Revolutionizing Hedge Fund Risk Management: The Power of Deep Learning and LSTM in Hedging Illiquid Assets

Abstract

In the dynamic sphere of financial markets, hedge funds have emerged as a critical force, navigating through volatility with advanced risk management techniques yet grappling with the challenges posed by illiquid assets. This study aims to transcend traditional option pricing models, which struggle under the complexities of hedge fund investments, by exploring the applicability of machine learning in financial risk management. Leveraging Deep Neural Networks (DNNs) and Long Short-Term Memory (LSTM) cells, the research introduces a model-free, data-driven approach for discrete-time hedging problems. Through a comparative analysis of simulated data and the implementation of LSTM architectures, the paper elucidates the potential of these machine learning techniques to enhance the precision of risk assessments and decision-making processes in hedge fund investments. The findings reveal that DNNs and LSTMs offer significant advancements over conventional models, effectively capturing long-term dependencies and complex patterns within financial time series data. Consequently, the study underscores the transformative impact of machine learning on the methodologies employed in financial risk management, proposing a novel paradigm that promises to mitigate the intricacies of hedging illiquid assets. This research not only contributes to the academic discourse but also paves the way for the development of more adaptive and resilient investment strategies in the face of market uncertainties.