Machine Learning

The goal is to generate tailored asset recommendations using inputs such as investment goals, risk appetite, demographics, and transaction history. There will be exploration on collaborative filtering, sequential modeling, and possibly large language models, to deliver more informed, asset recommendations that align with each investor’s unique needs.

Agentic Investment Research Analyst is an AI-powered multi-agent system designed to automate equity research process, collecting real-time financial news, extracting key metrics and sentiment, and compiling actionable, sector-specific investment reports and trade ideas. Built on advanced NLP and LLM technologies, this platform aims to give retail investors access to timely insights without the time intensive research process.

Using GDELT’s news-based signals, we will build a graph of country interactions and feed it into a GNN to understand how conflicts, alliances, and economic shifts ripple through global oil markets. The output then passed to a Gradient Boosted Tree to forecast price changes with more interpretability (and hopefully accuracy) than traditional models.

Experiment processing and engineering textual data from Social media platforms (Reddit, Facebook) into useful features that improves ML time series prediction when trading Crypto.

The team was able to incorporated multiple variations of models to investigate the different performances for different specifications of models. They managed to execute profitable automated forex trading using signals generated from ML Models.

Taking inspiration from Greenblatt’s Magic Formula, we decided to use ROC and EY as the only two features when training our machine learning models. We decided to apply this value investing strategy to SGX stocks and framed it as a machine learning problem.

To aid the analysis of the minutes and minimise potential inaccuracies, our team employed 2 models that satisfy different objectives. (i.e Top Modelling and Sentiment Analysis.