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

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