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Portfolio Choices For Big Data
Chen, Pixiong
Chen, Pixiong
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Abstract
In this dissertation, I conduct a study of online investor sentiment for stock returns prediction and portfolio policy decisions. A summary of my research is made in Chapter 1, and the details are in the following chapters. In Chapter 2, two data-driven econometric approaches are proposed to construct investor sentiment indices based on internet search queries, which are constructed by the partial least squares and LASSO methods, respectively. By examining the relationship between investor sentiment and stock risk premium on overall market level, our empirical findings are that these sentiment indices have predictive power both in and out of sample, and the out-of-sample predictability of the online investor sentiment indices proposed by the paper is robust for different horizons. Moreover, our investor sentiment indices are also able to predict the returns of cross-sectional characteristics portfolios. This predictability based on investor sentiment has economic value since it improves portfolio performance, in terms of certainty equivalent return gain and Sharpe ratio, for investors who conduct the optimal asset allocation.Chapter 3 utilizes machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment. Our empirical studies provide a strong evidence that some machine learning methods, such as the extreme gradient boosting or random forest, show significant predictive ability in terms of out-of-sample R-square with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which reveal a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value that it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of certainty equivalent return gain and Sharpe ratio. In Chapter 4, we study the online investor sentiment and propose using nonparametric generalized method of moment proposed by Cai (2003) to estimate the portfolio policy via an investment sentiment index for optimal asset allocations. We find that portfolio performance is improved by introducing the flexibility into portfolio policy and incorporation of investor sentiment, constructed based on machine learning methods, as a predictor that captures the time variations of investment opportunities. It is shown that the market timing depending on investor sentiment is nonlinear and varies across assets.
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Date
2022-12-31
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University of Kansas
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Keywords
Finance, Economics, Asset return, Machine learning, Nonlinearity, Nonparametric GMM, Online investor sentiment, Portfolio allocations