# Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting

arXiv: https://arxiv.org/abs/2208.14385 · HTML: https://mlsanity.com/paper/2208.14385/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [Application of Deep Q Learning with Simulation Results for Elevator Optimization](https://mlsanity.com/paper/2210.00065/) (2022)
- [Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies](https://mlsanity.com/paper/2211.15912/) (2022)

## Abstract (arXiv)

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.