A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks

We propose a nonparametric method for estimating the pricing formula of a derivative asset using learning networks. Although not a substitute for the more traditional arbitrage-based pricing formulas, network pricing formulas may be more accurate and computationally more efficient alternatives when the underlying asset's price dynamics are unknown, or when the pricing equation associated with no-arbitrage condition cannot be solved analytically. To assess the potential value of network pricing formulas, we simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis function networks, multilayer perceptron networks, and projection pursuit. To illustrate the practical relevance of our network pricing approach, we apply it to the pricing and delta-hedging of S&P 500 futures options from 1987 to 1991.

A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks

We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis functions, multilayer perceptrons, and projection pursuit. To illustrate practical relevance, we also apply our approach to S&P 500 futures options data from 1987 to 1991

Information Dissemination and Aggregation in Asset Markets with Simple Intelligent Traders

Various studies of asset markets have shown that traders are capable of learning and transmitting information through prices in many situations. In this paper we replace human traders with intelligent software agents in a series of simulated markets. Using these simple learning agents, we are able to replicate several features of the experiments with human subjects, regarding (1) dissemination of information from informed to uninformed traders, and (2) aggregation of information spread over different traders

Experimental Markets for Product Concepts

Market prices are well known to efficiently collect and aggregate diverse information regarding the value of commodities and assets. The role of markets has been particularly suitable to pricing financial securities. This article provides an alternative application of the pricing mechanism to marketing research - using pseudo-securities markets to measure preferences over new product concepts. Surveys, focus groups, concept tests and conjoint studies are methods traditionally used to measure individual and aggregate preferences. Unfortunately, these methods can be biased, costly and time-consuming to conduct. The present research is motivated by the desire to efficiently measure preferences and more accurately predict new product success, based on the efficiency and incentive-compatibility of security trading markets. The article describes a novel market research method, pro-vides insight into why the method should work, and compares the results of several trading experiments against other methodologies such as concept testing and conjoint analysis

Trainable, vision-based automated home cage behavioral phenotyping

We describe a fully trainable computer vision system enabling the automated analysis of complex mouse behaviors. Our system computes a sequence of feature descriptors for each video sequence and a classifier is used to learn a mapping from these features to behaviors of interest. We collected a very large manually annotated video database of mouse behaviors for training and testing the system. Our system performs on par with human scoring, as measured from the ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home cage behaviors of two standard inbred and two nonstandard mouse strains. From this data, we were able to predict the strain identity of individual mice with high accuracy.California Institute of Technology. Broad Fellows Program in Brain CircuitryNational Science Council of Taiwan (TMS-094-1-A032

Automated home-cage behavioral phenotyping of mice

We describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and a very large manually annotated video database used for training and testing the system. Our system outperforms leading commercial software and performs on par with human scoring, as measured from the ground-truth manual annotations of thousands of clips of freely behaving animals. We show that the home-cage behavior profiles provided by the system is sufficient to accurately predict the strain identity of individual animals in the case of two standard inbred and two non-standard mouse strains. Our software should complement existing sensor-based automated approaches and help develop an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of rodent behavior

Securities trading of concepts (STOC)

Identifying winning new product concepts can be a challenging process that requires insight into private consumer preferences. To measure consumer preferences for new product concepts, the authors apply a “securities trading of concepts,” or STOC, approach, in which new product concepts are traded as financial securities. The authors apply this method because market prices are known to efficiently collect and aggregate private information regarding the economic value of goods, services, and firms, particularly when trading financial securities. This research compares the STOC approach against stated-choice, conjoint, constant-sum, and longitudinal revealed-preference data. The authors also place STOC in the context of previous research on prediction markets and experimental economics. Across multiple product categories, the authors test whether STOC (1) is more cost efficient than other methods, (2) passes validity tests, (3) measures expectations of others, and (4) reveals individual preferences, not just those of the crowd. The results show that traders exhibit a self-preference bias when trading. Ultimately, STOC offers two key advantages over traditional market research methods: cost efficiency and scalability. For new product development teams deciding how to invest resources, this scalability may be especially important in the Web 2.0 world.United States. Office of Naval Research (Contract Number N00014-93-1-3085)National Science Foundation (U.S.). Information Technology Research (Contract Number IIS-0085836)National Science Foundation (U.S.). Knowledge and Distributed Intelligence Initiative (Contract Number DMS-9872936)National Science Foundation (U.S.) (Contract Number IIS-9800032)United States. Office of Naval Research (United States. Defense Advanced Research Projects Agency) (Contract Number N00014-00-1-0907

Demographic and clinical characteristics associated with glomerular filtration rates in living kidney donors

Due to the shortage of organs, living donor acceptance criteria are becoming less stringent. An accurate determination of the glomerular filtration rate (GFR) is critical in the evaluation of living kidney donors and a value exceeding 80 ml/min per 1.73 m2 is usually considered suitable. To improve strategies for kidney donor screening, an understanding of factors that affect GFR is needed. Here we studied the relationships between donor GFR measured by 125I-iothalamate clearances (mGFR) and age, gender, race, and decade of care in living kidney donors evaluated at the Cleveland Clinic from 1972 to 2005. We report the normal reference ranges for 1057 prospective donors (56% female, 11% African American). Females had slightly higher mGFR than males after adjustment for body surface area, but there were no differences due to race. The lower limit of normal for donors (5th percentile) was less than 80 ml/min per 1.73 m2 for females over age 45 and for males over age 40. We found a significant doubling in the rate of GFR decline in donors over age 45 as compared to younger donors. The age of the donors and body mass index increased over time, but their mGFR, adjusted for body surface area, significantly declined by 1.49±0.61 ml/min per 1.73 m2 per decade of testing. Our study shows that age and gender are important factors determining normal GFR in living kidney donors

A New Panel-Estimated GFR, Including beta(2)-Microglobulin and beta-Trace Protein and Not Including Race, Developed in a Diverse Population

RATIONALE AND OBJECTIVE: GFR estimation based on creatinine and cystatin C (eGFR(cr-cys)) is more accurate than eGFR based on either creatinine or cystatin C alone (eGFR(cr) or eGFR(cys)), but the inclusion of creatinine in eGFR(cr-cys) requires specification of a person’s race. Beta-2-microglobulin (B2M) and beta-trace protein (BTP) are alternative filtration markers that appear to be less influenced by race than creatinine. STUDY DESIGN: Study of diagnostic test accuracy. SETTING AND PARTICIPANTS: Development in pooled population of seven studies with 5017 participants with and without chronic kidney disease. External validation in a pooled population of seven other studies with 2245 participants. TESTS COMPARED: Panel eGFR using B2M and BTP in addition to cystatin C (three-marker panel) or creatinine and cystatin C (four-marker panel) with and without age and sex or race. OUTCOMES: GFR measured as the urinary clearance of iothalamate, plasma clearance of iohexol, or plasma clearance of Cr-EDTA RESULTS: Mean measured GFR was 58.1 and 83.2 ml/min/1.73m(2) and the proportion of blacks was 38.6% and 24.0%, in the development and validation populations, respectively. In development, addition of age and sex improved the performance of all equations compared to equations without age and sex, but addition of race did not further improve the performance. In validation, the four-marker panels were more accurate than the three-marker panels (p<0.001). The three-marker panel without race was more accurate than eGFR(cys) [1- P(30) of 15.6 vs 17.4% (p=0.014)], and the four-marker panel without race was as accurate as eGFR(cr-cys) [1- P(30) of 8.6 vs 9.4% (p=0.17)]. Results were generally consistent across subgroups. LIMITATIONS: No representation of participants with severe comorbid illness and from geographic areas outside of North America and Europe. CONCLUSIONS: The four-marker panel eGFR is as accurate as eGFR(cr-cys), without requiring specification of race. A more accurate race-free eGFR could be an important advance