Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

Family Portfolio Entrepreneurship

This paper seeks to extend our understanding of the growing field of Portfolio Entrepreneurship, the simultaneous ownership and engagement in several business activities (Westhead & Wright 1998; Carter & Ram 2003). Portfolio entrepreneurship has been identified as an important factor in both new venture creation and the economic landscape in general (Rosa & Scott 1996; 1999). We follow Carter and Ram's (2003) call to explore portfolio entrepreneurship within the family context. Specifically we address the why (cause) and how (process) of family portfolio entrepreneurship through comparative qualitative cases.