Stock Selection Model Based on Machine Learning with Wisdom of Experts and Crowds

摘要

Both stock recommendations from sell-side analysts and online user generated content from crowds have great significance in the stock market. We examine and compare different effects of analyst attitude and crowd sentiment on stock prices in this article with data from CSMAR. By estimating a multivariate linear regression model, we find that although the wisdom of both experts and crowds has impact on stock prices, the latter's impact on stock prices prevails. We also adopt LightGBM, a novel machine learning model, to predict stock trends based on empirical results. Portfolio returns of different models also suggest that crowd wisdom is more valuable for creating investment strategy than expert wisdom. And it is necessary to take the wisdom of both experts and crowds into consideration when making investment decision.