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A Brief History

In practice, recent failures and crises in the world of academic research exposed deep and systematic problems in how scientific research is conducted and evaluated, and how academia operates in general. These problems should not only be the concern of academics. Government funding of academic-research institutions is large enough that any taxpayer should want academia to function properly so that his tax dollars are not wasted. More seriously still, academic research and higher education can have huge spillover benefits that make the proper functioning of academia a matter of national importance. The lack of reality checks in academia has led to a series of costly failures, and the future of academic science rests on reasserting the quality control that reality provides.

Long Term Capital Management ("LTCM")

Recall the four-letter word, LTCM, and the mathematically elegant Black-Scholes formula (taught in almost all finance courses). At the time, it was widely believed that this model cracked the code of financial markets. It wasn’t until a $3.6 billion-dollar bailout supervised by the Federal Reserve for many, including the Nobel Laureates who developed the model, to realize that complex system involving human behavior cannot be explained by mathematical formulas alone.

Black Monday Stock Market Crash

The critical factor behind the largest single-day stock market crash on October 19, 1987 (Black Monday) can be traced back to quantitative portfolio insurance models failing to account for low-frequency second-order effects. The models used for both portfolio and liability insurance didn’t account for contagion: the viciously reinforcing cycles that feed on themselves. Portfolio insurance algorithms selling futures when markets drop causes markets to fall further, which in turn requires more selling, more selling means markets go down further, and the cycle continues. Although tail risks like these have only increased because of the interconnectedness of everything in the information age, models inherently discount them. Tails and second-order effects need to be of primary focus.

The Gaussian Copula to Model Mortgages

Extrapolations by experts pose a substantial risk because it’s not possible to know in advance what models will generate the best results. Relying on naive extrapolations for building predictive models will eventually cause losses. Recall David Li, the modern-day “famous actuary” cited on Wikipedia. Li hit upon a predictive model that helped fuel the financial crisis of 2007–2008. Li had extrapolated that joint life distributions for dying spouses was well suited for modeling credit default swaps. It was Li’s predictive model that supplied a method for bundling many different swaps in a CDO that could output the tranche-level correlations. With just a bit more mathematical alchemy, securities priced using his predictive model on subprime CDOs were rolling off the assembly line with AAA ratings. Li’s model is not just an example of a failed model. It went much further, in fact, it fueled the growth of the market itself and its inevitable crash in spectacular fashion. Stretching predictive models to ever-increasing applications look very similar to Li’s naive application of the Gaussian copula formula to mortgages. Note that Gauss originally devised his formula to measure the motion of stars.

The Founding of IAAS (i.e. The Solution)

Finding truths in complex systems involving humans requires intuition, experiment design, and getting your hands dirty. The financial disasters that models will inevitably create cannot be managed by traditional means. Glorified models taught in school with serious defects have contributed to massive destruction. In the cases mentioned above, models aided and abetted putting our financial security systems at risk. Despite this, modeling marches forward in an over-hyped fashion unperturbed by real-life lessons of the past. We believe profound change is needed, and solutions laser-focused on addressing these risks should be developed. Acting otherwise is tantamount to declaring ourselves impervious to empirical real-world developments.

As much as we focus on the use of quantitative models, attention should also be paid to their dangers. Merely footnoting embedded unrealistic assumptions that have led to past financial disasters should not be ignored. Unrealistic assumptions and their historic track records should be explored explicitly. Additional exams learning new modeling techniques will not remedy the risks outlined above. So IAAS was formed to bridge this gap between academia and practice.

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