α0 γ1fcf γ2δnoa ε 1 can be hen re-wrien as ROA 1 α0 γ1( ROA ΔNOA ) γ2δnoa ε which finally gives 1 ROA 1 α0 γ1roa ( γ2 γ1) ΔNOA ε. Therefore, if γ γ 1 2 1, we obain negaive coefficien on ΔNOA which means ha fuure earnings are less persisen in is ΔNOA componen han in FCF. 17 These inerpreaions are correc only in regressions in which all he componens of ΔNOA are used and if operaing profiabiliy is calculaed from profi afer all operaing iems are included. However, operaing profi repored by Worldscope does no include some operaing non-recurring iems (which are mosly he par of O_ΔPPE). Also, he relaion above assumes clean surplus accouning, i.e. ha all he gains and losses flow hrough he income saemen. The acual reporing sysem allows for some iems o be credied direcly o equiy, such as revaluaion of propery, plan and equipmen. These operaing iems are no included in operaing profi. 38

39 depreciaion as a measure of profiabiliy for he US marke. However, hey repor higher negaive coefficiens on hese wo variables han we find for he UK marke. Coefficien on ΔPPE of is significan only a he 10% level. Following ΔPPE, he coefficiens on he Level 4 measures of DD and O_ΔPPE and NEWINV are negaive and significan a he 10% level. Coefficiens on CAPEXP and NEWINV are no significan. Overall, we find supporing evidence for our second hypohesis on he negaive relaion beween fuure earnings and curren capial invesmen. I seems ha he reversal of he aggregae measures (ΔNNCOA, ΔNCOA and ΔPPE) in fuure earnings is driven by DD and O_ΔPPE. These resuls are also consisen wih he direcion of he sock reurn ess, suggesing ha he persisence of capial invesmen measures in fuure profiabiliy is relaed o he pricing of hese measures. 4.4 Regression ess of cross-secional variaion in he pricing of capial invesmens To es he variaion in he capial invesmen-reurn/profiabiliy relaion across he proxies of agency coss of managemen empire building (H5) and he likelihood of caering o he marke senimen (H6) we augmen equaions (2) and (4) wih he ineracion erms beween all explanaory variables and dummy variable represening he level of agency coss. 39

40 , 2 1, 2 1, 5 1, 1, 1, 0 1 _ * k k k m m j j j n n i i i k k k m j j j n i i i ε AGENCY H PRED RET δ ACC OTH γ CAPINV β PRED RET δ ACC OTH γ CAPINV β α ABNRET (5) 1 2 1, 2 1, 1, 1, _ * _ * _ m m j j j n n i i i m j j j n i i i ε AGENCY H ACC OTH δ CAPINV γ ACC OTH δ CAPINV γ AGENCY H ROA β ROA β AGENCY H α α ROA (6) Regressions are carried ou sequenially for he capial invesmen measures from Level 1 hrough 4, in a way described for regression equaion (2). H_AGENCY is a dummy variable ha akes 1 when free cash flows (FCF) is higher, leverage (LEV) is lower and SIZE is lower han is annual median, and 0 oherwise, respecively. I also akes he value of 1 when research and developmen expendiure (RD) is posiive and 0 when RD is zero or missing. All variables are defined in Table 2. Regressions are run separaely for each agency proxy. We expec o find negaive β and γ coefficiens in he ineracion erm, since he hypohesis saes ha he negaive relaion beween CAPINV and fuure reurns is more pronounced when agency problems are more likely o occur. Resuls are presened in Table 10. [Table 10 around here] We do no find any suppor for hese behavioural explanaions. Ineracion coefficiens from ABNRET regressions are mosly insignifican, and when significan hey are of he wrong sign. Similarly, he ineracion coefficiens from profiabiliy regressions are also largely insignifican. The only hree significan coefficiens from

41 ineracing ΔNOA wih RD, ΔNNCOA wih LEV and DD wih SIZE are negaive, as expeced. Ineresingly, his lower persisence does no ranslae in more negaive reurns when RD is high, LEV low and SIZE low for hese capial invesmen measures. The direcion of he resuls is consisen wih our previous ess ha found ha he organic growh from capial expendiures is no significan reurn predicor in he UK. CAPEXP is a variable for which he similar ess are run in he US by Timan, Wei and Xie (2004) and Polk and Sapienza (2009). 4.5 Regression ess of cross-secional variaion in he pricing of capial invesmens Conrolling for employee growh To disinguish reliabiliy and persisence (operaing leverage and ransiory iems) from oher compeing explanaions of invesmen s pricing and address join es hypohesis problem, we inroduce employee growh as non-accouning measure of invesmen in our ess. As firms grow, besides invesing in fixed asses, hey increase heir capaciy by hiring new employees. Alhough here is some level of subsiuion beween labour and capial in he firms producion funcion, here is some normal level of labour capial mix ha is mainained as companies grow. Therefore, employee growh should capure he level of growh in capaciy and should be posiively correlaed wih capial invesmen measures, and more se wih hose o which we assign high correlaion. Unabulaed resuls show ha he correlaion of percenage of growh in employees (EMPLGR) wih ΔNOA, ΔNNCOA, ΔNCOA and ΔPPE is subsanial, abou 0.4. EMPLGR is winsorized a +1 as he highes value o reduce he sensiiviy o ouliers. Less han 1% of oal observaions are winsorized in his way. The correlaion is somewha lower wih he individual Level 4 measures 41

42 wih medium/high correlaion wih invesmen: 0.18 wih CAPEXP, 0.32 wih O_ΔPPE and 0.21 wih NEWINV, bu i is no significan wih DD, as expeced. Consequenly, conrolling for employee growh should subsanially decrease he abiliy of capial invesmens measures o predic fuure sock reurns and profiabiliy, if he predicabiliy is he resul of invesmen per se. Conversely, if he employee growh does no affec he magniude of he predicabiliy of capial invesmen measures, hen i is more likely o be he resul of reliabiliy or operaing leverage and ransiory iems concerns. Furhermore, he expeced effec of adding employee growh should be sronger for variables which are expeced o be more correlaed wih invesmen (CAPEP, NEWINV and O_ΔPPE) if invesmen is he dominan explanaion. To es if he rae of hiring affecs he relaion beween capial invesmen and fuure reurns/profiabiliy, we firs regress ABNRET on EMPLGR conrolling for oher RET_PRED o examine if he EMPLGR iself predics fuure reurns: k _ k, 1 k 1 5 ABNRET α ρ EMPLGR δ RET PRED ε (7) Nex, we augmen base-line models (2) and (3) by adding he percenage change in employee growh (EMPLGR) as addiional explanaory variable. ABNRET 1 α 0 5 k 1 ρ EMPLGR k 1 δ RET _ PRED k, n i1 β CAPINV i ε 1 i, m j1 γ OTH _ ACC j j, (8) We perform similar procedure in respec wih profiabiliy ess: ROA α β ROA ρ EMPLGR ε (9)

43 ROA α β ROA ρ EMPLGR n γ CAPINV i i, j _ j, 1 i1 j1 m δ OTH ACC (10) Regressions in (8) and (10) are carried ou sequenially for he capial invesmen measures from Level 1 hrough 4, in a way described for regression equaion (2). All variables are defined in Table 2. The ime-series averages of coefficiens and associaed -saisics from annual cross-secional regressions are presened in Table 11, in Panel A for ABNRET ess and in Panel B for profiabiliy ess. [Table 11 around here] EMPLGR is a significan predicor of boh ABNRET and ROA +1 wihou accouning for oher capial invesmen measures. However, i is subsumed by accouning measures of growh when considered joinly. All capial invesmen measures ha were significan predicors of fuure reurns wihou conrolling for EMPLG remain so, albei coefficiens are reduced by around 14% for he Level 1, 2 and 3 measures, around 10% for O_ΔPPE and NEWINV, and only for abou 6% for DD. As for operaing profiabiliy, he coefficiens are very similar o hose wihou conrolling for EMPLGR. In summary, if invesmen informaion is indeed capured by EMPLGR, hen invesmen explanaion seems o conribue relaively lile o he predicion of fuure reurns and profiabiliy, and reliabiliy, operaing leverage and he effec of ransiory iems remain he main sources of he predicabiliy. ε Conrolling for indusry levels of capial invesmen One of he concerns in regarding wih he inerpreaion of resuls could be ha he abiliy of capial invesmen measures, and in paricular DD is relaed o he capial inensiy. More capial inensive indusries may be more sensiive o he growh in fixed capial (e.g. uiliies, elecommunicaions) han o growh in employees (e.g. 43

44 media, labour inensive manufacuring) and may have, on average, higher operaing leverage ha commands higher reurns and profiabiliy. To es wheher our resuls are driven by iner-indusry effec, we adjus capial invesmen measures, oher accruals and profiabiliy for he annual indusry median using 32 secor levels from FTSE/Dow Jones benchmark classificaion, and repea he models from regressions (2), (3) and (4): ABNRET 1 α 0 5 k 1 i1 β CAPINV _ ADJ δ RET _ PRED k n i k, ε i, 1 m j 1 γ OTH _ ACC _ ADJ j j, (11) ROA _ ADJ ADJ ε (12) 1 α0 β1roa _ 1 ROA _ ADJ 1 α 0 m j1 β ROA _ ADJ j 1 n i1 δ OTH _ ACC _ ADJ γ CAPINV i j, ε 1 _ ADJ i, (13) The ime-series averages of coefficiens and associaed -saisics from annual crosssecional regressions are presened in Table 12, in Panel A for ABNRET ess and in Panel B for profiabiliy ess. [Table 12 around here] The coefficiens on he Level 1, 2 and 3 variables, O_ΔPPE and NEWINV in reurn regression are similar o hose when unadjused measures are used. The coefficien on DD_ADJ is , lower han he coefficien on he unadjused measure of Ineresingly, CAPEXP_ADJ is now significan a he 5% level (-saisics of 2.33) wih he coefficien magniude of As for operaing profiabiliy, he persisence coefficien of ROA of is slighly lower han in unadjused version (0.820) wihou conrolling for oher variables. The similar paern is observed for ΔNOA, ΔNNCOA and ΔNCOA. All oher measures (ΔPPE, -DD and O_ ΔPPE) ha were significan a he 10% level in unadjused versions, now lose heir significance 44

45 and he coefficien poin esimaes are slighly lower. In summary, while major source of predicabiliy of reurns is inra-indusry variaion in capial invesmen measures, he srengh of predicabiliy for boh reurns and profiabiliy is somewha lower, in paricular for DD. Conrolling for acquisiion, disposal and exernal financing aciviies and operaing profiabiliy The finance lieraure documens he inverse relaion beween exernal growh hrough mergers and acquisiions and fuure reurns (e.g. Agrawal, Jaffe and Mandelker, 1992 and Loughran and Vijh, 1997 in he US). Bradshaw, Lee and Lin (2008) documen negaive abnormal reurns five years following he sell-offs in he UK. Bradshaw, Richardson and Sloan (2006) find ha he combined amoun of ne equiy and be issuance is negaively relaed o fuure size-adjuser reurns. Finally, Fama and French (2006) find some evidence ha operaing profiabiliy predics fuure reurns. We es wheher conrolling for hese facors will reduce he srengh of he predicabiliy of capial invesmen measures. Acquisiions sell-offs effecs are in paricular relaed o he predicive abiliy of O_ΔPPE as he individual measure, bu as well wih more aggregae measures. We do no have he daa on he ne fixed asses acquired ha is he par of O_ΔPPE, bu we use he oal amoun of ne asses acquired as he proxy for acquisiion (ACQ). Similarly, as we do no have he daa on he book value of disposed asses, we use Worldscope available proceeds from sale of fixed asses variable (DISP). Exernal financing changes (EXTFIN) are correlaed wih growh aciviies, so our inerpreaion could be erroneously assigned o invesmen insead o exernal financing. Finally, our capial invesmen measures are he par of operaing profiabiliy so i could be ha i is operaing profiabiliy iself 45

46 and no invesmen, or any of oher relaed explanaions ha drives he predicabiliy of capial invesmens in respec wih fuure reurns. We firs es if ACQ, DISP, EXTFIN and ROA are relaed o fuure reurns wihou accouning for CAPINV: ABNRET α ρ ACQ ρ DISP ρ EXTFIN ρ ROA ε (14) Nex we augmen equaion (2) wih he proxies for acquisiion, disposal and exernal financing, plus ROA in he reurn regression. ABNRET 1 α 0 n i1 ρ ACQ i 1 β CAPINV ρ i, 2 DISP ρ EXTFIN m j1 j γ OTH _ ACC 3 j, ρ ROA 5 k 1 4 δ k RET _ PRED k, ε 1 (15) We perform similar procedure in respec wih profiabiliy ess: ROA α β ROA ρ ACQ ρ DISP ρ EXTFIN ε (16) ROA 1 α 0 n i1 β ROA i 1 γ CAPINV ρ ACQ i, 1 m j1 j ρ 2 DISP ρ EXTFIN δ OTH _ ACC j, 3 ε 1 (17) The ime-series averages of coefficiens and associaed -saisics from annual cross-secional regressions are presened in Table 13, in Panel A for ABNRET ess and in Panel B for profiabiliy ess. [Table 13 around here] Wihou accouning for capial invesmens, only EXTFIN is significanly negaively relaed o fuure reurns and profiabiliy. Ineresingly, fuure profiabiliy one-year ahead increases as ACQ increases. Turning o our capial invesmen measures, we find ha he coefficiens in ABNRET regressions are very similar o hose when ACQ, DISP, EXTFIN and ROA are no presen. In fac, he magniude of he coefficien on O_ ΔPPE ha is expeced o be mos affeced, acually slighly 46

47 increases. The coefficien on DD decreases for around 13%. In line wih hese resuls, all magniudes of he coefficiens excep for DD in he profiabiliy regressions are slighly higher and wih higher -saisics. Only he coefficien on DD becomes insignifican. These resuls verify ha he main findings are no a manifesaion of some oher correlaed omied accouning variable. The predicabiliy of DD in respec wih fuure reurns is sill subsanial alhough somewha reduced. 5. Conclusion This sudy provides iniial evidence on he associaion beween fuure accouning profiabiliy and sock reurns wih curren measures of capial invesmen in he UK marke. We examine hese relaions using a comprehensive se of measures of capial invesmen, from aggregae changes in ne non-curren operaing asses o oher individual measures derived hrough gradual decomposiion of his variable. Firs, we find ha all major variables under examinaion excep capial expendiures are inversely relaed o fuure reurns. The rading sraegy of invesing in low-capial invesmen firms and shor-selling high-capial invesmen firms earns around 4.5% o 6.7% per year, depending on he definiion. The sronges individual predicor in he porfolio and reurn ess is depreciaion (6.7%), followed by oher changes in PPE (5.3%) and capial expendiures adjused for depreciaion (NEWINV, 4.5%). Second, we find he negaive associaion beween curren capial invesmen and fuure accouning profiabiliy for he same variables. In general, hese resuls remain afer conrolling for real invesmen by new hiring, iner-indusry variaions, acquisiion, disposal and exernal financing aciviies and curren operaing profiabiliy. We do no find suppor for agency-relaed behavioural explanaions of he observed relaions as he predicabiliy of capial invesmen measures does no 47

48 vary in cross-secion wih proxies for agency coss. The resuls are mosly consisen wih he reliabiliy, operaing leverage and overall persisence of ransiory iems explanaions of he pricing raher han wih he invesmen. Our findings indicae he researchers should be cauious when inerpreing he abiliy of accouning variables o predic fuure reurns since hese may be resul of boh underlying economics and accouning measuremen issues. Our paper has some limiaions. We are no able o fully disinguish beween hese alernaive explanaions, oher han invesmen per se. Therefore, fuure research could ry o disinguish wheher he relaion is driven by fundamenal informaion abou growh conained in he accouning informaion or by reporing choice. Our indusry ess indicae ha capial expendiures adjused for indusry median predic fuure reurns. Some relaed US-sudies use eiher cross-secional or ime-series measures of inefficien capial expendiures ha predic fuure reurns. Fuure research should ry o explore wheher some measure of inefficien invesmen is sronger predicor han he acual values. 48

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54 Table 1 Summary of Reliabiliy Assessmen, Assessmens of Correlaion wih Real Invesmen and Assessmen of Persisence in Fuure Earnings due o oher earnings properies by Non-Curren Operaing Accrual Caegory Invesmen Caegory Decomposiion Level Reliabiliy Assessmen Summary of Reasoning Behind Reliabiliy Assessmen Panel A: Levels 3 and 4 decomposiion of capial invesmen CAPEXP Level 4 Low CAPEXP involve subjecive capialisaion decision. DD Level 4 Low DD is subjec o he subjecive managemen s esimaes abou useful life, depreciaion mehod and residual value. ΔO_PPE Level 4 Low Caegory includes new PPE from acquisiion and book value of disposal componen ha are subjec o subjecive esimaes of fuure cash flows and discoun rae. NEWINV Level 4 Low Combinaion low reliabiliy of CAPEXP and DD ΔPPE Level 3 Low Combinaion of CAPEXP, DD and ΔO_PPE, all of hem caegories wih low reliabiliy. Assessmen of Correlaion wih Invesmen Medium/ High Medium Medium/High Medium/ High Medium/ High Summary of Reasoning Behind Correlaions Assessmen Caegory represens acual purchases of new PPE as an inpu ino producion bu does no accoun for asses sold. Caegory is correlaed wih concurren capial expendiures, bu also wih he exising PPE. Caegory includes new PPE from acquisiion and book value of disposal componen ha are likely o be highly relaed o acual sales of asses (divesiures). Impairmen is no an invesmen bu change in valuaion of exising asses in place. Caegory is he combinaion of CAPEXP (high correlaion) and DD (medium correlaion). Caegory is he combinaion of CAPEXP (high correlaion) and DD and ΔO_PPE (medium correlaion). Panel B: Levels 1 and 2 decomposiion of capial invesmen from Richardson e al. (2005) and Wu, Zhang and Zhang (2010) ΔNCOA Level 2 Low Caegory is dominaed by ΔPPE (low reliabiliy) and ΔINTANG (low reliabiliy due o subjecive High Caegory is dominaed by ΔPPE and ΔINTANG. They are complemenary inpus in producion; heir changes are likely o be highly correlaed. 54 Profiabiliy persisence and oher properies assessmen Medium Low Low Medium Oher aribues Caegory is correlaed wih depreciaion. I parially reflecs operaing leverage. DD is usually a fixed cos may represen operaing leverage. More levered firms are more volaile in erms of profiabiliy. Firms wih more volaile earnings have less persisen earnings. Impairmen is he form of imely recogniion of anicipaed losses. Asses could be sraegically disposed o realise gain. Gain/loss reflecs he reversal or he previous measuremen error in he asse capialisaion and depreciaion Boh componens of profi are ransiory in naure and decrease persisence. Caegory is correlaed wih depreciaion. I parially reflecs operaing leverage.

55 ΔNNCOA Level 1 Low/ Medium capialisaion decisions of inernally generaed inangibles). Combinaion of ΔNCOA (low reliabiliy) and ΔNCOL (medium reliabiliy). Medium/ High Combinaion of ΔNCOA (high correlaion) and ΔNCOL (medium correlaion). Noes: Reliabiliy assessmens are from Richardson e al. (2005) and assessmen of correlaion wih invesmen is from Wu, Zhang and Zhang (2010) for Level 1 and Level 2 decomposiion. CAPEXP is capial expendiures, DD is depreciaion and depleion, ΔO_PPE are oher changes in propery, plan and equipmen, ΔPPE is changes in propery plan and equipmen, NEWINV is CAPEXP less DD. ΔNCOA is changes in non-curren operaing asses defined as ΔPPE + ΔINTANG + ΔONCOA, where ΔINTANG is changes in inangible asses and ΔONCOA is changes in oher non-curren operaing asses. ΔNNCOA is changes in ne non-curren operaing asses defined as ΔNCOA ΔNCOL, where ΔNCOL is changes in non-curren operaing liabiliies. Deailed definiions wih associaed Worldscope codes are presened in Table 2. 55

56 Table 2 Variable Definiions Variable name Descripion and definiion ΔNOA Change in ne operaing asses = NOA NOA -1 NOA = Operaing asses (OA) Operaing liabiliies (OL); OA = Toal asses (Worldscope iem 0299) Cash and cash equivalens (Worldscope iem 02001) OL = Toal asses Book value of ordinary and preference shares (Worldscope iem 03995) Minoriy ineres (Worldscope iem 03426) - Toal deb (Worldscope iem 03255) Measures of capial invesmen (CAPINV) and oher accruals (OTH_ACC) from decomposiion of ΔNOA Level 1 iniial decomposiion: ΔNOA = ΔNNCOA + ΔWC ΔNNCOA Change in ne non-curren operaing asse = ΔNOA ΔWC ΔWC Change in non-cash working capial = WC WC -1 WC = Working capial (Worldscope iem 01351) Cash and cash equivalens (Worldscope iem 02001) + Toal deb (Worldscope iem 03255) - Long-erm deb (Worldscope iem 03251) Level 2 exended decomposiion: ΔNNCOA = ΔNCOA ΔNCOL ΔNCOA Change in non-curren operaing asses = NCOA NCOA -1 NCOA = OA Curren asses (Worldscope iem 02201) + Cash and cash equivalens (Worldscope iem 02001) ΔNCOL Change in non-curren operaing liabiliies = ΔNCOA ΔNNCOA Level 3 individual iems: ΔNCOA = ΔPPE + ΔINTANG + ΔONCOA ΔPPE Change in ne propery, plan and equipmen = PPE PPE -1 PPE (Worldscope iem 02501) ΔINTANG Change in inangible asses = INTANG INTANG -1 INTANG (Worldscope iem 02649) ΔONCOA Changes in oher non-curren operaing asses = ΔNCOA ΔPPE ΔINTANG Level 4 decomposiion of ΔPPE: ΔPPE = CAPEXP DD + O_ΔPPE = NEWINV + O_ΔPPE CAPEXP Capial expendiure (Worldscope iem 04601) DD Depreciaion and depleion (Worldscope iem 04049) O_ΔPPE NEWINV Oher changes in ne propery, plan and equipmen = ΔPPE CAPEXP + DD New invesmen = CAPEXP DD All variables are scaled by he average of closing and beginning oal asses (Worldscope iem 02999) in he period in which he variables are measured. ΔWC, ΔNCOL, ΔINTANG, ΔONCOA, CAPEXP, -DD, O_ΔPPE and NEWINV are winsorized a +1 and -1. These winsorized CAPINV variables are added back ogeher o consruc he measures of he higher aggregaion as follows: ΔPPE = winsorized CAPEXP winsorized DD + winsorized OΔPPE ΔNCOA = ΔPPE + winsorized ΔINTANG + winsorized ΔONCOA ΔNNCOA = ΔNCOA winsorized ΔNCOL ΔNOA = ΔNNCOA + winsorized ΔWC Reurn (ABNRET) and profiabiliy (ROA) ABNRET Abnormal reurns are a difference beween RET and buy-and-hold annual equally weighed reurns of a 4x4 size and book-o-marke sequenially formed porfolio calculaed beginning six monhs afer financial year-end, where size and book-o-marke porfolios are rebalanced monhly. ROA Operaing profi (Worldscope iem 01250)/Average oal asses ROA is winsorized a +1 and -1. Reurn predicors (RET_PRED) 56

57 lnsize lnbm Naural logarihm of marke value of equiy (08001) six monhs afer he financial-year end. Naural logarihm of book-o-marke raio calculaed by dividing book value of equiy (Worldscope iem 03501) he end-of-he monh marke value of equiy six monhs afer he financial year end. STREVRET Shor erm reversal reurns is calculaed for one monh prior o he beginning of cumulaing of fuure reurns. MOMRET Price momenum is calculaed as he reurn for he period saring one year before and ending one monh before he beginning of cumulaing of fuure reurns. LTREVRET Long erm reurn reversals are reurns for he period saring hree years before and ending one year before he beginning of cumulaing of fuure reurns. Proxies for he level of agency coss (AGENCY) FCF Difference beween cash flows from operaions scaled by average oal asses and winsorized a +1 and -1 and winsorized scaled DD. Cash flow from operaions are defined as: OP- WC+DDA, where DDA is Depreciaion, depleion and amorizaion (Worldscope iem 01151). SIZE Marke value of equiy (08001) six monhs afer he financial-year end. LEV Leverage is Toal deb (Worldscope iem 03255)/[Toal asses (Worldscope iem 02999)) + SIZE - Book value of ordinary and preference shares (Worldscope iem 03995)] RD Research and developmen (Worldscope iem 01201), scaled by avearge oal asse. RD is winsorized a +1 for he amouns higher han 1. If missing in he final sample, RD is se o zero. Oher variables EMPLGR Employee growh is he percenage change in he number of employees (Worldscope iem 07011) calculaed as (Employees - Employees -1 )/ Employees -1. EMPLGR is winsorized a he 99% level. ACQ DISP EXTFIN Fuure shor erm earnings growh Fuure longerm earnings growh Fuure shor erm sales Ne asse from acquisiion (Worldscope iem 04355) including excess of cos of acquired companies, scaled by he average oal asses (Worldscope iem 02999) and winsorized a 1 if scaled variable is higher han 1 and replaced wih 0 if ACQ is negaive. Disposal of fixed asses (Worldscope iem 04351) represening he amoun he company received from he sale of propery, plan and equipmen, scaled by he average oal asses (Worldscope iem 02999) and winsorized a 1 if scaled variable is higher han 1 and replaced wih missing if DISP is negaive. Exernal financing is he sum of winsorized ne equiy and ne deb issuance. Ne equiy issuance is ne proceeds sale/issue of common & preferred equiy (Worldscope iem 04251) less redeemed, reired, convered, ec. common & preferred equiy (Worldscope iem 04751) less oal common and preferred cash dividends paid (Worldscope iem 04551). Ne deb issuance is he amoun received by he company from he issuance of long erm deb, (converible and non-converible), increase in capialised lease obligaions, and deb acquired from acquisiions (Worldscope iem 04401) less reducion in long erm deb (Worldscope iem 04701) plus ne increase in shor erm borrowings (Worldscope iem 04821). Ne equiy and deb issuance are scaled by he average oal asses (Worldscope iem 02999) and winsorized a +1 and -1. Change in operaing profi one-year ahead = OP +1 - OP OP (Worldscope iem 01250) Fuure shor erm earnings growh is scaled by curren oal asses (Worldscope iem 02999) and winsorized a +1 and -1. Average OP in he fuure hree years less average operaing profi in he pas hree years scaled by he average oal asses (Worldscope iem 02999) in he pas hree years = ((OP +1 +OP +2 +OP +3 )/3 - (OP -1 +OP -2 +OP -3 )/3))/(TA -1 +TA -2 +TA -3 )/3 Fuure long-erm earnings growh is winsorized a +1 and -1. Change in sales one-year ahead = Sales +1 Sales Sales (Worldscope iem 01001) 57

58 growh Long-erm sales growh Fuure shor erm sales growh is scaled by curren oal asses (Worldscope iem 02999) and winsorized a he 1% and 99% levels. Average sales in he fuure hree years less average operaing profi in he pas hree years scaled by he average oal asses (Worldscope iem 02999) in he pas hree years = ((Sales +1 +Sales +2 +Sales +3 )/3 - (Sales -1 +Sales -2 +Sales -3 )/3))/(TA -1 +TA -2 +TA -3 )/3 Fuure long-erm earnings growh is winsorized a he 1% and 99% level. 58

59 Table 3 Descripive Saisics Mean Sd. Dev. 25% Median 75% Panel A: Level 1 iniial decomposiion and Level 2 exended decomposiion variables ΔNOA ΔNNCOA ΔWC ΔNCOA ΔNCOL Panel B: Level3 individual iems ΔPPE ΔINTANG ΔONCOA Panel C: Level4 decomposiion of ΔPPE CAPEXP DD O_ΔPPE NEWINV Noes: The sample consiss of 12,340 observaions over he period. ΔNCOL and DD are presened as negaive values in order o show he same direcion in changes in ne asses as oher measures of accruals. All variables are defined in Table 2. 59

60 Table 4 Correlaion Marix beween Capial Invesmen Variables Panel A: Correlaions beween Level 1 and Level 2 variables ΔNOA ΔNNCOA ΔWC ΔNCOA ΔNCOL ΔNOA ΔNNCOA ΔWC ΔNCOA ΔNCOL Panel B: Correlaions of ΔNCOA and Level 3 variables ΔNCOA ΔPPE ΔINTANG ΔONCOA ΔNCOA ΔPPE ΔINTANG ΔONCOA Panel C: Correlaions of ΔPPE and Level 4 variables ΔPPE CAPEXP DD O_ΔPPE NEWINV ΔPPE CAPEXP DD O_ΔPPE NEWINV Noes: The sample consiss of 12,340 observaions over he period. ΔNCOL and DD are presened as negaive values in order o show he same direcion in changes in ne asses as oher measures of accruals. Pearson correlaions are below and Spearman correlaions above he diagonal. All Pearson and Spearman correlaions higher han he absolue value of are saisically significan a he 1% level. All variables are defined in Table 2. 60

61 Table 5 Fuure Shor-Term and Long-Term Growh in Earnings and Sales Reurns and ROA Across Quiniles of Level 4 Measures of Capial Invesmens Earnings Sales Porfolio Quinile CAPEXP -DD O_ΔPPE NEWINV CAPEXP -DD O_ΔPPE NEWINV Panel A: Fuure shor-erm growh 1 (Low) (High) High - Low ** ** ** ** ** ** -saisic Panel B: Fuure long-erm growh 1 (Low) (High) High - Low ** ** ** ** ** ** -saisic Noes: Time-series averages of means of fuure shor-erm growh in earnings and sales (Panel A) and long-erm growh in earnings and sales (Panel B) across quiniles of he Level 4 measures of capial invesmen are presened, ogeher wih associaed -saisics on he mean of earnings and sales growh. Firms are sored ino he quiniles every year. The sample consiss of 10,603 (10,782) observaions for shor-erm earnings (sales) growh and 6,709 (7,036) observaions for long-erm earnings (sales) growh over he soring period. DD is presened as negaive values in order o show he same direcion in changes in ne asses as oher measures of accruals. Fuure shor-erm earnings (sales) growh is he difference beween operaing profi (sales) one year ahead and curren operaing profi (sales) scaled by curren oal asses. Fuure long-erm growh is he difference beween he average of operaing profi (sales) from year +1 hrough year +3 and he average of operaing profi (sales) from year -3 hrough year -1, scaled by he average of oal asses ) from year -3 hrough year -1. All variables are defined in Table 2. ** and * denoe saisical significance a he 5% and 10% levels, respecively, for a wo-ailed -saisic es. 61

62 Table 6 Time-Series Averages of Abnormal Reurns One-Year Ahead Across Quiniles of Measures of Capial Invesmen and Oher Accruals Panel A: Level 1 and Level 2 variables Porfolio quinile ΔNOA ΔNNCOA ΔWC ΔNCOA ΔNCOL 1 (Low) (High) Low High ** ** ** ** saisic No. of years posiive Panel B:Level 3 variables Porfolio quiniles ΔPPE ΔINTANG ΔONCOA 1 (Low) (High) Low - High ** saisic No. of years posiive Panel C: Level 4 variables Porfolio quinile CAPEXP DD O_ΔPPE NEWINV 1 (Low) (High) Low - High ** ** ** -saisic No. of years posiive Noes: Time-series averages of means of annual abnormal reurns one-year ahead across quiniles of he Level 1 and 2 measures of capial invesmen (Panel A), Level 3 (Panel B) and Level 4 (Panel C) are presened, ogeher wih associaed -saisics for he mean hedge reurns and he number of years wih posiive hedge reurns. Firms are sored ino he quiniles every year. The sample consiss of 12,340 observaions over he period. ΔNCOL and DD are presened as negaive values in order o show he same direcion in changes in ne asses as oher measures of accruals. All variables are defined in Table 2. ** and * denoe saisical significance a he 5% and 10% levels, respecively, for a wo-ailed -saisic es. 62

63 Table 7 Time-Series Averages of Abnormal Reurns One-Year Ahead of Trading Sraegy Based on Quiniles of Various Aggregae and Decomposed Measures of Capial Invesmen Conrolling for Oher Measures of Capial Invesmen Trading sraegy based on main variable: Conrolling for: % of common observaions in exreme quiniles Abnormal hedge reurns LOW HIGH Combined Nonoverlap Condiional Panel A: Relaion beween reurns from porfolios based on Level 1, 2 and 3 variables ΔNOA ΔNCNOA 64.6% 71.5% ** ** ** ΔNCNOA ΔNCOA 85.4% 91.9% ** * ΔNCOA ΔPPE 76.6% 73.5% ** Panel B: Relaion beween reurns from porfolios based on ΔPPE and Level 4 variables ΔPPE CAPEXP 27.5% 60.0% * ** ΔPPE -DD 31.9% 15.8% * ** ΔPPE O_ΔPPE 68.8% 59.6% ** * * ΔPPE NEWINV 49.9% 64.6% ** ** Panel C: Relaion beween reurns from porfolios based on Level 4 variables CAPEXP -DD 5.3% 7.9% ** -DD CAPEXP ** ** CAPEXP ΔO_PPE 14.8% 24.2% ΔO_PPE CAPEXP ** ** ΔO_PPE -DD 26.3% 15.4% ** ** -DD O_ΔPPE ** ** NEWINV -DD 35.9% 15.4% * ** -DD NEWINV ** ** NEWINV O_ΔPPE 21.7% 24.4% ** ** O_ΔPPE NEWINV * ** Noes: The percenage of common observaions wihin low and high quinile, respecively, is he oal number of common observaions wihin low (high) quinile from independen ranking of wo variables as indicaed in he firs wo columns divided by he sum of oal number of observaions in low (high) quinile for all sample years. Firms are ranked ino quiniles every year. Columns under common label Abnormal hedge reurns presen hedge reurns from various rading sraegies. Combined abnormal hedge reurns are abnormal reurns of porfolio from invesing in firms in low quinile of boh variables and shor-selling firms in high quinile of boh variables, where quiniles are based on independen ranking every year. Nonoverlap abnormal hedge reurns are abnormal reurns of porfolio from invesing in firms in low quinile of main variable ha a he same ime do no belong 63

64 o low quinile of conrolling variable and shor-selling firms in high quinile of main variable ha a he same ime do no belong o high quinile of conrolling variable, where quiniles are based on independen ranking every year. Condiional abnormal hedge reurns are based on condiional ranking. Firms are firs ranked ino quiniles based on conrolling variable and hen ranked ino sub-quiniles of he main variable. Condiional quiniles are combined sub-quiniles of he main variable. Abnormal hedge reurns are abnormal reurns of porfolio from invesing in firms in low condiional quiniles and shor-selling firms in high condiional quinile. Time-series averages of means of annual abnormal reurns are presened. The sample consiss of 12,340 observaions over he period. In Panel A, resuls for porfolios based on Level 1, 2 and 3 measures of capial invesmen are presened. In Panel B, resuls for porfolios based on ΔPPE and Level 4 variables. In Panel C, resuls for porfolios based on Level 4 variables only are presened. DD is presened as negaive values in order o show he same direcion in changes in ne asses as oher measures of accruals. All variables are defined in Table 2. ** and * denoe saisical significance a he 5% and 10% levels, respecively, for a wo-ailed -saisic es. 64

65 Table 8 Regression of Abnormal Reurns One-Year Ahead on Curren Measures of Capial Invesmen and Oher Accruals Panel A: Level 1 and Level 2 variables Inercep (0.76) (0.78) (0.91) (0.75) (0.92) (0.73) (0.69) ΔNOA ** (-4.18) ΔNNCOA ** ** (-3.61) (-3.57) ΔWC * (-1.69) (-1.75) (-1.80) ΔNCOA ** ** (-3.29) (-3.58) ΔNCOL (-0.13) (-1.42) lnsize (-0.67) (-0.67) (-0.93) (-0.63) (-0.91) (-0.63) (-0.56) lnbm (0.70) (0.69) (0.70) (0.66) (0.69) (0.71) (0.74) STREVRET ** ** ** ** ** ** ** (3.11) (3.16) (3.21) (3.19) (3.27) (3.10) (3.10) MOMRET ** ** ** ** ** ** ** (3.96) (4.06) (3.73) (4.07) (3.83) (3.96) (3.98) LTREVRET * * ** * ** * * (-2.01) (-2.10) (-2.29) (-2.11) (-2.37) (-2.04) (-2.07) 65

66 Panel B: Level 3 variables Inercep (0.65) (0.87) (0.88) (0.66) ΔPPE ** ** (-2.87) (-2.98) ΔINTANG (-0.98) (-0.89) ΔONCOA (0.57) (0.06) ΔNCOL (-1.29) (-0.66) (-0.26) (-1.46) ΔWC * (-1.62) (-1.84) (-1.74) (-1.71) lnsize (-0.55) (-0.83) (-0.87) (-0.52) lnbm (0.77) (0.74) (0.71) (0.77) STREVRET ** ** ** ** (3.19) (3.09) (3.20) (3.06) MOMRET ** ** ** ** (3.90) (3.85) (3.75) (3.99) LTREVRET * ** ** * (-2.05) (-2.28) (-2.33) (-1.98)

67 Panel C: Level 4 variables Inercep (0.90) (0.16) (0.74) (0.81) (-0.03) (0.65) CAPEXP (-0.04) (-1.72) DD ** ** (-2.66) (-3.13) O_ΔPPE ** ** ** (-2.37) (-2.50) (-2.46) NEWINV * (-1.53) (-1.86) ΔINTANG (-1.03) (-0.81) (-0.87) (-1.00) (-0.71) (-0.88) ΔONCOA (0.39) (0.53) (-0.04) (0.36) (-0.05) (-0.12) ΔNCOL (-0.57) (-0.57) (-1.23) (-0.69) (-1.42) (-1.41) ΔWC * * (-1.79) (-1.75) (-1.72) (-1.82) (-1.61) (-1.69) lnsize (-0.80) (-0.64) (-0.74) (-0.61) (-0.32) (-0.48) lnbm (0.70) (1.09) (0.65) (0.84) (1.16) (0.80) STREVRET ** ** ** ** ** ** (3.03) (3.10) (3.07) (3.04) (3.04) (3.02) MOMRET ** ** ** ** ** ** (3.87) (3.84) (3.93) (3.91) (3.98) (3.99) LTREVRET ** ** * * * * (-2.29) (-2.28) (-2.13) (-2.12) (-1.95) (-1.93) Noes: Each year in he sample period he following regressions are esimaed: 5 ABNRET α β CAPINV δ RET PRED ε (1) _ k k, 1 k 1 n m 5 α β CAPINV γ OTH _ ACC δ RET _ 1 0 i i, j j, k i1 j 1 k 1 ABNRET PRED ε (2) where CAPINV are individual capial invesmen measures a levels 1 o 4 of he gradual decomposiion of ΔNOA. OTH_ACC are oher accruals from he decomposiion of ΔNOA. RET_PRED are oher reurn predicors, lnsize, lnbm, STREVRET, MOMRET and LTREVRET. The regressions from equaion (2) are carried ou sequenially for he capial invesmen measures from Level 1 hrough 4, such ha n i1 CAPINV m OTH _ ACC i, j, i1 k, 1 ΔNOA. For example, in he regression a Level 4 capial invesmen variables CAPEXP, -DD and O_ΔPPE are measures of CAPINV while OTH_ACC include ΔINTANG, ΔONCOA, -ΔNCOL and ΔWC so ha CAPINV and OTH_ACC add o ΔNOA. 67

68 ΔNCOL and DD are presened as negaive values in order o show he same direcion in changes in ne asses as oher measures of accruals. The ime-series averages of coefficiens wih associaed -saisic on he mean coefficien (in parenheses) are repored in Panel A from regressions for measures of capial invesmen from Level 1 and 2, in Panel B from Level 3 and in Panel C from Level 4 decomposiion. The sample consiss of 12,340 observaions. All variables are defined in Table 2. ** and * denoe saisical significance a he 5% and 10% levels, respecively, for a wo-ailed - saisic es. 68

69 Table 9 Regression of Reurn on Asses (ROA) One-Year Ahead on Curren Measures of Capial invesmen and Oher Accruals Inercep ** ** ** ** ** ** (2.44) (3.08) (2.97) (2.96) (3.11) (1.49) (3.13) ROA ** ** ** ** ** ** ** (54.48) (54.78) (55.07) (55.00) (55.03) (54.79) (54.10) ΔNOA ** (-5.65) ΔNNCOA ** (-4.23) ΔNCOA ** (-4.11) ΔPPE * (-1.95) CAPEXP (-0.61) -DD * (-1.99) O_ΔPPE * * (-1.94) (-2.04) NEWINV (-0.79) ΔINTANG (-1.75) (-1.56) (-1.73) ΔONCOA * * * (-2.00) (-2.01) (-2.04) ΔNCOL (-1.14) (-1.13) (-1.13) (-1.24) ΔWC ** ** ** ** ** (-3.57) (-3.86) (-3.82) (-3.80) (-3.73) Mean adjused R Noes: Each year in he sample period he following regressions are esimaed: ROA α β ROA ε (3) n m ROA α β ROA γ CAPINV δ OTH ACC ε (4) _ i i, j j, 1 i1 j 1 where CAPINV are individual capial invesmen measures a levels 1 o 4 of he gradual decomposiion of ΔNOA. OTH_ACC are oher accruals from he decomposiion of ΔNOA. The regressions from equaion (4) are carried ou sequenially for he capial invesmen measures from Level 1 hrough 4, such ha n i1 CAPINV m OTH _ ACC i, j, i1 ΔNOA. For example, in he regression a Level 4 capial invesmen variables CAPEXP, -DD and O_ΔPPE are measures of CAPINV while OTH_ACC include ΔINTANG, ΔONCOA, -ΔNCOL and ΔWC so ha CAPINV and OTH_ACC add o ΔNOA. ΔNCOL and DD are presened as negaive values in order o show he same direcion in changes in ne 69

70 asses as oher measures of accruals. The ime-series averages of coefficiens wih associaed -saisic on he mean coefficien (in parenheses) are repored, and he ime-series mean adjused R 2 are repored. The sample consiss of 10,602 observaions. All variables are defined in Table 2. ** and * denoe saisical significance a he 5% and 10% levels, respecively, for a wo-ailed -saisic es. 70

71 - - Table 10 Relaion beween Measures of Capial Invesmens and Fuure Reurns and ROA Across Proxies for he Measures of Agency Coss Dependen variable ABNRET +1 ROA +1 AGENCY variable FCF LEV SIZE RD FCF LEV SIZE RD Panel A: Level 1, 2 and 3 variables ΔNOA*H_AGENCY ** (1.77) (0.60) (-0.05) (-0.24) (-0.82) (1.70) (-0.41) (-2.16) ΔNNCOA*H_AGENCY ** * (2.21) (0.49) (-0.47) (1.32) (-0.04) (-1.86) (-0.57) (-1.43) ΔNCOA*H_AGENCY ** (2.12) (0.55) (-0.55) (1.61) (-0.12) (-1.77) (-0.64) (-1.44) ΔPPE*H_AGENCY ** (2.66) (1.08) (-0.60) (1.08) (-1.41) (-1.29) (-1.18) (0.29) Panel B: Level 4 variables: CAPEXP, -DD and O_ΔPPE - CAPEXP*H_AGENCY (0.13) (-0.63) (-0.29) (-0.49) (-1.48) (-0.08) (-0.58) (0.40) -DD*H_AGENCY * * (1.93) (0.63) (-0.82) (-0.68) (0.29) (-1.16) (-2.03) (-1.72) O_ΔPPE*H_AGENCY ** (3.23) (-1.03) (0.27) (0.97) (-1.22) (-1.29) (-0.67) (0.19) - Panel C: Level 4 variables: NEWINV and O_ΔPPE - NEWINV*H_AGENCY (0.48) (-0.52) (-0.73) (-0.61) (-1.28) (-0.65) (-0.93) (-0.35) O_ΔPPE*H_AGENCY ** * (3.13) (-1.81) (0.19) (1.08) (-1.07) (-1.29) (-0.74) (0.55) - Noes: Each year in he period we rank firm-year observaions by he proxies for agency coss: free cash flows (FCF), leverage (LEV) and marke value of equiy (SIZE) below and above median. H_AGENCY is a dummy variable ha akes 1 when FCF is higher, LEV is lower and SIZE is lower han is annual median, and 0 oherwise, respecively. H_AGENCY is also a dummy variable equal 1 if research and developmen expendiure (RD) is posiive (N=3,999), and 0 if i is zero or missing (N=8,341). Each year he following regressions are esimaed: ABNRET and ROA 1 1 α 0 α 0 n i1 2n i i n1 β CAPINV i β CAPINV 1 i, i, i, m j 1 2m γ j j m1 γ OTH _ ACC j OTH _ ACC 1 j, α H _ AGENCY β ROA β 2n γi in1 CAPINV 2m j j m1 j, 2 δ OTH _ ACC 5 k k 1 10 k k 6 j, δ RET _ PRED δ RET _ PRED k, k, ROA * H _ AGENCY * H _ AGENCY ε * H _ AGENCY ε n i1 1 i 1 γ CAPINV i, m j 1 δ OTH _ ACC where CAPINV are individual capial invesmen measures a levels 1 o 4 of he gradual decomposiion of ΔNOA. OTH_ACC are oher accruals from he decomposiion of ΔNOA. RET_PRED are oher reurn predicors, lnsize, lnbm, STREVRET, MOMRET and LTREVRET. The regressions are carried ou sequenially for he capial invesmen measures from Level 1 j j, (5) (6) 71

72 hrough 4, such ha n i1 CAPINV m OTH _ ACC i, j, i1 ΔNOA. For example, in he regression a Level 4 capial invesmen variables CAPEXP, -DD and O_ΔPPE are measures of CAPINV while OTH_ACC include ΔINTANG, ΔONCOA, -ΔNCOL and ΔWC so ha CAPINV and OTH_ACC add o ΔNOA. DD is presened as negaive values in order o show he same direcion in changes in ne asses as oher measures of accruals. The ime-series mean ineracion coefficiens of CAPINV*H_AGENCY wih associaed -saisic on he mean coefficiens (in parenheses) for boh ABNRET and ROA regressions are repored for he measures of capial invesmen (CAPINV) from Level 1, 2 and 3 (Panel A) and Level 4 (Panels B and C) decomposiion. The sample consiss of 12,340 observaions for ABNRET regressions over he period and 10,602 observaions for ROA regressions over he period for explanaory variables. All variables are defined in Table 2. ** and * denoe saisical significance a he 5% and 10% levels, respecively, for a wo-ailed -saisic es. 72

73 Table 11 Regression of Abnormal Reurns One-Year Ahead and Reurn on Asses (ROA) One-Year Ahead on Curren Growh in Employees, Curren Measures of Capial invesmen and Oher Accruals Panel A: Relaion beween fuure abnormal reurns and curren capial invesmens conrolling for growh in employees Inercep (0.85) (0.64) (0.60) (0.57) (0.53) (-0.13) (0.53) EMPLGR ** * (-3.13) (-1.71) (-1.68) (-1.83) (-1.44) (-1.65) (-1.57) ΔNOA ** (-3.51) ΔNNCOA ** (-3.04) ΔNCOA ** (-3.03) ΔPPE ** (-2.50) CAPEXP (-1.70) -DD ** (-2.85) O_ΔPPE * * (-1.97) (-2.01) NEWINV * (-1.86) OTH_ACC included NO YES YES YES YES YES YES RET_PRED included YES YES YES YES YES YES YES 73

74 Panel B: Relaion beween fuure profiabiliy and curren capial invesmens conrolling for growh in employees Inercep ** ** ** ** ** ** (2.85) (3.27) (3.15) (3.13) (3.30) (1.53) (3.32) ROA ** ** ** ** ** ** ** (56.89) (56.97) (57.24) (57.07) (57.02) (56.31) (56.01) EMPLGR ** (-2.77) (-0.38) (-0.71) (-0.89) (-1.24) (-1.19) (-1.23) ΔNOA ** (-5.24) ΔNNCOA ** (-4.06) ΔNCOA ** (-3.95) ΔPPE * (-1.88) CAPEXP (-0.52) -DD ** (-2.19) O_ΔPPE * * (-1.77) (-1.82) NEWINV (-0.74) OTH_ACC included NO YES YES YES YES YES YES Noes: Each year in he sample period he following regressions are esimaed: ABNRET α ρ EMPLGR ε (7) ABNRET α 0 5 k 1 ρ EMPLGR k 1 δ RET _ PRED n i1 k, β CAPINV i ε 1 i, m j 1 γ OTH _ ACC Each year in he sample period he oher wo following regressions are esimaed: ROA α β ROA ρ EMPLGR ε (9) n ROA α β ROA ρ EMPLGR γ CAPINV δ OTH ACC ε (10) _ i i, j j, 1 i1 j1 where CAPINV are individual capial invesmen measures a levels 1 o 4 of he gradual decomposiion of ΔNOA. OTH_ACC are oher accruals from he decomposiion of ΔNOA. RET_PRED are oher reurn predicors, lnsize, lnbm, STREVRET, MOMRET and LTREVRET. The regressions from equaions (8) and (10) are carried ou sequenially for he capial invesmen measures from Level 1 hrough 4, such ha n i1 CAPINV m i, OTH _ ACC j, i1 ΔNOA j m j, (8). For example, in he regression a Level 4 capial invesmen variables CAPEXP, -DD and O_ΔPPE are measures of CAPINV while OTH_ACC include ΔINTANG, ΔONCOA, -ΔNCOL and ΔWC so ha CAPINV and OTH_ACC add o ΔNOA. DD is presened as negaive values in order o show he same direcion in changes in ne asses as oher 74

75 measures of accruals. The ime-series averages of coefficiens wih associaed -saisic on he mean coefficien (in parenheses) from ABNRET regressions (Panel A) and ROA +1 regressions (Panel B) for EMPLGR, CAPINV and ROA are repored. The sample consiss of 12,272 observaions in Panel A and 10,551 observaions in Panel B. All variables are defined in Table 2. ** and * denoe saisical significance a he 5% and 10% levels, respecively, for a wo-ailed -saisic es. 75

76 Table 12 Regression of Abnormal Reurns One-Year Ahead and Indusry-Adjused Reurn on Asses (ROA) One- Year Ahead on Indusry-Adjused Curren Measures of Capial invesmen and Oher Accruals Panel A: Relaion beween fuure abnormal reurns and curren indusry-adjused capial invesmens Inercep (0.78) (0.75) (0.71) (0.69) (0.58) (0.69) ΔNOA_ADJ ** (-4.62) ΔNNCOA_ADJ ** (-3.67) ΔNCOA_ADJ ** (-3.68) ΔPPE_ADJ ** (-2.99) CAPEXP_ADJ ** (-2.33) -DD_ADJ ** (-2.27) O_ΔPPE_ADJ ** ** (-2.58) (-2.54) NEWINV_ADJ * (-2.08) OTH_ACC_ADJ included YES YES YES YES YES YES RET_PRED included YES YES YES YES YES YES 76

77 Panel B: Relaion beween fuure indusry-adjused profiabiliy and curren capial invesmens conrolling for growh in employees Inercep ** ** ** ** ** ** ** (-5.06) (-5.00) (-4.91) (-5.16) (-4.85) (-4.59) (-4.59) ROA _ADJ ** ** ** ** ** ** ** (47.02) (46.29) (46.78) (46.95) (47.01) (46.19) (45.93) ΔNOA_ADJ ** (-4.96) ΔNNCOA_ADJ ** (-3.48) ΔNCOA_ADJ ** (-3.41) ΔPPE_ADJ (-1.61) CAPEXP_ADJ (-0.44) -DD_ADJ (-1.59) O_ΔPPE_ADJ (-1.59) (-1.70) NEWINV_ADJ (-0.60) OTH_ACC_ADJ included N YES YES YES YES YES YES Noes: Each year in he sample period he following regressions are esimaed: ABNRET 1 α 0 5 k 1 n i1 k β CAPINV _ ADJ i δ RET _ PRED k, ε i, 1 m j 1 γ OTH _ ACC _ ADJ j Each year in he sample period he oher following regressions are esimaed: ROA _ ADJ α β ROA ADJ ε (12) _ 1 n m α β ROA _ ADJ γ CAPINV _ ADJ δ OTH _ ACC _ i i, j i1 j1 ROA _ ADJ ADJ ε (13) where CAPINV_ADJ are individual indusry-adjused capial invesmen measures a levels 1 o 4 of he gradual decomposiion of ΔNOA_ADJ. OTH_ACC_ADJ are indusry-adjused oher accruals from he decomposiion of ΔNOA_ADJ. RET_PRED are oher reurn predicors, lnsize, lnbm, STREVRET, MOMRET and LTREVRET. ROA_ADJ is indusry-adjused operaing profiabiliy. The regressions from equaions (11) and (13) are carried ou sequenially for he capial invesmen measures from Level 1 hrough 4, such ha n i1 CAPINV _ ADJ m OTH _ ACC _ ADJ i, j, i1 j, j, 1 (11) ΔNOA _ ADJ. For example, in he regression a Level 4 capial invesmen variables CAPEXP_ADJ, -DD_ADJ and O_ΔPPE_ADJ are measures of CAPINV_ADJ while OTH_ACC_ADJ include ΔINTANG_ADJ, ΔONCOA_ADJ, -ΔNCOL_ADJ and 77

78 ΔWC_ADJ so ha CAPINV_ADJ and OTH_ACC_ADJ add o ΔNOA_ADJ. DD_ADJ is presened as negaive values in order o show he same direcion in changes in ne asses as oher measures of accruals. The ime-series averages of coefficiens wih associaed -saisic on he mean coefficien (in parenheses) from ABNRET regressions (Panel A) and ROA +1 regressions (Panel B) for CAPINV_ADJ and ROA_ADJ are repored. The sample consiss of 12,340 observaions in Panel A and 10,602 observaions in Panel B. All unadjused variables are defined in Table 2. The variables are indusry adjused by subracing annual indusry-median of respecive variable. Indusry is defined by 32 non-financial secor levels from he FTSE/Dow Jones Indusry Classificaion Benchmark. ** and * denoe saisical significance a he 5% and 10% levels, respecively, for a wo-ailed -saisic es. 78

79 Table 13 Regression of Abnormal Reurns One-Year Ahead and Reurn on Asses (ROA) One-Year Ahead on Operaing Profiabiliy, Acquisiion, Disposals, Exernal Financial Aciviies, Curren Measures of Capial invesmen and Oher Accruals Panel A: Relaion beween fuure abnormal reurns and curren capial invesmens conrolling for curren operaing profiabiliy, acquisiions, disposals and exernal financing Inercep (1.01) (0.86) (0.84) (0.80) (0.79) (0.29) (0.86) ACQ (-1.70) (-0.36) (-0.30) (-0.37) (-0.60) (-0.08) (-0.17) -DISP * * (0.71) (1.71) (1.68) (1.63) (1.74) (2.01) (1.81) EXTFIN ** ** ** ** ** ** ** (-4.54) (-2.69) (-2.74) (-2.78) (-3.03) (-3.37) (-3.68) ROA (0.85) (1.22) (1.21) (1.25) (1.29) (1.12) (1.18) ΔNOA ** (-3.96) ΔNNCOA ** (-2.98) ΔNCOA ** (-2.90) ΔPPE ** (-2.76) CAPEXP (-1.06) -DD ** (-2.72) O_ΔPPE ** ** (-2.46) (-2.40) NEWINV (-1.14) OTH_ACC included NO YES YES YES YES YES YES RET_PRED included YES YES YES YES YES YES YES 79

80 Panel B: Relaion beween fuure profiabiliy and curren capial invesmens conrolling for acquisiions, disposals and exernal financing Inercep ** ** ** ** ** ** (2.64) (2.95) (2.91) (2.90) (2.90) (1.29) (2.80) ROA ** ** ** ** ** ** ** (50.42) (50.82) (50.99) (50.73) (49.89) (48.21) (47.91) ACQ ** ** ** ** ** ** ** (3.75) (6.17) (4.83) (4.70) (5.05) (5.34) (5.33) -DISP (-0.82) (0.52) (0.64) (0.58) (0.38) (1.29) (1.02) EXTFIN ** (-2.75) (-1.24) (-1.27) (-1.31) (-1.37) (-1.43) (-1.57) ΔNOA ** (-5.67) ΔNNCOA ** (-4.29) ΔNCOA ** (-4.25) ΔPPE ** (-2.28) CAPEXP (0.05) -DD (-1.52) O_ΔPPE ** ** (-3.15) (-3.13) NEWINV (-0.08) OTH_ACC included NO YES YES YES YES YES YES Noes: Each year in he sample period he following regressions are esimaed: ABNRET α ρ ACQ ρ DISP ρ EXTFIN ρ ROA ε (14) ABNRET α i1 0 n ρ ACQ i 1 β CAPINV i, ρ DISP ρ EXTFIN 2 m j1 γ OTH _ ACC j 3 j, ρ ROA 4 5 k k 1 δ RET _ PRED Each year in he sample period he oher following regressions are esimaed: ROA α β ROA ρ ACQ ρ DISP ρ EXTFIN ε (16) k, ε 1 (15) 80

81 ROA 1 α 0 n i1 β ROA i 1 γ CAPINV ρ ACQ i, 1 m j1 ρ DISP ρ EXTFIN 2 δ OTH _ ACC j j, 3 ε 1 where ACQ is ne asses from acquisiions, DISP is disposal of fixed asses, EXTFIN is ne change in equiy and deb financing. CAPINV are individual capial invesmen measures a levels 1 o 4 of he gradual decomposiion of ΔNOA. OTH_ACC are oher accruals from he decomposiion of ΔNOA. RET_PRED are oher reurn predicors, lnsize, lnbm, STREVRET, MOMRET and LTREVRET. The regressions from equaions (15) and (17) are carried ou sequenially for he capial invesmen measures from Level 1 hrough 4, such ha n i1 CAPINV m OTH _ ACC i, j, i1 (17) ΔNOA. For example, in he regression a Level 4 capial invesmen variables CAPEXP, -DD and O_ΔPPE are measures of CAPINV while OTH_ACC include ΔINTANG, ΔONCOA, -ΔNCOL and ΔWC so ha CAPINV and OTH_ACC add o ΔNOA. DISP and DD are presened as negaive values in order o show he same direcion in changes in ne asses as oher measures of accruals. The ime-series averages of coefficiens wih associaed -saisic on he mean coefficien (in parenheses) from ABNRET regressions (Panel A) and ROA +1 regressions (Panel B) for ACQ, -DISP, EXTFIN, CAPINV and ROA are repored. The sample consiss of 12,059 observaions in Panel A and 10,369 observaions in Panel B. All variables are defined in Table 2. ** and * denoe saisical significance a he 5% and 10% levels, respecively, for a wo-ailed -saisic es. 81

82 Figure 1 Annual Abnormal Reurns from he Capial-Invesmen Based Sraegies Panel A: Annual hedge abnormal reurns from he capial invesmen in Levels 1, 2 and ΔNOA ΔNCNOA ΔNCOA ΔPPE Panel B: Annual hedge abnormal reurns from he Annual hedge abnormal reurns from he capial invesmen in Level CAPEXP -DD O_ΔPPE NEWINV Noes: Each year in he 1990 o 2004 period firms are sored ino quiniles by heir capial invesmen measures from Levels 1, 2 and 3 (Panel A) and Level 4 (Panel B) decomposiion. The sraegy consiss of invesing in low capial invesmen quinile and shoring firms in high capial invesmen quinile. Annual hedge reurns are ploed for each year by he capial invesmen measure. The sample consiss of 12,340 observaions. All variables are defined in Table 2. 82