Integrating feature selection and regression methods with technical indicators for predicting Apple Inc. stock prices

ArXiv ID: 2310.09903 “View on arXiv”

Authors: Unknown

Abstract

Stock price prediction is influenced by a variety of factors, including technical indicators, which makes Feature selection crucial for identifying the most relevant predictors. This study examines the impact of feature selection on stock price prediction accuracy using technical indicators. A total of 123 technical indicators and 10 regression models were evaluated using 13 years of Apple Inc. data. The primary goal is to identify the best combination of indicators and models for improved forecasting. The results show that a 3-day time window provides the highest prediction accuracy. Model performance was assessed using five error-based metrics. Among the models, Linear Regression and Ridge Regression achieved the best overall performance, each with a Mean Squared Error (MSE) of 0.00025. Applying feature selection significantly improved model accuracy. For example, the Multi-layered Perceptron Regression using Forward Selection improved by 56.47% over its baseline version. Support Vector Regression improved by 67.42%, and Linear Regression showed a 76.7% improvement when combined with Forward Selection. Ridge Regression also demonstrated a 72.82% enhancement. Additionally, Decision Tree, K-Nearest Neighbor, and Random Forest models showed varying levels of improvement when used with Backward Selection. The most effective technical indicators for stock price prediction were found to be Squeeze_pro, Percentage Price Oscillator, Thermo, Decay, Archer On-Balance Volume, Bollinger Bands, Squeeze, and Ichimoku. Overall, the study highlights that combining selected technical indicators with appropriate regression models can significantly enhance the accuracy and efficiency of stock price predictions.

Keywords: technical indicators, feature selection, stock price prediction, regression models, time-series analysis

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper employs standard regression and machine learning models (e.g., Ridge, SVM, Neural Networks) and wrapper feature selection methods without novel mathematical derivations or advanced theoretical proofs. However, it demonstrates strong empirical rigor through a rigorous evaluation on 13 years of real Apple Inc. data, using 123 technical indicators, 10 models, multiple time windows, and comprehensive error metrics (MSE, MAE, etc.), with reported percentage improvements in accuracy.

  flowchart TD
    A["Research Goal: Optimize Stock Price Prediction<br>using Feature Selection & Regression"] --> B["Data Input<br>13 Years of Apple Inc. Data"]

    B --> C{"Methodology Steps"}
    C --> D["Feature Generation<br>123 Technical Indicators"]
    C --> E["Feature Selection<br>Forward & Backward Selection"]
    D --> E
    E --> F["Model Evaluation<br>10 Regression Models"]

    F --> G{"Key Findings & Outcomes"}
    G --> H["Optimal Time Window: 3 Days"]
    G --> I["Best Models (MSE 0.00025):<br>Linear & Ridge Regression"]
    G --> J["Impact of Feature Selection:<br>56-76% Accuracy Improvement"]