Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms

ArXiv ID: 2309.00618 “View on arXiv”

Authors: Unknown

Abstract

Creating accurate predictions in the stock market has always been a significant challenge in finance. With the rise of machine learning as the next level in the forecasting area, this research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE in the short term from March 2020 to May 2022. We deploy, develop, and tune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models. We report the models that produce the highest accuracies from our evaluation metrics: RMSE, MAPE, MTT, and MPE. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite running longer (up to 10 seconds). Results from this study may improve by further tuning the individual parameters or introducing more exogenous variables.

Keywords: Machine Learning, XGBoost, Random Forest, Support Vector Regression, Forecasting, Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper primarily applies established machine learning models with standard metrics (RMSE, MAPE) and describes a backtesting setup with real market data and rolling windows, demonstrating high empirical rigor, but lacks advanced mathematical derivations or novel theoretical complexity.

  flowchart TD
    A["Research Goal<br>Forecast Short-Term Stock Prices"] --> B["Data Collection<br>NYSE Stocks Mar 2020 - May 2022"]
    B --> C["Methodology<br>Train/Test Split 240 Days"]
    C --> D{"Model Training<br>& Tuning"}
    D --> E["XGBoost"]
    D --> F["Random Forest"]
    D --> G["MLP"]
    D --> H["SVR"]
    E & F & G & H --> I["Evaluation Metrics<br>RMSE, MAPE, MTT, MPE"]
    I --> J{"Outcomes"}
    J --> K["Winner: XGBoost<br>Highest Accuracy"]
    J --> L["Trade-off: Runtime<br>Up to 10 seconds"]
    K & L --> M["Conclusion<br>Potential for Further Tuning"]