---
title: Diversification: Not a Free Lunch
url: https://www.ml-quant.com/papers/repec/gam-jjrfmx-v-17-y-2024-i-6-p-225-d-1402811/
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: RePEc:gam:jjrfmx:v:17:y:2024:i:6:p:225-:d:1402811
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F1911-8074%2F17%2F6%2F225%2Fpdf%3Bh%3Drepec%3Agam%3Ajjrfmx%3Av%3A17%3Ay%3A2024%3Ai%3A6%3Ap%3A225-%3Ad%3A1402811
featured: 2024-06-05
citations: unknown
topic: Asset Pricing & Factors
---


# Diversification: Not a Free Lunch

Not a Free Lunch: A study challenges the belief that diversification is risk-free, stating it can reduce returns if expected returns are estimated, based on historical returns of the S&P500 constituents.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F1911-8074%2F17%2F6%2F225%2Fpdf%3Bh%3Drepec%3Agam%3Ajjrfmx%3Av%3A17%3Ay%3A2024%3Ai%3A6%3Ap%3A225-%3Ad%3A1402811
- Identifier: RePEc:gam:jjrfmx:v:17:y:2024:i:6:p:225-:d:1402811
- Released: 2024-06-05
- First featured: Quant Letter No. 52 (2024-06-05): https://www.ml-quant.com/issues/2024-06-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors

## Related

- [Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series](https://www.ml-quant.com/papers/doi/10-1016-j-jocs-2024-102348/): The research proposes a method for estimating high-dimensional covariance matrices in latent factor models by clustering residual series, focusing on the idiosyncratic component.
- [When can weak latent factors be statistically inferred?](https://www.ml-quant.com/papers/arxiv/2407.03616/): The article introduces a new theory for principal component analysis (PCA) under the weak factor model. This theory accounts for cross-sectional dependent components and provides finite-sample characterizations for estimation error and statistical inference uncertainty level, improving upon previous research.
- [Macro Strikes Back: Term Structure of Risk Premia and Market Segmentation](https://www.ml-quant.com/papers/ssrn/4752696/): A unified framework reveals that most macroeconomic factors have increasing unconditional term structures with large risk premia at business cycle horizons.
- [In Search of the True Greenium](https://www.ml-quant.com/papers/ssrn/4744608/): The study introduces a robust green score and expected returns to calculate the greenium, the expected return of green securities compared to brown, which is found to be more negative in greener countries and over time.
- [Commodity Futures Characteristics and Asset Pricing Models](https://www.ml-quant.com/papers/ssrn/4746258/): The article shows that a latent-factor model using the Instrumented Principal Component Analysis methodology surpasses existing models in explaining variations in commodity futures returns, with momentum, expected shortfall, and idiosyncratic volatility as key factors.
- [NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks](https://www.ml-quant.com/papers/arxiv/2409.03024/): A Synthetic Mobility Dataset: The paper presents NUMOSIM, a synthetic mobility dataset for testing anomaly detection techniques, simulating realistic mobility scenarios and anomalies to improve geospatial mobility analysis.