A study of stock price analysis algorithms for new energy companies based on random forest and genetic algorithms

Abstract

China is the world's largest emerging energy market, but due to the rapid development of China's new energy industry special, the value of new energy companies are more susceptible to sharp increases and decreases brought about by external factors in the market compared to other companies. In this paper, we propose an algorithm that uses an effective cluster learning strategy (Random Forest) contrasted with a genetic algorithm. A new financial forecasting model GSRF is constructed by parameter optimization of random forest model through grid search algorithm, and the model is applied to short-term stock forecasting. This paper builds a stock price trend forecasting model based on both algorithms and uses the model to determine whether the cost of a stock will be higher than its cost at a given date. The experimental results show that the model built using GSRF stochastic forest has the highest return and the lowest risk compared to the return based on the traditional genetic algorithm trading strategy.