RiskLab Middle East
Building Upon the Ancient Wisdom of the Middle East
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Trustworthy Research
for Financial Intelligence
Data Structures
Data structuring for effective analysis, improved efficiency, reduced back-test overfitting, and reduced generalization error of financial machine learning models.
- Enhanced accuracy in modeling
- Reduced noise in datasets
- Efficient handling of large datasets
Feature Engineering
Selecting and transforming raw financial data into features that better represent the underlying factors affecting market movements.
- Improved prediction
- Interpretability of financial data
- Model Stability and Robustness
Cross-Validation
Evaluation of the model performance prior to testing is a challenging task by itself in the field of finance and econometrics and requires careful considerations.
- Reduced risk of model overfitting
- Improved model accuracy
- Reliable financial predictions
Machine Learning Applications
in Both Sides of the Business
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