Empirical studies of financial and labor economics

This dissertation consists of three essays in financial and labor economics. It provides empirical evidence for testing the efficient market hypothesis in some financial markets and for analyzing the trends of power couples’ concentration in large metropolitan areas. The first chapter investigates the Bitcoin market’s efficiency by examining the correlation between social media information and Bitcoin future returns. First, I extract Twitter sentiment information from the text analysis of more than 130,000 Bitcoin-related tweets. Granger causality tests confirm that market sentiment information affects Bitcoin returns in the short run. Moreover, I find that time series models that incorporate sentiment information better forecast Bitcoin future prices. Based on the predicted prices, I also implement an investment strategy that yields a sizeable return for investors. The second chapter examines episodes of exuberance and collapse in the Chinese stock market and the second-board market using a series of extended right-tailed augmented Dickey-Fuller tests. The empirical results suggest that multiple “bubbles” occurred in the Chinese stock market, although insufficient evidence is found to claim the same for the second-board market. The third chapter analyzes the trends of power couples’ concentration in large metropolitan areas of the United States between 1940 and 2010. The urbanization of college-educated couples between 1940 and 1990 was primarily due to the growth of dual-career households and the resulting severity of the co-location problem (Costa and Kahn, 2000). However, the concentration of college-educated couples in large metropolitan areas stopped increasing between 1990 and 2010. According to the results of a multinomial logit model and a triple difference-in-difference model, this is because the co-location effect faded away after 1990.

Author dc:creator

• Li, Mengmeng

• Economics
• Bitcoin
• Chinese stock market
• Empirical study
• Multiple bubbles
• Power couple
• Text data mining

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/2144/17726

OAI identifier oai:identifier

oai:open.bu.edu:2144/17726