Paper: arXiv 2512.15113

Authors: Mohit Beniwal

Abstract

Long-term price forecasting remains a formidable challenge due to the inherent uncertainty over the long term, despite some success in short-term predictions. Nonetheless, accurate long-term forecasts are essential for high-net-worth individuals, institutional investors, and traders. The proposed improved genetic algorithm-optimized support vector regression (IGA-SVR) model is specifically designed for long-term price prediction of global indices. The performance of the IGA-SVR model is rigorously evaluated and compared against the state-of-the-art baseline models, the Long Short-Term Memory (LSTM), and the forward-validating genetic algorithm optimized support vector regression (OGA-SVR). Extensive testing was conducted on the five global indices, namely Nifty, Dow Jones Industrial Average (DJI), DAX Performance Index (DAX), Nikkei 225 (N225), and Shanghai Stock Exchange Composite Index (SSE) from 2021 to 2024 of daily price prediction up to a year. Overall, the proposed IGA-SVR model achieved a reduction in MAPE by 19.87% compared to LSTM and 50.03% compared to OGA-SVR, demonstrating its superior performance in long-term daily price forecasting of global indices. Further, the execution time for LSTM was approximately 20 times higher than that of IGA-SVR, highlighting the high accuracy and computational efficiency of the proposed model. The genetic algorithm selects the optimal hyperparameters of SVR by minimizing the arithmetic mean of the Mean Absolute Percentage Error (MAPE) calculated over the full training dataset and the most recent five years of training data. This purposefully designed training methodology adjusts for recent trends while retaining long-term trend information, thereby offering enhanced generalization compared to the LSTM and rolling-forward validation approach employed by OGA-SVR, which forgets long-term trends and suffers from recency bias.

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper applies standard machine learning models (SVR, LSTM) with a custom genetic algorithm for hyperparameter tuning, which involves moderate mathematical complexity, while the empirical rigor is high due to extensive backtesting on multiple global indices over several years with clear performance metrics and computational efficiency comparisons.

Research Flowchart

  flowchart TD
  A["Research Goal: <br>Robust Long-Term Price Forecasting<br>for Global Stock Indices"] --> B["Data: 5 Global Indices<br>(Nifty, DJI, DAX, N225, SSE)<br>2021-2024"]
  B --> C["Methodology: <br>IGA-SVR Model"]
  C --> D["Genetic Algorithm Optimization<br>Minimizes weighted MAPE<br>(Recent + Long-term Data)"]
  D --> E["Training SVR<br>with Optimal Hyperparameters"]
  E --> F["Forecasting & Evaluation<br>vs. LSTM & OGA-SVR"]
  F --> G["Outcome: <br>Superior Performance"]
  G --> H["IGA-SVR Outcomes:<br>- 19.87% lower MAPE vs LSTM<br>- 50.03% lower MAPE vs OGA-SVR<br>- 20x faster execution than LSTM"]