The following are the possible areas where ML can be used :

  • Price prediction: Finding nonlinear relations, hierarchical relations and categorical variables.

  • Hedging: Reinforcement learning methods involve very few assumptions.

  • Portfolio construction and risk analysis: Machine learning methods outperform classical mean-variance portfolio optimization, with Sharpe ratio gains often exceeding 30%.

  • Structural breaks / outlier detection: Machine learning techniques can reduce the percentage of incorrect buy/sell signals due to structural breaks and outliers.

  • Bet sizing / alpha capture: Meta-labeling methods can improve buy-sell decisions.

  • Feature importance: As mentioned above, machine learning methods can identify patterns in high-dimensional space, identifying key features that are often missed as a result of a model’s misspecification.

  • Credit ratings / analyst recommendations: Machine learning algorithms have been successful in reproducing the recommendations produced by bank analysts and credit rating agencies, which often involve a number of models and subjective heuristics.

  • Unstructured data: Machine learning tools are often effective in analyzing unstructured data, as often arises in such arenas as analyses of news articles, corporation announcements, sales figures, transportation activity and more.

  • Execution: Machine learning may be used in analyzing trading and execution activities.

  • Detection of false investment strategies: Backtest overfitting and other forms of statistical error are the bane of the finance world. Machine learning strategies, in conjunction with other techniques, are effective in guarding against statistical overfitting and other forms of false discoveries.