1. Do Alpha Males Deliver Alpha? Testosterone and Hedge Funds by Yan Lu (University of Central Florida-Department of Finance) and Melvyn Teo (Singapore Management University – Lee Kong Chian School of Business)

2. The Games They Will Play: Tax Games, Roadblocks, and Glitches Under the New Legislation by Reuven S. Avi-Yonah (University of Michigan Law School), Lily L. Batchelder (New York University School of Law), J. Clifton Fleming Jr. (Brigham Young University – J. Reuben Clark Law School), David Gamage (Indiana University Maurer School of Law), Ari D. Glogower (Ohio State University (OSU) – Michael E. Moritz College of Law), Daniel Jacob Hemel (University of Chicago – Law School), David Kamin (New York University School of Law), Mitchell Kane (New York University (NYU)), Rebecca M. Kysar (Brooklyn Law School; Fordham University School of Law), David S. Miller (Proskauer Rose LLP), Darien Shanske (University of California, Davis – School of Law), Daniel Shaviro (New York University School of Law) and Manoj Viswanathan (University of California Hastings College of the Law)

3. Blockchain Technology: Principles and Applications by Marc Pilkington (Université Bourgogne Franche Comté)

4. The Games They Will Play: An Update on the Conference Committee Tax Bill by Reuven S. Avi-Yonah (University of Michigan Law School), Lily L. Batchelder (New York University School of Law), J. Clifton Fleming Jr. (Brigham Young University – J. Reuben Clark Law School), David Gamage (Indiana University Maurer School of Law), Ari D. Glogower (Ohio State University (OSU) – Michael E. Moritz College of Law), Daniel Jacob Hemel (University of Chicago – Law School), David Kamin (New York University School of Law), Mitchell Kane (New York University (NYU)), Rebecca M. Kysar (Brooklyn Law School; Fordham University School of Law), David S. Miller (Proskauer Rose LLP), Darien Shanske (University of California, Davis – School of Law), Daniel Shaviro (New York University School of Law) and Manoj Viswanathan (University of California Hastings College of the Law)

5. Advances in Financial Machine Learning (Chapter 1) by Marcos Lopez de Prado (Lawrence Berkeley National Laboratory)

The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent to readers of this SSRN article. Over the past two decades, I have seen many faces come and go, firms started and shut down. I have interviewed dozens of candidates from many failed machine learning funds. I wanted to collect, catalogue and explain some of the errors that led to the demise of those funds. – Marcos López de Prado