Huseyin Gulen

Bio: Huseyin Gulen is an academic researcher from Purdue University. The author has contributed to research in topics: Investment (macroeconomics) & Equity premium puzzle. The author has an hindex of 22, co-authored 51 publications receiving 6347 citations. Previous affiliations of Huseyin Gulen include Virginia Tech & Pamplin College of Business.

Policy Uncertainty and Corporate Investment

Abstract: Using the policy uncertainty index of Baker, Bloom, and Davis (2013), we document a strong negative relationship between firm-level capital investment and the aggregate level of uncertainty associated with future policy and regulatory outcomes. More importantly, we find evidence that the relation between policy uncertainty and capital investment is not uniform in the cross section, being significantly stronger for firms with a higher degree of investment irreversibility and for firms which are more dependent on government spending. Our results lend empirical support to the notion that policy uncertainty can depress corporate investment by inducing precautionary delays due to investment irreversibility.

Asset Growth and the Cross-Section of Stock Returns

Abstract: We test for firm-level asset investment effects in returns by examining the cross-sectional relation between firm asset growth and subsequent stock returns. As a test variable, we use the year-on-year percentage change in total assets. Asset growth rates are strong predictors of future abnormal returns. Asset growth retains its forecasting ability even on large capitalization stocks, a subgroup of firms for which other documented predictors of the cross-section lose much of their predictive ability. When we compare asset growth rates with the previously documented determinants of the cross-section of returns (i.e., book-to-market ratios, firm capitalization, lagged returns, accruals, and other growth measures), we find that a firm's annual asset growth rate emerges as an economically and statistically significant predictor of the cross-section of U.S. stock returns.

Asset Growth and the Cross-Section of Stock Returns

Abstract: We test for firm-level asset investment effects in returns by examining the crosssectional relation between firm asset growth and subsequent stock returns. Asset growth rates are strong predictors of future abnormal returns. Asset growth retains its forecasting ability even on large capitalization stocks. When we compare asset growth rates with the previously documented determinants of the cross-section of returns (i.e., book-to-market ratios, firm capitalization, lagged returns, accruals, and other growth measures), we find that a firm’s annual asset growth rate emerges as an economically and statistically significant predictor of the cross-section of U.S. stock returns. ONE OF THE PRIMARY FUNCTIONS OF CAPITAL MARKETS is the efficient pricing of real investment. As companies acquire and dispose of assets, economic efficiency demands that the market appropriately capitalizes such transactions. Yet, growing evidence identifies an important bias in the market’s capitalization of corporate asset investment and disinvestment. The findings suggest that corporate events associated with asset expansion (i.e., acquisitions, public equity offerings, public debt offerings, and bank loan initiations) tend to be followed by periods of abnormally low returns, whereas events associated with asset contraction (i.e., spinoffs, share repurchases, debt prepayments, and dividend initiations) tend to be followed by periods of abnormally high returns. 1 In

Informed Trading in Stock and Option Markets

Abstract: We investigate the contribution of option markets to price discovery, using a modification of Hasbrouck’s (1995) “information share” approach. Based on five years of stock and options data for 60 firms, we estimate the option market’s contribution to price discovery to be about 17% on average. Option market price discovery is related to trading volume and spreads in both markets, and stock volatility. Price discovery across option strike prices is related to leverage, trading volume, and spreads. Our results are consistent with theoretical arguments that informed investors trade in both stock and option markets, suggesting an important informational role for options. INVESTORS WHO HAVE ACCESS to private information can choose to trade in the stock market or in the options market. Given the high leverage achievable with options and the built-in downside protection, one might think the options market would be an ideal venue for informed trading. If informed traders do trade in the options market, we would expect to see price discovery in the options market. That is, we would expect at least some new information about the stock price to be reflected in option prices first. Establishing that price discovery straddles both the stock and options markets is important for several reasons. In a frictionless, dynamically complete market, options would be redundant securities. This paper contributes to the understanding of why options are relevant in actual markets, by providing the first unambiguous evidence that stock option trading contributes to price discovery in the underlying stock market. Further, we document that the level

Policy Uncertainty and Corporate Investment

Abstract: Using a news-based index of policy uncertainty, we document a strong negative relationship between firm-level capital investment and the aggregate level of uncertainty associated with future policy and regulatory outcomes. More importantly, we find evidence that the relation between policy uncertainty and capital investment is not uniform in the cross-section, being significantly stronger for firms with a higher degree of investment irreversibility and for firms that are more dependent on government spending. Our results lend empirical support to the notion that policy uncertainty can depress corporate investment by inducing precautionary delays due to investment irreversibility.

Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches

Abstract: 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.

Measuring Economic Policy Uncertainty

Abstract: We develop a new index of economic policy uncertainty (EPU) based on newspaper coverage frequency Several types of evidence – including human readings of 12,000 newspaper articles – indicate that our index proxies for movements in policy-related economic uncertainty Our US index spikes near tight presidential elections, Gulf Wars I and II, the 9/11 attacks, the failure of Lehman Brothers, the 2011 debt-ceiling dispute and other major battles over fiscal policy Using firm-level data, we find that policy uncertainty raises stock price volatility and reduces investment and employment in policy-sensitive sectors like defense, healthcare, and infrastructure construction At the macro level, policy uncertainty innovations foreshadow declines in investment, output, and employment in the United States and, in a panel VAR setting, for 12 major economies Extending our US index back to 1900, EPU rose dramatically in the 1930s (from late 1931) and has drifted upwards since the 1960s

The Cross-section of Expected Stock Returns

Abstract: This paper studies the cross-sectional properties of return forecasts derived from Fama-MacBeth regressions. These forecasts mimic how an investor could, in real time, combine many firm characteristics to obtain a composite estimate of a stock’s expected return. Empirically, the forecasts vary substantially across stocks and have strong predictive power for actual returns. For example, using ten-year rolling estimates of Fama- MacBeth slopes and a cross-sectional model with 15 firm characteristics (all based on low-frequency data), the expected-return estimates have a cross-sectional standard deviation of 0.87% monthly and a predictive slope for future monthly returns of 0.74, with a standard error of 0.07.