Essays in empirical asset pricing

Official URL: http://risc01.sabanciuniv.edu/record=b1680121 (Table of Contents)

This dissertation contains three articles. In the first article, I review the literature on liquidity. I focus on various liquidity proxies and their effects on the equity returns while restricting the review to the set of top journals in finance since this literature is quite immense. In the second article, I investigate the relationship between expected returns and liquidity measures in Borsa Istanbul. Firm-level cross-sectional regressions indicate that there is a positive relationship between various illiquidity measures and one-month to six-month ahead stock returns. Findings are robust after using different sample periods and controlling for well-known priced factors such as market beta, size, book-to-market and momentum. The portfolio analysis reveals that stocks that are in the highest illiquidity quintile earn 7.2% to 19.2% higher risk-adjusted annual returns than those in the lowest illiquidity quintile. The illiquidity premium is stronger for small stocks and stocks with higher return volatility and it increases (decreases) during periods of extremely low (high) market returns. In the third article, I investigate the stock return exposure to various illiquidity risk factors through alternative measures of factor betas and the performance of factor betas in predicting the cross-sectional variation in stock returns. As a parametric test, a two-step procedure is utilized to directly calculate the monthly factor betas in the first stage and then, the sensitivity of stock returns to these previously estimated factor betas is calculated in the second. The regression results show that there exists a significantly positive link between illiquidity beta and future stock returns. The results are robust after controlling for market, size, book-to-market and momentum factors. The portfolio analysis reveals that stocks in the high-beta portfolio generate about 5% higher annual returns compared to stocks in the low-beta portfolio.