Machine Learning in Capital Markets: Decision Support System for Outcome Analysis

Abstract

Decision support systems using Artificial Intelligence in the context of financial services include different application ranging from investment advice to financial trading. The analysis of order flow provides many challenges that can be addressed by Machine Learning (ML) techniques in order to determine an optimal dynamic trading strategy. The first step in this direction is represented by the outcome analysis of order flow: the model should identify strong predictors that determine a positive/negative outcome. The aim of this work is the proposal of a closed-loop ML approach based on decision tree (DT) model to perform outcome analysis on financial trading data. The overall approach is integrated in a Decision Support System for Outcome Analysis (DSS-OA). Taking into account the model complexity, the DT algorithm enables to generate explanations that allow the user to understand (i) how this outcome is reached (decision rules) and (ii) the most discriminative outcome predictors (feature importance). The closed-loop approach allows the users to interact directly with the proposed DSS-OA by retraining the algorithm with the goal to a finer-grained outcome analysis. The experimental results and comparisons demonstrated high-interpretability and predictive performance of the proposed DSS-OA by providing a valid and fast system for outcome analysis on financial trading data. Moreover, the Proof of Concept evaluation demonstrated the impact of the proposed DSS-OA in the outcome analysis scenario.

Keywords

• Finance
• decision support systems
• financial management
• machine learning
• decision trees
• outcome analysis