Mutual Funds

Learning to Manage Investment Portfolios beyond Simple Utility Functions ArXiv ID: 2510.26165 “View on arXiv” Authors: Maarten P. Scholl, Mahmoud Mahfouz, Anisoara Calinescu, J. Doyne Farmer Abstract While investment funds publicly disclose their objectives in broad terms, their managers optimize for complex combinations of competing goals that go beyond simple risk-return trade-offs. Traditional approaches attempt to model this through multi-objective utility functions, but face fundamental challenges in specification and parameterization. We propose a generative framework that learns latent representations of fund manager strategies without requiring explicit utility specification. Our approach directly models the conditional probability of a fund’s portfolio weights, given stock characteristics, historical returns, previous weights, and a latent variable representing the fund’s strategy. Unlike methods based on reinforcement learning or imitation learning, which require specified rewards or labeled expert objectives, our GAN-based architecture learns directly from the joint distribution of observed holdings and market data. We validate our framework on a dataset of 1436 U.S. equity mutual funds. The learned representations successfully capture known investment styles, such as “growth” and “value,” while also revealing implicit manager objectives. For instance, we find that while many funds exhibit characteristics of Markowitz-like optimization, they do so with heterogeneous realizations for turnover, concentration, and latent factors. To analyze and interpret the end-to-end model, we develop a series of tests that explain the model, and we show that the benchmark’s expert labeling are contained in our model’s encoding in a linear interpretable way. Our framework provides a data-driven approach for characterizing investment strategies for applications in market simulation, strategy attribution, and regulatory oversight. ...

An Empirical study on Mutual fund factor-risk-shifting and its intensity on Indian Equity Mutual funds ArXiv ID: 2510.19619 “View on arXiv” Authors: Rajesh ADJ Jeyaprakash, Senthil Arasu Balasubramanian, Vijay Maddikera Abstract Investment style groups investment approaches to predict portfolio return variations. This study examines the relationship between investment style, style consistency, and risk-adjusted returns of Indian equity mutual funds. The methodology involves estimating size and style beta coefficients, identifying breakpoints, analysing investment styles, and assessing risk-shifting intensity. Funds transition across styles over time, reflecting rotation, drift, or strengthening trends. Many Mid Blend funds remain in the same category, while others shift to Large Blend or Mid Value, indicating value-oriented strategies or large-cap exposure. Some funds adopt high-return styles like Small Value and Small Blend, aiming for alpha through small-cap equities. Performance changes following risk structure shifts are analyzed by comparing pre- and post-shift metrics, showing that style adjustments can enhance returns based on market conditions. This study contributes to mutual fund evaluation literature by highlighting the impact of style transitions on returns. ...

Do Mutual Funds Make Active and Skilled Liquidity Choices in Portfolio Management? Evidence from India ArXiv ID: 2510.02741 “View on arXiv” Authors: Pankaj K Agarwal, H K Pradhan, Konark Saxena Abstract This study examines active liquidity management by Indian open-ended equity mutual funds. We find that fund managers respond to inflows by increasing cash holdings, which are later used to purchase less-liquid stocks at favourable valuations. Funds with less liquid portfolios tend to maintain larger cash reserves to manage flows. Funds that make active liquidity choices yield statistically and economically significant gross and net returns. The performance differences between funds with varying activeness in altering liquidity highlight the importance of active liquidity management in markets with substantial cross-sectional liquidity differences such as India. ...

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. ...

Do Investors Value Sustainability? A Natural Experiment Examining Ranking and Fund Flows ArXiv ID: ssrn-3016092 “View on arXiv” Authors: Unknown Abstract Examining a shock to the salience of the sustainability of the US mutual fund market, we present causal evidence that investors marketwide value sustainability. Keywords: Sustainability, Mutual funds, Investor preferences, Fund flows, ESG investing Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on econometric analysis (difference-in-differences, local linear plots, fixed effects) rather than advanced mathematics, but is exceptionally data-heavy, using a large-scale natural experiment on $8 trillion in assets with precise flow measurements and experimental validation. flowchart TD A["Research Goal:<br>Do investors value sustainability?"] --> B["Methodology:<br>Natural experiment from sustainability ranking shock"] B --> C["Data/Inputs:<br>US mutual fund flows & sustainability scores"] C --> D["Computation:<br>Difference-in-differences analysis"] D --> E["Key Findings:<br>Investors increase flows to<br>higher sustainability funds post-shock"]

Is Money Really ‘Smart’? New Evidence on the Relation between Mutual Fund Flows, Manager Behavior, and Performance Persistence ArXiv ID: ssrn-414420 “View on arXiv” Authors: Unknown Abstract Mutual fund returns strongly persist over multi-year periods - that is the central finding of this paper. Further, consumer and fund manager behavior both play Keywords: Mutual Fund Persistence, Performance Persistence, Fund Manager Behavior, Investor Sentiment, Long-Term Returns, Mutual Funds Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper employs extensive empirical data analysis (CRSP mutual fund database, cross-sectional regressions, style adjustments) to test hypotheses about fund flows and performance, but uses relatively standard financial econometrics without complex mathematical derivations. flowchart TD A["Research Goal<br>Test Persistence of Mutual Fund Returns<br>and Roles of Manager Behavior & Flows"] --> B["Key Methodology<br>Longitudinal Performance Analysis"] B --> C{"Data / Inputs"} C --> C1["Multi-Year Mutual Fund Returns"] C --> C2["Manager Behavior Data"] C --> C3["Net Flows & Investor Sentiment"] C1 & C2 & C3 --> D["Computational Process<br>Regression & Persistence Metrics"] D --> E["Key Findings / Outcomes"] E --> E1["Strong Multi-Year Persistence Found"] E --> E2["Manager Behavior Explains Persistence"] E --> E3["Flows Reinforce Manager Behavior"]