---
title: Factor Investing: Market Implications
url: https://www.ml-quant.com/papers/ssrn/4679940/
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 4679940
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4679940
featured: 2024-01-03
citations: unknown
topic: Asset Pricing & Factors
---


# Factor Investing: Market Implications

Market Implications: Financial innovations like Exchange-Traded Funds and smart beta products, modeled as composite securities, simplify trading for investors and attract more factor investors.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4679940
- Identifier: SSRN 4679940
- Released: 2023-12-30
- First featured: Quant Letter No. 31 (2024-01-03): https://www.ml-quant.com/issues/2024-01-03/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors

## Related

- [Exploratory Control with Tsallis Entropy for Latent Factor Models](https://www.ml-quant.com/papers/arxiv/2211.07622/): The research uses Tsallis Entropy in models with latent factors to optimally control and explore the state space, proving that the optimal state distribution is q-Gaussian, which can be used in creating robust statistical arbitrage trading strategies.
- [No Sparsity in Asset Pricing: Evidence from a Generic Statistical Test](https://www.ml-quant.com/papers/ssrn/4730259/): The paper introduces a statistical test to identify sparsity in high-dimensional factor models, concluding that less than ten factors can explain stock returns and dense models perform better than sparse ones.
- [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.
- [Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models](https://www.ml-quant.com/papers/arxiv/2309.08800/): A new technique using dynamic time warping has been created to identify lead-lag relationships in multivariate time series systems, demonstrated in financial markets.
- [Power Sorting](https://www.ml-quant.com/papers/ssrn/4552208/): The article suggests a new method for creating characteristic-based equity factors called power sorting, showing its superior performance and applicability to multifactor strategies.
- [Alpha Now, Taxes Later: Tax-Efficient Long-Only Factor Investing](https://www.ml-quant.com/papers/ssrn/4538712/): Despite high turnover, factor investing can yield significant pre-tax and post-tax alphas, especially with value, quality, and safety buy-and-hold portfolios, making it a viable option for tax-aware investors.