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

ArXiv ID: 2402.17194 “View on arXiv”

Authors: Unknown

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.

Keywords: Stock Price Prediction, Random Forest, Machine Learning, Trend Analysis, Time Efficiency, Equities

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper applies standard Random Forest concepts with minimal advanced mathematics, focusing on descriptive explanations rather than complex derivations. Empirical rigor is low because it uses only four stocks, provides limited backtesting details, and lacks robust statistical validation or out-of-sample testing beyond basic OOB error and ROC curves.

  flowchart TD
    Goal["Research Goal: Predict Stock Price Trends<br/>(Rising, Sideways, Falling)"] --> Method
    subgraph Method ["Key Methodology"]
        Data["Input Data: 4 Stock Test Sets"] --> Preprocess
        Preprocess["Preprocessing & Feature Selection"] --> Model["Random Forest Model"]
        Model -->|Optimal Parameters| Train["Training & Validation"]
    end
    Train --> Compute["Computational Process:<br/>Model Evaluation on Test Set"]
    Compute --> Findings
    subgraph Findings ["Key Findings/Outcomes"]
        F1["Predictive Accuracy<br/>(Classification Performance)"]
        F2["Time Efficiency<br/>(Training & Inference Speed)"]
    end