Forthcoming Articles

Stock Return Asymmetry: Beyond Skewness

Lei Jiang, Ke Wu, Guofu Zhou, and Yifeng Zhu

In this paper, we propose two asymmetry measures for stock returns. Unlike the popular skewness measure, our measures are based on the distribution function of the data rather than just the third central moment. We present empirical evidence that greater upside asymmetries calculated using our new measures imply lower average returns in the cross-section of stocks. In contrast, when using the skewness measure, the relationship between asymmetry and returns is inconclusive.

Googling Investor Sentiment around the World

Zhenyu Gao, Haohan Ren, and Bohui Zhang

We study how investor sentiment affects stock prices around the world. Relying on households’ Google search behavior, we construct a weekly measure of sentiment for 38 countries during the 2004–2014 period. We validate the sentiment index in tests using sports outcomes and show that the sentiment measure is a contrarian predictor of country-level market returns. Furthermore, we document an important role of global sentiment in stock markets.

Does Unusual News Forecast Market Stress?

Paul Glasserman and Harry Mamaysky

An increase in “unusual” news with negative sentiment predicts an increase in stock market volatility. Unusual positive news forecasts lower volatility. Our analysis is based on over 360,000 articles on 50 large financial companies, published in 1996–2014. Unusualness interacted with sentiment forecasts company-specific and aggregate volatility several months ahead. Furthermore, unusual news is reflected more slowly in aggregate volatility than company-specific volatility. News measures from articles explicitly about the “market” — which are more easily accessible to investors — do not forecast volatility. The observed responses of volatility to news may be explained by attention constraints on investors.

The Dividend Term Structure

Jac. Kragt, Frank de Jong, and Joost Driessen

We estimate a model for the term structure of discounted risk-adjusted dividend growth using prices of dividend futures for the Eurostoxx 50. A 2-factor model capturing short-term mean reversion within a year and a medium-term component reverting at business-cycle horizon gives an excellent fit of these prices. Hence, investors update the valuation of dividends beyond the business cycle only to a limited degree. The 2-factor model, estimated on dividend futures data only, explains a large part of observed daily stock market returns. We also show that the two latent factors are related to various economic and financial variables.

Using Stocks or Portfolios in Tests of Factor Models

Andrew Ang, Jun Liu, and Krista B. Schwarz

We examine the efficiency of using individual stocks or portfolios as base assets to test asset pricing models using cross-sectional data. The literature has argued that creating portfolios reduces idiosyncratic volatility and allows more precise estimates of factor loadings, and consequently risk premia. We show analytically and empirically that smaller standard errors of portfolio beta estimates do not lead to smaller standard errors of cross-sectional coefficient estimates. Factor risk premia standard errors are determined by the cross-sectional distributions of factor loadings and residual risk. Portfolios destroy information by shrinking the dispersion of betas, leading to larger standard errors.

Can Corporate Income Tax Cuts Stimulate Innovation?

Julian Atanassov and Xiaoding Liu

We hypothesize that corporate income taxes distort firms’ incentives to innovate by reducing their pledgeable income. Using a differences-in-differences methodology, we document that large corporate income tax cuts boost corporate innovation. We find a similar but opposite effect for tax increases. Most of the change in innovation occurs two or more years after the tax change, and there’s no effect before the tax change. Exploring the mechanisms, we show that tax cuts have a stronger impact on innovation for firms with weaker governance, greater financial constraints, fewer tangible assets, smaller patent stock, and a greater degree of tax avoidance.

Venture Capital Communities

Amit Bubna, Sanjiv R. Das, and Nagpurnanand Prabhala

While venture capitalists (VCs) can choose from thousands of potential syndicate partners, many co-syndicate with small groups of preferred partners. We term these groups “VC communities.” We apply computational methods from the physical sciences to three decades of syndication data to identify these communities. We find that communities comprise VCs that are similar in age, connectedness, and functional style but undifferentiated in spatial locations. Machine learning tools classify communities into three groups roughly ordered by the age and reach. Community VC financing is associated with faster maturation and greater innovation especially for early-stage firms without an innovation history.

Competition and Operating Volatilities around the World

Tanakorn Makaew and Vojislav Maksimovic

Numerous papers have shown that developing economies are more volatile. We show despite greater aggregate and industry stability, performance and size of individual firms in developed countries are more volatile. In developing countries, market imperfections insulate incumbent firms from competition. Consistent with this, firms in developing countries have higher profit, higher market concentration, and less capital raising. Cross-country differences in operating risk and competition intensity are greater in external finance dependent industries where we expect higher impacts of capital market imperfections. We show the inverse relation between aggregate and firm-level volatilities has important implications on international studies of cash holding.

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