Large-scale Recommendation for Portfolio Optimization

• URL: http://arxiv.org/abs/2103.07768v1
• Date: Sat, 13 Mar 2021 18:22:48 GMT
• Title: Large-scale Recommendation for Portfolio Optimization
• Authors: Robin Swezey, Bruno Charron
• Abstract summary: Individual investors are massively using online brokers to trade stocks with convenient interfaces and low fees. We frame the problem faced by online brokers of replicating this level of service in a low-cost and automated manner. Because of the care required in recommending financial products, we focus on a risk-management approach tailored to each user's portfolio and risk profile.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Individual investors are now massively using online brokers to trade stocks with convenient interfaces and low fees, albeit losing the advice and personalization traditionally provided by full-service brokers. We frame the problem faced by online brokers of replicating this level of service in a low-cost and automated manner for a very large number of users. Because of the care required in recommending financial products, we focus on a risk-management approach tailored to each user's portfolio and risk profile. We show that our hybrid approach, based on Modern Portfolio Theory and Collaborative Filtering, provides a sound and effective solution. The method is applicable to stocks as well as other financial assets, and can be easily combined with various financial forecasting models. We validate our proposal by comparing it with several baselines in a domain expert-based study.

Related papers

• Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading [57.28635022507172]
  TiMi is a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment.<n>We propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection.
  arXiv  Detail & Related papers  (2025-10-06T13:08:55Z)
• Your AI, Not Your View: The Bias of LLMs in Investment Analysis [55.328782443604986]
  Large Language Models (LLMs) face frequent knowledge conflicts due to discrepancies between pre-trained parametric knowledge and real-time market data.<n>This paper offers the first quantitative analysis of confirmation bias in LLM-based investment analysis.<n>We observe a consistent preference for large-cap stocks and contrarian strategies across most models.
  arXiv  Detail & Related papers  (2025-07-28T16:09:38Z)
• Building crypto portfolios with agentic AI [46.348283638884425]
  The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility.<n>This paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations.
  arXiv  Detail & Related papers  (2025-07-11T18:03:51Z)
• 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)
• Large Language Model driven Policy Exploration for Recommender Systems [50.70228564385797]
  offline RL policies trained on static user data are vulnerable to distribution shift when deployed in dynamic online environments. Online RL-based RS also face challenges in production deployment due to the risks of exposing users to untrained or unstable policies. Large Language Models (LLMs) offer a promising solution to mimic user objectives and preferences for pre-training policies offline. We propose an Interaction-Augmented Learned Policy (iALP) that utilizes user preferences distilled from an LLM.
  arXiv  Detail & Related papers  (2025-01-23T16:37:44Z)
• Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation [0.0]
  We propose a multi-agent and self-adaptive portfolio optimisation framework integrated with attention mechanisms and time series, namely the MASAAT. By reconstructing the tokens of financial data in a sequence, the attention-based cross-sectional analysis module and temporal analysis module of each agent can effectively capture the correlations between assets and the dependencies between time points. The experimental results clearly demonstrate that the MASAAT framework achieves impressive enhancement when compared with many well-known portfolio optimsation approaches.
  arXiv  Detail & Related papers  (2024-04-13T09:10:05Z)
• Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling [27.770653904666776]
  Individual investors often disregard established investment theories, favoring their personal preferences instead. This presents a challenge for stock recommendation systems, which must not only provide strong investment performance but also respect these individual preferences. We propose a new model, Portfolio Temporal Graph Network Recommender, PfoTGNRec, which can handle time-varying collaborative signals and incorporates diversification-enhancing sampling.
  arXiv  Detail & Related papers  (2024-03-27T07:17:55Z)
• Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima [54.06000767038741]
  We analyze multimodal recommender systems from the novel perspective of flat local minima. We propose a concise yet effective gradient strategy called Mirror Gradient (MG) We find that the proposed MG can complement existing robust training methods and be easily extended to diverse advanced recommendation models.
  arXiv  Detail & Related papers  (2024-02-17T12:27:30Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance [96.73189436721465]
  We first present a multi-agent RL (MARL) method for multi-order execution considering practical constraints. We propose a learnable multi-round communication protocol, for the agents communicating the intended actions with each other. Experiments on the data from two real-world markets have illustrated superior performance with significantly better collaboration effectiveness.
  arXiv  Detail & Related papers  (2023-07-06T16:45:40Z)
• Model-Free Market Risk Hedging Using Crowding Networks [1.4786952412297811]
  Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. We analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. Our method provides an alternative way to hedge portfolio risk including tail risk, which does not require costly option-based strategies or complex numerical optimization.
  arXiv  Detail & Related papers  (2023-06-13T19:50:03Z)
• Mean-Variance Efficient Collaborative Filtering for Stock Recommendation [25.72157859795055]
  We propose a mean-variance efficient collaborative filtering (MVECF) model for stock recommendations.<n>Our model is specifically designed to improve the pareto optimality in a trade-off between the risk (variance of return) and return (mean return) by systemically handling uncertainties in stock prices.
  arXiv  Detail & Related papers  (2023-06-11T04:51:29Z)
• Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
  Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology. One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds. This introduces the problem of finding an optimal strategy for choosing price intervals. We formalize this problem as an online learning problem with non-stochastic rewards.
  arXiv  Detail & Related papers  (2023-02-01T17:21:40Z)
• A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management [0.0]
  We propose a new method using experts signals and historical price data to feed into our reinforcement learning framework. Our framework could gain 90 percent of the profit earned by the best expert.
  arXiv  Detail & Related papers  (2022-12-29T22:48:26Z)
• Incentive-Aware Recommender Systems in Two-Sided Markets [49.692453629365204]
  We propose a novel recommender system that aligns with agents' incentives while achieving myopically optimal performance. Our framework models this incentive-aware system as a multi-agent bandit problem in two-sided markets. Both algorithms satisfy an ex-post fairness criterion, which protects agents from over-exploitation.
  arXiv  Detail & Related papers  (2022-11-23T22:20:12Z)
• 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)
• Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications [110.54266632357673]
  We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
  arXiv  Detail & Related papers  (2020-05-09T01:32:03Z)

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.