  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.
