Recommender Systems in Financial Trading: Using machine-based conviction analysis in an explainable AI investment framework
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingRecommendation SystemsSimilar Papers 제목 키워드 기반
Quantitative Trading using Deep Q Learning
Reinforcement learning (RL) is a subfield of machine learning that has been used in many fields, such as robotics, gaming, and autonomous systems. There has been growing interest in using RL for quantitative trading, whe…
Q-Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Adversarial Attacks on Machine Learning Systems for High-Frequency Trading
Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain. Nonetheless, little is known about the robustness properties of these models. We study val…
Algorithmic TradingBIG-bench Machine LearningVocal Bursts Intensity PredictionInterpretability in Safety-Critical FinancialTrading Systems
Sophisticated machine learning (ML) models to inform trading in the financial sector create problems of interpretability and risk management. Seemingly robust forecasting models may behave erroneously in out of distribut…
ManagementPredicting risk/reward ratio in financial markets for asset management using machine learning
Financial market forecasting remains a formidable challenge despite the surge in computational capabilities and machine learning advancements. While numerous studies have underscored the precision of computer-generated m…
Algorithmic TradingAsset ManagementManagementExtracting the multi-timescale activity patterns of online financial markets
Online financial markets can be represented as complex systems where trading dynamics can be captured and characterized at different resolutions and time scales. In this work, we develop a methodology based on non-negati…