UC Davis

As the second-largest economy globally, China's economy has developed rapidly in recent years, and China's stock market has developed particularly fast as one of the most representative and important markets. Its total market capitalization had grown from 4.03 trillion US Dollars in 2010 to 8.52 trillion US Dollars in 2019. It is an important investment channel for individual investors on the supply side. It also provides financing opportunities for enterprises on the demand side. The fast growth has brought many systemic problems that need to be solved urgently, like imperfect regulation and informed trading. Considering these characteristics, I choose China's stock market as the theme for deep research and study. The first chapter studies the informed trading problem in China's A-Share market. It investigates the abnormal drop of stock returns on announcement day for the listed firms on A board in China's stock market. It finds that the firms who reported a huge irrational goodwill impairment experienced a more significant decline before the announcement day, and their rebounds in abnormal return are, on average, more powerful after the announcement day. There is a significant negative jump in stock return on announcement day for these abnormal firms, while the other firms' jump is not significant. The difference in difference model over various cut-off days suggests that external reasons like informed trading are not the dominating factor determining the decline in abnormal returns of the abnormal firms before announcement day. Rather, the internal difference between firms plays a more important role in the stock disaster.

The second chapter focuses on the profit potential of China's stock indexes by applying neural network models. The chapter uses a neural network approach to predict China's stock indexes' returns from the Shanghai Stock Exchange(SSE) and the Shenzhen Stock Exchange(SZSE). It compares the ARIMA model, Multiple Layer Perception(MLP) model, and Recurrent Neural Network(RNN) model in terms of mean squared error and forecast accuracy to study the added value of improvement from model architecture. It turns out that MLP and RNN models failed to provide a significantly better prediction than ARIMA models if using historical information of stock prices alone. The paper also uses a standard quantitative trading strategy to backtest the value of predictions from these three models. The paper finds that the ARIMA model predicts the SSE Composite Index well, while the RNN model is the best in predicting Industrial Index and Composite Index. Most of the strategy's annualized returns surpassed the return of the benchmark stock index in the bear market from 2017 to 2020, which offers investors a better choice in their stock trading.

While my first two chapters focus on China's stock market, my third part of the dissertation studies the US's options market. China has not yet established a developed options trading system like the US, and the options market is an indispensable supplement of a complete stock market. Actually, China's options trading for stock indexes started in 2019. As the first stock index options product in China's domestic market, CSI 300 stock index options were listed and traded on the China Financial Futures Exchange on Dec 23rd, 2019. Considering the fact, Chapter 3, joint work with my colleague Yitian Xiao, study the US's options market. It is well known that the options market enables traders to hedge positions in asset markets, thereby reducing risk exposure. Traders can choose from many different strike prices -- analogous to the coverage level in an insurance contract -- when hedging. In Chapter 3, we argue that the hedging motive leads a typical hedger to take positions slightly out of the money, i.e., just below the asset's current value. We develop a simple theoretical model and validate its predictions using data on S