A Case Study of Next Portfolio Prediction for Mutual Funds

ArXiv ID: 2410.18098 “View on arXiv”

Authors: Unknown

Abstract

Mutual funds aim to generate returns above market averages. While predicting their future portfolio allocations can bring economic advantages, the task remains challenging and largely unexplored. To fill that gap, this work frames mutual fund portfolio prediction as a Next Novel Basket Recommendation (NNBR) task, focusing on predicting novel items in a fund’s next portfolio. We create a comprehensive benchmark dataset using publicly available data and evaluate the performance of various recommender system models on the NNBR task. Our findings reveal that predicting novel items in mutual fund portfolios is inherently more challenging than predicting the entire portfolio or only repeated items. While state-of-the-art NBR models are outperformed by simple heuristics when considering both novel and repeated items together, autoencoder-based approaches demonstrate superior performance in predicting only new items. The insights gained from this study highlight the importance of considering domain-specific characteristics when applying recommender systems to mutual fund portfolio prediction. The performance gap between predicting the entire portfolio or repeated items and predicting novel items underscores the complexity of the NNBR task in this domain and the need for continued research to develop more robust and adaptable models for this critical financial application.

Keywords: Mutual Funds, Portfolio Prediction, Recommender Systems, Next Novel Basket Recommendation, Autoencoder, Equities (Mutual Funds)

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper applies recommender system models (like autoencoders and NBR methods) to financial data but the mathematics involves standard matrix factorization and probabilistic models rather than advanced derivatives. Empirical rigor is solid due to the creation of a benchmark dataset from SEC, Open FIGI, and Yahoo Finance, and the use of the RecBole library for reproducible evaluation.

  flowchart TD
    A["Research Goal: Predict Novel Items in Mutual Fund Portfolio"] --> B["Methodology: Formulate as Next Novel Basket Recommendation<br>Use Public Data to Build Comprehensive Benchmark"]
    B --> C["Computational Process: Apply & Evaluate Multiple Recommender System Models<br>Comparing SOTA vs. Simple Heuristics vs. Autoencoders"]
    C --> D{"Data Inputs: Mutual Fund Historical Portfolios<br>Equities, Holdings, Time Series"}
    C --> E["Key Findings & Outcomes"]
    E --> F["Prediction of novel items is more difficult<br>than repeated items or full portfolio"]
    E --> G["SOTA NBR models are outperformed by<br>simple heuristics for novel items"]
    E --> H["Autoencoder-based approaches show<br>superior performance on novel items"]
    E --> I["Domain-specific characteristics are critical<br>for financial recommender systems"]