Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies

TL;DR

This paper explores combining advanced machine learning models like RNN and LSTM with improved trading strategies and the Quasi-Reversibility Method to enhance stock option forecasting and investment outcomes.

Contribution

It introduces a novel integration of ML models and trading strategies, including the use of QRM, for more accurate stock option predictions and optimized trading decisions.

Findings

Improved prediction accuracy using combined ML models.

Enhanced trading strategies with simulated investment gains.

Application of Binomial Asset Pricing Model for optimized investment.

Abstract

This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data.