This paper advances a new technique for identifying, delineating, and analyzing microgeographies. It applies this technique to locate and measure agglomerations of high-growth, high-tech (HGHT) startup activity within 205 U.S. cities. Using data from 1995 to 2018 on venture-backed companies, I...

A hierarchical clustering based asset allocation method, which uses graph theory and machine learning techniques, is proposed. Hierarchical clustering refers to the formation of a recursive clustering, suggested by the data, not defined a priori. Several hierarchical clustering methods are...

This study investigates the price movement characteristics of banking issuers listed on the Indonesia Stock Exchange with macroeconomic indicators as an exogenous variable. By using the k-means clustering based on the monthly rate of return, banks are classified into three clusters, lower,...

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in http://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit mutation clustering structure. Our results are in-sample...

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in http://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer...