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Found 4 papers, 1 papers with code
Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies
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.
Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting
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.
Forecasting Stock Options Prices via the Solution of an Ill-Posed Problem for the Black-Scholes Equation
We also provide a convergence analysis for QRM.
An Evaluation of novel method of Ill-Posed Problem for the Black-Scholes Equation solution
It was proposed by Klibanov a new empirical mathematical method to work with the Black-Scholes equation.
Numerical Analysis Numerical Analysis 35R30, 65K05, 35R25, 65M30 G.1.8; G.1.6
