Stock Market Directional Bias Prediction Using ML Algorithms

ArXiv ID: 2310.16855 “View on arXiv”

Authors: Unknown

Abstract

The stock market has been established since the 13th century, but in the current epoch of time, it is substantially more practicable to anticipate the stock market than it was at any other point in time due to the tools and data that are available for both traditional and algorithmic trading. There are many different machine learning models that can do time-series forecasting in the context of machine learning. These models can be used to anticipate the future prices of assets and/or the directional bias of assets. In this study, we examine and contrast the effectiveness of three different machine learning algorithms, namely, logistic regression, decision tree, and random forest to forecast the movement of the assets traded on the Japanese stock market. In addition, the models are compared to a feed forward deep neural network, and it is found that all of the models consistently reach above 50% in directional bias forecasting for the stock market. The results of our study contribute to a better understanding of the complexity involved in stock market forecasting and give insight on the possible role that machine learning could play in this context.

Keywords: Logistic Regression, Decision Tree, Random Forest, Deep Neural Network, time-series forecasting, Equities (Japanese Stock Market)

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper employs standard ML classifiers (logistic regression, decision trees, random forest) with a basic binary classification task, resulting in low mathematical complexity. The empirical rigor is low because the backtest is limited to a single stock (Sony) with a simple data split, lacks risk/transaction cost analysis, and does not use walk-forward or cross-validation to address time-series leakage.

  flowchart TD
    A["Research Goal: Compare ML algorithms for stock market directional bias prediction"] --> B["Data Input: Japanese Equities<br>Time-Series Data"]
    B --> C["Methodology: Model Training & Evaluation<br>Logistic Regression | Decision Tree | Random Forest | DNN"]
    C --> D["Computational Process: Time-Series Forecasting<br>Predicting Up/Down Directional Bias"]
    D --> E["Outcome: Models consistently achieve >50% accuracy"]
    E --> F["Key Findings:<br>1. ML models feasible for stock market prediction<br>2. Japanese market shows predictable patterns<br>3. All algorithms outperform baseline"]