Multi-Perspective Analysis for Derivative Financial Product Prediction with Stacked Recurrent Neural Networks, Natural Language Processing and Large Language Model

Author

Ethan Lo, The Graduate Center, City University of New YorkFollow

Date of Degree

2-2024

Document Type

Capstone Project

Degree Name

M.S.

Program

Data Analysis & Visualization

Advisor

Michelle McSweeney

Subject Categories

Other Computer Engineering | Portfolio and Security Analysis

Keywords

Derivative Financial Product, Stacked Recurrent Neural Networks (RNN), Natural Language Processing (NLP), Large Language Model (LLMs), S&P 500, ChatGPT

Abstract

This study developed a multi-perspective, AI-powered model for predicting E-Mini S&P 500 Index Futures prices, tackling the challenging market dynamics of these derivative financial instruments. Leveraging FinBERT for analysis of Wall Street Journal data alongside technical indicators, trader positioning, and economic factors, my stacked recurrent neural network built with LSTMs and GRUs achieves significantly improved accuracy compared to single sub-models. Furthermore, ChatGPT generation of human-readable analysis reports demonstrates the feasibility of using large language models in financial analysis. This research pioneers the use of stacked RNNs and LLMs for multi-perspective financial analysis, offering a novel blueprint for automated prediction and customized reporting. Ultimately empowering hedge fund managers and institutional investors to navigate complex and dynamic markets with greater confidence.

Recommended Citation

Lo, Ethan, "Multi-Perspective Analysis for Derivative Financial Product Prediction with Stacked Recurrent Neural Networks, Natural Language Processing and Large Language Model" (2024). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/5700