1. Common-Knowledge Attacks on Democracy by Henry Farrell (George Washington University – Department of Political Science) and Bruce Schneier (Harvard University – Berkman Klein Center for Internet & Society)
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 in forensic finance–on things that are potentially, illegal, illicit, or immoral in financial markets.
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.
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 a top academic told me that he didn’t find the topic academically interesting or worthwhile. The paper elicited very different views from other academics–some many were quite interested, but it depends on one’s views about academic research. I think financial research should have applications and not just be useful for ivory-tower lunch discussions. I would like to encourage researchers to not just write papers to try to get tenure, but to pick areas they are passionate about, where one can hope to truly understand our world, and at least potentially, make a small difference. – John M. Griffin
This paper addresses a simple but important question: Have voters been rewarded for supporting the winning candidate in the 2016 presidential election? In this election more than most, the winner seemed to appeal to voters’ perceived sense of loss with promises like “bring the jobs back.” I wondered whether those promises had been fulfilled, at least from an economic perspective. The answer, which is mostly negative thus far, is particularly poignant now that GM has announced plans to shutter large factories in those very counties that helped swing the election from blue to red.
Will this trend continue? Or will these regions “start winning again” in the long run? I intend to continue updating this paper as new data is released. As scholars, we still have much to learn about how economic growth is distributed geographically — and how that distribution affects political outcomes. As voters, we can use this knowledge to judge our leaders’ promises more accurately and hold them accountable. – Anthony Orlando
This is a 257-page preview version of a brand new book “151 Trading Strategies”, which provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation , global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students. The book is being published by Palgrave Macmillan, an imprint of Springer Nature.
I got the idea and was inspired to write this book following the success of my paper “101 Formulaic Alphas”, which provides explicit formulas – that are also computer source code – for 101 real-life quantitative trading signals (alphas). “151 Trading Strategies” takes this concept to the next level: instead of focusing on quant trading alphas or any particular asset class, it goes across essentially all asset classes and a number of trading styles, so it comes as no surprise that it took almost 9 months to write it. — Zura Kakushadze