Work

Dynamic Graph Neural Networks for Financial Forecasting

Year: 2024

Type: dissertation

Abstract: The thesis proposes a stock price prediction model based on a Dynamic Graph Neural Network (DGNN), which represents stocks and their interactions through daily events, enriched by sequential models (GRU, Transformer, and MAMBA) for predicting the next day's closing price. The dataset includes stock data, macroeconomic data, and news, pre-processed to build a dynamic graph. The experimental results demonstrate the model's effectiveness in capturing market dynamics and predicting prices.

Source: Electronic Theses and Dissertations Repository (University of Pisa)

Author: TUPPUTI, DOMENICO

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W6999863134

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