---
title: Bank Deposits vs. Money Market Mutual Funds
url: https://www.ml-quant.com/papers/ssrn/5102030/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5102030
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5102030
featured: 2025-01-23
citations: unknown
topic: Portfolio & Allocation
---


# Bank Deposits vs. Money Market Mutual Funds

The research suggests that competition from U.S. money market mutual funds could affect the availability of bank loans.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5102030
- Identifier: SSRN 5102030
- Released: 2025-01-18
- First featured: Quant Letter No. 83 (2025-01-23): https://www.ml-quant.com/issues/2025-01-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [The Performance of Household-held Mutual Funds: Evidence from the Euro Area](https://www.ml-quant.com/papers/ssrn/4849419/): A study from 2009 to 2020 shows that Euro-area households prefer pricier, lower risk-adjusted return mutual funds, particularly equity funds, and tend to follow past returns.
- [Beat the Market: An Effective Intraday Momentum Strategy for S&P500 ETF (SPY)](https://www.ml-quant.com/papers/ssrn/4824172/): The study investigates the success of an intraday momentum strategy on SPY, an ETF tracking the SP500, which resulted in a 1985 total return from 2007 to 2024.
- [Determinants of Bank Performance: Evidence from Replicating Portfolios](https://www.ml-quant.com/papers/ssrn/4822187/): A new bank performance metric reveals that structural issues like cost inefficiencies primarily cause underperformance, with high-performing banks being less dependent on government aid and more shock-resistant.
- [STRAPSim: A Portfolio Similarity Metric for ETF Alignment and Portfolio Trades](https://www.ml-quant.com/papers/arxiv/2509.24151/): STRAPSim, a new method for assessing portfolio similarity, surpasses existing measures by using semantic similarity, portfolio share weighting, and residual-aware greedy alignment, proving beneficial in ETF benchmarking, portfolio creation, and systematic execution.
- [Reaching for Duration and Leverage in the Treasury Market](https://www.ml-quant.com/papers/ssrn/4816018/): The article reveals that the use of Treasury futures by mutual funds varies significantly over time and across funds, influencing the variation in aggregate Treasury futures open interest.
- [Machine Learning Mutual Fund Flows](https://www.ml-quant.com/papers/ssrn/4812038/): Nonlinear machine learning models are more effective than linear models in predicting future fund flows, with past flows and the Morningstar rating as key predictors.