The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance

TL;DR

This paper assesses the effectiveness of random forest models combined with AI techniques in predicting stock market trends, focusing on accuracy and efficiency for practical investment decision-making.

Contribution

It introduces an evaluation of random forest models with AI for stock trend prediction, emphasizing optimal parameters and performance metrics.

Findings

Random forest models achieve high predictive accuracy.

The models demonstrate efficient computation times.

Effective parameter tuning improves forecast reliability.

Abstract

The stock market is a crucial component of the financial market, playing a vital role in wealth accumulation for investors, financing costs for listed companies, and the stable development of the national macroeconomy. Significant fluctuations in the stock market can damage the interests of stock investors and cause an imbalance in the industrial structure, which can interfere with the macro level development of the national economy. The prediction of stock price trends is a popular research topic in academia. Predicting the three trends of stock pricesrising, sideways, and falling can assist investors in making informed decisions about buying, holding, or selling stocks. Establishing an effective forecasting model for predicting these trends is of substantial practical importance. This paper evaluates the predictive performance of random forest models combined with artificial intelligence on a test set of four stocks using optimal parameters. The evaluation considers both predictive accuracy and time efficiency.