Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting
This paper explores using CNNs and QRM to improve stock option price predictions based on the Black-Scholes equation.
What it examines
This paper explores using Convolutional Neural Networks (CNN) combined with the Quasi-Reversibility Method (QRM) to predict stock option prices. It aims to improve prediction accuracy by leveraging machine learning to analyze historical data and solve the Black-Scholes equation as an ill-posed inverse problem.
What it concludes
The research shows that CNN combined with QRM can improve stock option predictions, with potential applications in financial trading strategies. Future work could explore different data sets, additional market factors, and alternative machine learning models to further enhance prediction accuracy.
Evidence objects
The research shows that CNN combined with QRM can improve stock option predictions, with potential applications in financial trading strategies. Future work could explore different data sets, additional market factors, and alternative machine learning models to further enhance prediction accuracy.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation… ▽ More This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation to analyze data for 92,846 companies. We solve the Black-Scholes (BS) equation forwards in time as an ill-posed inverse problem, using the Quasi-Reversibility Method (QRM), to predict option price for the future one day. For each company, we have 13 elements including stock and option daily prices, volatility, minimizer, etc. Because the market is so complicated that there exists no perfect model, we apply ML to train algorithms to make the best prediction. The current stage of research combines QRM with Convolutional Neural Networks (CNN), which learn information across a large number of data points simultaneously. We implement CNN to generate new results by validating and testing on sample market data. We test different ways of applying CNN and compare our CNN models with previous models to see if achieving a higher profit rate is possible. △ Less
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