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Full-Text Articles in Business
Arbitrage Risk, Investor Sentiment And Maximum Daily Returns, Kenneth A. Tah
Arbitrage Risk, Investor Sentiment And Maximum Daily Returns, Kenneth A. Tah
Doctoral Dissertations
We test the cross-sectional relation between daily maximum return (MAX) and return in the following month for stocks with high and low idiosyncratic volatility. We use portfolio level analysis and firm-level cross-sectional regression to find that the negative and significant relation between MAX and expected stock return (known as the "MAX effect") is a non-January phenomenon observed predominantly on a sample of stocks with high idiosyncratic volatility. We find that the effect of investor sentiment on the MAX effect depends on arbitrage risk. Our findings suggest that arbitrageurs find it difficult to correct the mispricing of stocks with extreme positive …
Anchoring Bias, Idiosyncratic Volatility And The Cross-Section Of Stock Returns, Cedric Tresor Luma Mbanga
Anchoring Bias, Idiosyncratic Volatility And The Cross-Section Of Stock Returns, Cedric Tresor Luma Mbanga
Doctoral Dissertations
Ang, Hodrick, Xing and Zhang (2006) document an anomaly in the cross-section of stock returns. They show that high idiosyncratic volatility (IVOL) firms earn lower returns in the following month. Specifically, they find after sorting stocks in quintile portfolios based on the previous month's IVOL that a zero-investment portfolio long the most volatile quintile of stocks and short the least yields about -1% during the subsequent month. The evidence reported in Ang, Hodrick, Xing and Zhang (2006) is primarily puzzling because traditional asset pricing theories suggest that (i) only systematic risk should be priced, (ii) to the extent that markets …
Investor Sentiment Aligned: A Powerful Predictor Of Stock Returns, Dashan Huang, Fuwei Jiang, Jun Tu, Guofu Zhou
Investor Sentiment Aligned: A Powerful Predictor Of Stock Returns, Dashan Huang, Fuwei Jiang, Jun Tu, Guofu Zhou
Research Collection Lee Kong Chian School Of Business
We propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices have both in and out of sample, and the predictability becomes both statistically and economically significant. In addition, it outperforms well-recognized macroeconomic variables and can also predict cross-sectional stock returns sorted by industry, size, value, and momentum. The driving force of the predictive power appears to stem from investors' biased beliefs about future cash flows.