---
title: Time-Series Predictability in Portfolio Management
url: https://www.ml-quant.com/papers/ssrn/4492826/
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 4492826
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4492826
featured: 2023-07-05
citations: unknown
topic: Portfolio & Allocation
---


# Time-Series Predictability in Portfolio Management

Factor investing benefits from timeseries predictability, with managed market portfolios generating strong alphas.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4492826
- Identifier: SSRN 4492826
- Released: 2022-03-12
- First featured: Quant Letter No. 6 (2023-07-05): https://www.ml-quant.com/issues/2023-07-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [On Unified Adaptive Black-Litterman Mean-Variance Portfolio Management](https://www.ml-quant.com/papers/arxiv/2307.03391/): The paper presents a new adaptive portfolio management framework that merges dynamic Black-Litterman optimization with the general factor model and Elastic Net regression, showing computational benefits and promising trading results.
- [Factor-based Portfolio Optimization with Forward Returns](https://www.ml-quant.com/papers/repec/eee-ecolet-v-228-y-2023-i-c-s0165176523001623/): The research applies a factor model and machine learning to include forward-looking information in portfolio optimization, which reduces idiosyncratic noise and enhances out-of-sample performance.
- [VaR and ES Forecasting in Large Portfolios: A Dynamic Factor Model Approach](https://www.ml-quant.com/papers/repec/eee-ecosta-v-27-y-2023-i-c-p-1-15/): A Dynamic Factor Model Approach: The article introduces two superior methods for predicting and estimating Value-at-Risk (VaR) and Expected Shortfall (ES) in large portfolios.
- [Black-Litterman, Bayesian Shrinkage, and Factor Models in Portfolio Selection: You Can Have It All](https://www.ml-quant.com/papers/arxiv/2308.09264/): The paper introduces a Bayesian model that combines shrinkage estimation with view inclusion, applied to Fama-French approach factor models, outperforming simple and optimal portfolios based on sample estimators.
- [HighDimensional Portfolio Optimization with Tree-Structured Factor Model](https://www.ml-quant.com/papers/repec/eee-pacfin-v-81-y-2023-i-c-s0927538x23001774/): The paper proposes a new portfolio optimization method that uses multiple characteristic information to predict stock returns and risk exposures, demonstrating its effectiveness in achieving higher Sharpe ratios, smaller standard deviations, and lower turnover.
- [Smart Beta ETFs & Increased Flow Sensitivity to Multi-Factor Alphas](https://www.ml-quant.com/papers/ssrn/4620486/): Smart beta ETFs trading activity significantly impacts mutual fund flow sensitivity, especially in funds with high nonmarket risk factor exposure.
