Financial Portfolio Management and Optimization to Maximize Returns Using a Combination of HRP and Sentiment Analysis

Abstract

Current portfolio optimization techniques, try to exploit the correlation between stocks using historical data. Many algorithms have been developed based on this for optimizing portfolio weights of which Hierarchical Risk Parity (HRP) is one of them. However future stock prices prove to have a stronger impact on gaining the best out of stock markets. This paper proposes HRP-S which uses traditional HRP along with sentiment analysis to suggest an optimized portfolio weight distribution. This allows capturing historic stock correlations and investor sentiments affecting the stock prices in the near future. This paper gives the implementation and a working example of HRP-S for the period of January 2019–December 2019.