---
title: Portfolio Construction: Low Risk High Variability
url: https://www.ml-quant.com/papers/ssrn/5105457/
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 5105457
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5105457
featured: 2025-02-05
citations: unknown
topic: Portfolio & Allocation
---


# Portfolio Construction: Low Risk High Variability

Low Risk High Variability: Research indicates that stocks with less volatility yield higher returns, with portfolio construction and transaction costs significantly impacting low-risk portfolio performance.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5105457
- Identifier: SSRN 5105457
- Released: 2025-01-21
- First featured: Quant Letter No. 84 (2025-02-05): https://www.ml-quant.com/issues/2025-02-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [Portfolio Optimization under Transaction Costs with Recursive Preferences](https://www.ml-quant.com/papers/arxiv/2402.08387/): The Merton investment-consumption problem is expanded to incorporate transaction costs and stochastic differential utility, using new math techniques to understand all parameter combinations and previously difficult aspects.
- [Adaptive Online Portfolio Selection](https://www.ml-quant.com/papers/repec/eee-ejores-v-321-y-2025-i-1-p-214-230/): The article introduces a new online portfolio selection strategy that considers transaction costs and uses an adaptive scheme for sequential parameter decision, yielding higher cumulative returns and competitive Sharpe ratios than existing strategies.
- [Adaptive Online Portfolio Selection with Transaction Costs](https://www.ml-quant.com/papers/repec/taf-quantf-v-24-y-2023-i-1-p-59-82/): The research proposes a new algorithm for online portfolio selection that improves return prediction accuracy by considering peer impact.
- [Optimizing Investment Strategies with Lazy Factor and Probability Weighting: A Price Portfolio Forecasting and Mean-Variance Model with Transaction Costs Approach](https://www.ml-quant.com/papers/arxiv/2306.07928/): A new investment strategy model has been developed and tested on a dataset.
- [A Cholesky decomposition-based asset selection heuristic for sparse tangent portfolio optimization](https://www.ml-quant.com/papers/arxiv/2502.11701/): A new asset selection method for mean-variance portfolios has been proposed, allowing for quicker optimization and construction of portfolios with fewer assets.
- [Multi-hypothesis prediction for portfolio optimization: A structured ensemble learning approach to risk diversification](https://www.ml-quant.com/papers/arxiv/2501.03919/): The paper introduces a framework for portfolio allocation that uses multiple hypotheses prediction through structured ensemble models, allowing for control of portfolio diversification before decision-making.