Online Investor Sentiment via Machine Learning

Online Investor Sentiment via Machine Learning

Blog Article

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters.Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances Vaporizer Parts with high-dimensional investor sentiment proxies.They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium.Moreover, this Wooden Train Set predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio.