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A Supervised Machine Learning in Financial Forecasting: Identifying Effective Models for the BIST100 Index

Cansu Ergenç

Cansu Ergenç

Ankara Yıldırım Beyazıt University, Business SchoolAnkara, Türkiye
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Ergenç, Cansu
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Rafet Aktaş

Rafet Aktaş

Ankara Yıldırım Beyazıt University, Business SchoolAnkara, Türkiye
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Aktaş, Rafet
  
Sep 08, 2025
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Published Online: Sep 08, 2025
Page range: 66 - 90
DOI: https://doi.org/10.2478/revecp-2025-0005
Keywords
© 2025 Cansu Ergenç et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1:

Data Preprocessing for Machine Learning
Data Preprocessing for Machine Learning

Fig. 2:

Experimental design of study
Experimental design of study

Fig. 3:

Tree-based models fitting results. Each subplot shows the predicted vs. actual closing prices for a specific Tree-Based Model (Decision Trees, Bagging, Random Forests, AdaBoost, GBM, LightGBM, XGBoost, CatBoost) applied to the BIST100 dataset. The blue line represents the actual closing prices, while the orange line represents the model’s predicted values. Closer alignment between the lines indicates higher predictive accuracy.
Tree-based models fitting results. Each subplot shows the predicted vs. actual closing prices for a specific Tree-Based Model (Decision Trees, Bagging, Random Forests, AdaBoost, GBM, LightGBM, XGBoost, CatBoost) applied to the BIST100 dataset. The blue line represents the actual closing prices, while the orange line represents the model’s predicted values. Closer alignment between the lines indicates higher predictive accuracy.

Fig. 4:

Neural Network-Based Models fitting results. This figure illustrates the predicted vs. actual financial performance for Neural Network-Based Models (ANN, CNN, RNN, LSTM) using the BIST100 dataset. Lower deviation from the diagonal line indicates better predictive accuracy.
Neural Network-Based Models fitting results. This figure illustrates the predicted vs. actual financial performance for Neural Network-Based Models (ANN, CNN, RNN, LSTM) using the BIST100 dataset. Lower deviation from the diagonal line indicates better predictive accuracy.

Fig. 5:

Instance-based Learning Models fitting results. This figure compares the predicted vs. actual financial performance for Instance-Based Learning Models (KNN, SVM) on the BIST100 dataset. Performance differences are reflected in the proximity of predictions to the actual values.
Instance-based Learning Models fitting results. This figure compares the predicted vs. actual financial performance for Instance-Based Learning Models (KNN, SVM) on the BIST100 dataset. Performance differences are reflected in the proximity of predictions to the actual values.

MSE, RMSE, MAE, MAPE and rRMSE values

 MSE RMSE MAE MAPE rRMSE
Decision Trees 3.258e–05 0.0057 0.0032 2.4591 0.0283
Bagging 1.671e–05 0.0041 0.0026 2.1876 0.0202
Random Forests 1.635e–05 0.0041 0.0024 1.9541 0.0201
Adaboost 2.272e–05 0.0047 0.0025 2.0166 0.0236
GBM 2.035e–05 0.0045 0.0023 1.6585 0.0223
LightGBM 2.739e–05 0.0052 0.0026 1.7510 0.0259
XGBoost 2.662e–05 0.0051 0.0025 1.7688 0.0256
CatBoost 3.129e–05 0.0055 0.0037 3.0035 0.0277
ANN 0.00042 0.0205 0.0151 13.6640 0.1019
CNNs 2.886e–05 0.0053 0.0039 4.1893 0.0266
RNNs 6.565e–05 0.0081 0.0060 5.6011 0.0402
LSTMs 0.0001 0.0106 0.0078 7.3147 0.0528
KNN 0.0001 0.0101 0.0071 7.0558 0.0501
SVM 1.592e–05 0.0039 0.0031 3.5148 0.0198

Characteristics of the dataset

Variable Description
Date Date of observation
Open Opening price on the given day
High Highest price on the given day
Low Lowest price on the given day
Close Closing price on the given day
Simple Moving Average (SMA) Average of closing prices over a specified period
Weighted Moving Average (WMA) Average of closing prices over a specified period, weighted by recent prices
Momentum (MOM) Rate of change of the closing price over a specified period
Stochastic %K (STCK) Current closing price relative to the range over a recent period
Stochastic %D (STCD) Moving average of the Stochastic %K
Relative Strength Index (RSI) Measures the magnitude of recent price changes to evaluate overbought or oversold conditions
Moving Average Convergence Divergence (MACD) Difference between the 12-day and 26-day exponential moving averages (EMAs)
Larry William’s %R (LWR) Momentum indicator measuring overbought and oversold levels
Accumulation/Distribution Oscillator (ADO) Indicator combining price and volume to show how much of a stock is being accumulated or distributed
Commodity Channel Index (CCI) Measures the deviation of the price from its average over a specified period, indicating overbought or oversold conditions
Language:
English
Publication timeframe:
2 times per year
Journal Subjects:
Business and Economics, Political Economics, Economic Theory, Systems and Structures
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