4 papers
Interpretable Hypothesis-Driven Trading:A Rigorous Walk-Forward Validation Framework for Market Microstructure Signals
Gagan Deep, Akash Deep, William Lamptey
We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothes…
Probability Weighting Meets Heavy Tails: An Econometric Framework for Behavioral Asset Pricing
Akash Deep, Svetlozar T. Rachev, Frank J. Fabozzi
We develop an econometric framework integrating heavy-tailed Student's distributions with behavioral probability weighting while preserving infinite divisibility. Using 432{,}7…
Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach
Akash Deep, Chris Monico, W. Brent Lindquist +2
We propose a machine learning-based extension of the classical binomial option pricing model that incorporates key market microstructure effects. Traditional models assume friction…
Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading
Akash Deep, Abootaleb Shirvani, Chris Monico +2
Because of the theoretical challenges posed by the Efficient Market Hypothesis to technical analysis, the effectiveness of technical indicators in high-frequency trading remains in…