«By Tarun Chordia and Lakshmanan Shivakumar May 23, 2005 Contacts Chordia Shivakumar* Voice: (404)727-1620 (44) 20-7262-5050 Ext. 3333 Fax: ...»
Earnings and Price Momentum
Tarun Chordia and Lakshmanan Shivakumar
May 23, 2005
Voice: (404)727-1620 (44) 20-7262-5050 Ext. 3333
Fax: (404)727-5238 (44) 20 7724 6573
E-mail: Tarun_Chordia@bus.emory.edu email@example.com
Address: Goizueta Business School London Business School
Emory University London NW1 4SA
Atlanta, GA 30322-2710 United Kingdom Acknowledgments We thank an anonymous referee, Ray Ball, Michael Brennan, Greg Clinch, Francisco Gomes, Paul Irvine, Narasimhan Jegadeesh, Josef Lakonishok, Maureen McNichols, Stefan Nagel, Bhaskaran Swaminathan, Jacob Thomas, and seminar participants at Case Western University, London Business School, University of Chicago, University of Rochester and the LBS accounting symposium for helpful comments. The second author was supported by the Dean’s Fund for Research at the London Business School. All errors are our own.
* Corresponding author Earnings and Price Momentum Abstract This paper examines whether earnings momentum and price momentum are related.
Both, in time-series as well as in cross-sectional asset pricing tests we find that price momentum is captured by the systematic component of earnings momentum. In time series as well as in cross-sectional asset pricing tests, the predictive power of past returns is subsumed by a zero investment portfolio that is long on stocks with high earnings surprises and short on stocks with low earnings surprises. Further, returns to the earnings-based zero inve stment portfolio are significantly related to future macroeconomic activities, including growth in GDP, industrial production, consumption, labor income, inflation and T-bill returns.
In a seminal paper, Fama (1998), once again makes the case for the efficient markets hypothesis.
Even with the recent interest in behavioral finance, that is driven by data that conflicts with the standard frictionless asset pricing models, Fama argues that the null should still be one of market efficiency. However, Fama does concede that the existence of two robust and persistent anomalies still pose challenges to the efficient markets paradigm. These two anomalies are (i) the post-earnings-announcement-drift or earnings momentum, first documented by Ball and Brown (1968) and (ii) the short-run return continuations or price momentum, documented by Jegadeesh and Titman (1993). Earnings momentum refers to the fact that firms reporting unexpectedly high earnings subsequently outperform firms reporting unexpectedly low earnings.
The superior performance lasts for about nine months after the earnings announcements. The price momentum strategy that buys past winners and sells past losers earns abnormal returns for a period of up to a year after the inception of the strategy.
In this paper, we study whether earnings momentum and price momentum are related. Our analysis extends the study by Chan, Jegadeesh and Lakonishok (1996) who also investigate whether the predictability of future returns based on past returns is subsumed by individual stock earnings surprises in cross-sectional tests. However, if price momentum is related to macroeconomic variables, as shown by Chordia and Shivakumar (2002), Ahn, Conrad and Dittmar (2003) and Avramov and Chordia (2005), then firm-specific characteristics (such as, earnings surprises) will be insufficient to capture price momentum. We seek a relation between price momentum and the systematic component of earnings momentum.
Based on the most recent earnings surprise1 we sort firms into decile portfolios and then examine whether a zero investment portfolio (denoted PMN for positive minus negative) that is long in the highest earnings surprise portfolio and is short in the lowest earnings surprise portfolio captures the price momentum phenomenon. Both in time series and cross-sectional asset pricing tests, we find that payoffs to price momentum strategies are captured by the earnings-based portfolio, PMN. For instance, the price momentum effect (as measured by a portfolio, denoted WML, that is long on past winners and short on past losers), which is about 76 basis points per month, is The earnings surprise is measured by standardized unexpected earnings or SUE which is defined as the earnings in quarter t less earnings in quarter t-4 standardized by the standard deviation of earnings changes over the last eight quarters.
reduced to essentially zero in time-series tests after controlling for the exposure of firms to PMN.
Since PMN is a diversified portfolio, it is unlikely to reflect any firm-specific information. Thus, the above results are consistent with price momentum being primarily related to the systematic component of earnings momentum.
To better understand the ability of PMN to explain price momentum, we analyze the properties of PMN. During our sample period from January 1972 through December 1999, the payoffs to the PMN portfolio average a significant 90 basis points per month and these payoffs are not subsumed by the Fama-French (1993) factors or the momentum factor of Carhart (1997). Thus, the earnings momentum anomaly subsumes the price momentum anomaly but is not itself subsumed by the price momentum anomaly. The correlation between PMN and the price momentum based portfolio, WML, is 0.66. Also, WML is more volatile than PMN. These results suggest that price momentum is a noisy proxy for earnings momentum. This is consistent with the results in Hong, Lee and Swaminathan (2003) who examine earnings and price momentum in eleven international equity markets and find that price momentum exists only in those countries where earnings momentum is profitable.
Using a variety of measures to capture future macroeconomic conditions, we show that the return on PMN forecasts future business conditions. In part icular, we find that the return on PMN is correlated with future growth in GDP, industrial production, consumption, labor income, inflation and T-bill returns. These correlations persist even after controlling for the Fama -French factors.
These results suggest that the PMN portfolio may be viewed as a risk factor that earns a risk premium. 2 However, PMN is negatively related to the business cycle as measured by GDP growth. Portfolios that vary counter-cyclically to the business cycle should not earn a positive risk premium. Thus, while PMN is related to the business cycle, it is unlikely to proxy for a risk factor. Overall, these results suggest that earnings momentum (or post-earnings-announcementCochrane (2000) has suggested that “the central and unfinished task of absolute asset pricing is to understand and measure the sources of aggregate or macroeconomic risk that drives asset prices.” Based on a survey of 392 CFOs, Graham and Harvey (2001) find that next to market risk, macroeconomic risks (such as business cycle risk and inflation risks) are the most important risk factors that firms’ consider in computing their cost of capital.
drift) contains a systematic component related to the macroeconomy, but that this component is unlikely to represent a (macroeconomic) risk factor.3 Chordia and Shivakumar (2005) suggest that earnings momentum or the post-earningsannouncement-drift can be attributable to inflation illusion. The inflation illusion hypothesis, which was proposed by Modigliani and Cohn (1979), suggests that while bond market investors correctly anticipate the impact of inflation on discount rates, stock market investors fail to incorporate inflation when forecasting future earnings growth rate. Thus, when inflation rises, investors do not adjust the future earnings growth, even though they fully adjust the discount rates. A direct implication of this hypothesis is that if the earnings growth, in response to inflation, varies across stocks, then inflation illusion would induce mis-valuation in the crosssection. Chordia and Shivakumar (2005) show that the effect of inflation on earnings growth increases monotonically across the SUE-sorted portfolios. Due to inflation illusion, stocks whose earnings growth is positively related to inflation are undervalued, whereas those with earnings growth negatively related to inflation are overvalued. The subsequent correction of this under- and overvaluation drives the post-earnings-announcement-drift.
This paper also contributes to the on-going debate on the sources of profits to price momentum.
Several studies have suggested that the momentum profits are driven by cognitive biases on part of investors (e.g., Daniel, Hirshleifer and Subrahmanyam (1998), and Barberis, Shleifer and Vishny (1998)). In contrast, Chordia and Shivakumar (2002), Ahn, Conrad and Dittmar (2003) and Avramov and Chordia (2005) argue that the price momentum payoffs are related to the business cycle. On the other hand, Korajcyzk and Sadka (2004) argue that the momentum phenomenon persists due to frictions in the price adjustment process caused by transaction costs.
The finding that price momentum is subsumed by a common factor related to the macroeconomy is significant since it does not rely on capital market frictions to explain the price momentum effect.
This paper narrows the search for an explanation to the anomalies by documenting that the price momentum anomaly is a manifestation of the earnings momentum anomaly. The two anomalies Daniel, Hirshleifer and Subrahmanyam (2001) derive an asset pricing model that includes non-risk factors.
that Fama (1998) cites as being above suspicion may, in fact, correspond to the same anomaly, namely, the earnings momentum or the post-earnings-announcement-drift anomaly. Moreover, our results indicate that the price momentum based factor, WML, in the Carhart (1997) fourfactor model is merely a noisy proxy for the earnings momentum based factor, PMN. This implies that PMN rather than WML is the more appropriate factor to use in asset pricing tests. Of course, both PMN and WML are empirically motivated and neither may represent a state variable in the Merton (1973) sense.
The rest of the paper is organized as follows. Section I discusses the two momentum strategies, while the following section discusses the formation of portfolios based on these momentum strategies. Section III presents time-series and cross-sectional asset pricing tests. Section IV presents the properties of PMN, while section V analyses the link between PMN and the macroeconomy. Section VI concludes.
I. Momentum Strategies As mentioned, the two anomalies that we focus on in this paper are (1) price momentum and (2) earnings momentum. Price momentum was first documented by Jegadeesh and Titman (1993).
The profitability of price momentum strategies has been particularly intriguing, as, among all the anomalies examined by Fama and French (1996), it remains the only anomaly that is unexplained by the Fama and French (1993) three-factor model. Jegadeesh and Titman (2001) show that profits to momentum strategies of about 1% per month have continued in the 1990s, suggesting that their initial results were not due to data mining. Furthermore, the robustness of this strategy has been confirmed using data from stock markets other than the US, where the profitability of this strategy was initially identified. Rouwenhorst (1998) finds momentum payoffs to be significantly positive in twelve other countries that were examined in his study.
Earnings momentum or the post-earnings-announcement-drift was first documented by Ball and Brown (1968). Foster, Olsen and Shevlin (1984) and Bernard and Thomas (1989) among others have confirmed the robustness of the Ball and Brown (1968) findings using more recent data.
Foster, Olsen and Shevlin (1984) document an annualized payoff of 25% from earnings momentum strategies. Hew, Skerratt, Strong and Walker (1996) and Booth, Kallunki and Martikainen (1996) have extended the post-earnings-announcement -drift evidence to non-US data. The post-earnings-announcement-drift is also a robust anomaly and has defied rational explanations. The phenomenon has been attributed to a delayed price response to information.
Since stock prices are likely to be driven by earnings, we will test whether the price momentum and the post-earnings-announcement-drift phenomenon are related.
II. The Zero-investment portfolios To study the impact of earnings momentum on price momentum, we first create earnings portfolios that capture the post-earnings-announcement-drift phenomenon. Each month, all NYSE-AMEX firms on the monthly CRSP files and with data on COMPUSTAT are sorted into deciles based on their standardized unexpected earnings (SUE) from the most recent earnings announcement. 4 We sort firms in each month into deciles based on the earnings in this quarter less earnings four quarters ago. For cross-sectional comparison, we standardize this change in earnings by the standard deviation of the earnings changes in the prior eight quarters. We prefer standardizing earnings changes by the standard deviation rather than by stock price, market capitalization, total assets or sales as these variables may themselves proxy for size or expected returns. Sorting firms on earnings changes scaled by these variables could bias us towards capturin g cross-sectional differences in expected returns associated with these variables.
Moreover, our methodology is consistent with prior studies in accounting that investigate the post-earnings -announcement-drift phenomenon (see Bernard and Thomas, 1989). 5 We implement this sort each month using the same methodology as Chan, Jegadeesh and Lakonishok (1996). Thus, in each portfolio formation month, we sort firms using only the most recent earnings announced by the firms. To avoid using stale earnings, we require the most recent earnings to be announced no earlier than four months before the end of the formation month.
Decile portfolios, which we also refer to as SUE portfolios, are formed by weighting equally all firms in the decile rankings. The positions are held for the following six months, t through t+5, which is designated as the holding period. We follow Jegadeesh and Titman (1993) in forming Data on earnings announcement is available for most Nasdaq stocks only from 1984. Including Nasdaq stocks in our analyses has no qualitative impact on our results.
We repeated the analyses after allowing for a drift in earnings as done in Bernard and Thomas (1989). The results remain qualitatively unchanged with this modification.