Abstracts and downloadable pdf versions of published papers.

"Market States and Momentum" with Roberto Gutierrez Jr. and Allaudeen Hameed, The Journal of Finance.

We test overreaction theories of short-run momentum and long-run reversal in the cross section of stock returns. Consistent with these theories, we find that momentum profits depend on the state of the market. From 1929 to 1995, the mean monthly momentum profit following positive market returns is 0.93%, whereas the mean profit following negative market returns is negative 0.37%. The up-market momentum reverses in the long-run, consistent with the market correcting a prior overreaction. Our results are robust to the conditioning information in macroeconomic factors. Moreover, we find that macroeconomic factors are unable to explain momentum profits after simple methodological adjustments to take account of microstructure concerns.

"Value versus Glamour," with Jennifer Conrad and Gautam Kaul, The Journal of Finance.

The fragility of the one-factor Capital asset Pricing Model (CAPM) in explaining the cross-section of expected returns of securities has led to a resurgence of research aimed at discovering variables that might better explain observed returns. Most of this research uses sorting procedures to uncover relations between firm characteristics (such as “value” or “glamour”) and equity returns. In this paper, we examine the propensity of these sorting strategies to generate statistically and economically significant profits due to data-snooping biases induced by our collective familiarity with the data. We construct four simulation-based measures of data-snooping with the differences between them hinging on the extent of snooping. Under plausible assumptions, we show that our familiarity with the data can account for substantial fractions, but not all, of the in-sample relations between well-known value and glamour firm characteristics reported in the literature that uses single (or one-way) sorts. However, the biases in studies that simultaneously condition return on multiple characteristics (an increasing tendency in the literature) are much larger; two-way sorts conducted on simulated data are able to generate most of the profits observed in the real data. We also conduct out-of-sample experiments that confirm the findings of our in-sample simulations.

“On the Predictability of Stock Returns in Real Time," with Bill Marcum and Roberto Gutierrez Jr., forthcoming, Journal of Business.

Researchers have documented an abundance of evidence that stock returns are predictable ex post (using trading rules identified with full-period information). We address in this study whether the cross section of stock returns is predictable ex ante (using trading rules identified with prior information only). We ask if a real-time investor could have used book-to-market equity, firm size, and one-year lagged returns to forecast stock returns during the 1974 to 1997 period. For this purpose, we develop variations on common recursive out-of-sample methods. Despite persistently good in-sample performances across all simulations, only one class of real-time simulations (out of three) outperforms a passive index. This finding suggests that the market is difficult to beat in real time. Moreover, even the specification with the highest level of out-of-sample profits falls far short of the in-sample evidence of predictability. Our findings indicate a marked difference between ex post and ex ante predictability and suggest that the current notion of predictability in the literature is exaggerated.

Bonus!! Link to NY Times article on this paper.

"Evidence of Predictability in the Cross-Section of Bank Stock Returns," with Gary Patterson and William Jackson, Journal of Banking and Finance, Vol. 27, 2003.

In this paper, we examine the predictability of the cross-section of bank stock returns by taking advantage of the unique set of industry characteristics that prevail in the financial services sector. We examine predictability in the cross-section of bank stock returns using information contained in individual bank fundamental variables such as income from derivative usage, previous loan commitments, loan-loss reserves, earnings, and leverage. We find that variables related to non-interest income, loan-loss reserves, earnings, leverage, and standby letters of credit are all univariately important in forecasting the cross-section of bank stock returns. Surprisingly, neither book-to-market nor firm size is important in our sample. We examine whether this cross-sectional predictability is due to increased risk, or another explanation, such as investor under or overreaction. Our results suggest that this predictability is not due to increased risk, but rather is consistent with investor underreaction to changes in banks' fundamental variables. Furthermore, out-of-sample testing demonstrates this underreaction appears to be exploitable using simple cross-sectional trading strategies.

"A Rose.com by Any Other Name," coauthored with Orlin Dimitrov and P. Raghavendra Rau, December 2001, The Journal of Finance.

We document a striking positive stock price reaction to the announcement of corporate name changes to Internet-related dotcom names. This “dotcom” effect produces cumulative abnormal returns on the order of 74 percent for the ten days surrounding the announcement day. The effect does not appear to be transitory; there is no evidence of a post-announcement negative drift. The announcement day effect is also similar across all firms, regardless of the firm’s level of involvement with the Internet. A mere association with the Internet seems enough to provide a firm with a large and permanent value increase.

Bonus!! Link to NY Times article on this paper.

“Asymmetric Information and the Predictability of Real Estate Returns,” co-authored with David Downs and Gary Patterson, The Journal of Real Estate Finance and Economics, Vol. 20, Issue 2, 2000.

This paper examines systematic price changes associated with the heterogeneity of investors’ information sets in real estate asset markets. The empirical implications rely on a theoretical economy in which information asymmetry alters the dynamic relation between returns and trading volume. We employ a filter-rule methodology to determine predictability in returns and augment the return-based conditioning set with trading volume. The additional conditioning information is necessary since the model is underspecified when predictability is based on returns alone. Our results are consistent with the co-existence of informational and non-informational exchange in the speculative markets for real estate assets. This conclusion lends support to the claim that investors are confronted with an adverse selection problem in the publicly traded real estate markets. Importantly, these results are unique in addressing the time-variation in information asymmetry.

“Filter Rules Based on Price and Volume in Individual Security Overreaction,” Review of Financial Studies, Volume 12, Number 4, 1999.

I present evidence of predictability in a sample constructed as to minimize concerns about time-varying risk premia and market-microstructure effects. I use filter rules on lagged return and lagged volume information to uncover weekly overreaction profits on large-capitalization NYSE and Amex securities. I find that decreasing-volume stocks experi­ence greater reversals. Increasing-volume stocks exhibit weaker rever­sals, and positive autocorrelation. A real-time simulation of the filter strategies suggests that an investor who pursues the filter strategy with relatively low transaction costs will strongly outperform an investor who follows a buy-and-hold strategy.

"Real Estate Securities and a Filter-based, Short-term Trading Strategy," co-authored with David Downs and Gary Patterson, The Journal of Real Estate Research, Volume. 18, Number 2, 1999.

Anecdotal evidence provides overwhelming support to the belief that sophisticated real estate investors profit by timing long-run real estate cycles. This study examines the performance benefits that sophisticated investors may derive from short-run cycles in real estate, specifically, through the publicly traded real estate markets. Using a simple strategy that filters out noise in REIT price reversals, we are able to show that a contrarian strategy is many times more profitable than the associated execution costs. Furthermore, we find that the REIT market has been sufficiently liquid to execute this trading strategy. This last point is directly related to the filter strategy since only REITs with large price movements satisfy the hypothetical investor’s selection criteria.

Abstracts and downloadable pdf versions of working papers.

“What Best Explains The Cross-Section Of Stock Returns? Exploring the Asset Growth Effect” with Huseyin Gulen and Michael Schill.

We examine the cross-sectional relation between firm asset growth and subsequent stock returns. As a test variable, we use a simple and comprehensive measure of firm asset growth, the year-on-year percentage change in total assets (Compustat Data Item 6). Sorting U.S. firms by previous year asset growth rates, we find that from 1963 to 2003 firms in the highest growth decile earn annualized raw returns of approximately 6%, and firms in the lowest growth decile earn annualized raw returns of approximately 26%, a spread of 20% per year. When we compare asset growth rates with other important determinants of the cross-section of returns (i.e., book-to-market ratios, firm capitalization, and momentum), we find that asset growth has the strongest effect on subsequent returns. We observe that controlling for firm asset growth fundamentally changes some standard cross-sectional relations. For example, the momentum effect is reversed for low growth firms. The asset growth effect is remarkably consistent over our sample period, with low growth firms outperforming high growth firms in 37 out of 40 years. Overall, a firm’s annual asset growth rate appears to be the most important variable in predicting the cross-section of US stock returns.

"The Other January Effect," with John J. McConnell and Alexei V. Ovtchinnikov

“Streetlore” has touted the market return in January as a predictor of market returns for the remainder of the year since at least 1973. We systematically examine the predictive power of January returns over the period 1940-2003 and find that January returns have predictive power for market returns over the next 11 months of the year. The effect persists after controlling for macroeconomic/business cycle variables that have been shown to predict stock returns, the Presidential Cycle in returns, and investor sentiment and persists among both large and small capitalization stocks and among both value and glamour stocks. Additionally, we find that January has predictive power for two of the three premiums in the Fama-French (1993) three-factor model of asset pricing.
 

"Changing names with style: Mutual fund name changes and their effects on fund flows," with Huseyin Gulen and P. Raghavendra Rau

We investigate the effects of conditional name changes in the mutual fund industry. Specifically, we examine whether mutual funds change their names to take advantage of the current hot investment styles, and what effects these name changes have on the flows in and out of the funds, and to the funds’ subsequent returns. We find that name changes tend to occur in waves; funds tend to change their names to be associated with the current high return style or to disassociate themselves from the current low return styles. The year after a fund changes its name to reflect a current hot style or moves away from a current cold style, the fund experiences an average cumulative excess flow of 28%, despite no increase in performance compared to its pre-name change performance. The increase in flows is similar across funds whose holdings match the style implied by their new name and those whose holdings do not, adding support to a growing body of literature suggesting that investors are “irrationally” influenced by cosmetic effects.

Bonus!! Link to Wall Street Journal article on this paper.

Double Bonus!! Link to New York Times article on this paper.

"The Game of the Name: Valuation Effects of Name Changes in a Market Downturn" with P. Raghavendra Rau, Ajay Patel, Igor Osobov, and Ajay Khorana

We investigate stock price reactions to Internet related name changes in a market downturn. In contrast to the Internet boom period, during which there was a surge of dot.com additions, in the bust period, there is a dramatic reduction in the pace of dot.com additions accompanied by a rapid increase in dot.com name deletions. Following the Internet “crash” of mid-2000, investors react positively to name changes for firms that remove dot.com from their name. This dot.com deletion effect produces cumulative abnormal returns on the order of 64 percent for the sixty days surrounding the announcement day. Our results add support to a growing body of literature that documents that investors are potentially influenced by cosmetic effects and that managers rationally time corporate actions to take advantage of these biases.

Bonus!! Link to Wall Street Journal article on this paper.

"Investing in size and book-to-market portfolios using information about the macroeconomy: some new trading rules, "with Maria Vassalou and Huseyin Gulen

We propose new trading strategies that invest in size and book-to-market (B/M) decile portfolios. These trading strategies are based on a forecast model that uses mainly business cycle-related variables as predictors. Extensive out-of-sample experiments show profitable predictability in the returns of the decile portfolios. In particular, the proposed strategies outperform passive investments in the same deciles, as well as SMB- and HML-type of strategies. A key characteristic of the proposed strategies is that the long and short positions can be invested in different decile portfolios across time. This is in contrast to the traditional SMB- and HML-type of strategies that always go long and short on the same portfolios. Active strategies that involve the market portfolio, SMB and HML are also examined. A significant level of predictability is identified for SMB. Our results suggest that time variation in SMB and HML is linked to variations in aggregate, macroeconomic, nondiversifiable risk. Thus, our results most closely support a risk-based explanation for SMB and HML.

“Is Time-Series Based Predictability Evident in Real Time?,” with Huseyin Gulen.

We show that out-of-sample tests used in the time-series predictability literature may suffer from test-size problems related to the common practice of exogenous specification of critical parameters, such as the choice of predictive variables, traded assets, and in-sample estimation periods. We perform specification searches across these parameters and find that rejections of the null hypothesis of no predictability are very sensitive to minor variations in parameter specification. We perform simulations using random factors to determine if the observed predictability in the data is real. The simulations suggest that much of the literatures’ out-of-sample evidence of time-series based predictability is consistent with data-snooping.

“Market Inefficiency and the ‘Price Effect’: Links among Size, P/E, Market-to-Book, Reversals and other Anomalies,” with Richard Rendleman.

We develop a very simple hypothesis that any inefficiency (or almost any reasonable specification of inefficiency) in stock market pricing should reveal itself empirically as a stock price effect. That is, if the stock market is inefficient, low-priced stocks should earn higher risk-adjusted returns than high-priced stocks. Moreover, to the extent that the size, P/E, market-to-book and winners/losers effects are linked by low price, any general inefficiency in the pricing of stocks should also reveal itself as an empirical irregularity or anomaly with respect to these price-linked stock selection criteria.
Our results show that pricing error plays an extremely important role in explaining "excess returns" due to the book-to-market, P/E and "winners and losers" effects. Moreover, the results provide compelling evidence that the size effect is also due, in large part, to pricing error. The implications of these results are obvious. If much of the excess return patterns are due to pricing error, the use of variables such as book-to-market and size as risk proxies may be overdone.