Abstract:

This paper investigates forecasting the BIST100 stock index using cross-capital flow analysis. It employs feature engineering and the Orthogonal Matching Pursuit (OMP) model to navigate the intricacies of financial time series prediction. The study meticulously selects features such as lagged values, moving averages, and volatility metrics, normalized to ensure unbiased model impact. The OMP model is carefully optimized to handle the dimensionality of financial data, avoiding overfitting through a sparsity constraint. This approach yields an R-squared score of 0.88, indicating a solid capability to capture index variance. Visual comparisons between actual and predicted values further validate the model's accuracy. The paper highlights the importance of methodological precision in developing models capable of discerning complex patterns, offering valuable insights for investment strategies. Implications of the study show that cross-capital movements and macroeconomic variables are a good fit with ML to predict the Stock Market despite the complexity of financial markets.

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