The finding of reversals in weekly returns has been attributed to a combination of microstructure issues and overreaction to information. I provide new evidence eliminating overreaction as a source of reversal. I show that well-known weekly contrarian profits are followed by a long run of momentum profits. In fact, these profits are strong enough to produce a significant momentum effect over the full year following portfolio formation. Thus, the market does not appear to view extreme weekly returns as excessive, as implied by an overreaction story. To the contrary, this return continuation is consistent with underreaction to the news driving extreme weekly returns. This is supported by cross-sectional tests in which I find this week's news is positively related to next week's returns. The evidence presented here is consistent with growing evidence that underreaction to firm-specific information is a pervasive feature of price formation. Therefore, if any short-run contrarian profits can be realized, they are better viewed as compensation for providing liquidity than as a reward for arbitrage.

The finding of reversals in weekly returns has been attributed to a combination of microstructure issues and overreaction to information. I provide new evidence eliminating overreaction as a source of reversal. I show that well-known weekly contrarian profits are followed by a long run of momentum profits. In fact, these profits are strong enough to produce a significant momentum effect over the full year following portfolio formation. Thus, the market does not appear to view extreme weekly returns as excessive, as implied by an overreaction story. To the contrary, this return continuation is consistent with underreaction to the news driving extreme weekly returns. This is supported by cross-sectional tests in which I find this week's news is positively related to next week's returns. The evidence presented here is consistent with growing evidence that underreaction to firm-specific information is a pervasive feature of price formation. Therefore, if any short-run contrarian profits can be realized, they are better viewed as compensation for providing liquidity than as a reward for arbitrage.

This dissertation examines whether simultaneously conditioning on the multidimensional characteristics of information signals can help predict returns that are of economic significance. We use large price changes, public announcements, and large volume increases to proxy for the magnitude, dissemination, and precision of information signals. Abnormal returns following large price change events are found to be unimportant. As we condition on other characteristics of information signals, the abnormal returns become large. Large price change events accompanied by both a public announcement and an increase in volume have a 20-day abnormal return of almost 2% for positive events and -1.68% for negative events. The type of news provides further refinement. If the news relates to earnings announcements, management earnings forecasts, or analyst recommendations then the 20-day abnormal returns becomes much larger: ranging from 3% to 4% for positive events and about -2.25% for negative events. For these news events, we also find that the underreaction is greater for positive (negative) event firms that underperformed (overperformed) the market in the prior period, earning 20-day post-event abnormal returns of 4.85% (-3.50%). This evidence is consistent with the Barberis, Shleifer, and Vishny (1998) model of investor sentiment that suggests that investors are slow to change their beliefs. The evidence from our sample does not provide much support for strategic trading models under information asymmetry. Finally, an out-of-sample trading strategy generates 20-day post-event statistically significant abnormal return of 2.18% for positive events and -2.40% for negative events. Net of transaction costs, the abnormal returns are a statistically significant 1.04% for positive events and a statistically significant -1.51% for negative events. / Ph. D.

I investigate the information content in the implied volatility spread, which is the spread in implied volatilities between a pair of call and put options with the same strike price and time-to-maturity. By constructing the implied volatility time series for each stock, I show that stocks with larger implied volatility spreads tend to have higher future returns during 2003-2013. I also find that even volatilities implied from untraded options contain such information about future stock performance. The trading strategy based on the information contained in the actively traded options does not necessarily outperform its counterpart derived from the untraded options. This is inconsistent with the previous research suggesting that the information contained in the implied volatility spread largely results from the price pressure induced by informed trading in option markets. Further analysis suggests that option illiquidity is associated with the implied volatility spread, and the magnitude of this spread contains information about the risk-neutral distribution of the underlying stock return. A larger spread is associated with smaller risk-neutral variance, more negative risk-neutral skewness, and seemingly larger risk-neutral kurtosis, and this association is primarily driven by the systematic components in risk-neutral higher moments. I design a calibration study which reveals that the non-normality of the underlying risk-neutral return distribution relative to the Brownian motion can give rise to the implied volatility spread through the channel of early exercise premium.

This dissertation consists of two essays in asset allocation. In the first essay, I measure the value of active money management. I explore this issue by comprehensively examining the parametric rule proposed by Brandt, Santa-Clara and Valkanov (2009) (the BSV rule) out-of-sample for portfolio selection among 3516 stocks in CRSP and comparing this rule to the mean-variance (MV) rule and the naïve 1/N rule recently advocated by DeMiguel, Garlappi and Uppal (2009). The BSV rule outperforms both the MV and 1/N rules and the outperformance is robust to investment horizons and stock market states. The BSV rule is effective for investors with different preferences or investment opportunities. The effectiveness of the BSV rule is robust to data screening criteria, estimation periods, portfolio performance evaluation models, the business cycle, and stock market states. In the second essay, I explore the question of whether macroeconomic state variables are able to predict cross-sectional stock returns from the perspective of asset allocation. I find that conditioning on macroeconomic state variables leads to optimal portfolios with a Carhart alpha that is 125 basis points per month higher than unconditional optimal portfolios out-of-sample. Unfortunately, conditioning on macroeconomic states is subject to an "overfitting" problem and can lead investors to experience unexpected huge losses. My results suggest that macroeconomic state variables mare able to predict cross-sectional stock returns but risk-averse investors need to combine other funds (e.g. market portfolio) to take advantage of this predictability.

Yes / This study uses the quality of coverage decisions as a new metric to evaluate the performance of star and non-star analysts. We find that the coverage decisions of star analysts are better predictors of returns than those of non-star analysts. The return predictability of star analysts’ coverage decisions is stronger for informationally opaque stocks. We further exploit the staggered short selling deregulations, Google’s withdrawal, and the anti-corruption campaign as three quasi-natural experiments that create plausibly exogenous variations in the quality of information environment. These experiments show that the predictive power of star analysts’ coverage decisions strengthens (weakens) following a sharp deterioration (improvement) in firms’ information environment, consistent with the notion that star analysts possess superior ability to identify mispriced stocks. Overall, star analysts make better coverage decisions and play a superior role as information intermediaries, especially in poor information environment.

This thesis consists of three papers that make independendet contributions to the field of financial economics. As such, the papers, Chapter 2, Chapter 3, and Chapter 4, can be read independently of each other. In Chapter 2, we construct a simple measure of the aggregate illiquidity of hedge fund portfolios, and show that it has strong in- and out-of-sample forecasting power for 72 portfolios of international equities, U.S. corporate bonds, and currencies, over the 1994 to 2011 period. The forecasting ability of hedge fund illiquidity for asset returns is, in most cases, greater than, and provides independent information relative to, well-known predictive variables for each of these asset classes. We construct a simple equilibrium model to rationalise our findings and empirically verify auxiliary predictions of the model. In Chapter 3, I analyse the risk-shifting of hedge funds. Since the information on hedge fund holdings is very restricted, researchers have used the variance of returns as a proxy for risk. I propose a new method for measuring the time-varying variance. I use this method to investigate whether equity long-short hedge funds engage in risk-shifting driven by their past performance relative to their peers. I find that hedge funds which have strongly underperformed or outperformed their peers in recent months increase their exposure to the core strategy, i.e. the equity long-short strategy, and to non-core strategies. The risk shifting is mitigated for hedge funds with long redemption periods. Chapter 4 contributes to the equity premium prediction literature. I improve the forecast performance of typical single variable predictive regressions used in the equity premium prediction literature through Bayesian priors derived from consumption-based asset pricing models. To implement these model-based priors, I develop a Bayesian procedure which is rooted in the macroeconometrics literature. I find that the model-based priors can increase the explanatory power, measured by the out-of-sample R², of the single variable predictive regressions by several percentage points.

Hedge fund activism announcements are associated with positive market reactions, and they introduce information asymmetry between insiders and outside investors. Target firm insiders have superior information about the campaign and play an important role in the campaign negotiation. This study examines insiders’ behavior as information asymmetry rises following the campaign announcement. Insiders increase trading in their own firms in response to the campaign announcement. These post-announcement insider trades have additional return predictability than insider trades in other times. Post-announcement insider buys predict higher probabilities of achieving successful campaign outcomes including management turnovers, increases in payout, and corporate restructurings, and higher value of these outcomes. I also find evidence that insiders use campaign resistance and trading interactively to achieve higher wealth gain.

This thesis consists of one stock return predictability study and two international risk exposure studies. The first study shows that the statistical significance of out-of-sample predictability of market returns given by Kelly and Pruitt (2013), using a partial least squares methodology, constructed from the valuation ratios of portfolios, is overstated for two reasons. Firstly, the analysis is conducted on gross returns rather than excess returns, and this raises the apparent predictability of the equity premium due to the inclusion of predictable movements of interest rates. Secondly, the bootstrap statistics used to assess out-of-sample significance do not account for small-sample bias in the estimated coefficients. This bias is well known to affect in-sample tests of significance and I show that it is also important for out-of-sample tests of significance. Accounting for both these effects can radically change the conclusions; for example, the recursive out-of-sample R2 values for the sample period 1965-2010 are insignificant for the prediction of one-year excess returns, and one-month returns, except in the case of the book-to-market ratios of six size- and value-sorted portfolios which are significant at the 10% level. The second study examines whether U.S. common stocks are exposed to international risks, which I define as shocks to foreign markets that are orthogonal to U.S. market returns. By sorting stocks on past exposure to this risk factor I show that it is possible to create portfolios with an ex-post spread in exposure to international risk. I examine whether the international risk is priced in the cross-section of U.S. stocks, and find that for small stocks an increase in exposure to international risk results in lower returns relative to the Fama-French three-factor model. I conduct similar analysis on a measure of the international value premium and find little evidence of this risk being priced in U.S. stocks. The third study examines whether a portfolios of U.S. stocks can mimic foreign index returns, thereby providing investors with the benefits of international diversification without the need to invest directly in assets that trade abroad. I test this proposition using index data from seven developed markets and eight emerging markets over the period 1975-2013. Portfolios of U.S. stocks are constructed out-of-sample to mimic these international indices using a step-wise procedure that selects from a variety of industry portfolios, stocks of multinational corporations, country funds and American depositary receipts. I also use a partial least squares approach to form mimicking portfolios. I show that investors are able to gain considerable exposure to emerging market indices using domestically traded stocks. However, for developed market indices it is difficult to obtain home-made exposure beyond the simple exposure of foreign indices to the U.S. market factor. Using mean-variance spanning tests I find that, with few exceptions, international indices do not improve over the investment frontier provided by the domestically constructed alternative of investing in the U.S. market index and portfolios of industries and multinational corporations.

A key prediction of the Capital Asset Pricing Model (CAPM) is that idiosyncratic risk is not priced by investors because in the absence of frictions it can be fully diversified away. In the presence of constraints on diversification, refinements of the CAPM conclude that the part of idiosyncratic risk that is not diversified should be priced. Recent empirical studies yielded mixed evidence with some studies finding positive correlation between idiosyncratic risk and stock returns, while other studies reported none or even negative correlation. In this thesis we revisit the problem whether idiosyncratic risk is priced by the stock market and what the probable causes for the mixed evidence produced by other studies, using monthly data for the US market covering the period from 1980 until 2013. We find that one-period volatility forecasts are not significantly correlated with stock returns. On the other hand, the mean-reverting unconditional volatility is a robust predictor of returns. Consistent with economic theory, the size of the premium depends on the degree of 'knowledge' of the security among market participants. In particular, the premium for Nasdaq-traded stocks is higher than that for NYSE and Amex stocks. We also find stronger correlation between idiosyncratic risk and returns during recessions, which may suggest interaction of risk premium with decreased risk tolerance or other investment considerations like flight to safety or liquidity requirements. The difference between the correlations between the idiosyncratic volatility estimators used by other studies and the true risk metric - the mean-reverting volatility - is the likely cause for the mixed evidence produced by other studies. Our results are robust with respect to liquidity, momentum, return reversals, unadjusted price, liquidity, credit quality, omitted factors, and hold at daily frequency.