Suggest materials to research

Explanation

Sometimes we're interested by a topic and might even have questions in mind. Chatting with AI tools can give you some ideas on where to start researching. 

Example Prompt

"Hello, I am a third-year undergraduate student studying for a BSc in Actuarial Science at a UK university. I am writing a report for an imaginary client which requires me to use stochastic modelling to project stock market movements. Can you find some examples of research on using stochastic modelling to project stock market movements from the last 12 months to help aid my understanding? Thank you!"

AI response  

Here are some recent examples of research from the past year that explore stochastic modelling and its application to stock market projections:

📚 Recent Research on Stochastic Modelling for Stock Market Movements

1. Deep Learning Meets Stochasticity

• Title: An Evaluation of Deep Learning Models for Stock Market Trend Prediction
• Authors: Gonzalo Lopez Gil, Paul Duhamel-Sebline, Andrew McCarren
• Published: August 2024
• Summary: This study evaluates advanced deep learning models—including xLSTM-TS, Temporal Fusion Transformers, and N-BEATS—for forecasting stock trends. It emphasizes the stochastic nature of market data and uses wavelet denoising to improve prediction accuracy.
• Findings: The xLSTM-TS model achieved a test accuracy of 72.82% on the EWZ ETF dataset, outperforming other models.
• Read the paper

2. Classic Stochastic Models vs. Machine Learning

• Title: A Performance Analysis of Stochastic Processes and Machine Learning Algorithms in Stock Market Prediction
• Author: Mohammed Bouasabah
• Published: July 2024
• Summary: This paper compares traditional stochastic models—like Geometric Brownian Motion (GBM), Vasicek, and Cox–Ingersoll–Ross (CIR)—with machine learning algorithms such as SVM and Random Forest.
• Findings: Stochastic models, especially CIR, showed strong predictive performance, though machine learning offered more flexibility.
• Read the full text

3. Survey of Techniques

• Title: A Survey of Recent Machine Learning Techniques for Stock Prediction
• Published: 2024
• Summary: This article reviews various machine learning models used for stock prediction and discusses how they handle stochasticity in financial data.
• Read the article

These papers should give you a solid foundation to understand how stochastic modelling is evolving in financial forecasting.