Paper: arXiv 2412.03038

Abstract

Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors’ budgets among different assets to balance the risk and return of an investment. In this study, we propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of two main phases, i.e., return-related maximization and risk control. Specifically, in the return-related maximization phase, we introduce two auxiliary objectives, i.e., return rate prediction, and return rate ranking, combined with portfolio optimization to remit the overfitting problem and improve the generalization of the trained model to future markets. Subsequently, in the risk control phase, we propose two methods, i.e., portfolio interpolation and portfolio improvement, to achieve fine-grained risk control and fast risk adaption to a user-specified risk level. For the portfolio interpolation method, we theoretically prove that the risk can be perfectly controlled if the to-be-set risk level is in a proper interval. In addition, we also show that the return rate of the adjusted portfolio after portfolio interpolation is no less than that of the min-variance optimization, as long as the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method can achieve greater return rates while keeping the same risk level compared to portfolio interpolation. Extensive experiments are conducted on three real-world datasets. The results demonstrate the effectiveness and efficiency of the proposed framework.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel multi-objective framework with theoretical proofs (e.g., for portfolio interpolation) and extensive experiments on three real-world datasets, accompanied by open-sourced code.

Research Flowchart

  flowchart TD
  A["Research Goal: Portfolio Management with Controllable Risk"] --> B["Phase 1: Return-Related Maximization"]
  B --> B1["Inputs: Historical Market Data"]
  B --> B2["Auxiliary Objectives: Return Prediction & Ranking"]
  B --> B3["Portfolio Optimization to prevent overfitting"]
  C["Phase 2: Risk Control"] --> C1["Input: User-specified Risk Level"]
  C --> C2["Method A: Portfolio Interpolation<br/>*Theoretical guarantee of risk control*"]
  C --> C3["Method B: Portfolio Improvement<br/>*Higher returns at same risk level*"]
  B3 --> D["Computational Process"]
  C2 --> D
  C3 --> D
  D --> E["Key Findings & Outcomes"]
  E --> E1["Effective & Efficient Framework"]
  E --> E2["Generalization to future markets"]
  E --> E3["Fine-grained, fast risk adaptation"]

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1 of 69 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.