Machine Learning in Financial Instruments Pricing

Dear Colleagues,

The valuation of financial instruments presents one of the most persistent challenges in quantitative finance. This Special Issue, “Machine Learning in Financial Instruments Pricing”, invites contributions that tackle this challenge with modern data-driven methods.

This initiative highlights how advanced computational/mathematical and data-driven techniques are reshaping the foundational approach to pricing and risk management. As financial products grow more complex and markets evolve, reliance solely on traditional analytical models has become insufficient, creating a demand for methods that can learn directly from data and market dynamics. Machine learning provides a powerful framework for tackling problems that challenge traditional models, such as capturing non-linearities and scaling to high dimensions. This directly addresses critical needs for improved accuracy and computational efficiency in real-world pricing scenarios.

This Special Issue seeks to explore cutting-edge research at the intersection of financial mathematics, quantitative finance, and machine learning. We invite high-quality original research and review articles that contribute novel methodologies, empirical analyses, or comprehensive surveys on the application of machine learning to the valuation of complex financial instruments. Topics of interest include, but are not limited to, the following:

Financial Instruments and Context:

Derivatives pricing (options, swaps, futures/forwards);
Structured products and securitizations;
Exotic and path-dependent options;
Credit derivatives and counterparty credit risk;
Fixed income and interest rate derivatives;
Valuation adjustment (XVA) frameworks.

Pricing in Incomplete Markets:

Core methodologies and machine learning techniques;
Neural networks and deep Learning (e.g., PDE Nets, diffusion models);
Reinforcement learning for optimal pricing and hedging;
Generative models for market scenario simulation;
Gaussian processes and bayesian methods for uncertainty quantification;
Tree-based models (random forests, gradient boosting);
Dimensionality reduction and feature engineering for financial data;
Surrogate modeling and calibration of high-fidelity models;
Hybrid models combining ML with stochastic calculus.

Applications and Challenges:

High-dimensional pricing problems;
model calibration and parameter inference;
efficient long monte carlo simulation;
real-time pricing and hedging;
Explainable AI (XAI) in finance;
data-driven discovery of governing equations;
market microstructure effects on pricing.

Dr. David Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• financial mathematics
• derivatives pricing
• deep learning
• complex instruments
• model calibration
• incomplete market
• reinforcement learning