Be Wary of Black-Box Trading Algorithms

Black-box algorithms now account for nearly a third of all U. S. stock trades. It is a mistake to think that these algorithms possess superhuman intelligence. In reality, computers do not have the common sense and wisdom that humans have accumulated by living. Trading algorithms are particularly dangerous because they are so efficient at discovering statistical patterns—but so utterly useless in judging whether the discovered patterns are meaningful

It is Time to Kill the Economic Theory of Suicide

A seminal paper by Hamermesh and Soss modeled suicide as a rational economic decision based on a comparison of the financial costs and benefits of staying alive. This study investigates the theoretical and empirical validity of that model

It is Time to Kill the Economic Theory of Suicide

A seminal paper by Hamermesh and Soss modeled suicide as a rational economic decision based on a comparison of the financial costs and benefits of staying alive. Their model is fundamentally flawed and their prediction that suicide rates increase with age is wrong

The Paradox of Big Data

Data-mining is often used to discover patterns in Big Data. It is tempting believe that because an unearthed pattern is unusual it must be meaningful, but patterns are inevitable in Big Data and usually meaningless. The paradox of Big Data is that data mining is most seductive when there are a large number of variables, but a large number of variables exacerbates the perils of data mining

The Chinese Real Estate Bubble

China has seen extraordinary economic growth for the past two decades, coupled with a booming housing market. Following the 2008 financial crisis, however, observers began worrying that the Chinese real estate market had been gripped by a speculative bubble. We use residential rent and price data to assess whether these fears are justified. We conclude that residential real estate markets are bubbly in Beijing and Shanghai, with the Beijing housing market frothier than the Shanghai market

Rapid detection and quantification of features such as damage or flaws in composite and metallic structures

An apparatus, system, and method for non-destructible evaluation (NDE) of a material use thermography to rapidly detect and/or generally locate a feature such as, for example, damage or a defect in the material. The apparatus, system, and method also use ultrasound to specifically locate the feature in the material for quantification and/or evaluation either by an operator or by an external device suited for such purpose. Accordingly, the apparatus, system and method are particularly useful for NDE in applications such as the analysis of the structure of an aircraft, for example, in which the scale of the material to be analyzed is large, thus requiring the rapid NDE afforded by thermography, and in which quantification and/or evaluation of a feature must be performed with precision, thus requiring the relatively high-resolution NDE afforded by ultrasound

College Football: Doing Less With More and More With Less

There is a substantial and highly significant correlation between the performance of widely followed college football teams and the pre-college recruiting scores received by their players. This correlation implies a regression toward the mean that should be taken into account in the identification of under-performing and over-performing teams and can also be used to improve pre-season predictions of the performance of teams with highly rated and lowly rated recruits

The Name Game: The Importance of Resourcefulness, Ruses, and Recall in Stock Ticker Symbols

Previous research reported that a portfolio of stocks with clever ticker symbols outperformed the overall market by a significant margin during the years 1984 to 2005. This paper reports the performance of those stocks during the subsequent years 2006 to 2018, and also investigates the 2006-2008 performance of a new set of clever-ticker stocks. Both clever-ticker portfolios beat the market by a substantial margin, supporting the resiliency of the clever-ticker phenomenon

A efficient and practical 3D face scanner using near infrared and visible photometric stereo

AbstractThis paper is concerned with the acquisition of model data for automatic 3D face recognition applications. As 3D methods become progressively more popular in face recognition research, the need for fast and accurate data capture has become crucial. This paper is motivated by this need and offers three primary contributions. Firstly, the paper demonstrates that four-source photometric stereo offers a potential means for data capture that is computationally nd financially viable and easily deployable in commercial settings. We have shown that both visible light and less ntrusive near infrared light is suitable for facial illumination. The second contribution is a detailed set of experimental esults that compare the accuracy of the device to ground truth, which was captured using a commercial projected pattern range finder. Importantly, we show that not only is near infrared light a valid alternative to the more commonly xploited visible light, but that it actually gives more accurate reconstructions. Finally, we assess the validity of the Lambertian assumption on skin reflectance data and show that better results may be obtained by incorporating more dvanced reflectance functions, such as the Oren–Nayar model