Sensitivity Analysis Of Stock Market With Respect To Monetary Policy PDF Download

This thesis has three essays that study the intersections of macroeconomics, finance, and text analysis. The topics include executives' attention and financial decisions, economic policy uncertainty and stock market forecasting, and the stock market performance in the time of the Covid-19 pandemic. The essays hope to provide unique measurements of attention and uncertainty, empirical evidence, and theories to understand the connections and differences between classic theories and agents' behavior in actual economic activities. The first essay is my job market paper. I examine the attention of executive managers and their financing behavior, focusing on the information acquisition process. Corporations are sensitive to both macroeconomic and firm-specific challenges. Executives must choose overall attention capacity and divide finite attention between these topics. By using natural language processing and quarterly earnings call transcripts, I assess the information content of this dialog. The attention capacity quantifies the effective information used to make borrowing decisions, consisting of information processing macro and firm-specific issues. The attention allocation measures the ratio of attention paid to macroeconomics. Executives make two critical decisions during the information acquiring process. First, executives decide the overall attention capacity, determined by the general uncertainty. Second, executives decide the optimal attention allocated between macro and firm-specific topics. In the rise of uncertainty from either subject, executives' attention capacity increases (scale effect) and assign greater awareness to this topic (substitution effect). I show that the substitution effect is higher than the scale effect. Using an optimal static capital structure model with endogenous information choice, I demonstrate that an executive can tolerate a higher leverage rate when actively acquiring information. Thus, the information decision process is crucial to understanding the recent rising leverage phenomenon.The second essay examines the relationship between the stock market performance and the economic activities in the time of Covid-19. Stock prices and workplace mobility trace out striking clockwise paths in daily data from mid-February to late May 2020. Global stock prices fell 30 percent from February 17 to March 12, before mobility declined. Over the next 11 days, stocks fell another 10 percentage points as mobility dropped 40 percent. From March 23 to April 9, stocks recovered half their losses, and mobility decreased further. From April 9 to late May, both stocks and mobility rose modestly. This dynamic plays out across the 35 countries in our sample, with notable departures in China, South Korea, and Taiwan. The size of the global stock market crash in reaction to the pandemic is many times larger than a standard asset-pricing model implies. Looking more closely at the world's two largest economies, the pandemic had greater effects on stock market levels and volatilities in the U.S. than in China, even before it became evident that early U.S. containment efforts would flounder. Newspaper-based narrative evidence confirms the dominant - and historically unprecedented - the role of pandemic-related developments in the stock market behavior of both countries. The third essay tests the prediction power of the mainland China Economic Policy Uncertainty in forecasting the Chinese stock market. Rational asset pricing theory indicates that the fluctuations of the real economy have a significant impact on the stock market. The Chinese stock market is highly regulated and sensitive to regulations and market policies uncertainty. Using an efficient Dynamic Model Averaging (eDMA) model, this paper investigates how well the newspaper-based Economic Policy Uncertainty (EPU) index can predict the returns of the Chinese Shanghai Stock Exchange Index. Empirical evidence shows that EPU mutes the impact of monetary policy as a predictor. Also, eDMA significantly improves the forecasting performance compared to other forecasting methodologies.