Three Essays on Asset Price Forecasting

This dissertation is a collection of essays examining current issues in asset price forecasting. The first chapter of this dissertation discusses the relationship between investor sentiment and excess stock return. This essay takes a novel approach in estimating investor sentiment use social media posts. In this study, I construct daily, equity-specific, investor sentiment indexes from Twitter and test the efficient market theory. We use a multinomial inverse regression to build the dictionary of relevant words and phrases for construction of the indexes. We find that our investor sentiment measure has a positive and statistically significant effect on individual stock returns. These findings are robust to different models and specifications. Chapter 2 examines the ability of international sector predictors to forecast US housing price inflation. Under floating exchange rate regimes, the Dornbusch model predicts shocks to domestic or foreign economies will be reflected in exchange rates. When exchange rates are fixed, shocks are likely to affect the net foreign asset holdings. In this study, I examine the role of the exchange rates and the net change in foreign asset holdings in improving US real estate inflation forecasts. I conduct in-sample and out-of-sample comparison of forecasting models relative to an autoregressive baseline model. I find that inclusion of foreign sector variables can improve the US real estate inflation forecasts by up to 40 percent. This improvement is mostly driven by changes in the net foreign asset holdings at longer horizons. The results are robust to samples at the metropolitan level although with different gains. Chapter 3 continues with this line research. Here, I determine the ability of net capital inflows from regions to forecast US housing inflation. Over the last decade, there has been a high correlation between balance of payment measures (Current Account deficits and Net Financial Accounts). The international finance theory has focused on determining the cause of this relationship. Specifically, this theory has found that deregulation in credit markets, accommodative US monetary policy, and fixed exchange rates caused US housing prices and balance of payments measures to move together. In 2015, BEA released new estimates of balance of payments measures in line with international standards, such that now bilateral financial account data has been created. In this study, I use a number of components from bilateral financial account data, to forecast US housing prices. Further, to empirically test the implications of the international finance theory, I use factor analysis methods to create an bilateral financial account index to forecast US housing prices. Overall, I find that many of these measures are able to produce improved forecasts of up to 50 percent

Stock Returns and Investor Sentiment: Textual Analysis and Social Media

The behavioral finance literature has found that investor sentiment has predictive ability for equity returns. This differs from standard finance theory, which provides no role for investor sentiment. We examine the relationship between investor sentiment and stock returns by employing textual analysis on social media posts. We find that our investor sentiment measure has a positive and significant effect on abnormal stock returns. These findings are consistent across a number of different models and specifications, providing further evidence against non-behavioral theories

Employee Satisfaction and Stock Returns During the COVID-19 Pandemic

The COVID-19 Pandemic has had an unprecedented impact on how employees and employers operate. Employees, directly affected by workplace changes, may provide information regarding future efficiencies. As a result, crowdsourced employee satisfaction (ES) reviews mentioning the COVID-19 Pandemic may contain useful information regarding the future profitability of these firms. We utilize crowdsourced COVID-19 Pandemic specific ES obtained from Glassdoor.com to determine the impact on abnormal stock returns for public firms from March-December 2020. We find evidence that higher COVID-19 ES is related to higher abnormal stock returns. While non-COVID ES is found not to be related to abnormal stock returns