Anureet Saxena

Anureet Saxena is currently in the process of setting up his own firm focused on combining fundamental and quantitative approaches to investing. Most recently Anureet was a senior portfolio manager on the Systematic Strategies Team for Columbia Threadneedle Investments. Previously, he was Director of Quantitative Research at McKinley Capital Management and member of the Scientific Advisory Board for over six years. Prior to joining McKinley Capital Management, Anureet was a Portfolio Manager at Lazard Asset Management, and held various positions at Allianz Global Investors, Qontigo (formerly Axioma Inc), and Assiduous Investment.

Anureet has taught in the Quantitative and Computational Finance (QCF) program at Georgia Institute of Technology and Quantitative Methods program at Krannert School of Management, Anureet is the winner of 2004 Egon Balas best paper award, 2008 Gerald L. Thompson dissertation award and 2014 excellence in Economics scholarship by Krannert School of Management. Anureet earned his B.Tech in Computer Science and Engineering from IIT Bombay, M.S and Ph.D in Management Science from Tepper School of Business (Carnegie Mellon University), M.S in Economics from Krannert School of Management (Purdue University), and a certificate in Accounting from UC Berkeley (extension program). Anureet is a CFA, CIPM and CQF charter holder.

Title of the Talk:

Factor Alignment Problems in Quantitative Portfolio Construction

Abstract of Talk:

Construction of optimized portfolios entails a complex interaction between three key entities, namely, the risk factors, the alpha factors, and the constraints. The problems that arise due to mutual misalignment between these three entities are collectively referred to as Factor Alignment Problems (FAP). Examples of FAP include risk underestimation of optimized portfolios, undesirable exposures to factors with hidden and unaccounted systematic risk, consistent failure in achieving ex-ante performance targets, and inability to harvest high quality alphas into above-average IR. This talk presents a detailed investigation of FAP culminating with a practical and commercially viable solution in the form of augmented risk models.