Combining Alphas via Bounded Regression
We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (we...
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MDPI AG
2015-11-01
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| Series: | Risks |
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| Online Access: | http://www.mdpi.com/2227-9091/3/4/474 |
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doaj-2a6cd43d0fb4416db0c92b258589499f2020-11-25T00:23:35ZengMDPI AGRisks2227-90912015-11-013447449010.3390/risks3040474risks3040474Combining Alphas via Bounded RegressionZura Kakushadze0Quantigicr Solutions LLC, 1127 High Ridge Road #135, Stamford, CT 06905, USAWe give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.http://www.mdpi.com/2227-9091/3/4/474hedge fundalpha streamalpha weightsportfolio turnoverinvestment allocationweighted regressiondiversificationboundsoptimizationfactor models |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Zura Kakushadze |
| spellingShingle |
Zura Kakushadze Combining Alphas via Bounded Regression Risks hedge fund alpha stream alpha weights portfolio turnover investment allocation weighted regression diversification bounds optimization factor models |
| author_facet |
Zura Kakushadze |
| author_sort |
Zura Kakushadze |
| title |
Combining Alphas via Bounded Regression |
| title_short |
Combining Alphas via Bounded Regression |
| title_full |
Combining Alphas via Bounded Regression |
| title_fullStr |
Combining Alphas via Bounded Regression |
| title_full_unstemmed |
Combining Alphas via Bounded Regression |
| title_sort |
combining alphas via bounded regression |
| publisher |
MDPI AG |
| series |
Risks |
| issn |
2227-9091 |
| publishDate |
2015-11-01 |
| description |
We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples. |
| topic |
hedge fund alpha stream alpha weights portfolio turnover investment allocation weighted regression diversification bounds optimization factor models |
| url |
http://www.mdpi.com/2227-9091/3/4/474 |
| work_keys_str_mv |
AT zurakakushadze combiningalphasviaboundedregression |
| _version_ |
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