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Exploring the Impact of the U.S. Macroeconomy on the Chinese Stock Market - Based on Market and Individual Stock Dimensions

Profile: Course Project, 2022

This paper Employed the PLS method to analyze the volatility of the Chinese stock market, using 120 macroeconomic variables from the FRED-MD database in the United States. The results indicate a significant relationship with factors such as the global economic system, supply chain dynamics, and the China-US trade war. This paper also calculated the systemic risk posed by Chinese companies to the US macroeconomy, using macroeconomic indicators and individual stock returns, and applied PCA and Sparse-PCA for dimensionality reduction. Found that Sparse-PCA improves the economic interpretability of the 10 principal components more effectively than traditional PCA methods

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Fundamental Factors and Stock Returns - Based on Machine Learning Methods

Profile: Course Project, 2022

This paper utilized 207 fundamental and volume-price factors in the US stock market from January 1985 to October 2022, and employed 10 machine learning algorithms, including linear regression, penalized linear regression, tree methods, and neural networks, to synthesize factor signals and construct investment portfolios. Empirical findings demonstrated that machine learning algorithms effectively discerned the relationship between anomalies (factors) and returns. With a 1-year training window and monthly rebalancing, long-short portfolios yielded average annualized returns between 16.5% and 22.8%, with Sharpe ratios ranging from 0.69 to 1.43. Adjusting the training window to 3 months and 24 months resulted in annualized returns and Sharpe ratios spanning from 11.9% to 19.6%, 0.57 to 1.18; and from 4.96% to 21.2%, 0.59 to 1.54, respectively

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