David Edelman

New formula for estimating fetal weight below 1000 g : Comparison with existing formulas

Most estimated fetal weight formulas have been derived and tested with larger fetuses, yet accuracy in predicting birth weight is more critical at the limit of viability. Complete data from 142 pregnancies in which delivery took place within 7 days of an ultrasonographic examination were used to create an appropriate formula for fetuses less than 1000 g and compare it with 10 currently available formulas. Our formula (In [BW] = 0.66 x 1n [HC] + 1.04 x 1n [AC] + 0.985 x 1n [FL]) was significantly more accurate than all other formulas and also performed better on a prospective cohort of 27 fetuses with estimated fetal weight less than 1000 g. Of the existing formulas, the Hadlock formula (using head circumference, abdominal circumference, femur length) was the most accurate, being significantly more accurate than all but the Woo formula with all but the Woo formula.

Option model calibration using a bacterial foraging optimization algorithm

The Bacterial Foraging Optimization (BFO) algorithm is a biologically inspired computation technique which is based on mimicking the foraging behavior of E.coli bacteria. This paper illustrates how a BFO algorithm can be constructed and applied to solve parameter estimation of a EGARCH-M model which is then used for calibration of a volatility option pricing model. The results from the algorithm are shown to be robust and extendable, suggesting the potential of applying the BFO for financial modeling.

Adapting support vector machine methods for horserace odds prediction

The methodology of Support Vector Machine Methods is adapted in a straightforward manner to enable the analysis of stratified outcomes such as horseracing results. As the strength of the Support Vector Machine approach lies in its apparent ability to produce generalisable models when the dimensionality of the inputs is large relative to the the number of observations, such a methodology would appear to be particularly appropriate in the horseracing context, where often the number of input variables deemed as being potentially relevant can be difficult to reconcile with the scarcity of relevant race results. The methods are applied to a relatively small (200 races in-sample) sample of Australian racing data and tested on 100 races out-of-sample with promising results, especially considering the relatively large number (12) of input variables used.

First Trimester Aneuploidy Screening Using Nuchal Iranslucency, Free Beta Human Chorionic Gonadotrophin and Maternal Age

Summary: Screening for aneuploidy using maternal age has a low detection rate and high false positive rate. Second trimester maternal serum screening increases trisomy 21 detection and decreases die false positive rate. First trimester screening would enable definitive diagnosis with chorionic villus sampling, and simple surgical termination of affected pregnancies would still be an option. Nuchal translucency (NT), free beta human chorionic gonadotrophin (fβHCG) and maternal age were assessed in 302 patients before chorionic villus sampling. NT positively and fβHCG negatively correlated with gestation, but neither correlated with maternal age nor with each other. Both NT and fβHCG were increased in trisomy 21. NT was increased and fβHCG was decreased in trisomy 18. Multivariate discriminant analysis enabled 87.5% detection of trisomy 21 in this high‐risk population, for a 14% false positive rate. In a simulated normal population, using a risk cut‐off of 1 in 250, 71% detection was achieved for a 7% false positive rate. The combination of NT, fβHCG and maternal age is a simple, readily available and viable first trimester screening strategy.

Tote arbitrage and lock opportunities in racetrack betting

A game-theoretic approach to the investigation of arbitrage opportunities based on combinations of exotic wagers for the same event is described. This situation is also known as a ‘lock’ or ‘Dutch Book’. The technique is applied to recent totalizator data from Australian thoroughbred races. It appears that such opportunities appear fairly regularly, at least according to published final dividends for various bet types. The method is demonstrated in some detail on a particular example.

On the Financial Value of Information

Results of Kelly [5] and Breiman [2] relating optimal growth rates for gambling and investing to information distances are generalised to include return distributions for virtually any type of game or asset. These results are achieved by first introducing the notion of the optimal financial derivative instrument for a given gamble or investment and then solving the related optimisation problem. For assets varying continuously over time, a formula for optimal dynamic portfolio adjustment follows for commonly occurring models, assuming no transaction costs. The latter results are applied to assets with normal and lognormal returns. The results for these are demonstrated using simulation.

An Introduction to Natural Computing in Finance

The field of Natural Computing (NC) has advanced rapidly over the past decade. One significant offshoot of this progress has been the application of NC methods in finance. This paper provides an introduction to a wide range of financial problems to which NC methods have been usefully applied. The paper also identifies open issues and suggests multiple future directions for the application of NC methods in finance.

A confidence interval for the center of an unknown unimodal distribution based on a sample of size 1

Abstract Contrary to popular belief, it is sometimes possible to produce a confidence interval for the center (e.g., mode) of a distribution based on a sample of size 1 without having any previous knowledge of the degree of its dispersion. This (possibly counterintuitive) fact was first proved by Abbot and Rosenblatt, with the first application being developed by Robert Machol, for the case of a normal variable with unknown variance. Although he and others have subsequently generalized these results, they have previously appeared only in the engineering literature, so statisticians may not be aware of them. It is the purpose of this article to present a simple proof of the validity of the confidence interval procedure for the mode of an unknown unimodal density (i.e., a density that is decreasing as its argument moves away from the mode) based on a single observation, with a comment about similar procedures for the center of a symmetric density and that of a normal density (both mean and variance being un...

Adaptive universal portfolios

In this article, we consider Covers universal portfolio and the problem of multi-period investment in a nonparametric setting. We show that Covers universal portfolio is equivalent to a Bayes estimator of the optimal growth portfolio. However, as noted by Cover, it can take a long time for the universal portfolio to produce significant growth. Therefore, we propose the adaptive universal portfolio, which retains much of the qualitative nature of Covers universal portfolio while enhancing early performance. An empirical study is carried out over a range of exchange traded funds over a 5 year period, which exhibits the enhanced early performance generated by the adaptive universal portfolio.

Estimation of an EGARCH Volatility Option Pricing Model using a Bacteria Foraging Optimisation Algorithm

The bacterial foraging optimisation algorithm is a novel natural computing algorithm which is based on mimicking the foraging behavior of E.coli bacteria. This chapter illustrates how a bacteria foraging optimisation algorithm (BFOA) can be constructed. The utility of this algorithm is tested by comparing its performance on a series of benchmark functions against that of the canonical genetic algorithm (GA). Following this, the algorithm’s performance is further assessed by applying it to estimate parameters for an EGARCH model which can then be applied for pricing volatility options. The results suggest that the BFOA can be used as a complementary technique to conventional statistical computing techniques in parameter estimation for financial models.