A Closer Look At The Short-Term Return Reversal

2y ago
23 Views
3 Downloads
247.41 KB
17 Pages
Last View : 8d ago
Last Download : 2m ago
Upload by : Annika Witter
Transcription

MANAGEMENT SCIENCEVol. 60, No. 3, March 2014, pp. 658–674ISSN 0025-1909 (print) ISSN 1526-5501 (online)http://dx.doi.org/10.1287/mnsc.2013.1766 2014 INFORMSDownloaded from informs.org by [128.171.195.158] on 07 October 2014, at 11:56 . For personal use only, all rights reserved.A Closer Look at the Short-Term Return ReversalZhi DaFinance Department, Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556, zda@nd.eduQianqiu LiuShidler College of Business, University of Hawaii, Honolulu, Hawaii 96822, qianqiu@hawaii.eduErnst SchaumburgFederal Reserve Bank of New York, New York, New York 10045, ernst.schaumburg@gmail.comStock returns unexplained by “fundamentals,” such as cash flow news, are more likely to reverse in the shortrun than those linked to fundamental news. Making novel use of analyst forecast revisions to measure cashflow news, a simple enhanced reversal strategy generates a risk-adjusted return four times the size of the standard reversal strategy. Importantly, isolating the component of past returns not driven by fundamentals providesa cleaner setting for testing existing theories of short-term reversals. Using this approach, we find that bothliquidity shocks and investor sentiment contribute to the observed short-term reversal, but in different ways:Specifically, the reversal profit is attributable to liquidity shocks on the long side because fire sales more likelydemand liquidity, and it is attributable to investor sentiment on the short side because short-sale constraintsprevent the immediate elimination of overvaluation.Keywords: short-term return reversal; liquidity; sentiment; fundamental newsHistory: Received September 25, 2012; accepted April 5, 2013, by Brad Barber, finance. Published online inArticles in Advance September 3, 2013.1.Introductiondemand curve of a stock is downward sloping and/orthe supply curve is upward sloping, as in Grossmanand Miller (1988) and Jegadeesh and Titman (1995).In the model of Campbell et al. (1993), for example,uninformed trades lead to a temporary price concession that, when absorbed by liquidity providers,results in a reversal in price that serves as compensation for those who provide liquidity. Consistent withsuch a mechanism, Avramov et al. (2006) find that thestandard reversal strategy profits mainly derive frompositions in small, high turnover, and illiquid stocks.In fact, Pastor and Stambaugh (2003) suggest directlymeasuring the degree of illiquidity by the occurrenceof an initial price change and subsequent reversal.We label this second explanation the liquidity-basedexplanation.These two explanations are not necessarily mutually exclusive. A natural question follows: do liquidity shocks and investor sentiment play different rolesin driving short-term return reversal? The answer tothis question clearly has important bearings on thedebate about market efficiency and asset pricing models in general. We attempt to address this question intwo steps.In the first step, we recognize that under both explanations, reversal profits should come from the portion of past returns unexplained by the “fundamental”Short-term return reversal in the stock market, a wellestablished phenomenon for more than 40 years, hasbeen shown to be both robust and of economic significance.1 Jegadeesh (1990), for example, documentsprofits of about 2% per month over 1934–1987 usinga reversal strategy that buys and sells stocks on thebasis of their prior-month returns and holds themfor one month. These profits are not readily explainable by direct transaction costs. In an efficient market with a slowly varying stochastic discount factor,asset prices should follow a martingale over shorttime horizons even though they exhibit predictablevariations over longer horizons (see, e.g., Sims 1984).Two possible explanations for short-term reversal profits have received much attention in the literature. Shiller (1984), Black (1986), Stiglitz (1989),Summers and Summers (1989), and Subrahmanyam(2005), among others, have suggested that short-termreversal profits are evidence that market prices mayreflect investor overreaction to information, or fads, orsimply cognitive errors. We label this the sentimentbased explanation. Another explanation is based onthe price pressure that can occur when the short-term1See Fama (1965), Jegadeesh (1990), and Lehmann (1990).658

Da, Liu, and Schaumburg: A Closer Look at the Short-Term Return ReversalDownloaded from informs.org by [128.171.195.158] on 07 October 2014, at 11:56 . For personal use only, all rights reserved.Management Science 60(3), pp. 658–674, 2014 INFORMSchange, which we label as residual return. We can thinkof the “fundamental” component of the stock returnto contain three components: (1) the expected returnthat reflects the rational compensation of risk, (2) cashflow news that is due to changing expectations aboutfundamental future cash flows, and (3) discount ratenews that is due to changing expectations about rational future discount rates. If we can purge these threecomponents out of the realized return to obtain theresidual return, we can better isolate the nonfundamental return component, be it sentiment-induced mispricing or a price concession triggered by a liquidityshock.2 Importantly, by focusing on this cleaner sourceof short-term reversal, we arguably have a superiortesting ground for studying alternative explanationsof short-term reversal.Among the three “fundamental” components ofstock return, the discount rate news component isprobably small at weekly or monthly frequency underthe common belief that the stochastic discount factoris slow moving. For this reason, we focus our attention on controlling for the two remaining fundamental components using suitably constructed proxies.To measure the expected return component, weuse the Fama and French (1993) three-factor model,although we stress that the specific choice of modelfor the expected return is not crucial for studyingshort-term return reversal since the expected return issmall relative to the realized return at high frequencyin virtually all commonly used asset pricing models.A novel feature of our empirical exercise is that wemeasure the cash flow news component directly usingrevisions of equity analyst consensus forecasts following the procedures described in Da and Warachka(2009). Similar approaches are used by Easton andMonahan (2005) and Chen et al. (2013). Crucially, theuse of analyst earnings forecasts allows us to measurecash flow news at monthly frequency in real time,which is necessary for implementing the short-termreversal strategy. Furthermore, computing monthlyrevisions mitigates analyst forecast biases that persistover this short horizon.Throughout this paper, we control for industryeffects. In other words, all stock returns and theircomponents will effectively be measured in excessof their industry averages. Such industry controlhas several benefits. First, any residual analyst forecast biases, as long as they are roughly constantacross stocks within the same industry, will be alleviated. Second, the industry control also helps toremove any common components in expected returns2For clarification, since we allow stock price to temporarily deviatefrom its fundamental value, the three “fundamental” componentsno longer add up to the realized stock return as in the standardCampbell and Shiller (1988) decomposition framework.659and discount rate news. Moskowitz and Grinblatt(1999) document a strong and prevalent momentumeffect in industry components of stock returns. Ourindustry controls, by taking out the industry momentum effect, will mechanically enhance the short-termreturn reversal. They do not drive our results though.We verify that our results are qualitatively similareven if we do not apply industry controls at all.Our key variable of interest, the residual return,is computed by subtracting the estimated expectedreturn and cash flow news from the realized return.Notwithstanding the measurement errors associatedwith our empirical estimates, it is important to notethat as long as our estimates are informative about thetrue expected return and cash flow news, the residual return should help to isolate the “true” driverof short-term reversal. Our residual return is in spiritsimilar to the “intangible” return in Daniel andTitman (2006). Daniel and Titman (2006) focuses onlong-term return reversal and shows that “intangible” return, or the component of past five-year returnsorthogonal to the firm’s past accounting performance,predicts future long-run return reversal. In contrast,we focus on short-term return reversal at monthly frequency. Computing residual returns over such a shorthorizon is only made possible by our novel use ofanalyst forecasts.By focusing on the residual return, we enhance theprofitability of the short-term reversal strategy substantially. During our sample period from 1982 to2009, a residual-based short-term reversal strategy thatsorts stocks into deciles within each industry onthe basis of prior-month residual return generatesa monthly alpha of 1.34% with a highly significantt-value of 9.28. Such an alpha is large considering thefact that our sample includes a subset of relativelylarge and liquid stocks due to the requirement forregular analyst coverage. As a comparison, the standard reversal strategy only generates a monthly alphaof 0.33% with an insignificant t-value of 1.37 in thesame sample. Overall, the short-term reversal effectstill exists even among large stocks for the more recentyears, and it is the industry momentum effect thatmakes it difficult to find. The success of the residualbased reversal strategy survives transaction cost analysis and a battery of additional robustness checks.The enhanced reversal strategy offers a superiortesting ground for evaluating different explanationsof short-term reversal. In the second step, we obtainfresh insights from separately analyzing the long andshort legs of the residual-based reversal strategy.We find the profits from buying losers (the longside in the residual-based strategy), after risk adjustment, to load positively and significantly on thelagged aggregate Amihud (2002) illiquidity measureand realized volatility of the S&P 500 index. Thus,

Downloaded from informs.org by [128.171.195.158] on 07 October 2014, at 11:56 . For personal use only, all rights reserved.660Da, Liu, and Schaumburg: A Closer Look at the Short-Term Return Reversalthese profits are more likely reflecting compensationsfor liquidity provision since they are higher whenthe level of illiquidity (proxied by the Amihud (2002)measure) is high and when the required compensation for liquidity provision is likely to be high(proxied by the realized volatility). Overall, this finding is consistent with the theoretical prediction ofShleifer and Vishny (1992) and the empirical evidencein Coval and Stafford (2007). Recent losers are morelikely to be financially distressed, and constrainedinvestors are forced to sell, causing a large price concession. The later price recovery thus reflects compensation for liquidity provision. Nagel (2012) alsorelates short-term return reversal to liquidity provision. Our novel approach allows us to extend Nagel’s(2012) analysis by showing that liquidity provisionappears more important for explaining the reversalon recent losers since fire sales are more likely thanfire purchase. Our results are therefore complementary to the findings in Avramov et al. (2006) and suggest that liquidity shocks are particularly relevant onthe long side.In contrast, we find the profits from selling winners (the short side in the residual-based strategy),after risk adjustment, to load positively and significantly on two lagged measures of investor sentiment that reflect optimism and equity overvaluation. The two measures are the monthly numberof initial public offerings (IPOs) and monthly equityshare in new issues. Hirshleifer and Jiang (2010) consider security issuance as a proxy for aggregate overvaluation. Baker and Wurgler (2006) also use thenumber of IPOs and equity issuance in constructingtheir investor sentiment index.3 The fact that investorsentiment drives the reversal of recent winners is consistent with the existence of short-sale constraints,which limit the ability of rational traders to exploitoverpricing immediately (see Miller 1977). Consistent with Miller’s (1977) argument, Stambaugh et al.(2012) show that many asset pricing anomalies arestronger following high levels of sentiment and thatthis effect is attributable only to the short-legs.4 Again,by isolating recent “nonfundamental” price change,our analysis shows that Miller’s (1977) argument alsoextends to the short-term return reversal, even amonglarge stocks.The differential role played by liquidity shock andinvestor sentiment holds up strikingly consistentlyacross 10 different subsamples constructed according to stock characteristics such as size, book-tomarket (BM), analyst coverage (number of analysts3We do not focus on other components of the sentiment indexrelated to turnover or closed-end fund discount since they mightbe driven by liquidity as well.4Stambaugh et al. (2012) do not examine the short-term reversalanomaly.Management Science 60(3), pp. 658–674, 2014 INFORMS(NOA)), analyst forecast dispersion (DISP), and liquidity. Liquidity shocks always seem to be explainingthe reversal on recent losers, whereas investor sentiment always seems to be driving the reversal onrecent winners.The differential role played by liquidity shock andinvestor sentiment is also confirmed using crosssectional regression analysis. Using the stock-levelAmihud (2002) illiquidity measure, we find thatincreased stock illiquidity leads to stronger reversalonly among recent losers, confirming that liquidityshocks are driving the long-side of reversal profit.When we split our sample into stocks with optionstraded and stocks without options traded, we find noreversal among recent winners with options traded.Stocks with option traded are less likely to face binding short-sale constraints; hence, recent winners withoptions are less likely to be overpriced, explainingtheir lack of reversal in the near future. This resultsuggests that positive investment sentiment, combined with short-sale constraint, is consistent with theshort-side of the reversal profit.To summarize, in this paper, we take a fresh lookat an old asset pricing anomaly: the short-termreturn reversal. In the process, we make severalcontributions. First, we argue that to study the causesof short-term reversal, one should first partial outthe well-known effects associated with fundamentalnews. Second, we propose a novel use of analyst forecast data to proxy for cash flow news and isolate thecomponent of past returns that drives the short-termreversal. Finally, using our “clean” measure of shortterm reversal, we are able to show that both liquidity shocks and investor sentiment contribute to theobserved short-term reversal, but in different ways:Specifically, the reversal profit is attributable to liquidity shocks on the long side and investor sentimenton the short side.The rest of this paper is organized as follows. Section 2 discusses our empirical implementation anddescribes our sample. Section 3 contains the empirical results. Section 4 discusses the differential rolesplayed by investor sentiment and liquidity shock indriving the reversal. Section 5 concludes.2.Empirical Measurement2.1. Expected ReturnsTo compute conditional expected stock returns, weneed to use a pricing model. To be consistent with themethodology used to risk-adjust returns in our empirical results, we estimate the conditional expectedreturn using the Fama and French (1993) three-factormodel: t Et 6rf 7 MKT1 t Et 6MKT7 SMB1 t Et 6SMB7 HML1 t Et 6HML70(1)

Da, Liu, and Schaumburg: A Closer Look at the Short-Term Return Reversal661Downloaded from informs.org by [128.171.195.158] on 07 October 2014, at 11:56 . For personal use only, all rights reserved.Management Science 60(3), pp. 658–674, 2014 INFORMSWe note, however, that our empirical results do notappear to hinge on the choice of pricing model,(e.g., CAPM or augmented five-factor Fama andFrench (1993) model, which adds momentum andshort-term reversal factors to the three-factor model).To avoid any look-ahead bias, the factor betas areestimated using monthly returns in the previous fiveyear rolling window (with a minimum of 36 monthsof observations), whereas the factor risk premium isset equal to the average factor return in our samplingperiod.2.2. Cash Flow NewsTo directly compute fundamental cash flow news atmonthly frequency, we follow Easton and Monahan(2005) and Da and Warachka (2009) and measure cashflow news using revisions in equity analyst earnings forecasts. Crucially, the use of analyst earningsforecasts allows us to measure cash flow news atmonthly frequencies in real time, which is necessaryfor implementing the short-term reversal strategy.Furthermore, computing monthly revisions mitigatesany analyst forecast biases that persist over this shorthorizon.We obtain the analyst consensus earnings forecasts from the Institutional Brokers Estimate System(I/B/E/S) summary unadjusted file. I/B/E/S produces these consensus earnings forecasts each month,typically on the third Thursday of the month. To better match returns to earnings forecast revisions, formost parts of our analysis, we examine the I/B/E/Smonth ranging from the current I/B/E/S consensusforecast issuance date (third Thursday this month)to the next consensus forecast issuance date (thirdThursday next month), although we do confirm thatusing the simple calendar month produces very similar results. We initially include all unadjusted consensus earnings forecasts between January 1982 andMarch 2009. Unadjusted I/B/E/S forecasts are notadjusted by share splits after their issuance date.5We keep consensus earnings forecasts for the current and subsequent fiscal year (A1t 1 A2t ), along withits long-term growth forecast (LTGt ). The earningsforecasts are denominated in dollars per share, andthe t subscript denotes when a forecast is employed.The long-term growth forecast represents an annualized percentage growth rate. This forecast has nofixed maturity date, but pertains to the next three tofive years.We first define a simple proxy for the cash flowinnovation using only revisions in the earnings5As detailed in Diether et al. (2002), the earnings per share aftera share split is often a small number that I/B/E/S rounds to thenearest cent. This rounding procedure can distort certain properties of dollar-denominated analyst forecasts such as revisions andforecast errors.forecast for the current fiscal year (A1t ):6 A1 A1t t 1 Bt for no-earnings-announcement month1FREV t 1 E1t 1 A1t Bt for earnings-announcement month1where E1 is the actual earnings per share, and Bt isthe book value per share. In other words, FREV isequal to the analyst forecast revision (scaled) whenthere is no earning announcement and equal to theearnings surprise (scaled) during the month of fiscalyear earnings announcement.More precisely, we compute cash flow innovationsfollowing Da and Warachka (2009) by taking advantage of multiple earnings forecasts for different maturities. Some modifications are made to account for thefact that we are computing cash flow innovations forindividual stocks rather than for portfolios of stocks.We discuss the details below.Let Xt1 t j denote the expectation of future earnings(Xt j ); here the additional subscript refers to an expectation at time t. A three-stage growth model thatparallels the formulation in Frankel and Lee (1998)as well as Pastor et al. (2008) infers these earningsexpectations from analyst forecasts. In the first stage,expected earnings are computed directly from analystforecasts until year 5 as follows:7Xt1 t 1 A1t 1Xt1 t 2 A2t 1Xt1 t 3 A

Da, Liu, and Schaumburg: A Closer Look at the Short-Term Return Reversal Management Science 60(3), pp. 658–674, 2014 INFORMS 659 change, which we label as residual return. We can think of the “fundamental” component of the stock return to contain three components: (1) the expected

Related Documents:

May 02, 2018 · D. Program Evaluation ͟The organization has provided a description of the framework for how each program will be evaluated. The framework should include all the elements below: ͟The evaluation methods are cost-effective for the organization ͟Quantitative and qualitative data is being collected (at Basics tier, data collection must have begun)

Silat is a combative art of self-defense and survival rooted from Matay archipelago. It was traced at thé early of Langkasuka Kingdom (2nd century CE) till thé reign of Melaka (Malaysia) Sultanate era (13th century). Silat has now evolved to become part of social culture and tradition with thé appearance of a fine physical and spiritual .

On an exceptional basis, Member States may request UNESCO to provide thé candidates with access to thé platform so they can complète thé form by themselves. Thèse requests must be addressed to esd rize unesco. or by 15 A ril 2021 UNESCO will provide thé nomineewith accessto thé platform via their émail address.

̶The leading indicator of employee engagement is based on the quality of the relationship between employee and supervisor Empower your managers! ̶Help them understand the impact on the organization ̶Share important changes, plan options, tasks, and deadlines ̶Provide key messages and talking points ̶Prepare them to answer employee questions

Dr. Sunita Bharatwal** Dr. Pawan Garga*** Abstract Customer satisfaction is derived from thè functionalities and values, a product or Service can provide. The current study aims to segregate thè dimensions of ordine Service quality and gather insights on its impact on web shopping. The trends of purchases have

Chính Văn.- Còn đức Thế tôn thì tuệ giác cực kỳ trong sạch 8: hiện hành bất nhị 9, đạt đến vô tướng 10, đứng vào chỗ đứng của các đức Thế tôn 11, thể hiện tính bình đẳng của các Ngài, đến chỗ không còn chướng ngại 12, giáo pháp không thể khuynh đảo, tâm thức không bị cản trở, cái được

Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. Crawford M., Marsh D. The driving force : food in human evolution and the future.

Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. 3 Crawford M., Marsh D. The driving force : food in human evolution and the future.