A Framework of Applying Kelly Stationary Index to Stock Trading in Taiwan Market

Portfolio management and money management have always been important issues for investors and researchers in the financial field. The Kelly criterion is a theoretical approach of money management, and is a mathematical method for optimizing long-term expected return. Kelly criterion requires the future outcomes distribution as input, which can be predicted through the techniques of machine learning (ML). With the revolutionary growth of the amount of information, big data is the key to boost ML prediction, therefore, we introduce a general Kelly framework, including the strength of Kelly, ML, and big data.In addition, we propose the Kelly stationary index (KSI) to quantity the stationarity of the stock's outcomes distribution, which will affect the trading period and forecasting frequency. We calculate the KSI of each constituent stock of Taiwan's 50, and apply the Kelly criterion strategy to verify the effectiveness of KSI. The experimental results show that there is a moderate downhill relationship between the strategy performance and KSI with the -0.591 of correlation coefficient. It also indicates that the closer the estimated distribution is to the actual distribution, the higher the expected profit. In the future, we will use KSI for money management, strategy development, and apply KSI into the general Kelly framework.