Forecasting the dynamics of financial markets. Empirical evidence in the long term

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1 Leonardo Franci (Ialy), Andi Duqi (Ialy), Giuseppe Torluccio (Ialy) Forecasing he dynamics of financial markes. Empirical evidence in he long erm Absrac This sudy aims o verify wheher here are any macroeconomic variables ha have significan power in predicing he dynamics of financial markes. In paricular, he paper wans o idenify an economeric model ha can guide he sraegies of operaors in building heir invesmen porfolios. The analysis considers he US marke during a period of rapid economic change and high volailiy of sock prices. The auhors evidence ha a number of variables have sysemaically influenced he evoluion of he sock marke during he period under review. These variables are he macroeconomic indicaors ha relae o he senimen of consumers and companies, he erm srucure, he premium for he risk of defaul, he rae of growh of he moneary base, he oil price and sea freigh raes. Keywords: macroeconomic news, sock marke, regression wih disribued delays. JEL Classificaion: G10, G11, G17, N20. Inroducion The relaionship beween macroeconomic variables and he dynamics of he financial markes has radiionally been considered crucial by invesors, who have o observe he economic conex prior o making heir own porfolio choices. In fac, all of he decisions aken by raional invesors conain an implici and/or explici evaluaion of he presen and fuure economic condiions. Alhough neoclassical financial heory has idenified wihin he sysemaic risk he risk componen ha is inheren in any ype of financial insrumen, i has failed o indicae which macroeconomic variables are able o produce a persisen impac on he sock exchange rend. The necessiy of invesigaing his relaionship arises more deeply from he need of invesors o make projecions abou heir invesmen sraegies in order o increase he payoffs from heir sock porfolios. The aim of his sudy is herefore o deermine, hrough he analysis of a panel of daa relaed o he US marke over a seven-year period, an economeric model ha is able o anicipae he rend of he sock marke, based on cerain macroeconomic variables. In an aemp o define a concepual reference frame, he nex secion provides a review of he curren lieraure on his opic. Then, in secion 2 we carry ou an illusraion of he mehodology used o idenify he macroeconomic variables ha may have a significan impac on sock performance. In secion 3, we conduc differen saisical analyses and inerpre he empirical findings in he ligh of previous research. Finally, afer having verified he robusness of he model, we perform an invesmen sraegy by creaing an ou-of-sample porfolio based on our model s assumpions. Leonardo Franci, Andi Duqi, Giuseppe Torluccio, Lieraure review The possibiliy ha macroeconomic indicaors affec asse prices has been he focus of a considerable number of sudies during he las 30 years. Prior research has produced numerous heoreical models, bu none appears enirely saisfacory. A firs effor in his direcion was made by Ross (1976), who affirmed ha he price of financial aciviies is influenced by a series of risk facors ha define he macroeconomic conex, for example, he oil price, he ineres rae rend, inflaion and GDP. Subsequenly, Fama and Schwer (1977) found ha real sock reurns are negaively correlaed o he expeced and unexpeced inflaion componens. They also indicaed ha indusrial producion and he growh of GDP provide a good capaciy for forecasing he fuure yields of cerain financial aciviies. Culer, Poerba and Summers (1989) affirmed ha a sudden and unexpeced increase in he indusrial producion growh rae would rigger a significan upward movemen of sock prices. Furher, he findings of a sudy carried ou by DeFina (1991) highlighed how an unexpeced increase in he inflaion rae produces adverse effecs on firms earnings. This relaionship is explained by wo facors: firs, by he naure of commercial conracs concluded by firms 1 ; and second, by he presence in he ax legislaion of a series of elemens ha amplify inflaion effecs, enhance fiscal pressure and reduce firms profis 2. 1 Quie ofen, firms conclude nominal conracs wih heir cusomers and suppliers. A ypical example of a nominal conrac is ha beween a manufacuring firm and is wholesalers, which fixes ex ane he amoun payable by he laer for he fuure purchase of each compleed lo. 2 As an example, depreciaion of angible asses reduces earnings before axes. However, an unexpeced flucuaion in he inflaion rae reduces he real depreciaion value, increasing he real ax value. FIFO mehodology for he evaluaion of invenories may lead, during inflaion periods, o an increase in profis and, herefore, a higher axaion value. 93

2 Oher sudies have concenraed on he abiliy of he moneary policy o influence he course of financial markes. Homa and Jaffee (1971) indicaed ha growh in he moneary base had preceded good performance in erms of quarerly earnings resuls for he period of Kaul (1987) found ha he negaive correlaion beween he real sock reurns and inflaion in he pos-war period may have been he resul of a non-cyclic moneary policy. Hardouvelis (1987) examined he reacion of he financial markes o news iems relaing o fifeen differen macroeconomic variables. The resuls showed ha news iems abou moneary policies had significanly influenced sock prices during he period of Asprem (1989) conduced an analysis of he relaionship beween marke indices and macro daa in en European counries. His resuls highlighed he fac ha a negaive correlaion emerges beween he rae of increase in he moneary base and he sock marke. An addiional variable ha has been considered fundamenal in influencing he fuure rend of he sock marke is he oil price. However, he lieraure has no provided conclusive resuls on his opic. Kling (1985) assered ha an increase in he oil price is accompanied by a downward movemen in he financial markes. Chen, Rol and Ross (1986), on he oher hand, showed ha variaions in he oil price have no significan effecs on sock prices. Huang, Masulis and Soll (1996), however, using daily daa from 1979 o 1990, found no significan evidence of a relaionship. A number of empirical sudies have indicaed ha he erm srucure possesses a predicive capaciy regarding he fuure rend of sock prices. Campbell (1987) noiced ha he exising spread beween shor-erm governmen bonds has significan predicive power for fuure sock reurns. Such evidence was laer confirmed by Fama and French (1989), who exended he validiy of he model o he spreads among mid-erm and long-erm governmen bonds. Chen, Rol and Ross (1986) showed ha a variaion in he slope of he erm srucure produces significan effecs on share prices. Keim and Sambaugh (1986) underlined a posiive correlaion beween shifs in he erm srucure and flucuaions in US sock prices. More recen lieraure has provided furher conribuions wih regard o he long-erm link beween macroeconomic daa and he financial markes. Rapach (2001) sudied he effecs of he shocks produced by he money supply and by he aggregae demand and supply on sock reurns, idenifying conradicory resuls. Drawing on sudies previously conduced by Lee (1992), Flannery and Proopapadakis (2002) 94 developed an analysis of he way in which he daily sock quoaions respond o macroeconomic news. Guidolin and Ono (2006) underook an empirical analysis based on monhly daa for he period of and demonsraed ha, in he long erm, here is a subsanially sable relaionship beween differen financial markes (sock, bonds and money marke) and he main macroeconomic aggregaes. In he las few years, here have also been conribuions aiming o affirm he opposie relaionship, suggesing ha financial marke performance is helpful in forecasing he fuure rends of cerain macroeconomic indicaors. In his regard, James, Koreisha and Parch (1985) evidenced ha sock reurns enable he forecasing of fuure flucuaions in expeced inflaion and he nominal ineres rae. Furher, by evaluaing he findings of previous researchers, Lee (1992) explained how sock reurns are good indicaors of fuure real economic growh, which is measured by indusrial producion. However, in conras o he above argumens, a sudy carried ou by Canova and De Nicolò (2000) has found ha US sock reurns do no have any significan forecasing power on fuure rends in inflaion and real economic growh. There has been lile aemp o sudy he abiliy of macroeconomic variables o affec he price flucuaions of financial insrumens. The inernaional lieraure has also produced only a few sudies focusing on he abiliy of hese variables o predic he volailiy of financial markes. Ederingon and Lee (1993) examined he effec of 22 repors relaed o US macroeconomic daa on flucuaions in fuures on reasury bills and exchange raes during a hreeyear period. The sudy showed how flucuaions in fuures prices (in paricular, hose on governmen bonds) reach a high level of volailiy in he firs fifeen minues following he disseminaion of news relaed o five macroeconomics variables: he unemploymen rae, he consumpion price index, he rade balance, GDP and reail sales. We conribue o he exising lieraure by using special regression echniques o highligh he significan dependence beween he main macroeconomic variables and fuure rends in he US sock index (S&P500). 2. Sample selecion and mehodology The daa used in his paper were colleced on a monhly basis 1 from January 2002 o December The high frequency and lenghy ime span enables he inclusion in he analysis he impac of 1 Daa source: Daasream.

3 some exraordinary hisorical evens on he capial markes, including he recen crises riggered by subprime loans. Addiionally, he use of sufficienly numerous hisorical series guaranees significan oucomes from he economeric analysis, hus miigaing he disorion effecs of poenially excepional evens when only shor periods are considered. Basically, sock prices are condiional on wo facors: fuure expeced dividends and he discoun rae 1. We assume in advance ha only hose macroeconomic variables ha influence he aforemenioned facors on a regular basis can be revealed as poenial anicipaory indicaors of he fuure rend in sock prices. In order o esablish he exisence of a poenial correlaion beween he macro indicaors and he rend in sock prices, we apply a ime-series regression model wih disribued delays 2. This has been widely proposed in he lieraure for sudying he curren and fuure causal dynamic effecs of a change in an independen variable (X ) on he dependen (Y ) (Pierce, 1975; Sock and Wason, 2003). Pracically, Y can be esimaed as a linear combinaion of he curren and r lagged values 3 of he variable X, namely: r Y X, (1) i0 i ( i) where is he consan; i are he regression coefficiens or dynamic mulipliers; is he error erm. Equaion (1) allows he deerminaion of he poenial dependence and respecive inensiy among he variables ha are he subjec of our sudy. The esimaion of he regression coefficiens wih disribued delays is conduced hrough he radiional echnique of ordinary leas squares (OLS). Prior o proceeding wih an empirical verificaion, a preliminary saisical analysis is carried ou on he ime series in order o ascerain wheher he hypohesis of saionariy holds for i. For his purpose, he original ime series are esed using he uni roo Phillips-Perron es 4. In order o avoid he evenual exisence of uni roos in he original ime series (ha is, heir non-saionariy), we calculae he firs difference or he firs differences of he logarihms Empirical resuls In his secion we ascerain wheher here is any causal effec beween he lagged variaions of several macroeconomic variables and curren changes in he S&P500 index. The analysis was divided ino wo pars. In he firs par, he predicive abiliy of each individual macroeconomic variable was esed. Subsequenly, wo predicion models were proposed, ino which we insered only hose regressors ha had been shown o be mos significan in he shor erm An overview of he macroeconomic variables used as regressors. The analysis menioned above was performed using several US macroeconomic indicaors. Such indicaors, which are summarized in Table 1, conain imporan informaion on growh and inflaion. The analysis of hese variables synhesizes he dynamics of he economic cycle. As has been observed previously in he lieraure, analyss repors and invesors preferences are widely condiioned and driven by hese macroeconomic daa. The Federal Reserve iself conducs exremely scrupulous conrol and analysis of he configuraion of hese daa, so ha i can opimize is policy in relaion o ineres raes. In paricular, six of hese variables refer o ineres raes and raw maerial prices: he erm spread (difference beween he 10-year or 30-year Treasury Bond yield and he 3-monh Treasury Bill yield), he Fed Funds rae, he risk premium (difference beween he 30-year Treasury Bond yield and he yield of lowcrediworhiness corporae bonds), he defaul premium (difference beween he yields of high-raed and low-raed corporae bonds), he oil price and he rend in he Balic Dry Index. The resuls of a sudy conduced by Esrella and Hardouvelis (1991) revealed how he slope of he erm srucure has ofen preceded fuure sages of expansion or recession in he real economy, o he exen ha he erm spread has been included in he Federal Reserve s Conference Board s Index of Leading Indicaors. 1 These facors produce a conradicory effec on he sock price. The prospecs of a growing economy in he fuure rigger an increase in he expeced profis, bu hey also cause an increase in he ineres raes applied o such fuure profis. Likewise, he prospec of a fuure decline in he economy riggers a fall in expeced profis and a corresponding reducion of he price discoun rae. In order o know which of he wo effecs prevails, i is essenial o observe he sae of healh of he real economy. During downurns in he economic cycle, he effecs of posiive economic repors have a sronger influence on expeced profis han on ineres raes. The opposie occurs during periods of expansion, when he impac on ineres raes is generally predominan. 2 We presume ha he macroeconomic variables are exogenous facors wih respec o he formaion of sock prices in he financial markes. 3 Specifically, for each variable, delays from zero o 12 monhs have been insered in he regression. 4 Phillips and Perron s es saisics can be viewed as Dickey-Fuller saisics ha have been made robus o serial correlaion by using he Newey-Wes (1987) heeroskedasiciy and auocorrelaion consisen covariance marix esimaor. For breviy, we do no presen he full resuls here. 5 The use of logarihms can be jusified by he fac ha many economic series are characerized by exponenial growh and/or by he fac ha, for many economic series, he sandard deviaion is approximaely proporional o he level. In he laer case, he sandard deviaion of he logarihm is approximaely consan. The variaion of he logarihm of a variable calculaed in hundreds, or he firs log difference Y, is equal o: Y d log( Y ) log 100. Y 1 95

4 Table 1. Descripive saiscs of main variables Symbol Variable Source Unis Mean SD P5 Median P95 Dependen variable S&P500 Sandard & Poors 500 Index Sandard & Poor s b.p Independen variables BDI Balic Dry index Balic Exchange b.p CC Consumers confidence The Conference Board Number CPMI Chicago PMI Naional Associaion of Purchasing Managers Number CUR Capaciy uilizaion rae Federal Reserve % DP Defaul premium USA Corporae bond yield Moody's BAA USA Corporae bond yield Moody's AAA % FFR Fed Funds rae Federal Reserve % HBP Building permis Bureau of he Census ($/000) IP Indusrial producion Federal Reserve Number ISM ISM index Insiue for Supply Managemen Number M2 M2 aggregae The Conference Board $/Billions MCSI Michigan consumer senimen index Universiy of Michigan Number NOR Indusrial orders Bureau of he Census $/Millions OP Oil price (Bren) ICE $/Barrels RP Risk premium USA Corporae bond yield Moody's BAA T- Bond 30Y yield % TS10 Term srucure 10Y T-Bond 10Y (30Y) yield T-Bill 3M yield % TS30 Term srucure 30Y T-Bond 30Y yield T-Bill 3M yield % UCL Unemploymen claims Deparmen of labor ($/000) The Fed Funds rae has been included in he analysis as a represenaive indicaor of moneary policy. Even he pas risk premium has proved o be a valid indicaor in predeermining he evoluion of he sock marke (Friedman and Kuner, 1992), as has he defaul premium (Fama and French, 1989; Gerler and Lown, 2000). An increase in eiher of hese spreads is generally linked o a drop in sock prices. Finally, flucuaions in he oil price and in he price of sea freigh raes for raw maerials are ypically considered revealing indicaors abou he world economy. In Table 2 (see he Appendix), we presen he correlaion coefficiens beween he S&P500 and he main variables under differen lags. The remaining macroeconomic variables used here measure differen aspecs of he real economy, including he senimen of consumers and he purchasing managers of manufacuring firms, rends in he real esae and indusrial secors, he oal money supply in he economic sysem, and he condiions of he labor marke Macroeconomic variables and sock marke performance. This secion presens he findings of he empirical analysis based on he resuls shown in Tables 3-5. The resuls in Table 3 (see he Appendix) show ha, a differen periods, cerain macroeconomic indicaors have anicipaed he S&P500 index. Among hese, he Balic Dry index, which summarizes he variaion in he sea freigh raes of so-called dry goods during 2009, has seen an increasing rend ha has preceded he marke index rend by some monhs. 96 In fac, as we can observe from he -ess, he mos significan lag is he one-monh lag. Addiionally, he esimaed dynamic mulipliers show ha an increase in he Balic Dry index leads o an almos immediae increase in he sock markes. Moreover, he fac ha he cumulaive muliplier increases up o he eighh monh demonsraes ha an increase in sea freigh raes produces a persisen and significan impac on he level of sock prices. The repored resuls show ha oil quoaions and sock marke reurns develop a muually posiive relaionship wihin he shor erm. However, in he medium erm, a posiive variaion in crude oil prices is accompanied by a negaive variaion in he S&P500 index. A delay of five monhs provides resuls ha are paricularly significan (Table 4 see in he Appendix). Anoher indicaor ha anicipaes he fuure movemens of he sock index is he consumers confidence index (CC). During he recen financial crisis, his indicaor sared a downward rend, anicipaing he sock markes by a few monhs, and reaching minimum levels by February 2009, which was exacly one monh before he sock marke reached is lowes value. The saisical analysis shows posiive and significan regression coefficiens for he firs wo lags of he indicaor. The analysis of he dynamic mulipliers is paricularly ineresing; i shows ha an increase in consumers confidence leads o an immediae increase in sock prices. Similar findings are obained for he Michigan Consumer Senimen Index (MCSI). In he pas, his indicaor has anicipaed downward rends in he sock index by abou nine monhs.

5 Wih regard o he Chicago Purchasing Managers Index (PMI) rend analysis, we can observe ha i has been able o anicipae by one monh he fall in he S&P500 index from he peaks reached in Ocober From he saisical analysis, we can also observe he high significance of he firs wo lags. We noice equally relevan resuls from he rend analysis of he producion index of he Insiue for Supply Managemen (ISM), which indicaes ha several paricularly low values of his indicaor have anicipaed he posiive performance of he sock markes in he following monhs. The mos significan coefficiens are relaed o lags of hree o five monhs. Turning o unemploymen figures, he analysis highlighs ha his indicaor began an upward rend a few monhs before he sock index reached is highes quoaion in December The saisical analysis indicaes negaive regression coefficiens ha are significan for he firs hree lags of he variable. Addiionally, he analysis of he dynamic mulipliers signals ha an increase in requess for unemploymen benefi is accompanied by an immediae decrease in sock prices. Looking a he cumulaive dynamic mulipliers, we can observe ha deerioraion in labor marke condiions produces a persisen negaive impac on he level of sock prices for a period of abou four monhs. From he perspecive of moneary policy, i is ineresing o noe ha he federal funds rae has reached is maximum/minimum levels one monh in advance of he sock index. Likewise, he cumulaive dynamic mulipliers relevan o he M2 aggregae indicae ha he posiive impac of a Fed iniiaive on he sock markes ends five or six monhs afer he injecion of new liquidiy ino he sysem. Finally, i is worh menioning he good forecasing capaciy of he spreads beween corporae bond yields for differen risk raings, and hose beween governmen bonds of differen duraions. We can see ha he spread grew rapidly in he leas favourable sages of he economic cycle, reaching is maximum by December 2008, and hen falling during 2009 when he prospecs for he economy sared o improve. These resuls show ha he firs five spread lags are highly significan in predicing he fuure evoluion of he S&P500 index. Similar findings are obained for he risk premium variable. The spread beween governmen bond yields wih differen expiry daes, in he course of he las en years, has proven o be a good predicor of fuure upward and downward movemens in he S&P500 index. The saisical analysis indicaes a negaive relaion beween pas variaions in he spread and he curren sock price, demonsraing he capaciy of he spread o anicipae fuure rends in he sock marke Shor-erm predicion models. The proposed predicion models have been consruced in an effor o offer a useful ool for invesors who deermine heir porfolio sraegies on he basis of an asse allocaion policy mainly oriened owards shor-erm marke opporuniies. The following predicion models have been evaluaed using lags in hose macroeconomic variables revealed o be quie significan over a shor period: S & P500 BDI DP, 2 S & P500 TS10 1 BDI DP TS10 CC 1 1 M 2 1 (2) (3) Model (2) only includes variables linked o he ineres rae and he prices of raw maerials. Model (3), on he oher hand, also includes variables ha refer o cerain real aspecs of he economy and, in paricular, o he senimen of consumers and money aggregae variaions. The empirical resuls are presened in Table 5. Boh models presen significan regression coefficiens a he 1% level, heir adj. R 2 are and 0.572, respecively, and hey saisfy all specificaion ess 1. The sandard errors are given in parenheses, below are he esimaes of he coefficiens. Table 5. Time series regression for models (2) and (3) BDI-1 CC-1 M2-1 TS10-1 DP-3 Variable Model (2) Model (3) 0.092*** (5.21) *** (-3.66) *** (-4.93) 0.105*** (6.87) 0.211*** (5.93) 3.514** (3.26) *** (-4.35) *** (-4.96) Adj. R F *** *** Noes: For every macroeconomic variable, we presen regression coefficiens, wih -es resuls in parenheses; he regression adj. R 2 and he F-saisic are lised below. ***, ** and * indicae significance levels of 1%, 5% and 10%, respecively. Inercep is no repored. 1 The purpose of he specificaion ess is o verify he hypohesis ha he residuals are normally disribued, wih means equal o zero and consan variance (homoskedasiciy), and ha hey are no self-correlaed. If such condiions were no me, i would be necessary o inervene in he ime series, o sabilize he variance or avoid auocorrelaion. To his end, he Jarque-Bera es is conduced firs of all; i is used o ascerain normaliy. We proceed by esing he homoskedasiciy of he residuals by applying he Breusch-Pagan es and he absence of auocorrelaion using he Breusch-Godfrey es. 97

6 2 4. Robusness checks We esed he robusness of our models by checking heir performance using ou-of-sample daa analysis. A number of saisical mehods can be used o imple- we men predicion models in real ime. In his case, applied he ou-of-sample pseudo-forecas mehod. We chose a dae near he end of he sample, esimaed he forecas model up o ha dae, and hen used he model o make a forecas on he remaining sample daa, ha is, he daa beyond he dae we had chosen. Employed in his way, he model offers a series of pseudo-forecass and, herefore, a series of pseudo- residuals. The reason why we refer o hese as ou-of- sample pseudo-forecasss is ha hey are no real ou- in of-sample forecass. The laer would occur only real ime, or raher, wihou knowledge of he fuure values of a ime series. This mehod allows us o make forecass in order o evaluae he adequacy of he ap- us o esimae he RMSFE 1. This las indicaor plied model. The use of he mehod illusraed above also enabled can be used boh o measure he uncerainy of afore- or, more cas mehod and o build forecas inervals precisely, inervals ha are prediced o have a cerain probabiliy of conaining he fuure value of a variable. The ou-of-sample pseudo-forecascalculaed by applying he forecas of he 2010 S&P500 index, models presened in equaions (2) and (3), are shown in Figures 1 and 2, along wih he real index values. In boh cases, he forecass approximaely follow he effecive rend of he S&P500 index. However, he former are on average higher han he observed values 2. In oher words, he predicions anicipae he sock index variaions. Such forecas disorions sugges ha boh proposed models are unsable and he insabiliy has led o index variaion forecass ha are sysemaically higher han he observed values. For his reason, before making reali would be ime applicaions of he above models, useful o aemp o idenify he source of he disorion and creae a modified version of he models. Source: Daasream. Fig. 1. S& &P500 and ou-of-sample pseudo-forecass calculaed using model (2), year 2010 Source: Daasream. Fig. 2. S& &P500 and ou-of-sample pseudo-forecass calculaed using model (3), year The roo mean squared forecas error is a measure of he forecas error or, more precisely, of he error ypicallyy obained from using a forecas model. RMSFE is equal o he square roo of he mean squared forecas: RMSFE 98 E( Y Y 1 ˆ 1) 2 2 The mean forecas errors are equal o 3.86 for he model represened in equaion (2) and b..p. for he model represened in equaion (3). The pseudo ou-of-sample forecass RMSFEs are boh higher han hose esimaed in he sample.

7 Conclusions The aim of his paper was o prove empirically he capaciy of cerain macroeconomic variables, and heir pas variaions, o predic he rend in he S&P500 index. Wihin he limis of our experimen, he analysis developed herein has shown a saisfacory abiliy o esablish he main macroeconomic indicaors wih significan predicive power over he dynamics of he sock index. The implemenaion of appropriae sraegies aimed a benefiing from hese relaionships could be useful for invesors wishing o efficienly reallocae heir invesmen porfolios. In paricular, his paper References Invesmen Managemen and Financial Innovaions, Volume 9, Issue 3, 2012 could be useful for hose invesmen managers who adop a op-down approach in building heir porfolios, where macroeconomic variables are relevan in deermining he amoun of capial o be invesed in various secors of he financial marke. In implemening hese models here should be a careful evaluaion of heir learning level. In fac, he performance of he models will gradually improve overime as hey learn from he daa hey process. Addiionally, many furher robusness checks should be performed by esing he models on differen imeseries of hisorical daa and real-ime daa simulaions. 1. Asprem, M. (1989). Sock prices, asse porfolios and macroeconomic variables in en European counries, Journal of Banking and Finance, 13, pp Campbell, J. (1987). Sock reurns and he erm srucure, Journal of Financial Economics, 18, pp Canova, F. and G. De Nicolò (2000). Sock reurns, erm srucure, inflaion and real aciviy: An inernaional perspecive, Macroeconomic Dynamics, 4, pp Chen, N., R. Rol, S.A. Ross (1986). Economic forces and he sock marke, Journal of Business, 59, pp Culer, D., J. Poerba, L. Summers (1989). Wha moves sock prices?, Journal of Porfolio Managemen, 15, pp DeFina, R.H. (1991). Does inflaion depresses he sock marke? Federal Reserve Bank of Philadelphia Business Review, 12, pp Ederingon, L. and J. Lee (1993). How markes process informaion: News releases and volailiy, Journal of Finance, 48, pp Esrella, A. and G. Hardouvelis (1991). The erm srucure as a predicor of real economic aciviy, Journal of Finance, 46, pp Fama, E. and K. French (1989). Business condiions and expeced reurns on socks and bonds, Journal of Financial Economics, 25, pp Fama, E. and W. Schwer (1977). Asse reurns and inflaion, Journal of Financial Economics, 5, pp Flannery, M. and A. Proopapadakis (2002). Macroeconomics facors do influence aggregae sock reurns, Review of Financial Sudies, 15, pp Friedman, B. and K.N. Kuner (1992). Money, income, prices and ineres raes, American Economic Review, 82, pp Gerler, M. and C. Lown (2000). The informaion in he high yield bond spread for he business cycle: Evidence and some implicaions, NBER working paper No Guidolin, M. and S. Ono (2006). Are he dynamic linkages beween he macroeconomy and asse price ime varying?, Journal of Economic and Business, 58, pp Hardouvelis, G. (1987). Macroeconomic informaion and sock prices, Journal of Economics and Business, 39, pp Homa, K. and D. Jaffee (1971). The supply of money and common sock prices, Journal of Finance, 26, pp Huang, R., R. Masulis, and H. Soll (1996). Energy shocks and financial markes, Journal of Fuures Markes, 16, pp James, C., S. Koreisha, and M. Parch (1985). A VARMA analysis of causal relaions among sock reurns, real oupu, and nominal ineres raes, Journal of Finance, 40, pp Kaul, G. (1987). Sock reurns and inflaion: The role of he moneary secor, Journal of Financial Economics, 18, pp Keim, D. and R.F. Sambaugh (1986). Predicing reurns in he sock and bond markes, Journal of Financial Economics, 17, pp Kling, J.L. (1985). Oil price shocks and sock marke behavior, Journal of Porfolio Managemen, 12, pp Lee, B. (1992). Causal relaions among sock reurns, ineres raes, real aciviy, and inflaion, Journal of Finance, 47, pp Pierce, D.A. (1975). Forecasing in dynamic models wih sochasic regression, Journal of Economerics, 3, pp Rapach, D. (2001). Macro shocks and real sock prices, Journal of Economics and Business, 53, pp Ross, S. (1976). The arbirage heory of capial asse pricing, Journal of Economic Theory, 13, pp Sock, J. and M. Wason (2003). How did leading indicaor forecass perform during he 2001 recession?, Federal Reserve Bank of Richmond Economic Quarerly, 89/3. 99

8 Appendix Table 2. Pairwise correlaions beween S&P500 index and curren and lagged values of independen variables Lags BDI CC CPMI FFR HBP IP ISM M2 MCSI NOR OP UCL DP RP TS10 TS * 0.416* 0.322* 0.284* 0.41* * * 0.28* * * * * 0.421* 0.275* 0.307* * * 0.233* * * * * * * 0.221* * * 0.262* * * 0.258* 0.3* * * * * * * -0.27* * * * * 0.215* * * * * * * * * * * Noe: * Indicae significan values a he 5% level. 100

9 Table 3. Time-series regression wih disribued delays. Dependen variable: S&P500 index Lags BDI CC CPMI CUR FFR HBP IP ISM M2 MCSI NOR OP UCL DP RP TS10 TS * 0.170*** 0.225*** * 0.342*** *** -4.61** 0.205* 0.585* ** *** -6.14*** (1.69) (4.05) (4.04) (0.31) (2.01) (4.13) (0.35) (4.14) (-3.17) (2.47) 2.45 (1.86) (-3.22) (-4.65) (-3.63) (-0.41) (-0.78) 0.082*** 0.157*** 0.219*** *** * ** * (3.19) (3.68) (3.88) (0.33) (1.22) (0.5) (0.23) (1.11) (-0.45) (3.47) 0.2 (-0.17) (-2.43) (-2.8) (-2.37) (-1.86) (-1.85) *** 0.150*** 2.511*** ** * ** * (-0.35) (2.99) (2.67) (2.92) (-0.76) (0.15) (2.77) (0.77) (-1.83) (1.42) 2.42 (1.09) (-0.47) (3.19) (0.86) (1.03) (2.2) *** ** 2.336*** * *** (0.99) (0.04) (-1.20) (3.61) (-0.93) (2.71) (3.47) (1.55) (0.6) (-1.02) 0.71 (1.29) (-2.33) (-4.35) (-1.38) (0.8) (1.77) 0.051** ** ** ** ** -7.74*** (2.01) (-0.35) (2.27) (-1.36) (0.98) (2.83) (-1.53) (-0.60) (-0.24) (2.9) 1.42 (0.54) (-0.72) (-3.26) (-3.64) (-1.58) (-0.62) *** 0.126** ** *** (-0.04) (3.41) (2.11) (-1.27) (1.96) (-0.86) (-1.37) (1.98) (-1.35) (1.23) (-3.02) (0.62) (3.79) (0.58) (-1.01) (-0.37) * ** * (-0.66) (-0.52) (-1.22) (1.43) (-2.59) (-3.22) (1.41) (0.42) (0.66) (1) (0.6) (1.75) (-0.46) (2.23) (0.06) (0.59) ** ** * * (-1.06) (0.91) (-2.17) (-2.16) (-0.95) (1.04) (-2.16) (-0.24) (2.42) (1.7) (-0.41) (-0.34) (-1.77) (-0.6) (0.84) (1.13) 0.062** ** (2.33) (1.02) (-0.47) (-1.34) (-0.26) (0.04) (-1.27) (-0.2) (-0.94) (1.76) (-1.44) (0.98) (0.32) (-2.98) (-0.87) (-0.87) * * ** ** (-1.72) (1.29) (-0.54) (-1.39) (1.19) (-2.33) (-1.42) (0.16) (-0.95) (3.26) 0.53 (-1.33) (1.11) (-0.31) (3.12) (-0.84) (-1.45) * * * (0.64) (-1) (-0.54) (1.85) (1.44) (1.8) (1.81) (0.68) (0.43) (0.45) 0.86 (2.21) (0) (-2.2) (-1.83) (-1.17) (-1.77) ** * ** -3.74* -5.33** (-1.05) (1.09) (-0.84) (-0.34) (-1.51) (-0.25) (-0.46) (0.18) (1.62) (2.67) 0.56 (-2.46) (-1.28) (-0.92) (-3.16) (-2.3) (-3.19) * * (0.36) (0) (-0.64) (0) (0.99) (0.14) (-0.1) (0.62) (-1.3) (0.78) (-0.41) (-0.56) (-0.48) (2.59) (-0.5) (-1.04) Adj. R F Noes: For every macroeconomic variable, we presen regression coefficiens wih -es in parenheses; he regression Adj. R 2 and he F-saisic are lised below. ***, ** and * indicae significance levels of 1%, 5% and 10%, respecively. 101

10 Table 4. Dynamic effecs of pas variaions in macroeconomic variables on curren values of S&P500 index: esimaes of dynamic mulipliers (MD) and cumulaive dynamic mulipliers (MC) BDI CC CPMI CUR FFR HBP IP ISM M2 Lags MD MC MD MC MD MC MD MC MD MC MD MC MD MC MD MC MD MCSI NOR OP UCL DP RP TS10 TS30 Lags MD MC MD MC MD MC MD MC MD MC MD MC MD MC MD MC

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