1. Why Not Taxation and Representation? A Note on the American Revolution by Sebastian Galiani (University of Maryland – Department of Economics) and Gustavo Torrens (Indiana University)

“Why Not Taxation and Representation? A Note on the American Revolution” began a couple years ago, as our airport project. For personal and professional reasons, we were both traveling a lot. Somehow, between unintelligible boarding announcements, we started reading about colonial America and the American Revolution. (For us some of the most exciting research questions emerge from a dialogue between history and economic theory.) Eventually, two issues pop out from our readings. First, how little was written about the British perspective on the matter. Second, it was never clear to us why Britain and the American colonies were unable to reach an agreement that would have avoided war and independence. The paper grew as an attempt to solve this puzzle.

Our answer is simple. Granting American colonies representation in the British Parliament would have shifted the balance of power within Britain in favor of radical political reform. American representatives would have formed a coalition with the incipient democratic movement in England (they could not commit to a different course of action), which would have posed a serious threat to the position of the landed gentry. Fearful of this outcome, the British chose to go to war rather than grant parliamentary representation to the American elites.

We hope the paper triggers a new perspective on the American Revolution and, more importantly, on the way we understand independence processes. This is crucial to improve our understanding of the long run institutional paths followed by different countries after independence (the long shadow of colonial history on economic and political institutions). – Gustavo Torrens and Sebastian Galiani

2. A Brief Introduction to the Basics of Game Theory by Matthew O. Jackson (Stanford University – Department of Economics)

3. A Century of Evidence on Trend-Following Investing by Brian Hurst (AQR Capital Management, LLC) and Yao Hua Ooi (AQR Capital Management, LLC) and Lasse Pedersen (AQR Capital Management, LLC)

4. Battlefield Casualties and Ballot Box Defeat: Did the Bush-Obama Wars Cost Clinton the White House? by Douglas Kriner (Boston University – Department of Political Science) and Francis Shen (University of Minnesota Law School)

We have been writing about the causes and political consequences of inequality in military sacrifice for more than a decade. But the issue typically gets overlooked, and this was the case with analysis of the 2016 presidential election. Few commentators argue that antiwar sentiment was a key to Trump’s victory. But we think that’s a credible argument, and this paper lays out the data to support it.

As we write at the start of the paper: Imagine a country continuously at war for nearly two decades. Imagine that the wars were supported by both Democratic and Republican presidents. Continue to imagine that the country fighting these wars relied only on a small group of citizens—a group so small that those who served in theater constituted less than 1 percent of the nation’s population, while those who died or were wounded in battle comprised far less than 1/10th of 1 percent of the nation’s population.  And finally, imagine that these soldiers, their families, friends, and neighbors felt that their sacrifice and needs had long been ignored by politicians in Washington. Would voters in these hard hit communities get angry? And would they seize an opportunity to express that anger at both political parties? We think the answer is yes. And the proof is the 2016 victory of Donald J. Trump.

Trump’s campaign rhetoric was well-crafted to appeal to voters in communities that have borne the brunt of fifteen years of fighting.  He pledged both to rebuild the military, and to be more restrained in its use – to avoid the “stupid” wars of his predecessors.   Our analysis suggests that this gambit may have tilted the election.  Even controlling in a statistical model for many other alternative explanations, we find that there is a significant and meaningful relationship between a community’s rate of military sacrifice and its support for Trump. Our statistical model suggests that if three states key to Trump’s victory – Pennsylvania, Michigan, and Wisconsin – had suffered even a modestly lower casualty rate, all three could have flipped from red to blue and sent Hillary Clinton to the White House. We argue that politicians from both parties would do well to more directly recognize and address the needs of those communities whose young women and men are making the ultimate sacrifice for the country. -Francis Shen

5. Taking Corrections Literally But Not Seriously? The Effects of Information on Factual Beliefs and Candidate Favorability by Brendan Nyhan (Dartmouth College) and Ethan Porter (George Washington University) and Jason Reifler (University of Exeter) and Thomas Wood (Ohio State University (OSU))

This paper is the result of two research teams working together to further our understanding of the U.S. public’s willingness to accept factual corrections of their political leaders. Brendan and Jason have written about the risk that factual corrections will inspire “backfire” among survey respondents when the respondent shares the ideological affiliation of the corrected politician. By contrast, Tom and Ethan conducted a series of studies which found that people are typically willing to accept factual corrections even when the content of the correction is ideologically challenging. During the 2016 presidential campaign, the role of facts was the subject of intense debate, which inspired a joint research project asking the following questions: Would people accept factual corrections about statements made by their preferred candidate? Or would they reject empirical evidence, choosing loyalty to party and candidate instead? Finally, would they demonstrate factual backfire, where corrective information might make them systematically less informed?

To find out, we ran two experimental studies about two misleading statements made by then-candidate Donald Trump – his speech at the Republican National Convention suggesting crime was rising dramatically and a statement during the first debate suggesting major job losses in Michigan and Ohio. Encouragingly, we found that exposing people to corrective information after showing them one of these statements increased the accuracy of their beliefs on average even among Trump supporters. The corrections did not backfire. However, people did not change their minds about the candidates either. Supporters of President Trump didn’t become any less supportive of him even as they otherwise rejected his misstatements. Based on this paper, it seems that Americans are willing to accept factual corrections, but we shouldn’t expect those corrections to affect which candidates they support. – Ethan Porter

1. Availability Cascades and Risk Regulation by Timur Kuran (Duke University – Department of Economics) and Cass Sunstein (Harvard Law School)

This paper came about through serendipity: Timur Kuran was visiting at the University of Chicago, and I was a great admirer of his, and we became fast friends. We had a shared interest: the relationship among behavioral science, politics, belief formation, and law. We noticed that psychological work on the availability heuristic had not been brought into contact with social influences, and in particular with then-recent thinking about informational cascades and reputational cascades. Viola: availability cascades. Because I was especially interested in risk regulation, that became a natural for us.

In a way, we had no idea what was coming. The rise of the internet, and of social media, has shown the power of availability cascades, in ways that we could not have anticipated. Also, our focus on risk regulation disciplined the paper, which was good, but allowed us only to glimpse at potential applications, for example in religious and ethnic strife, in authoritarian regimes, in massive and abrupt social movements. We have planned, periodically, to do a book on all this (or perhaps two) – and we are hoping to gear up pretty soon. – Cass Sunstein

3. A Brief Introduction to the Basics of Game Theory by Matthew O. Jackson (Stanford University – Department of Economics)

4. Why Do Some Terrorist Attacks Receive More Media Attention Than Others? by Erin Kearns (Georgia State University – College of Arts and Sciences) and Allison Betus (Georgia State University) and Anthony Lemieux (Georgia State University – Global Studies Institute)

Terrorist attacks often dominate news coverage as reporters seek to provide the public with information about the event, its perpetrators, and the victims. Yet, not all incidents receive equal attention. Why do some terrorist attacks get extensive media coverage, while others get less or even no coverage? Building from previous research on biases in entertainment and news media, we expect that the perpetrator’s social identity is a driving factor behind this discrepancy. Drawing from our own research and that of colleagues, we also expect that media coverage would be influenced by the type of target and the number of fatalities. We also expect that perpetrators who are arrested will receive more coverage, particularly as their case moves through the criminal justice system.

To test our argument, we looked at media coverage for all terror attacks within the United States between 2006 and 2015 (this has been updated since the original working paper was posted on SSRN). The Global Terrorism Database (GTD) provides systematic, unbiased coding of terrorism around the world from 1970 to 2015. We draw from the GTD’s coding to identify 136 terrorism incidents that meet their definition and thus should be reported on as such in the media. Media coverage came from two sources: LexisNexis and CNN.com. We limited coverage to US-based sources between the date of the attack and the end of 2016. To be included in our dataset, the primary focus of the article had to be the event, the perpetrator(s), or the victim(s). We identified 3,541 articles on the 136 events.

Using negative binomial regression, we estimated models to test our argument and compared it to a number of counterarguments. We found that Muslim perpetrators receive over 350% more coverage, even when controlling for target type, fatalities, and being arrested. In terms of relative effects, a non-Muslim would have to kill about seven more people than a Muslim to receive the same degree of coverage on average. The Boston Bombing and the Fort Hood shooting comprised nearly 25% of all media coverage in our dataset, which raises concerns that these incidents are driving our results. We estimated models without these cases and the results were approximately the same, yet the impact of a Muslim perpetrator was actually a bit larger. Our findings were robust against additional counterarguments including the attack: occurring near a significant date, targeting Muslims or minorities more broadly, having an unknown perpetrator and group, accounting for casualties, and not meeting all criteria of the GTD’s definition. In sum, the media disproportionately covers attacks when the perpetrator is Muslim. This may have important implications for how Americans think and feel about both terrorism threats and the Muslim community. To better understand media coverage of terrorism, we are building from the current project to explore factors that impact whether or not “terrorism” and “terrorist” are used to describe incidents and their perpetrators.  – Allison Betus

5. Historical Returns of the Market Portfolio by Ronald Doeswijk (Independent) and Trevin Lam (Rabobank) and Laurens Swinkels (Erasmus University Rotterdam (EUR))

This study maps 56 years of annual returns for the GMP by composing a new and unique dataset that basically comprises all assets in which financial investors have invested. Our sample from 1960 through 2015 covers an inflationary and a disinflationary environment, seven recessions as well as several crises. Composing this unique dataset also involved hard core data collection from dusty, now yellow, OECD books in the basement of the library of Erasmus University in Rotterdam. These books are invaluable since the data have not been processed digitally. Only by flipping through the pages of the books, shooting pictures of the relevant pages and processing these data manually, we were able to produce the results in this paper.

We believe this study to be valuable for several reasons. First, from a theoretical point of view, by combining the risk-free asset with the optimal portfolio on the efficient frontier we are able to construct the Capital Allocation Line. Every possible portfolio on this line has an ex-ante superior risk-adjusted expected return compared to other portfolios on the efficient frontier. Second, we aim to estimate the historical returns of the invested market portfolio as closely as possible, and therefore one could see our return series as a benchmark return for the multi-asset investor. Third, this new data enables an extensive analysis of return and risk characteristics of the global market portfolio and the asset categories over the period 1960 to 2015.

Some key findings in our study are that the global market portfolio realizes a compounded real return of 4.38% with a standard deviation of 11.6% in our sample period. Next, the reward for the average investor is a compounded return of 3.24%-points above the saver’s. Finally, we show that with simple alternative heuristic allocation schemes it is possible to achieve a better reward for risk, in particular for downward risk. We conclude that all investors together did a reasonable job in determining the global market portfolio, but there is room for improvement.

We are looking forward to conduct more research on strategic allocation in the future to add valuable studies to the academic literature! – Trevin Lam

1. Is it Ethical to Teach that Beta and CAPM Explain Something? by Pablo Fernandez (University of Navarra – IESE Business School)

My answer to the question in the title is NO. It is crystal clear that CAPM and its Betas do not explain anything about expected or required returns. There are mountains of evidence to support my stance.

If, for any reason, a person teaches that Beta and CAPM explain something and he knows that they do not explain anything, such a person is lying. To lie is not ethical. If the person “believes” that Beta and CAPM explain something, his “belief” is due to ignorance (he has not studied enough, he has not done enough calculations, he just repeats what he heard to others…). For a professor, it is not ethical to teach about a subject that he does not know enough about. – Pablo Fernández

2. Lazy Prices by Lauren Cohen (Harvard Business School) and Christopher Malloy (Harvard Business School) and Quoc Nguyen (University of Illinois at Chicago – Department of Finance)

Big Picture

Taking a step back, we provide evidence on the power of defaults and the implications of inertia in decision-making.  We show how these touch even the most watched and poured-over markets in the world. Taking a twist on monotony and boilerplates – by focusing on deviations from default behavior – we can see how powerful these “breaks” or “deviations” from the norm can be for conveying valuable future information. In an entirely non-experimental setting, across thousands of firms and almost 20 years of data, breaks from default behavior have large implications for corporations, and asset prices more generally. Given the pervasiveness of inertia in human behavior across settings, the implications of breaks from default behaviors in the corporate, financial, and political settings provide a critical, yet underappreciated area, in both finance and economics. –  Lauren Cohen

3. CDS Rate Construction Methods by Machine Learning Techniques by Raymond Brummelhuis (University of Reims Champagne-Ardenne) and Zhongmin Luo (Standard Chartered Bank, London)

If you are intrigued by AlphaGo, driverless cars, robots, you might want to know a bit more about a rapidly evolving area called Machine Learning, an area destined to change our lives in many areas. If you have interest in finance industry, you might want to know one major challenge (referred to as Shortage of Liquidity problem in the paper) faced by banks today in their efforts to improve their Counterparty Credit Risk measurement and management, a problematic area highlighted by the downfall of Lehman during the financial crisis in 2008. Our paper presents 8 most popular classifiers in Machine Learning, each illustrated by real-world example, can be a great guide for you. Meanwhile, we take an interdisciplinary approach by applying Machine Learning techniques to solve the Shortage of Liquidity challenge faced by finance industry. In derivative pricing and risk management, it is often necessary to measure the default risk of counterparties based on CDS quotes for the counterparties. Frustratingly, you will not find liquid CDS quotes for most of the counterparties in the CDS market. According to European Banking Authority’s survey for major European banks, over 75% of their counterparties do not have liquid quotes for CDS; therefore, banks have to come up with so-called CDS proxies in derivative pricing and risk management. Numerous literature published on derivative pricing, risk management and XVA (a collective term for Value Adjustments such as CVA, FVA, etc.) have conveniently assumed the existence of liquid CDS quotes and few have directly addressed the real-world predicament that confronts the finance industry in this area.

The paper, firstly, shows that two existing widely used CDS Proxy methods ignore counterparty specific default risk and potentially introduce arbitrage opportunity. Secondly, we introduce and apply well-established Machine Learning Techniques, specifically, Classification Method to provide arbitrage-free solution to the predicament discussed above. Thirdly, we systematically compare the performances of 156 classifiers out of 8 most popular classification families exclusively based on financial market data to confirm and contrast some of the findings of existing literature; when properly parameterized, we show top three classifiers (SVM, Ensemble and Neural Network) produce highly accurate classification results based on k-fold Cross Validation. Fourthly, we investigate the impacts of high correlations between financial feature variables on classification accuracies. As future direction for research, we highlight in the paper that the Machine Learning techniques introduced in the paper can be applied to proxy other financial market variables in derivative pricing and risk management as well as private equity investments. Being excited about the interests shown by our readers, we plan to continue with the research by applying Machine Learning techniques to solve practical problems in real financial world. – Zhongmin Luo

4. A Brief Introduction to the Basics of Game Theory by Matthew O. Jackson (Stanford University – Department of Economics)

5. Historical Returns of the Market Portfolio by Ronald Doeswijk (Independent) and Trevin Lam (Rabobank) and Laurens Swinkels (Erasmus University Rotterdam (EUR))

This study is the first that documents returns for the global market portfolio for a decades long period that contains various market conditions. To illustrate, our sample from 1960 through 2015 covers an inflationary and a disinflationary environment, seven recessions as well as several crises. Our global market portfolio basically contains all assets in which financial investors have invested. Composing this unique dataset also involved hard core data collection from dusty, now yellow, OECD books in the basement of the library of Erasmus University in Rotterdam. These books are invaluable since the data have not been processed digitally. Only by flipping through the pages of the books, shooting pictures of the relevant pages and processing these data manually, we were able to produce the results in this paper.

We believe this study to be valuable for several reasons. First, from a theoretical point of view, by combining the risk-free asset with the optimal portfolio on the efficient frontier we are able to construct the Capital Allocation Line. Every possible portfolio on this line has an ex-ante superior risk-adjusted expected return compared to other portfolios on the efficient frontier. Second, we aim to estimate the historical returns of the invested market portfolio as closely as possible, and therefore one could see our return series as a benchmark return for the multi-asset investor. Third, this new data enables an extensive analysis of return and risk characteristics of the global market portfolio and the asset categories over the period 1960 to 2015.

Some key findings in our study are that the global market portfolio realizes a compounded real return of 4.38% with a standard deviation of 11.6% in our sample period. Next, the reward for the average investor is a compounded return of 3.24%-points above the saver’s. Finally, we show that with simple alternative heuristic allocation schemes it is possible to achieve a better reward for risk, in particular for downward risk. We conclude that all investors together did a reasonable job in determining the global market portfolio, but there is room for improvement.

We are looking forward to conducting more research on strategic allocation in the future to add valuable studies to the academic literature! – Trevin Lam

1. Manipulation in the VIX? by John Griffin (University of Texas at Austin – Department of Finance) and Amin Shams (University of Texas at Austin – Department of Finance )

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. Putting Integrity Into Finance: A Purely Positive Approach by Werner Erhard (Independent) and Michael Jensen (SSRN)

3. Contabilidad: qué Dice y qué no Dice (Accounting: What it is and What it is Not) by Pablo Fernandez (University of Navarra – IESE Business School)

The most common problems of interpretation of accounting figures come from ignoring that “every accounting figure (except for the year) is an opinion, NOT a fact”. – Pablo Fernández

4. A Brief Introduction to the Basics of Game Theory by Matthew O. Jackson (Stanford University – Department of Economics)

5. Squaring Venture Capital Valuations with Reality by Will Gornall (University of British Columbia (UBC) – Sauder School of Business) and Ilya Strebulaev (Stanford University – Graduate School of Business)

1. Defense Against the Dark Arts of Copyright Trolling by Matthew Sag (Loyola University Chicago School of Law) and Jake Haskell (Independent)

Lawsuits targeting anonymous defendants accused of illegal file sharing have transformed the copyright litigation landscape over the past few years. The practice is called copyright trolling because of the opportunistic way plaintiffs exploit information asymmetries, the high cost of federal court litigation, and the extravagant threat of statutory damages for copyright infringement to leverage settlements from the guilty and the innocent alike. I have written about various aspects of this phenomenon in previous articles, but in Defense Against the Dark Arts of Copyright Trolling my coauthor and I set out to do something more immediately helpful.

Many ordinary working people are coerced into settling meritless or mistargeted copyright trolling suits because so few lawyers are willing or able to take on these cases. Jake Haskell (a former student of mine at Loyola University Chicago) and I thought that we could level the playing field by exposing the tactics and weaknesses of the typical copyright troll. We wrote this article as a public service and we were unsure whether anyone would want to publish it at all. We certainly didn’t expect 4000 people to download it! We are grateful for the interest that so many lawyers, tech bloggers and others have expressed in this project.  – Matthew Sag

Matt Sag was my Copyright professor when I was in school, and he’s been interested in copyright trolling for a while. He got me interested in the subject, and I worked with him on the paper after I passed the bar. We both saw this as a social justice issue. Many of the people affected by the sort of predatory litigation trends we describe are low-income, unfamiliar or wary of the courts, or otherwise vulnerable.

We were very much hoping to be able to cut through most of the technical goop that permeates file-sharing cases and touch the heart of the issue, so that even attorneys unfamiliar with copyright law and intimidated by internet jargon could help. Matt wanted to show a clear roadmap of the the courts’ position on the issues, and I wanted to work on countering the troll’s typical arguments.

We are stoked to see how many people are reading this article! We hope it can be useful.  – Jake Haskell 

2. Do Stocks Outperform Treasury Bills? by Hendrik Bessembinder (Arizona State University)

3. Medieval and Early Modern Coinage and Its Problems by Meir Kohn (Dartmouth College – Department of Economics)

4. ‘Rape-Adjacent’: Imagining Legal Responses to Nonconsensual Condom Removal by Alexandra Brodsky (Yale Law School)

5. Presidents Lack the Authority to Abolish or Diminish National Monuments by Mark Squillace (University of Colorado Law School) and Eric Biber (University of California, Berkeley – School of Law) and Nicholas Bryner (Emmett Institute on Climate Change and the Environment, UCLA School of Law) and Sean Hecht (University of California, Los Angeles (UCLA) – School of Law)