Low-Risk Benchmarking Transcends Rebalancing Methods
| Autor: | Joseph D. Haley, Gregory N. Hight |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050208 finance
business.industry Strategy and Management 05 social sciences Diversification (finance) Regression analysis 030206 dentistry Benchmarking Portfolio construction 03 medical and health sciences Expected shortfall 0302 clinical medicine Minimum-variance unbiased estimator Management of Technology and Innovation 0502 economics and business Econometrics business Finance Modern portfolio theory Risk management Mathematics |
| Zdroj: | The Journal of Investing. 30:7-30 |
| ISSN: | 2168-8613 1068-0896 |
| DOI: | 10.3905/joi.2020.1.159 |
| Popis: | The dismal performance of managed investment and the success of equal allocation and minimum variance models prompt these questions: Can rebalancing driven by minimum variance and maximum diversification rebalancing outperform naive models? Can a minimum variance model produce higher risk-adjusted returns than a maximum diversification model when security selection favors low-correlated assets? This study uses expected shortfall to measure risk, bootstrapping to transform fat-tailed distributions so they are suitable for t-tests, and factor analysis to help explain the relative performance of the models. The minimum variance and maximum diversification models outperformed naive models, and the minimum variance models produced higher risk-adjusted returns than the maximum diversification model. Market factors adequately explained differences in returns between rebalancing models, but adding a factor for the effect of rebalancing procedures improved all models. All models constrained by the lower risk benchmark produced higher risk-adjusted returns than corresponding models constrained by the higher risk benchmark. This outcome suggests many rebalancing models—perhaps even return-based models—could produce superior risk-adjusted returns if lower risk benchmarks constrain risk. TOPICS:Portfolio theory, portfolio construction, risk management, factor-based models, exchange-traded funds and applications Key Findings ▪ The two minimum variance models produced statistically significant superior risk-adjusted returns. ▪ All models constrained by the lower risk benchmark produced higher risk-adjusted returns than corresponding models constrained by the higher risk benchmark. ▪ Although market factors explained differences in model returns, adjusted R2 values increased when regression models included a rebalancing factor. |
| Databáze: | OpenAIRE |
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