Sorting Stocks Research Articles

The Common Components of Idiosyncratic Volatility

We show that the pricing of idiosyncratic volatility (IV) is due to unaccounted systematic risk, which affects a large number of asset pricing anomalies. A single common IV component explains one third of variation in IV. Mispricing arises when sorting stocks by the part of IV predicted by exposure to the common IV component, but not when sorting by residual IV. We propose the return difference between stocks with high and low predicted IV, PIV, as a proxy for the missing risk factor. PIV is significantly related to the common components of the returns of several important anomaly-based trading strategies. Controlling for PIV helps explain the earnings surprise, net stock issues, profitability, as well as illiquidity anomalies, and completely explains the financial distress anomaly. Overall our results suggest that the IV puzzle reflects systematic risk related to the average stock volatility and credit risk.

Value and growth stocks in Brazil: risks and returns for one - and two-dimensional portfolios under different economic conditions

For empirical purposes, value stocks are usually defined as those traded at low price-to-earnings ratios (stock prices divided by earnings per share), low price-to-book ratios (stock prices divided by book value per share) or high dividend yields (dividends per share divided by stock prices). Growth stocks, on the other hand, are traded at high price-to-earnings ratios, high price-to-book ratios or low dividend yields. Academic research so far produced, international and Brazilian alike, shows that value stocks outperform growth stocks, challenging the Efficient Market Hypothesis, which states that the market prices of traded stocks are the best estimate of their intrinsic values. Most studies use a single ratio to sort stocks on percentiles; risks (generally defined as beta or standard deviations) and returns are then calculated for the resulting value and growth portfolios. In the present paper, we aim to further contribute to the growing literature on the field by applying a method not previously tested on the Brazilian market. We build portfolios sorted by the price-to-earnings and price-to-book ratios alone and by a combination of both in order to assess value and growth stocks' risks and returns on the Brazilian stock market between 1989 and 2009. Furthermore, our risk analysis may be regarded as the paper's main contribution, since its approach departs from conventional risk concepts, as we not only test for beta: portfolios' returns are measured under different economic conditions. Results support a pervasive value premium in the Brazilian stock market. Risk analysis shows that this premium holds under every economic condition analyzed, suggesting that value stocks are indeed less risky. Beta proved not to be a satisfactory risk measure. Portfolios sorted by the price-to-earnings ratio yielded the best results.

Does Realized Skewness and Kurtosis Predict the Cross-Section of Equity Returns?

Yes. We use intraday data to compute weekly realized variance, skewness and kurtosis for individual equities and assess whether this week’s realized moments predict next week’s stock returns in the cross-section. We sort stocks each week according to their past realized moments, form decile portfolios and analyze subsequent weekly returns. The data shows a very strong negative relationship between stock returns and realized skewness and a clear positive relation between stock returns and realized kurtosis. We do not find a strong relationship between realized volatility and stock returns. A trading strategy that buys stocks in the lowest realized skewness decile and sells stocks in the highest realized skewness decile generates an average weekly return of 43 basis points with a t-statistic of 8.91. A similar strategy that buys stocks with high realized kurtosis and sells stocks with low realized kurtosis produces a weekly return of 16 basis points with a t-statistic of 2.12. Our results are robust across sample periods, portfolio weightings, and proxies for firm characteristics, and they are not captured by the Fama-French and Carhart factors.

Do Realized Skewness and Kurtosis Predict the Cross-Section of Equity Returns?

Yes. We use intraday data to compute weekly realized variance, skewness and kurtosis for individual equities and assess whether this week?s realized moments predict next week?s stock returns in the cross-section. We sort stocks each week according to their past realized moments, form decile portfolios and analyze subsequent weekly returns. We ?nd a very strong negative relationship between realized skewness and next week?s stock returns, and a positive relationship between realized kurtosis and next week?s stock returns. We do not ?nd a strong relationship between realized volatility and stock returns. A trading strategy that buys stocks in the lowest realized skewness decile and sells stocks in the highest realized skewness decile generates an average weekly return of 43 basis points with a t-statistic of 8:91. A similar strategy that buys stocks with high realized kurtosis and sells stocks with low realized kurtosis produces a weekly return of 16 basis points with a t-statistic of 2:98. Our results are robust across sample periods, portfolio weightings, and proxies for ?rm characteristics, and they are not captured by the Fama-French and Carhart factors.

Sources of Momentum Profits: Evidence on the Irrelevance of Characteristics

Several recent studies document that sorting stocks first on certain stock-level characteristics and then on past returns results in elevated momentum profits. We show that such strategies enhance momentum profits simply by trading in stocks with more extreme past returns. Adjusted for this effect, elevated momentum profits resulting from characteristics (size, R², turnover, age, analyst coverage, analyst forecast dispersion, market-to-book, price, illiquidity, credit rating) disappear almost entirely. Interaction patterns have been used to support behavioral and limits-to-arbitrage explanations of momentum; our findings imply that explanations of momentum should instead focus on the link between momentum profits and extreme past returns.

On the estimation of the cost of equity in Latin America

This paper researches the sources of stock market risk influencing the pricing of 921 Latin American stocks and computes their corresponding opportunity cost (COE) over the period 1997–2004 by firm and sector. Running an adjusted version of the Capital Asset Pricing Model (CAPM) it finds that systematic risk accounts on average for more than 32% of COE total variance. This implies that potential CAPM mispricing related to undiversified idiosyncratic risk in Latin America has been relatively lower (but absolutely higher) than in United States and other European and Asian stock markets (such as the United Kingdom, Canada or Japan). A first robustness test for the omission of international sources of un-diversifiable risk suggests that both global market and real currencies portfolios do not add significant information to domestic market portfolios. Moreover, a second robustness check offers further evidence that well-diversified portfolios constructed by sorting stocks according to their size and book-to-market ratios a la Fama and French do not improve the goodness of fit in the regressions based on the adjusted version of CAPM.

The Value Premium and Time‐Varying Volatility

Abstract: Numerous studies have documented the failure of the static and conditional capital asset pricing models to explain the difference in returns between value and growth stocks. This paper examines the post‐1963 value premium by employing a model that captures the time‐varying total risk of the value‐minus‐growth portfolios. Our results show that the time‐series of value premia is strongly and positively correlated with its volatility. This conclusion is robust to the criterion used to sort stocks into value and growth portfolios and to the country under review (the US and the UK). Our paper is consistent with evidence on the possible role of idiosyncratic risk in explaining equity returns, and also with a separate strand of literature concerning the relative lack of reversibility of value firms' investment decisions.

Cross-section of option returns and volatility

We study the cross-section of stock option returns by sorting stocks on the difference between historical realized volatility and at-the-money implied volatility. We find that a zero-cost trading strategy that is long (short) in the portfolio with a large positive (negative) difference between these two volatility measures produces an economically and statistically significant average monthly return. The results are robust to different market conditions, to stock risks-characteristics, to various industry groupings, to option liquidity characteristics, and are not explained by usual risk factor models.

Skewness from High-Frequency Data Predicts the Cross-Section of Stock Returns

Theoretical and empirical studies document a negative relation between stock returns and individual skewness. In these studies, individual skewness has been defined with predictive models, industry groups and even with options' skewness. However, measures of skewness computed only from stock returns, such as historical skewness, do not confirm this negative relation. We propose a model-free measure of individual skewness directly obtained from high-frequency intraday prices, which we call realized skewness. We test whether realized skewness predicts future stock returns by sorting stocks every week according to realized skewness, forming five portfolios and analyzing subsequent weekly returns. We find a negative relation between realized skewness and stock returns in the cross section. A trading strategy that buys stocks in the lowest realized skewness quintile and sells stocks in the highest realized skewness quintile generates an average raw return of 38 basis points per week with a t-statistic of 9.15. This result is robust to different market periods, portfolio weightings, firm characteristics proxies and is not explained by the Fama-French-Carhart factors.

Information and Cross-Border Equity Holdings

This paper studies, both theoretically and empirically, dispersion in cross-border equity holdings. We propose a rational expectations equilibrium model in which agents have information about asset specific and/or common components of stocks' payoffs. The model produces closed-form solutions for asset prices and investor holdings (positions). A numerical analysis can generate home bias levels similar to those found in existing studies as well as reverse home bias (i.e., investors from one country overweigh stocks from another country). The last section of the paper analyzes cross-border mutual fund holdings of 5,781 stocks from 21 developed countries. We create a proxy variable for the degree of asset specific information about a stock and another proxy for the degree of common component information. Double sorting stocks using our information proxies produces ownership dispersion similar to that implied by the model. In regression analysis, our information proxies explain holding levels even after including variables that have previously been linked to home bias.

Idiosyncratic Volatility and the Cross Section of Expected Returns

AbstractThis paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that i) the data frequency used to estimate idiosyncratic volatility, ii) the weighting scheme used to compute average portfolio returns, iii) the breakpoints utilized to sort stocks into quintile portfolios, and iv) using a screen for size, price, and liquidity play critical roles in determining the existence and significance of a relation between idiosyncratic risk and the cross section of expected returns. Portfoliolevel analyses based on two different measures of idiosyncratic volatility (estimated using daily and monthly data), three weighting schemes (value-weighted, equal-weighted, inverse volatility-weighted), three breakpoints (CRSP, NYSE, equal market share), and two different samples (NYSE/AMEX/NASDAQ and NYSE) indicate that no robustly significant relation exists between idiosyncratic volatility and expected returns.

Trading Volume Liquidity and Investment Styles

The purpose of this study is to better understand stock market trading volume liquidity, measured at the individual stock level, and its relationship with and potential impact on stock performance for a variety of well-known investment styles. We focus on two universes of generally liquid stocks (chosen because they are a primary focus of U.S. institutional investors such as mutual fund managers and pension fund managers), the stocks that make up the Standard & Poor's (S&P) 500 Index, and a broader index of the top 1,000 stocks measured by market capitalization (closely mimicking the Russell 1000 Index stocks). We show that three liquidity-related measures - trailing 3-month trading volume (i.e., shares), dollar value of trading volume, and turnover - are monotonically related to price-to-book (PB) and market capitalization (MKT), and momentum strategies based on both past 6-month winners and losers (MOM) tend to experience higher liquidity. When we sort stocks based on each of these liquidity measures we find that the more liquid stocks based on trading volume and turnover tend to have higher subsequent returns (1 through 12-month holding periods) than the less liquid stocks, although the reverse is true based on dollar volume. We then focus on the trading volume measure (which produces the greatest dispersion of returns) and run CAPM and Fama-French (3-factor and 4-factor) model regressions that show that the most heavily traded quintile of stocks experiences significant superior performance. We create a new measure that we call the trading volume factor, in the spirit of the Fama-French factors, and investigate its properties. We find that its beta is generally significant when added to the Fama-French 4-factor model, regressed against portfolio quintile returns based on PB, MKT and MOM sorts.

Idiosyncratic Volatility and the Cross-Section of Expected Returns

This paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that (i) data frequency used to estimate idiosyncratic volatility, (ii) weighting scheme used to compute average portfolio returns, (iii) breakpoints utilized to sort stocks into quintile portfolios, and (iv) using a screen for size, price and liquidity play a critical role in determining the existence and significance of a relation between idiosyncratic risk and the cross-section of expected returns. Portfolio-level analyses based on two different measures of idiosyncratic volatility (estimated using daily and monthly data), three weighting schemes (value-weighted, equal-weighted, inverse-volatility-weighted), three breakpoints (CRSP, NYSE, equal-market-share), and two different samples (NYSE/AMEX/NASDAQ and NYSE) indicate that there is no robust, significant relation between idiosyncratic volatility and expected returns.

Irreversible Investment, Real Activity and the Value Premium

We conduct an empirical investigation of the principal predictions of an emerging strand of models linking firms' real investment behavior under irreversibility and asset return dynamics. A string of recent papers (Berk, Green and Naik (1999), Gomes, Kogan and Zhang (2003), Zhang (2003), Carlson, Fisher and Giammarino (2003), Kogan (2003) and Cooper (2003) ties firms' characteristics, such as their book-to-market ratio, to their systematic risk within the framework of real options models. In these models systematic risk is conditional in nature. Book-to-market appears to explain the cross-section of average stock return and predicts return in the time series, because it is correlated with the systematic risk of the firm, namely the firm's conditional loadings with respect to risk factors. Empirical tests that do not adequately account for the conditional nature of pricing could therefore reject the (static) CAPM and find a book-to-market effect. We present a model that builds on the papers mentioned, that facilitates our empirical work. Our model yields a relationship among book-to-market, economically fundamental observable variables capturing the true, conditional, systematic risk of firms, and conditional moments of stock returns. We find substantial empirical support for the model's predictions and consequently for a link between book-to-market and risk. However, book-to-market retains some incremental information about expected returns which is not explained by our measure for systematic risk. Irreversibility of real investment plays a central role in the string of models discussed above. The irreversibility constraint occasionally binds, implying that at times firms will have excess capital capacity. The models predict that the conditional first and second moments of returns are positively correlated with excess capital capacity, and more so when the degree of investment irreversibility is high. The explanation for this in the different papers is rather distinct but they all share this feature. Since each model generates a value premium among firms that all use the same technology, it seems reasonable to sort stocks by industries. We therefore perform a battery of tests on industry portfolios. We begin by using the time series mean and volatility of capacity utilization (data on capacity utilization is given by the Federal Reserve) as a measure to rank industries by the degree to which investment is irreversible. We then continue to use capacity utilization as a measure for excess capital capacity. Our model yields an alternative measure of excess capital capacity, namely the capital-to-output ratio. We use both measures in our tests. We find that our economic variables explain return volatility, time variation in market beta and are strong predictors or returns in the time series. These findings provide strong support for the predictions stemming from the new literature.

Does Disposition Drive Momentum?

We test the hypothesis that the dispositon effect is a behavioral bias that drives stock price momentum. Using data from a large Shanghai brokerage firm, we estimate the magnitude of the disposition effect for a sample of 13,460 Chinese investors and firms. We find that a large majority of Chinese investors exhibit the disposition effect. An investor’s disposition coefficient estimated with one year of data forecasts that investor’s disposition effect and investment performance in subsequent years. More disposition-prone investors tend to trade less frequently and in smaller sizes than other investors. While past returns do not forecast future returns in our relatively short sample of Shanghai Stock Exchange stocks, sorting stocks by the net unrealized gains or losses of disposition-prone investors generates a statistically significant winner/loser spread of seven percent per year. Our results suggest that disposition does indeed drive momentum.

On the Cross Section of Conditionally Expected Stock Returns

We test and find evidence against Sharpe (1964) and Lintner's (1965) CAPM using portfolios motivated by Campbell's (1993) ICAPM, which is our alternative hypothesis. That is, we sort stocks equally into ten portfolios according to out-of-sample forecasts formed using predictive variables advocated by recent authors. The average out-of-sample realized returns of these portfolios increase monotonically from the first decile(stocks with the lowest expected returns) to the tenth decile (stocks with the highest expected returns), and the difference between the tenth and first deciles is a statistically significant 4.8 percent per year. These results are shown to be distinct from the momentum anomaly documented by Jegadeesh and Titman (1993). As expected, these portfolio returns pose a challenge to CAPM; however, they are explained by a variant of Campbell's ICAPM, in which risk factors also include the variables used to form the expected returns, in addition to stock market returns.

Parallels Between the Cross-Sectional Predictability of Stock and Country Returns

Book-to-market ratio (BE/ME), market equity (ME), and one- year past return (momentum) (MOM) help explain the cross- section of expected individual stock returns within the U.S. and within other countries. Examining equity markets as a whole, in contrast to individual stocks, we uncover strong parallels between the explanatory power of these variables for individual stocks and for countries. First, country versions of BE/ME, ME, and MOM help explain the cross-section of expected country returns. Second, the January seasonal in ME's explanatory power for stocks also appears for countries. Third, portfolios formed by sorting stocks and countries on these variables produce similar patterns in profitability before and after the portfolio formation date.