Cross-section Of Future Returns Research Articles

베타의 변동성과 주식의 기대수익률

This paper presents the stocks with high volatility of beta (VolBeta) underperform those with low VolBeta in the Korean stock market. The main findings are summarized as follows: First, the portfolio with high VolBeta has lower return relative to the one with low VolBeta. The raw and Fama-French four factor risk-adjusted return on zero-cost portfolio buying the highest and selling lowest VolBeta deciles are -1.11% and -1.45%, respectively. Second, firm-level Fama-MacBeth cross-sectional regressions show negative and statistically significant relation between VolBeta in this month and the cross-section of future returns. The results are robust to controls for firm chracteristics reportedly affecting stock returns. Third, overpricing of the stocks with high VolBeta is mostly pronounced in the ones with high idiosyncratic volatility. On the contrary, overpricing disappears in the high VolBeta stocks with low idiosyncratic volatility. The results suggest stocks with higher arbitrage risk measured by idiosyncratic volatility are more likely to be mispriced, consistent with Stambaugh, Yu, Yuan(2015).

Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns

Which variables provide independent information about the cross-section of future returns? Portfolio sorts and Fama-MacBeth regressions cannot easily answer this question when the number of candidate variables is large and when cross-terms might be important. We introduce a new method based on ideas from the machine learning literature that can be used in this context. Applying the method to past-return-based prediction of future returns, short-term returns become the most important predictors. A trading strategy based on our findings has an information ratio twice as high as a Fama-MacBeth regression accounting for two-way interactions. Transaction costs do not explain the results.

The cross section of expected holding period returns and their dynamics: A present value approach

We provide a tractable model of firm-level expected holding period returns using two firm fundamentals—book-to-market ratio and return on equity—and study the cross-sectional properties of the model-implied expected returns. We find that firm-level expected returns and expected profitability are time-varying but highly persistent and that forecasts of holding period returns strongly predict the cross section of future returns up to three years ahead. We show a highly significant predictive pooled regression slope for future quarterly returns of 0.86. The popular factor-based expected return models have either an insignificant or a significantly negative association with future returns. In supplemental analyses, we show that these forecasts are also informative of the time series variation in aggregate conditions. For a representative firm, the slope of the conditional expected return curve is more positive in good times, when expected short-run returns are relatively low, and the model-implied forecaster of aggregate returns exhibits modest predictive ability. Collectively, we provide a simple, theoretically motivated, and practically useful approach to estimating multi-period-ahead expected returns.

Cost of Capital Dynamics Implied by Firm Fundamentals

We provide a tractable model of firm-level expected holding period returns using two firm fundamentals ― book-to-market ratio and ROE ― and study the cross-sectional properties of the model-implied expected returns. We find that: 1) firm level expected returns and expected profitability are time-varying, but highly persistent; 2) forecasts of holding period returns strongly predict the cross section of future returns up to three years ahead. We document a highly significant predictive pooled regression slope for future quarterly returns of 0.86, whereas the popular factor-based expected return models have either an insignificant or a significantly negative association with future returns. In supplemental analyses, we show that these forecasts are also informative of the time-series variation in aggregate conditions: 1) for a representative firm, the slope of the conditional expected return curve is more positive in good times, when expected short-run returns are relatively low; 2) the model-implied forecaster of aggregate returns exhibits modest predictive ability. Collectively, we provide a simple, theoretically-motivated, and practically useful approach to estimating multi-period ahead expected returns.

Investor Sentiment and Analysts' Earnings Forecast Errors

We correlate analysts' forecast errors with temporal variation in investor sentiment. We find that when sentiment is high, analysts' forecasts of one-year-ahead earnings and long-term earnings growth are relatively more optimistic for “uncertain” or “difficult-to-value” firms. Adding these forecast errors to a regression of stock returns on sentiment absorbs a sizable fraction of the explanatory power of sentiment for the cross section of future returns. This finding provides direct support for the notion that investor sentiment affects the earnings expectations of hard-to-value firms. Additional tests suggest that this bias in expectations is unlikely to be strategic in nature. Our results provide new insight into the mechanism through which investor sentiment affects returns.This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.

Alternative Estimates of the Cost of Equity Capital for Australian Firms

We estimate the cost of equity capital for a sample of Australian firms using the CAPM and valuation models which are based on analyst forecasts of earnings and dividends. We find that the use of a composite of time-series and analyst forecasts rather than raw analyst forecasts results in cost of capital estimates that better explain the cross-sectional variation in future stock returns. Cost of capital estimates from the CAPM provide the weakest explanation of future stock returns. Cost of capital estimates from the valuation models are also found to be significantly related to key firm characteristics such as size, book-to-market and financial leverage. A variant of the dividend discount model provides the best cost of capital estimates when judged by their ability to explain the cross-section of future returns and their association with firm risk characteristics.

Corporate Political Contributions and Stock Returns

We develop a new and comprehensive database of firm-level contributions to U.S. political campaigns from 1979 to 2004. We construct variables that measure the extent of firm support for candidates. We find that these measures are positively and significantly correlated with the cross-section of future returns. The effect is strongest for firms that support a greater number of candidates which hold office in the same state that the firm is based. In addition, there are stronger effects for firms whose contributions are slanted toward House candidates and Democrats.