Impact of Expert-Following Strategies in Financial Asset Recommendation

• URL: http://arxiv.org/abs/2607.14556v1
• Date: Thu, 16 Jul 2026 04:34:57 GMT
• Title: Impact of Expert-Following Strategies in Financial Asset Recommendation
• Abstract summary: We propose the Expert-Following Strategies: a framework that identifies top-performing investors based on their historical ROI.<n>Our experiments using real-world transaction histories show that our strategy achieves statistically significant improvement over the market-average baseline in both ROI and nDCG simultaneously.
• Score: 41.54865096569508
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial institutions hold rich transaction histories, yet delivering recommendations that simultaneously maximize investment returns and ensure preference alignment remains a significant challenge. Existing approaches, namely return-based and preference-based strategies, each optimize a single objective, resulting in a fundamental trade-off between profitability (ROI) and relevance (nDCG). In this paper, we propose the Expert-Following Strategies: a framework that identifies top-performing investors based on their historical ROI and recommends the assets they purchased, scored by ROI-weighted purchase frequency. Our experiments using real-world transaction histories show that our strategy achieves statistically significant improvement over the market-average baseline in both ROI and nDCG simultaneously across all four thresholds.

Related papers

• Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization [82.03139922490796]
  Reinforcement learning (RL) has shown significant promise for sequential portfolio optimization tasks, such as stock trading, where the objective is to maximize cumulative returns while minimizing risks using historical data.<n>Traditional RL approaches often produce policies that merely memorize the optimal yet impractical buying and selling behaviors within the fixed dataset.<n>Our approach frames portfolio optimization as a new type of partial-offline RL problem and makes two technical contributions.
  arXiv  Detail & Related papers  (2025-05-19T06:37:25Z)
• Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach [2.2835610890984164]
  This study proposes a volatility-guided portfolio optimization framework that dynamically constructs portfolios based on investors' risk profiles.<n>The efficacy of the proposed methodology is established using stocks from the Dow $30$ index.
  arXiv  Detail & Related papers  (2025-04-20T10:17:37Z)
• Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards [3.9795751586546766]
  This paper introduces a novel agent-based approach for enhancing existing portfolio strategies using Proximal Policy Optimization (PPO)<n>Rather than focusing solely on traditional portfolio construction, our approach aims to improve an already high-performing strategy through dynamic rebalancing driven by PPO and Oracle agents.
  arXiv  Detail & Related papers  (2025-02-04T11:45:59Z)
• Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization [49.396692286192206]
  We study the use of deep reinforcement learning for responsible portfolio optimization by incorporating ESG states and objectives. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation.
  arXiv  Detail & Related papers  (2024-03-25T12:04:03Z)
• Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning [42.303733194571905]
  We seek to find and automatize an optimal credit card limit adjustment policy by employing reinforcement learning techniques. Our research establishes a conceptual structure for applying reinforcement learning framework to credit limit adjustment.
  arXiv  Detail & Related papers  (2023-06-27T16:10:36Z)
• E2EAI: End-to-End Deep Learning Framework for Active Investing [123.52358449455231]
  We propose an E2E that covers almost the entire process of factor investing through factor selection, factor combination, stock selection, and portfolio construction. Experiments on real stock market data demonstrate the effectiveness of our end-to-end deep leaning framework in active investing.
  arXiv  Detail & Related papers  (2023-05-25T10:27:07Z)
• Asset Allocation: From Markowitz to Deep Reinforcement Learning [2.0305676256390934]
  Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets. We conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques.
  arXiv  Detail & Related papers  (2022-07-14T14:44:04Z)
• Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning [13.23731449431572]
  We propose a full-cycle data-driven investment robo-advising framework, consisting of two ML agents. The proposed investment pipeline is applied on real market data from April 1, 2016 to February 1, 2021 and has shown to consistently outperform the S&P 500 benchmark portfolio.
  arXiv  Detail & Related papers  (2021-05-19T17:20:03Z)
• Universal Trading for Order Execution with Oracle Policy Distillation [99.57416828489568]
  We propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. We show that our framework can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information.
  arXiv  Detail & Related papers  (2021-01-28T05:52:18Z)
• Learning Risk Preferences from Investment Portfolios Using Inverse Optimization [25.19470942583387]
  This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization. We demonstrate our methods on real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings.
  arXiv  Detail & Related papers  (2020-10-04T21:29:29Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.