---
title: Common Advisor Effect in Allocations
url: https://www.ml-quant.com/papers/ssrn/4822200/
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 4822200
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4822200
featured: 2024-05-15
citations: unknown
topic: Portfolio & Allocation
---


# Common Advisor Effect in Allocations

The study reveals that pension funds with the same asset manager or actuary tend to make similar asset allocation decisions, which may not align with their unique characteristics or sophistication level.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4822200
- Identifier: SSRN 4822200
- Released: 2024-05-09
- First featured: Quant Letter No. 49 (2024-05-15): https://www.ml-quant.com/issues/2024-05-15/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [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.
- [Determinants of Bank Performance: Evidence from Replicating Portfolios](https://www.ml-quant.com/papers/ssrn/4822187/): A new bank performance metric reveals that structural issues like cost inefficiencies primarily cause underperformance, with high-performing banks being less dependent on government aid and more shock-resistant.
- [Bond Portfolio Optimization at Life Insurance Companies: Duration Spread Ratio Optimization vs. Mean-Variance Optimization](https://www.ml-quant.com/papers/ssrn/4825814/): The research compares the effects of integrating credit risk and interest rate risk in bond portfolio optimization with traditional risk measures, introducing a new approach called Duration Spread Ratio (DSR) optimization that outperforms in all scenarios.
- [Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management](https://www.ml-quant.com/papers/arxiv/2405.05449/): The paper presents KDD, a hybrid method combining portfolio theory and reinforcement learning for optimal investment portfolios, achieving high profitability with low risk.
- [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.
- [Single-stage Portfolio Optimization with Automated Machine Learning for M6](https://www.ml-quant.com/papers/ssrn/4836123/): The M6 forecasting competition paper introduces a data-driven approach that directly optimizes portfolio weights, achieving a 9.5 global rate of return and an information ratio of 5.045.
