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Machine Learning and Investment Analytics for Trading

Advanced Analytics for Investors

Summary

Methods for developing trading strategies based on AI and ML, both in short-term, and longer-term investing, are gaining popularity.

 

AI and ML are being used to develop trading strategies, develop liquidity searching algorithms, risk management, defining and assessing credit ratings, predicting defaults, hedging overlays, portfolio clustering and portfolio recommendations.

 

This is being achieved by using historical data for autonomous predictive modelling, external factors, SME overlays, and visualisation with allowances for adjustments. This results in consistent forecast scenarios with consideration for every significant predictive factor including:

 

  • Macro Factors/Fundamentals
  • Competitive Intensity, Valuations, Industry, Analysis
  • Credit Fundamentals
  • Volatility, Spread Dispersion, Default Rates, Credit Risk
  • Asset Class Volatility
  • News, Social Media and Commentary through Machine Learning and NLP

Data and Factors Considered

Featurisation and Modelling Process

Investement Analytics Flowchart

Outcomes

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