In both corporate finance and asset pricing empirical work, researchers are often confronted with panel data. In these data sets, the residuals may be correlated across firms and across time, and OLS standard errors can be biased. Historically, the two literatures have used different solutions to this problem. Corporate finance has relied on clustered standard errors, while asset pricing has used the Fama-MacBeth procedure to estimate standard errors. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct) standard errors and when they diverge. The intent is to provide intuition as to why the different approaches sometimes give different answers and give researchers guidance for their use.

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Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches

We provide a model that links a security’s market liquidity — i.e., the ease of trading it — and traders’ funding liquidity — i.e., their availability of funds. Traders provide market liquidity and their ability to do so depends on their funding, that is, their capital and the margins charged by their financiers. In times of crisis, reductions in market liquidity and funding liquidity are mutually reinforcing, leading to a liquidity spiral. The model explains the empirically documented features that market liquidity (i) can suddenly dry up (i.e. is fragile), (ii) has commonality across securities, (iii) is related to volatility, (iv) experiences “flight to liquidity” events, and (v) comoves with the market. Finally, the model shows how the Fed can improve current market liquidity by committing to improve funding in a potential future crisis.

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Market Liquidity and Funding Liquidity

We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. In addition, we find that stocks with high idiosyncratic volatility relative to the Fama and French (1993) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.

The Cross-Section of Volatility and Expected Returns

Investor sentiment, defined broadly, is a belief about future cash flows and investment risks that is not justified by the facts at hand. The question is no longer whether investor sentiment affects stock prices, but how to measure investor sentiment and quantify its effects. One approach is "bottom up," using biases in individual investor psychology, such as overconfidence, representativeness, and conservatism, to explain how individual investors underreact or overreact to past returns or fundamentals. The investor sentiment approach that we develop in this paper is, by contrast, distinctly "top down" and macroeconomic: we take the origin of investor sentiment as exogenous and focus on its empirical effects. We show that it is quite possible to measure investor sentiment and that waves of sentiment have clearly discernible, important, and regular effects on individual firms and on the stock market as a whole. The top-down approach builds on the two broader and more irrefutable assumptions of behavioral finance -- sentiment and the limits to arbitrage -- to explain which stocks are likely to be most affected by sentiment. In particular, stocks that are difficult to arbitrage or to value are most affected by sentiment.

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Investor Sentiment in the Stock Market

A common approach to evaluating robustness to omitted variable bias is to observe coefficient movements after inclusion of controls. This is informative only if selection on observables is informative about selection on unobservables. Although this link is known in theory in existing literature, very few empirical articles approach this formally. I develop an extension of the theory that connects bias explicitly to coefficient stability. I show that it is necessary to take into account coefficient and R-squared movements. I develop a formal bounding argument. I show two validation exercises and discuss application to the economics literature. Supplementary materials for this article are available online.

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Unobservable Selection and Coefficient Stability: Theory and Evidence

We investigate whether differences in individuals’ experiences of macro-economic shocks affect longterm risk attitudes, as is often suggested for the generation that experienced the Great Depression. Using data from the Survey of Consumer Finances from 1964-2004, we find that birth-cohorts that have experienced high stock market returns throughout their life report lower risk aversion, are more likely to be stock market participants, and, if they participate, invest a higher fraction of liquid wealth in stocks. We also find that cohorts that have experience high inflation are less likely to hold bonds. These results are estimated controlling for age, year effects, and a broad set of household characteristics. Our estimates indicate that stock market returns and inflation early in life affect risk-taking several decades later. However, more recent returns have a stronger effect, which fades away slowly as time progresses. Thus, the experience of risky asset payoffs over the course of an individuals’ life affects subsequent risk-taking. Our results explain, for example, the relatively low rates of stock market participation among young households in the early 1980s (following the disappointing stock market returns in the 1970s depression) and the relatively high participation rates of young investors in the late 1990s (following the boom years in the 1990s).

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Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?*

Short sales, institutional investors and the cross-section of stock returns

It has become standard practice in the cross-sectional asset-pricing literature to evaluate models based on how well they explain average returns on size-B/M portfolios, something many models seem to do remarkably well. In this paper, we review and critique the empirical methods used in the literature. We argue that asset-pricing tests are often highly misleading, in the sense that apparently strong explanatory power (high cross-sectional R2s and small pricing errors) in fact provides quite weak support for a model. We offer a number of suggestions for improving empirical tests and evidence that several proposed models don't work as well as originally advertised.

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A Skeptical Appraisal of Asset Pricing Tests

This paper documents that carry traders are subject to crash risk: i.e. exchange rate movements between high-interest-rate and low-interest-rate currencies are negatively skewed. We argue that this negative skewness is due to sudden unwinding of carry trades, which tend to occur in periods in which risk appetite and funding liquidity decrease. Funding liquidity measures predict exchange rate movements, and controlling for liquidity helps explain the uncovered interest-rate puzzle. Carry-trade losses reduce future crash risk, but increase the price of crash risk. We also document excess co-movement among currencies with similar interest rate. Our findings are consistent with a model in which carry traders are subject to funding liquidity constraints.

Carry Trades and Currency Crashes

http://web.mit.edu/lewellen/www/Documents/AssetPricingTests.pdf

Stefan Nagel

Papers

Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?*

Short sales, institutional investors and the cross-section of stock returns

A Skeptical Appraisal of Asset Pricing Tests

Carry Trades and Currency Crashes

A skeptical appraisal of asset pricing tests.