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- Zura Kakushadze
- 2015

We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction is related to the largest eigenvalue and the corresponding eigenvector of the alpha… (More)

- Zura Kakushadze, Willie Yu
- 2016

We present a novel method for extracting cancer signatures by applying statistical risk models (http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an “overall” mode of somatic mutational noise. We give a prescription for factoring out this noise and source… (More)

We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: (1) longer horizon risk factors (value, growth, etc.)… (More)

- Zura Kakushadze, Willie Yu
- 2017

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix… (More)

- Zura Kakushadze
- J. Informetrics
- 2016

We propose a new index to quantify SSRN downloads. Unlike the SSRN downloads rank, which is based on the total number of an author’s SSRN downloads, our index also reflects the author’s productivity by taking into account the download numbers for the papers. Our index is inspired by – but is not the same as – Hirsch’s -index for citations, which cannot be… (More)

- Zura Kakushadze
- 2015

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.… (More)

We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis. We extrapolate to other cancer sites by using estimated… (More)

- Zura Kakushadze, Willie Yu
- Genes
- 2017

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389… (More)

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