I am excited about research that is practical and helps us understand the way our financial system work, and maybe make them better. I typically do research on things that are potentially, illegal, illicit, or immoral in financial markets; topics in forensic finance. Although there have credible allegations and evidence that LIBOR, FX, swaps, gold, silver, ect., seem to have been gamed, there is surprisingly extremely little academic work in these fields, perhaps because academics like to work in areas where others are working. But, the gaming of financial markets through financial sophistry is financial thievery and quite harmful to the trust that our financial system depends on. Amin Shams and I became interested in the settlement process in this market after further study, because it had features that make it have the potential to be gamed. Nevertheless, I was skeptical, but the more we researched the market and the more data we received, the more interesting it appeared.
I think academics can help shed light on markets features that allow gaming and those that do not. A credible and robust non-result can also be interesting and I have published work showing no questionable activity as well. The downloads are interesting because some academics told me that they didn’t find the paper very interesting, perhaps because it was too applied. I think financial research should have applications and not just be useful for ivory-tower lunch discussions. The liveliest and heated ivory-tower lunch discussions I recall were always on applied topics. I would like to encourage young researchers to not just write papers to try to get tenure, but to pick areas they are passionate about and where one can, at least potentially, make a small difference. -John M. Griffin
2. What is Program Evaluation? A Beginners Guide (Presentation Slides) by Gene Shackman (The Global Social Change Research Project)
I am hoping this guide may be useful to anyone who wants to know about the very basic ideas and methods of evaluation. Evaluation can be useful, but only if people understand it. I am hoping this will help clients, potential clients, funders, stakeholders, and the public better understand evaluation and a little of how it works. That way, people can have a realistic idea of how it can be used, and how it cannot be used. – Gene Shackman
The paper offers no results, neither theoretical nor empirical. It takes a satirical approach to this important issue and it is meant to be thought-provoking. While everyone would agree that there are journals with higher standards than others, the problem is caused by the obsession of reducing the profession’s scientific evaluation of papers to a crude counting measure. The “true stories” described in the paper are unfortunately true, and we should reflect whether these practices are conducive to our ultimate goal, which is the production of better research. I hope you will enjoy reading “Top5itis.” Thanks for your attention. – Roberto Serrano
4. 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)
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