Predicting the Bid-Ask Spread of Equity Options - A Machine Learning Approach Applied to AMZN and AMD

Sep 17, 2026

Uploaded by Skyler

This research investigates the application of machine learning techniques to predict the bid-ask spread of equity options, a key measure of market liquidity and trading costs. While most existing literature focuses on pricing and volatility estimation, this study addresses direct spread modelling using real-world options data from Amazon (AMZN) and AMD. The project builds a complete pipeline with feature engineering, exploratory analysis, and training of linear and nonlinear models. Results show machine learning models outperform traditional regressions, with CatBoost delivering the best accuracy, and SHAP/LIME improving transparency for traders, risk analysts, and regulators.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

1 Introduction

1.1 Background

1.2 Motivation and Research Gap

Predicting the Bid-Ask Spread of Equity Options: A Machine Learning Approach Applied to Amazon and AMD MSc Research Project FinTech Pablo José González Pardo Student ID: x24120511 School of Computing National College of Ireland Supervisor: Brian Byrne National College of Ireland MSc Project Submission Sheet School of Computing Student Name: Pablo José González Pardo Student ID: X24120511 Programme: MSc FinTech Year: 2024-2025 Module: MSc Research Project Supervisor: Brian Byrne Submission Due Date: 11th August 2025 Project Title: Predicting the Bid-Ask Spread of Equity Options: A Machine Learning Approach Applied to AMZN and AMD Word Count: 5910 Page Count: 21 I hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project. ALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action. Signature: Pablo José González Pardo Date: 10th of August 2025 PLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST | Attach a completed copy of this sheet to each project (including multiple copies) | □ | | --- | --- | | Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ | | You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It isnot sufficient to keep a copy on computer. | □ | Assignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office. | Office Use Only | | | --- | --- | | Signature: | | | Date: | | | Penalty Applied (if applicable): | | Predicting the Bid-Ask Spread of Equity Options: A Machine Learning Approach Applied toAMZNandAMD Pablo José González Pardo x24120511 Abstract This research investigates the application of machine learning techniques to predict the bid-ask spread of equity options, a key measure of market liquidity and trading costs. While most existing literature focuses on pricing and volatility estimation, this study addresses the topic of direct spread modelling using real world options data from Amazon (AMZN) and AMD, the project develops a full pipeline including feature engineering, exploratory analysis, and the training of both linear and nonlinear models. Results show that machine learning models significantly outperform traditional linear regressions, especially in capturing nonlinear interactions between variables. Among all models, CatBoost consistently achieves the highest predictive accuracy. Furthermore, explainability tools like SHAP and LIME are used to assess feature importance and enhance model transparency. The findings contribute to both academic understanding and practical applications, providing useful insights for traders, risk analysts, and regulators interested in the behaviour of liquidity in derivatives markets. 1 Introduction 1.1 Background Options could be defined as financial contracts that give the holder the right to buy or sell a financial instrument at a specific price for a certain period of time, each option has the following key features: • Strike price: The price at which an option can be exercised. • Expiration date: The date at which an option expires. • Option premium: The price at which an option is purchased. • Stock price: The price of the underlying asset. There are two types of options, call options, that give the holder the right, but not the obligation, to buy the underlying asset at the strike price on or before expiration and put options that give the hold

FAQs

What does the project predict?

The project predicts the bid-ask spread of equity options, defined as the difference between ask and bid prices.

Which datasets are used in the study?

The study uses real-world options data from Amazon (AMZN) and AMD.

Why are explainability tools used?

SHAP and LIME are used to evaluate feature importance and improve model transparency.

Sep 17, 2026

Uploaded by Skyler