---
title: Machine Learning Approach for Predicting U.S. ETFs’ Tracking Errors – Implications on U.S. Invested Fund
url: https://www.ml-quant.com/papers/ssrn/4726993/
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 4726993
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4726993
featured: 2024-02-21
citations: 0
topic: Portfolio & Allocation
---


# Machine Learning Approach for Predicting U.S. ETFs’ Tracking Errors – Implications on U.S. Invested Fund

Machine learning methods, specifically Random Forest and Gradient Boosting Decision Tree, are found to be more effective in predicting U.S. ETF’s tracking errors, with U.S. assets and expense ratio being key factors.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4726993
- Identifier: SSRN 4726993
- Released: 2023-10-16
- First featured: Quant Letter No. 38 (2024-02-21): https://www.ml-quant.com/issues/2024-02-21/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [Who Clears the Market When Passive Investors Trade?](https://www.ml-quant.com/papers/ssrn/4777585/): The article investigates the role of firms in providing shares to passive investors, particularly in response to index funds' buying.
- [Imputing Mutual Fund Trades](https://www.ml-quant.com/papers/ssrn/4678139/): The paper introduces a new method to estimate daily mutual fund trades in individual stocks using daily stock prices, returns, and quarterly fund holdings, showing high accuracy for larger trades.
- [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.
- [Active ETFs Cloned from Mutual Funds: Competing for Investor Flows](https://www.ml-quant.com/papers/ssrn/4653702/): The research indicates that less transparent active ETFs do not affect mutual fund investor flows, instead, the reputation of the cloned mutual funds helps the new ETFs attract more flows.
- [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.
- [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.