Résumé:
Assessing the uncertainty associated with asset price fluctuations is crucial in finance, with
volatility being a key measure. The ability to predict this volatility is vital for making
effective investment decisions, managing risk and pricing products. This research aims to
explain the concept of volatility and its importance for financial institutions and global
businesses. It provides a comprehensive overview of machine learning techniques, with
emphasis on artificial neural networks, in the context of classification problems such as
predicting rises and falls in the BIST 100 index. machine learning models and artificial neural
networks is detailed, including the process of data collection, data preparation, algorithm
selection and model optimization. Each step is essential to develop reliable and efficient
models. The models are then implemented and the results analyzed, comparing the
performance of different models to identify the best model for predicting market volatility.
The research results show that random forest models and decision trees achieve the best
results in predicting financial market volatility compared to other models tested. The research
results constitute a valuable contribution to the field of forecasting financial market volatility
using machine learning and artificial neural network techniques. The study highlights the
potential of these techniques to significantly improve forecast accuracy, analyze risks and
make sound investment decisions. By demonstrating the effectiveness of machine learning
and artificial neural network models in predicting financial market volatility, this study opens
vast possibilities for improving investment performance and risk management in finance. The
results also suggest the need for further research to develop more complex and accurate
models, taking into account the ever-changing economic and financial landscape
