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Author: Qing Bai Publisher: ISBN: Category : Languages : en Pages : 96
Book Description
The dissertation consists of two essays. Essay I examines the return predictability by firm level R & D and innovation measures and shows that technology spillover helps to explain the positive innovation-return relation. Essay II propose a novel measure of conditional value premium based on firm's stock split announcement. This measure is shown to have a strong predicting power over value premium both in sample and out of sample. Essay I: I show that technology spillovers are important information phenomena that benefit both other innovators (as emphasized in the Industrial Organization literature) and stock market investors. I find that the premium associated with R & D and patenting activities is largely restricted to firms located in more isolated technology spaces with fewer spillovers. Moreover, there is a strong lead-lag effect among firms engaging in innovative activities: the stock prices of firms in more isolated technology spaces react more slowly to new information than do the stock prices of firms in more competitive technology spaces. Finally, announcement-day returns to patent grants are greater for more technologically important patents (measured by forward citations), but only for firms in more crowded technology spaces. My results indicate that investors are able to value innovative investments by exploiting the information flows associated with greater technology spillovers. Essay II: I propose a novel conditional value premium measure based on the present-value relation that the stock price impact of a firm's public announcement reveals the firm's expected discount rates. Specifically, because most splitting stocks are growth stocks on which, by construction, the value premium has strong influence, the average splitting stock announcement-day returns track closely conditional value premium. I find very similar results using announcements of divested asset acquisitions in which acquirers are usually growth firms. Consistent with risk-based explanations, my conditional value premium measure correlates positively with future GDP growth and helps explain the cross-section of stock returns.
Author: Qing Bai Publisher: ISBN: Category : Languages : en Pages : 96
Book Description
The dissertation consists of two essays. Essay I examines the return predictability by firm level R & D and innovation measures and shows that technology spillover helps to explain the positive innovation-return relation. Essay II propose a novel measure of conditional value premium based on firm's stock split announcement. This measure is shown to have a strong predicting power over value premium both in sample and out of sample. Essay I: I show that technology spillovers are important information phenomena that benefit both other innovators (as emphasized in the Industrial Organization literature) and stock market investors. I find that the premium associated with R & D and patenting activities is largely restricted to firms located in more isolated technology spaces with fewer spillovers. Moreover, there is a strong lead-lag effect among firms engaging in innovative activities: the stock prices of firms in more isolated technology spaces react more slowly to new information than do the stock prices of firms in more competitive technology spaces. Finally, announcement-day returns to patent grants are greater for more technologically important patents (measured by forward citations), but only for firms in more crowded technology spaces. My results indicate that investors are able to value innovative investments by exploiting the information flows associated with greater technology spillovers. Essay II: I propose a novel conditional value premium measure based on the present-value relation that the stock price impact of a firm's public announcement reveals the firm's expected discount rates. Specifically, because most splitting stocks are growth stocks on which, by construction, the value premium has strong influence, the average splitting stock announcement-day returns track closely conditional value premium. I find very similar results using announcements of divested asset acquisitions in which acquirers are usually growth firms. Consistent with risk-based explanations, my conditional value premium measure correlates positively with future GDP growth and helps explain the cross-section of stock returns.
Author: Gregory William Eaton Publisher: ISBN: Category : Languages : en Pages : 304
Book Description
I examine the effects of stock liquidity on asset values and whether aggregate stock liquidity and other forecasting instruments predict stock market returns. In the first chapter, I use tick-size reductions in equity markets as sources of exogenous variation in liquidity to examine the causal effect of transaction costs on firm value. In contrast to the prevailing view, I find that increased liquidity has a marginal or, in some cases, negative impact on firm value. The second chapter evaluates the predictive content of aggregate liquidity for economic activity and stock returns. We decompose illiquidity into a component capturing aggregate volatility and a volatility-adjusted component and find strong evidence that the component of illiquidity uncorrelated with volatility forecasts stock market returns. The third chapter provides new evidence on the stock return forecasting performance of alternative corporate payout yields. We find that the net payout yield forecasts stock returns and generally outperforms the commonly used dividend yield. Additionally, we show that the choice of cash flow used to construct the payout yield is economically significant. An agent relying on the incorrect payout measure as a forecasting instrument is willing to pay an economically significant amount to switch to the optimal policy.
Author: Ruojun Wu Publisher: ISBN: Category : Bayesian statistical decision theory Languages : en Pages : 137
Book Description
This dissertation studies the effect of parameter uncertainty on the return predictability and volatility of the stock market. The first two chapters focus on the decomposition of market volatility, and the third chapter studies the return predictability. When facing imperfect information, the investors tend to form a learning scheme that encompasses both historical data and prior beliefs. In the variance decomposition framework, the introducing of learning directly impacts the way that return forecasts are revised and consequently the relative component of market volatility based on these forecasts, namely the price movements from revision on future discount rates and those from future cash flows. According to the empirical study in Chapter 1, the former is not necessarily the major driving force of market volatility, which provides an alternative view on what moves stock prices. Learning is modeled and estimated by Bayesian method. Chapter 2 follows the topic in Chapter 1 and studies the role of persistent state variables in return decomposition in order to provide more robust inference on variance decomposition. In Chapter 3 we propose to utilize theoretical constraints to help predict market returns when in sample data is very noisy and creates model uncertainty for the investors. The constraints are also incorporated by Bayesian method. We show in the out-of-sample forecast experiment that models with theoretical constraints produce better forecasts.
Author: Shunlin Liang Publisher: ISBN: Category : Industrial management Languages : en Pages :
Book Description
This dissertation consists of two essays on disaster risk and equity return predictability. The first essay proposes new measures of firm-level and market level disaster risk from deviation of put-call symmetry, which is free from being contaminated by the asymmetry between option traders and equity investors. Compared with other known measures of disaster risk, the market-level disaster risk measure robustly predicts aggregate market returns, with out-of-sample (R^2=6.86%) for the next twelve months. The cross-sectional analysis shows that firm-level disaster risk also explains variations in expected stock returns. Stocks with high firm-level disaster risk earn an annual four-factor subsequent alpha 8.0% higher than stocks with low firm-level disaster risk. I explore potential mechanisms giving rise to these asset pricing facts. The second essay finds that the investor’s learning of higher moments can account for the time-variation, size, and volatility of equity premium. I estimate the investor’s belief on skewness and kurtosis of consumption and dividend growth, and assume investor’s Bayesian learning about a skew student’s t-distribution with unknown fixed parameters. The predictive regressions show that more negative skewness and higher kurtosis predict higher subsequent market excess returns, which implies the investor’s learning generates the time variation of equity premium although the true distribution is static. The calibrated asset pricing model shows that the investor’s learning also explains the size and volatility of the equity premium observed in the data when the investor has a preference for early resolution of uncertainty.
Author: Qing Bai
Author: Qing Bai
Author: Lei Jiang
Author: Shu Yan
Author: Bradley Steele Paye
Author: Yuzhao Zhang
Author: Amit Goyal
Author: Zeming Li
Author: Gregory William Eaton
Author: Ruojun Wu
Author: Shunlin Liang