Smart Money, Noise Trading and Stock Price Behavior

This paper derives and estimates an equilibrium model of stock price behavior in which exogenous "noise traders" interact with risk-averse "smart money" investors. The model assumes that changes in exponentially detrended dividends and prices are normally distributed, and that smart money investors have constant absolute risk aversion. In equilibrium, the stock price is the present value of expected dividends, discounted at the riskless interest rate, less a constant risk premium, plus a term which is due to noise trading. The model expresses both stock prices and dividends as sums of unobserved components in continuous time. The model is able to explain the volatility and predictability of U.S. stock returns in the period 1871-1986 in either of two ways. Either the discount rate is 4% or below, and the constant risk premium is large; or the discount rate is 5% or above, and noise trading, correlated with fundamentals, increases the volatility of stock prices. The data are not well able to distinguish between these explanations.

The flash crash: The impact of high frequency trading on an electronic market

The Flash Crash, a brief period of extreme market volatility on May 6, 2010, raised questions about the current structure of the U.S. financial markets. We use audit-trail data to describe the structure of the E-mini S&P 500 stock index futures market on May 6. We ask three questions. How did High Frequency Traders (HFTs) trade on May 6? What may have triggered the Flash Crash? What role did HFTs play in the Flash Crash? We conclude that HFTs did not trigger the Flash Crash, but their responses to the unusually large selling pressure on that day exacerbated market volatility

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

Lithic technological responses to Late Pleistocene glacial cycling at Pinnacle Point Site 5-6, South Africa

There are multiple hypotheses for human responses to glacial cycling in the Late Pleistocene, including changes in population size, interconnectedness, and mobility. Lithic technological analysis informs us of human responses to environmental change because lithic assemblage characteristics are a reflection of raw material transport, reduction, and discard behaviors that depend on hunter-gatherer social and economic decisions. Pinnacle Point Site 5-6 (PP5-6), Western Cape, South Africa is an ideal locality for examining the influence of glacial cycling on early modern human behaviors because it preserves a long sequence spanning marine isotope stages (MIS) 5, 4, and 3 and is associated with robust records of paleoenvironmental change. The analysis presented here addresses the question, what, if any, lithic assemblage traits at PP5-6 represent changing behavioral responses to the MIS 5-4-3 interglacial-glacial cycle? It statistically evaluates changes in 93 traits with no a priori assumptions about which traits may significantly associate with MIS. In contrast to other studies that claim that there is little relationship between broad-scale patterns of climate change and lithic technology, we identified the following characteristics that are associated with MIS 4: increased use of quartz, increased evidence for outcrop sources of quartzite and silcrete, increased evidence for earlier stages of reduction in silcrete, evidence for increased flaking efficiency in all raw material types, and changes in tool types and function for silcrete. Based on these results, we suggest that foragers responded to MIS 4 glacial environmental conditions at PP5-6 with increased population or group sizes, 'place provisioning', longer and/or more intense site occupations, and decreased residential mobility. Several other traits, including silcrete frequency, do not exhibit an association with MIS. Backed pieces, once they appear in the PP5-6 record during MIS 4, persist through MIS 3. Changing paleoenvironments explain some, but not all temporal technological variability at PP5-6.Social Science and Humanities Research Council of Canada; NORAM; American-Scandinavian Foundation; Fundacao para a Ciencia e Tecnologia [SFRH/BPD/73598/2010]; IGERT [DGE 0801634]; Hyde Family Foundations; Institute of Human Origins; National Science Foundation [BCS-9912465, BCS-0130713, BCS-0524087, BCS-1138073]; John Templeton Foundation to the Institute of Human Origins at Arizona State Universit

Defining optimal soybean seeding rates and associated risk across North America

Soybean [Glycine max (L.) Merr.] seeding rate research across North America is typically conducted in small geo-political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment.Fil: Gaspar, Adam P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mourtzinis, Spyridon. University of Wisconsin; Estados UnidosFil: Kyle, Don. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Galdi, Eric. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Lindsey, Laura E.. Ohio State University; Estados UnidosFil: Hamman, William P.. Ohio State University; Estados UnidosFil: Matcham, Emma G. University of Wisconsin; Estados UnidosFil: Kandel, Hans J.. North Dakota State University; Estados UnidosFil: Schmitz, Peder. North Dakota State University; Estados UnidosFil: Stanley, Jordan D.. North Dakota State University; Estados UnidosFil: Schmidt, John P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mueller, Daren S.. University of Iowa; Estados UnidosFil: Nafziger, Emerson D.. University of Illinois; Estados UnidosFil: Ross, Jeremy. University of Arkansas for Medical Sciences; Estados UnidosFil: Carter, Paul R.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Varenhorst, Adam J.. University of South Dakota; Estados UnidosFil: Wise, Kiersten A.. University of Kentucky; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Kansas State University; Estados UnidosFil: Chilvers, Martin I.. Michigan State University; Estados UnidosFil: Hauswedell, Brady. University of South Dakota; Estados UnidosFil: Tenuta, Albert U.. University of Guelph; CanadáFil: Conley, Shawn P.. University of Wisconsin; Estados Unido