Journal Of Business & Economics Research Volume 1, Number 11

Transcription

1 Profis From Buying Losers And Selling Winners In The London Sock Exchange Anonios Anoniou ( Universiy of Durham, UK Emilios C. Galariois ( Universiy of Durham, UK Spyros I. Spyrou ( Ahens Universiy of Economics and Business, Greece Absrac DeBond and Thaler (1985) have challenged he noions of marke efficiency and of raional invesor behaviour. According o heir findings sock porfolios ha experience negaive reurns end o ouperform porfolios ha experience posiive reurns, during he subsequen period. In oher words, sock reurns may be predicable, and his may be due o excessive invesor opimism and pessimism. This paper invesigaes he exisence of such conrarian profis for socks lised in he London Sock Exchange. The resuls indicae ha conrarian sraegies are profiable for UK socks and more pronounced for exreme marke capialisaion socks. These profis persis even afer he sample is adjused for marke fricions, and irrespecive of wheher raw or risk-adjused reurns are used. 1. Inroducion DeBond and Thaler (1985) challenge he noions of marke efficiency and of raional invesor behaviour by demonsraing ha porfolios ha experience negaive reurns end o ouperform ones ha experience posiive reurns, by up o 25% during he subsequen period. They sugges ha he observed predicabiliy is due o negaive serial correlaion and sems from exreme invesor opimism and pessimism. This paper invesigaes he exisence of such conrarian profis for socks lised in he London Sock Exchange (LSE henceforh) a leading global equiy marke. There are hree main issues ha differeniae his paper from previous UK empirical sudies. Firsly, i considers shor-erm conrarian sraegies, since invesors end o have shor invesmen horizons and are unlikely o se up very long-erm sraegies. Secondly, i considers riskadjusmens based on hree-facor models raher han adjusmens ha only consider a single (marke) facor. Thirdly, i invesigaes wheher he resuls are affeced by marke microsrucure biases, such as bid ask bias and infrequen rading. The paper is organised as follows: in he following wo secions we briefly review some of he mos imporan sudies in he empirical lieraure and presen he sample daa. Nex, we es for serial correlaion since negaive serial correlaion can ransform winners o losers and losers o winners. In oher words, if negaive serial correlaion is presen in he daa a sraegy ha shors each period pas winners and longs each period pas losers could be profiable. Finally, we presen he conrarian rading sraegies and also examine wheher he resuls are due o marke microsrucure biases. 2. A Brief Review of he Lieraure In he firs sudy ha iniiaed he debae DeBond and Thaler (1985, 1987) find ha US long-erm sock reurns are predicable. Tha is, porfolios ha experience negaive reurns end o ouperform porfolios ha experience posiive reurn, by up o 25% during he subsequen period. In following sudies on US socks, Jegadeesh (1990), Jegadesh & Timan (1995), Lehman (1990) produce resuls ha end o indicae ha his may also be he case for shor-erm conrarian sraegies. A firs possible explanaion of he effec was pu forward by Chan (1988) 59

2 and Ball and Kohari (1989) who argued ha his phenomenon is due o risk miss measuremen and changes in equilibrium required reurns. Zarowin (1990) offers anoher possible explanaion arguing ha losers have a endency o be smaller sized firms han winners, in he US marke. Sill oher auhors argue ha he explanaion lies in marke fricions such as bid-ask biases and infrequen rading which are no properly accouned (e.g. Conrad and Kaul, 1993). Lo and MacKinlay (1990) poin ou ha hese sraegies may be profiable even when he reurns of some socks reac faser o informaion han he reurns of oher socks (a lead-lag effec). However, Jegadesh and Timan (1995) find ha despie he presence of a size-relaed lead-lag srucure mos of conrarian profis are due o firmspecific overreacion. In empirical sudies ha involve long-erm UK marke evidence Poerba and Summers (1988) find negaive serial correlaion consisen wih overreacion, while Dissanaike (1997) employs conrarian sraegies adjused for risk and also finds ha pas losers ouperform pas winners. Furhermore, Brouwer e al., (1997) come o similar conclusions, Richards (1997) shows ha overreacion is unrelaed o risk and anomalies, and Balvers e al., (1999) confirm mean reversion and overreacion. Finally, Bayas and Cakiki (1999) find posiive and significan profis for conrarian porfolios, and Clare and Thomas (1995) find long-erm evidence ha seem o be consisen wih overreacion. They argue however ha he resuls are relaed o he size-effec. 3. Daa The paper uses weekly price observaions for all socks lised on he LSE ha had a leas 260 consecuive observaions, beween 1984 and The FTSE100 Price Index is employed as a proxy for he common facor (marke porfolio). Reurns are coninuously compounded, defined as he firs difference of he logarihmic price levels, and all daa are colleced from Daasream Inernaional. Table 1 presens saisics on he number of firms available for each year and heir marke values. For example, he larges number of firms (1645) in a single year are for 1990, while he smalles one is me in 1985 (1164 firms). The smalles marke value of a firm is below 0.01 million Serling for years 1989 hrough 1996, while he maximum marke value is for year 2000 (119,814.1 million serling). Mean marke values range from million in 1985 o 1,234.6 million in Table 1 Toal Number of Firms in he Sample and Marke Values per year Year Min. Value Max. Value Mean Value Toal no. of firms < < < < < < < < Noe: Values in million of Serling. 60

3 For he empirical analysis, socks are assigned o five sub-samples based on marke capialisaion (i.e. smalles, small, medium, large, larges firm sub-samples) as follows: every year all socks are ranked based on he previous year-end sock marke capialisaion and subsequenly grouped o five sub-samples ha each conain 20% of firms. The procedure is repeaed every year, for a period of sixeen years. Descripive reurn saisics based on closing prices are presened in Table 2. The average weekly reurn for all socks is 0.05% wih a sandard error of 0.017, while he highes mean reurn is ha of he smalles sock subsample (0.001). The larges sock sub-sample has he second highes reurn and he highes sandard error (0.020). Table 2 Descripive Saisics of Sock Reurns All Smalles Small Medium Large Larges Mean Sandard Error Minimum Maximum Jarque-Berra Negaive Serial Correlaion An imporan issue in he examinaion of he predicabiliy of sock reurns and profis from conrarian sraegies is he exisence of negaive serial correlaion in sock reurns. Tha is, negaive serial correlaion can ransform winners o losers and losers o winners, in oher words, a sraegy ha shors each period pas winners and longs each period pas losers could be profiable. Thus, as a firs sep in he analysis, his paper invesigaes wheher negaive serial correlaion is presen in he daa. However, since auhors have poined ou ha how one defines abnormal reurns is imporan for he profiabiliy of conrarian sraegies (see for example Chopra e al., 1992), he paper does no only examine simple raw reurns bu also reurns adjused for risk. We do his in wo ways: (a) wih a single facor model as is done in mos previous sudies and (b) wih a hree-facor model similar o he one suggesed by Fama and French (1996). More specifically, reurns are firs defined as he residuals (e i, ) from a marke model as follows: r i ai bi rm, ei, (1) where r i, is he raw reurn of sock i a ime, r m, is he reurn of he marke porfolio (m) a ime, and e i, is he marke-adjused reurn for sock i a ime. Nex, reurns are defined as he residuals (e i, ) from a hree-facor model as follows: r i ai bmrm, bsmbsmb bhml HML ei, (2) In (2) he facor SMB (Small Minus Big) is he difference beween he reurn on a porfolio of small socks and he reurn on a porfolio of large socks. The facor HML (High Minus Low) is he difference beween he reurn on a porfolio of high book-o-marke socks and he reurn on a porfolio of low book-o-marke socks. The facors are consruced in a similar manner as in Fama and French,

4 The resuls are presened in Table 3, and sugges ha negaive serial correlaion is presen in he daa. Wih raw reurns (Panel A) 453 of he sample firms exhibi negaive 1 s order serial correlaion. When marke risk is considered (Panel B), 643 firms exhibi 1 s order negaive serial correlaion, while when reurns are adjused for facors similar o he FF ones, 739 firms exhibi 1 s order serial correlaion. Pu simply, in he las case more han half of he firms in he sample are negaively serially correlaed in he 1 s order, and his could indicae ha pas losers are less risky han pas winners. In order o examine wheher firms ha rade infrequenly affec he above resuls we also es for serial correlaion in he daa afer excluding all socks ha rade infrequenly. The resuls are similar o he resuls presened in Table 3, and are no repored here (bu are available upon reques). To summarise he resuls hus far, negaive correlaion appears presen in UK sock reurns even afer adjusing for various risk facors, in line wih long erm evidence by Balvers e al., (1999), and Poerba and Summers (1988). Table 3 Serial Correlaion & Significance (All Firms) Order 1 s 2 nd 3 rd 4 h Number of 5% * Panel A: Raw Reurns * * * Number of 5% * Panel B: Risk-Adjused Reurns * * * Panel C: Three-facor adjused Reurns Number of 5% * * Noes: * Denoes firms wih significan negaive serial correlaion a he 5% level Table 3 presens socks wih negaive serial correlaion * * 5. The Trading Sraegy As shown above sock reurns in he LSE exhibi negaive serial correlaion. This could poenially creae profis for a rading sraegy ha buys pas losers and sells pas winners. The rading sraegy employed in his paper is a sandard shor-erm conrarian sraegy, i.e. i consiss of a porfolio ha every period is shor in he previous period s winners and long in he previous period s losers. The sraegy is also employed in previous sudies such as Jegadeesh and Timan (1995), Lo and Mackinley (1990), among ohers. More specifically, he zero-invesmen porfolios are re-balanced every week, and he profis, are esimaed as: N 1 ( ri, 1 r 1 ) ri, (3) N i1 where, r 1 is he lagged reurn on an equally weighed porfolio ha conains all socks, r i,-1 is he reurn on sock i a ime -1, and N is he number of socks in he sample. However, as discussed above, many auhors have argued ha conrarian profis may be due o biases such as bid-ask biases or infrequen rading. In order o examine wheher any observed profis are due o marke fricions we also recalculae conrarian profis employing bid-o-bid prices raher han closing prices, and also we recalculae profis afer excluding infrequenly rading firms. The resuls are repored in Table 4 for all size sub-samples and he full sample. In Panel A, we repor he average conrarian profi for all sub-samples when closing prices are used o compue reurns. In Panel B, we repor profis when bid-o-bid prices are employed, while in Panel C we repor 62

5 profis when infrequenly rading firms are excluded. Panel D and E repor profis for risk adjused reurns when infrequenly rading firms are excluded. Risk adjusmen akes place by means of a single facor model (Panel D) and a hree-facor model (Panel E), as discussed above. As can be seen from Table 4 (Panel A) conrarian profis are saisically significan for he smalles, large, and larges sub-sample. For example, he average weekly conrarian profi (x10 3 ) is and for he smalles and he larges sub-sample respecively. Noe ha, Jegadeesh and Timan, for he US marke find higher average weekly conrarian profis and also while in he US conrarian profis decline as one moves from small o large socks, he opposie seems o happen in he LSE. The profis wih he bid-o-bid prices (Panel B) appear lower, hus, i appears ha a bid-ask bias may affec resuls. For example, he average weekly profi for he all sock-group is from and saisically insignifican. When infrequenly rading firms are excluded (Panel C) average weekly profis also appear lower. For example, he average weekly conrarian profi for larges socks is now from in Panel A, while for smalles socks i is from The resuls so far seem o sugges ha par of he profis may be due o infrequen rading and also ha conrarian sraegies are profiable only for he wo exreme size sub-samples. The average weekly conrarian profis ha are obained from risk adjused reurns (Panels E and D) exhibi a differen picure. Firsly, profis are posiive and saisically and significan a he 5% level for nearly all subsamples. Secondly profis decline as one moves from smalles o large sock sub-samples (similar o he resuls of previous sudies for he US marke. Table 4 Conrarian Profis (π) All Smalles Small Medium Large Larges Panel A: Closing prices (all socks) π x (2.320)* (2.753)* (-0.749) (0.426) (-4.480)* (2.495)* Panel B: Bid-o-Bid Prices π x (1.046) (1.032) (0.613) (-5.255)* (-3.323)* (5.556)* Panel C: Excluding ha Trade Infrequenly π x (-0.101) (1.953)** (-1.528) (-2.143)* (-5.138)* (7.056)* Panel D: Single-Facor Risk-Adjused Reurns (Frequen Trading ) π x (0.197) (5.185)* (2.995)* (1.703)** (-0.263) (8.537)* Panel E: Three-facor Risk-Adjused Reurns (Frequen Trading ) π x (1.647)** Noe: -saisics appear in parenheses (6.097)* (4.874)* (3.109)* (1.997)* (10.369)* 63

6 In order o examine he economic significance of hese conrarian profis, he conrarian profi per Serling long () are esimaed as follows as suggesed by Bacmann and Dubois More specifically he conrarian profi per Serling long () is: N 1 wi, ri, 11, k (4) N w 1 11 i, 1 where w i, ( ri, 1 rm, 1 ) if r i, 1 r or 0 oherwise. is defined o provide profis only when each asse s m, 1 N 1 lagged reurns are lower han he lagged average reurns of all socks in he sample, in which case he posiion on ha asse nex period would be long. Obaining a weighed average of reurns () resuls o reurns per Serling long. The resuls for conrarian profis per Serling long are repored in Table 5 (organised exacly as Table 4) and sugges ha conrarian profis are posiive and economically significan for mos sub-samples only when reurns are adjused for risk. For example, when a hree-facor model is used o adjus for risk he conrarian profi per Euro long for he smalles sock sub-sample is wih a significan -saisic of The conrarian profi per Euro long for he larges sock sub-sample is wih a significan -saisic of Table 5 Conrarian Profis Per Euro Long () All Smalles Small Medium Large Larges Panel A: Closing prices (all socks) (1.561) (3.366)* (-0.121) (-2.690)* (-2.675)* (1.429) Panel B: Bid-o-Bid Prices (0.908) (1.063) (0.699) (-4.032)* (-1.860)** (3.035)* Panel C: Excluding ha Trade Infrequenly (0.574) (2.265)* (-0.977) (-3.502)* (-2.231)* (3.656)* Panel D: Single-Facor Risk-Adjused Reurns (Frequen Trading ) (-0.448) (4.454)* (3.169)* (0.153) (0.084) (6.447)* Panel E: Three-facor Risk-Adjused Reurns (Frequen Trading ) Noes: -saisics appear in parenheses (0.151) (5.094)* (4.517)* (1.007) (1.369) (7.938)* 64

7 To summarise hus far, conrarian sraegies produce saisically (π) and economically (ψ) significan profis in he LSE, irrespecive of how sock reurns are defined. In addiion, he wo mos "profiable" sub-samples appear o be he wo exreme size sub-samples. Furhermore, profis decline as one moves from he smalles sock sub-sample o larger sock sub-samples. The paper s findings so far on shor-erm profiabiliy are in line in mos aspecs wih sudies for he US marke, and consisen wih long-erm findings for he UK sock marke (e.g. Dissanaike, 1997, Brouwer e al., 1997, ec). Concluding Remarks This paper invesigaes he exisence of shor-erm conrarian profis for socks lised in he LSE. The main resul ha emerges from he empirical analysis is ha conrarian sraegies produce saisically and economically significan profis ha are no explained by infrequen rading, bid-ask biases, and risk. Furhermore, profis are more pronounced for exreme marke capializaion socks (smalles - larges), and LSE invesors could hus employ conrarian sraegies for such socks. The resuls presened in he paper seem o sugges ha pas prices predic fuure reurns, implying ha he marke is no efficien wih respec o hisorical informaion. This is consisen wih previous resuls for he UK marke on long-erm price reversals. Furhermore, i is demonsraed ha shor-erm conrarian profis are no specific for US daa. Wih respec o marke paricipans, he facs show ha conrarian sraegies in he LSE are also profiable for shor-erm horizons and more imporanly, profis are no due o aking on excess risk direcly or indirecly, since profis exis for risk adjused reurns of even large and more liquid socks. A quesion ha arises a his poin is on he facors ha drive hese profis; i.e. are hey firm specific or marke wide facors, and if boh of hese ses of facors conribue, o wha exen does each one do. I would hus be very ineresing o examine his issue furher. This however, is he work of anoher working paper by he Anoniou, Galariois and Spyrou (2003). References 1. Anoniou, A., Galariois E.C., Spyrou, S.I., Shor-erm conrarian sraegies in he London Sock Exchange: are hey profiable? Which facors affec hem?, Working paper, Cenre for Empirical Research in Finance, Durham Business School. 2. Bacmann, J.F., Dubois, M., Conrarian sraegies and cross-auocorrelaions in sock reurns: evidence from France, Social Science Research Nework Elecronic Library, & European Financial Managemen Associaion 1998 Meeing. 3. Ball, R., Kohari, S.P., Non-saionary expeced reurns: implicaions for ess of markes efficiency and serial correlaion in reurns, Journal of Financial Economics, 25, Balvers, R., Wu Y., Gilliland, E., Mean reversion across naional sock markes and parameric conrarian invesmen sraegies, Journal of Finance, 55, Bayas, A., Cakici, N., Do markes overreac? Inernaional evidence, Journal of Banking and Finance, 23, Brouwer, I., Van Der Pu, J., Veld, C., Conrarian invesmen sraegies in a European conex, Journal of Business Finance & Accouning, 24, Chan, C., On he conrarian invesmen sraegy, Journal of Business, 61, Chopra, N., Lakonishok, J., Rier, J., Do socks overreac?, Journal of Financial Economics, 31, Chordia, T., Shivakumar, R., Momenum, Business Cycle, and Time varying expeced reurns, Journal of Finance, 57,

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