ARE PAIRS TRADING PROFITS ROBUST TO TRADING COSTS?

We examine the impact of trading costs on pairs trading profitability in the U.S. equity market, 1963 to 2009. After controlling for commissions, market impact, and short selling fees, pairs trading remains profitable, albeit at much more modest levels. Specifically, we document a risk‐adjusted return of about 30 basis points per month among portfolios of well‐matched pairs that are formed within refined industry groups. Pairs trading exhibits a lower risk and lower return profile than a short‐term reversal strategy that sorts stocks relative to their industry peers. Notably, both these types of contrarian investing are largely unprofitable after 2002.     

Cross-sectional performance and investor sentiment in a multiple risk factor model

Economists have long recognized the importance of information veracity in valuing risky securities. Market participants concerned about the credibility of information measures may require additional compensation to entice them to hold stocks with less transparent information. These same securities are expected to display greater sensitivities to measures of market sentiment. We find that investor sentiment sensitivities increase directly with multiple measures of opacity in the cross-section. Next we examine the extent to which sentiment sensitivities are priced in an asset pricing context. Using the Jha et al. (2009) model of conditional performance evaluation, we find an inverse relation between ex ante known investor sentiment and the marginal performance of opaque stocks. In contrast, translucent stocks exhibit relatively little variability in performance across levels of sentiment.     

A comprehensive long-term analysis of S&P 500 index additions and deletions

We investigate the long-term effects of S&P 500 index additions and deletions on a sample of stocks from 1962 to 2003 and find a significant long-term price increase for both added and deleted stocks, with deleted stocks outperforming added stocks. The long-term price increase for added stocks can be attributed to increases in institutional ownership, liquidity, and analyst coverage, and a decrease in the shadow cost in the long-term. However, while deletion has no significant effect on analyst coverage and shadow cost, we find a rebound in the institutional ownership and liquidity of deleted stocks. The difference in the long-term price increase of added and deleted stocks can be explained by analyst coverage and operating performance.     

Measuring investor sentiment with mutual fund flows

We investigate a proxy for monthly shifts between bond funds and equity funds in the USA: aggregate net exchanges of equity funds. This measure (which is negatively related to changes in VIX) is positively contemporaneously correlated with aggregate stock market excess returns: One standard deviation of net exchanges is related to 1.95% of market excess return. Our main new finding is that 85% (all) of the contemporaneous relation is reversed within four (ten) months. The effect is stronger in smaller stocks and in growth stocks. These findings support the notion of “noise” in aggregate market prices induced by investor sentiment.     

Mutual fund flows,expected returns,and the real economy

This paper investigates the relation between mutual fund flows and the real economy. The findings of this paper support the theory that the positive co-movement of flows into equity funds and stock market returns is explained by a common response to macroeconomic news. Variables that predict the real economy as well as the equity premium – in particular dividend-price ratio, default spread, relative T-Bill rate and consumption-wealth ratio – are related to fund flows and can account for the correlation of flows and market returns. Furthermore, consistent with the information-response hypothesis, mutual fund flows are forward-looking and predict real economic activity.     

Local institutional investors,information asymmetries,and equity returns

We examine the informational role of geographically proximate institutions in stock markets. We find that both the level of and change in local institutional ownership predict future stock returns, particularly for firms with high information asymmetry; in contrast, such predictive abilities are relatively weak for nonlocal institutional ownership. The local advantage is especially evident for local investment advisors, high local ownership institutions, and high local turnover institutions. We also find that the stocks that local institutional investors hold (trade) earn higher excess returns around future earnings announcements than those that nonlocal institutional investors hold (trade).     

Do arbitrageurs amplify economic shocks?

We test the hypothesis that arbitrageurs amplify economic shocks in equity markets. The ability of speculators to hold short positions depends on asset values. Shorts are often reduced following good news about a stock. Therefore, the prices of highly shorted stocks are excessively sensitive to shocks compared with stocks with little short interest. We confirm this hypothesis using several empirical strategies including two quasi-experiments. In particular, we establish that the price of highly shorted stocks overshoots after good earnings news due to short covering compared with other stocks.     

Anomalies: Is it the Economy?

There is still no consensus on whether small firm or value stock anomalies exist. We examine the last half of the 20th century and apply a six‐factor macroeconomic model to test for the presence of these abnormal returns. Using four proxies for value, we find that detecting this anomaly is sensitive to choice of proxy, the magnitude of the abnormal returns varies over time, and the anomaly does not persist through time. Additional tests provide evidence that abnormal returns for small, value‐oriented, and growth‐oriented firms differ significantly under restrictive versus expansive monetary policy regimes.     

Price trends and patterns in technical analysis: A theoretical and empirical examination

While many technical trading rules are based upon patterns in asset prices, we lack convincing explanations of how and why these patterns arise, and why trading rules based on technical analysis are profitable. This paper provides a model that explains the success of certain trading rules that are based on patterns in past prices. We point to the importance of confirmation bias, which has been shown to play a key role in other types of decision making. Traders who acquire information and trade on the basis of that information tend to bias their interpretation of subsequent information in the direction of their original view. This produces autocorrelations and patterns of price movement that can predict future prices, such as the “head-and-shoulders” and “double-top” patterns. The model also predicts that sequential price jumps for a particular stock will be positively autocorrelated. We test this prediction and find that jumps exhibit statistically and economically significant positive autocorrelations.     

Asset fire sales (and purchases) in equity markets

This paper examines institutional price pressure in equity markets by studying mutual fund transactions caused by capital flows from 1980 to 2004. Funds experiencing large outflows tend to decrease existing positions, which creates price pressure in the securities held in common by distressed funds. Similarly, the tendency among funds experiencing large inflows to expand existing positions creates positive price pressure in overlapping holdings. Investors who trade against constrained mutual funds earn significant returns for providing liquidity. In addition, future flow-driven transactions are predictable, creating an incentive to front-run the anticipated forced trades by funds experiencing extreme capital flows.     

Dispersion in analysts’ earnings forecasts and credit rating

This paper shows that the puzzling negative cross-sectional relation between dispersion in analysts’ earnings forecasts and future stock returns may be explained by financial distress, as proxied by credit rating downgrades. Focusing on a sample of firms rated by Standard & Poor's (S&P), we show that the profitability of dispersion-based trading strategies concentrates in a small number of the worst-rated firms and is significant only during periods of deteriorating credit conditions. In such periods, the negative dispersion–return relation emerges as low-rated firms experience substantial price drop along with considerable increase in forecast dispersion. Moreover, even for this small universe of worst-rated firms, the dispersion–return relation is non-existent when either the dispersion measure or return is adjusted by credit risk. The results are robust to previously proposed explanations for the dispersion effect such as short-sale constraints and leverage.     

Residual momentum

Conventional momentum strategies exhibit substantial time-varying exposures to the Fama and French factors. We show that these exposures can be reduced by ranking stocks on residual stock returns instead of total returns. As a consequence, residual momentum earns risk-adjusted profits that are about twice as large as those associated with total return momentum; is more consistent over time; and less concentrated in the extremes of the cross-section of stocks. Our results are inconsistent with the notion that the momentum phenomenon can be attributed to a priced risk factor or market microstructure effects.     

Measuring the effects of geographical distance on stock market correlation

Recent studies suggest that the correlation of stock returns increases with decreasing geographical distance. However, there is some debate on the appropriate methodology for measuring the effects of distance on correlation. We modify a regression approach suggested in the literature and complement it with an approach from spatial statistics, the mark correlation function. For the stocks contained in the S&P 500 that we examine, both approaches lead to similar results. Contrary to previous studies we find that beyond 50 miles geographical proximity is irrelevant for stock return correlations. For distances below 50 miles, we can show that the magnitude of local correlations varies with investor sentiment.     

Explaining the idiosyncratic volatility puzzle using Stochastic Discount Factors

I use Stochastic Discount Factors to examine the sources of the idiosyncratic volatility premium. I find that non-zero risk aversion and firms’ non-systematic coskewness determine the premium on idiosyncratic volatility risk. The firm’s non-systematic coskewness measures the comovement of the asset’s volatility with the market return. When I control for the non-systematic coskewness factor, I find no significant relation between idiosyncratic volatility and stock expected returns. My results are robust across different sample periods and firm characteristics.     

Hedge funds,managerial skill,and macroeconomic variables

This paper evaluates hedge fund performance through portfolio strategies that incorporate predictability based on macroeconomic variables. Incorporating predictability substantially improves out-of-sample performance for the entire universe of hedge funds as well as for various investment styles. While we also allow for predictability in fund risk loadings and benchmark returns, the major source of investment profitability is predictability in managerial skills. In particular, long-only strategies that incorporate predictability in managerial skills outperform their Fung and Hsieh (2004) benchmarks by over 17% per year. The economic value of predictability obtains for different rebalancing horizons and alternative benchmark models. It is also robust to adjustments for backfill bias, incubation bias, illiquidity, fund termination, and style composition.     

Caught on tape: Institutional trading,stock returns,and earnings announcements

Many questions about institutional trading can only be answered if one tracks high-frequency changes in institutional ownership. In the United States, however, institutions are only required to report their ownership quarterly in 13-F filings. We infer daily institutional trading behavior from the “tape”, the Transactions and Quotes database of the New York Stock Exchange, using a sophisticated method that best predicts quarterly 13-F data from trades of different sizes. We find that daily institutional trades are highly persistent and respond positively to recent daily returns but negatively to longer-term past daily returns. Institutional trades, particularly sells, appear to generate short-term losses—possibly reflecting institutional demand for liquidity—but longer-term profits. One source of these profits is that institutions anticipate both earnings surprises and post-earnings announcement drift. These results are different from those obtained using a standard size cutoff rule for institutional trades.