Recent financial literature suggests that the variation in the dividend–price ratio is significantly related to the expected returns but not to the expected dividend growth. In other words, stock returns are predictable but dividend growth is not. However, most of this evidence comes from the U.S. at the aggregate level, and there is a lack of research that relates to this topic in the New Zealand stock market. This research examines the predictive power of the dividend–price ratio using New Zealand stock market data from 1931 to 2012. The results confirm the claim in the U.S data that returns are predictable but dividend growth is not in the New Zealand stock market data. This research also investigates whether the return predictability is associated with risk-pricing or mispricing; whether the return predictability is due to the fundamental relationship among the dividend–price ratio, future returns and future dividend growth, or whether it is due to the effects of historical events; whether out-of-sample forecasts will have the same patterns as in-sample predictions; and whether individual company returns are predictable.

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.

This dissertation consists of three chapters describing the applications of factor models in different fields of asset pricing. The first chapter addresses the following issue: Prominent volatility-based factor pricing models focus exclusively on the second moment of asset returns, and hence, tend to identify volatile factors but with little risk premia. This chapter demonstrates that a simple asset return transform can arbitrarily upset the ranking of volatility-based factors, but not their prices of risks. Accordingly, we propose a new framework to identify factors based on their prices of risks, or the so-called principally priced risk factors (PPRFs). We construct these factors by generalizing the standard Sharpe ratio for a single asset to a set of assets, incorporating information from both the first and second moments of asset returns. The PPRF framework improves out-of-sample pricing performance in both equity and currency markets. The second chapter identifies the origins of covariance in institutional trading. Conceptually, we introduce two perspectives: the asset perspective, which prioritizes assets as the key market fundamentals, and the manager perspective, which prioritizes fund managers as the key market fundamentals that drive institutional trading covariance. Empirically, we establish that the asset perspective is the primary driver of covariance in institutional trading. Our analysis documents two further empirical patterns. First, returns stemming from the covariance in institutional trading from the asset perspective have higher volatility, offering valuable insights into the demand-based asset pricing literature. Second, the persistence in trading often breaks down during economic downturns, suggesting potential connections to the uncertainty-based business cycle literature. Finally, the third chapter examines the impact of changes in monetary policy rules on the asset valuations of firms with different profitability. I have the following two empirical findings. First, during periods of hawkish monetary policies, the 'profitability premium'— the expected extra return on investments in more profitable firms — tends to increase. Second, when analyzing the factors mediating this effect, changes in inflation expectations play a more significant role in influencing the profitability premium during transitions to a hawkish monetary regime, compared to the effects of real interest rate adjustments on production costs. These observations suggest a possible mechanism by which monetary policy may have different long-term effects on firms with different characteristics. / Doctor of Philosophy / This dissertation explores factor models in asset pricing across three chapters. The first chapter critiques volatility-based models that focus on asset return variance and introduces a new framework for identifying factors based on risk prices, enhancing pricing performance in equity and currency markets. The second chapter investigates the origins of covariance in institutional trading, emphasizing the asset perspective as the dominant influence and documenting higher volatility and breakdowns in trading persistence during economic downturns. The third chapter examines the effects of monetary policy changes on firm asset valuations, finding that hawkish policies increase the profitability premium, significantly influenced by shifts in inflation expectations rather than changes in real interest rates. These insights highlight the nuanced impacts of market fundamentals and monetary policy on asset pricing and firm profitability.

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.