Recommender Systems in Financial Trading: Using machine-based conviction analysis in an explainable AI investment framework

ArXiv ID: 2404.11080 “View on arXiv”

Authors: Unknown

Abstract

Traditionally, assets are selected for inclusion in a portfolio (long or short) by human analysts. Teams of human portfolio managers (PMs) seek to weigh and balance these securities using optimisation methods and other portfolio construction processes. Often, human PMs consider human analyst recommendations against the backdrop of the analyst’s recommendation track record and the applicability of the analyst to the recommendation they provide. Many firms regularly ask analysts to provide a “conviction” level on their recommendations. In the eyes of PMs, understanding a human analyst’s track record has typically come down to basic spread sheet tabulation or, at best, a “virtual portfolio” paper trading book to keep track of results of recommendations. Analysts’ conviction around their recommendations and their “paper trading” track record are two crucial workflow components between analysts and portfolio construction. Many human PMs may not even appreciate that they factor these data points into their decision-making logic. This chapter explores how Artificial Intelligence (AI) can be used to replicate these two steps and bridge the gap between AI data analytics and AI-based portfolio construction methods. This field of AI is referred to as Recommender Systems (RS). This chapter will further explore what metadata that RS systems functionally supply to downstream systems and their features.

Keywords: Recommender Systems, portfolio construction, artificial intelligence, asset selection, conviction scoring

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Philosophers
• Why: The paper provides a conceptual overview and a fictional case study of applying recommender systems to finance, focusing on workflow and explainability rather than advanced mathematical derivations or backtesting results.

  flowchart TD
    A["Research Goal:<br>Automate Analyst Conviction<br>& Track Record in Portfolio Construction"] --> B["Data Inputs:<br>Analyst Recommendations<br>Historical Performance Data<br>Asset Metadata"]
    B --> C["Computational Process:<br>Recommender System Model<br>with Conviction Scoring<br>& Explainable AI"]
    C --> D["Hybrid Portfolio Construction:<br>Integrate AI Recommendations<br>with Traditional PM Optimization"]
    D --> E["Key Outcomes:<br>1. AI-driven Conviction Metrics<br>2. Enhanced Asset Selection<br>3. Transparent Decision Logic<br>4. Improved Portfolio Performance"]