Wavelet multiscale analysis for hedge funds: scaling and strategies

The wide acceptance of Hedge Funds by Institutional Investors and Pension Funds has led to an explosive growth in assets under management. These investors are drawn to Hedge Funds due to the seemingly low correlation with traditional investments and the attractive returns. The correlations and market risk (the Beta in the Capital Asset Pricing Model) of Hedge Funds are generally calculated using monthly returns data, which may produce misleading results as Hedge Funds often hold illiquid exchange-traded securities or difficult to price over-the- counter securities. In this paper, the Maximum Overlap Discrete Wavelet Transform (MODWT) is applied to measure the scaling properties of Hedge Fund correlation and market risk with respect to the S&P 500. It is found that the level of correlation and market risk varies greatly according to the strategy studied and the time scale examined. Finally, the effects of scaling properties on the risk profile of a portfolio made up of Hedge Funds is studied using correlation matrices calculated over different time horizons

A multiscale view on inverse statistics and gain/loss asymmetry in financial time series

Researchers have studied the first passage time of financial time series and observed that the smallest time interval needed for a stock index to move a given distance is typically shorter for negative than for positive price movements. The same is not observed for the index constituents, the individual stocks. We use the discrete wavelet transform to illustrate that this is a long rather than short time scale phenomenon -- if enough low frequency content of the price process is removed, the asymmetry disappears. We also propose a new model, which explain the asymmetry by prolonged, correlated down movements of individual stocks

Probability of local bifurcation type from a fixed point: A random matrix perspective

Results regarding probable bifurcations from fixed points are presented in the context of general dynamical systems (real, random matrices), time-delay dynamical systems (companion matrices), and a set of mappings known for their properties as universal approximators (neural networks). The eigenvalue spectra is considered both numerically and analytically using previous work of Edelman et. al. Based upon the numerical evidence, various conjectures are presented. The conclusion is that in many circumstances, most bifurcations from fixed points of large dynamical systems will be due to complex eigenvalues. Nevertheless, surprising situations are presented for which the aforementioned conclusion is not general, e.g. real random matrices with Gaussian elements with a large positive mean and finite variance.Comment: 21 pages, 19 figure

Hybrid of swarm intelligent algorithms in medical applications

In this paper, we designed a hybrid of swarm intelligence algorithms to diagnose hepatitis, breast tissue, and dermatology conditions in patients with such infection. The effectiveness of hybrid swarm intelligent algorithms was studied since no single algorithm is effective in solving all types of problems. In this study, feed forward and Elman recurrent neural network (ERN) with swarm intelligent algorithms is used for the classification of the mentioned diseases. The capabilities of six (6) global optimization learning algorithms were studied and their performances in training as well as testing were compared. These algorithms include: hybrid of Cuckoo Search algorithm and Levenberg-Marquardt (LM) (CSLM), Cuckoo Search algorithm (CS) and backpropagation (BP) (CSBP), CS and ERN (CSERN), Artificial Bee Colony (ABC) and LM (ABCLM), ABC and BP (ABCBP), Genetic Algorithm (GA) and BP (GANN). Simulation comparative results indicated that the classification accuracy and run time of the CSLM outperform the CSERN, GANN, ABCBP, ABCLM, and CSBP in the breast tissue dataset. On the other hand, the CSERN performs better than the CSLM, GANN, ABCBP, ABCLM, and CSBP in both th

Chlamydiatrachomatis and placental inflammation in early preterm delivery

Chlamydiatrachomatis may infect the placenta and subsequently lead to preterm delivery. Our aim was to evaluate the relationship between the presence of Chlamydiatrachomatis and signs of placental inflammation in women who delivered at 32 weeks gestation or less. Setting: placental histology and clinical data were prospectively obtained from 304 women and newborns at the Erasmus MC-Sophia, Rotterdam, the Netherlands. C.trachomatis testing of placentas was done retrospectively using PCR. C.trachomatis was detected in 76 (25%) placentas. Histological evidence of placental inflammation was present in 123 (40%) placentas: in 41/76 (54%) placentas with C.trachomatis versus 82/228 (36%) placentas without C.trachomatis infection (OR 2.1, 95% CI 1.2–3.5). C.trachomatis infection correlated with the progression (P = 0.009) and intensity (P = 0.007) of materno-fetal placental inflammation. C.trachomatis DNA was frequently detected in the placenta of women with early preterm delivery, and was associated with histopathological signs of placental inflammation

Chlamydia trachomatis infection in early neonatal period

BACKGROUND: The clinical characteristics of Chlamydia trachomatis respiratory tract infections in Japanese neonates were investigated. METHODS: Clinical, laboratory and microbiological characteristics of five infants with pneumonia due to C. trachomatis in early neonatal period were analyzed. RESULTS: Only C. trachomatis was identified in 4 infants. Both C. trachomatis and cytomegalovirus was identified in one. Wheezing, tachypnea and cyanosis were common in infants. Mothers of five infants had negative chlamydial EIAs at 20 weeks of gestation. CONCLUSIONS: We identified five cases of C. trachomatis respiratory tract infections in early neonatal period with the possibility of intrauterine infection. Targeted screening, early diagnosis, and effective treatment of perinatal and neonatal chlamydial infections seems to be necessar

Evaluation and optimization of a commercial enzyme linked immunosorbent assay for detection of Chlamydophila pneumoniae IgA antibodies

<p>Abstract</p> <p>Background</p> <p>Serologic diagnosis of <it>Chlamydophila pneumoniae </it>(Cpn) infection routinely involves assays for the presence of IgG and IgM antibodies to Cpn. Although IgA antibodies to Cpn have been found to be of interest in the diagnosis of chronic infections, their significance in serological diagnosis remains unclear. The microimmunofluorescence (MIF) test is the current method for the measurement of Cpn antibodies. While commercial enzyme linked immunosorbent assays (ELISA) have been developed, they have not been fully validated. We therefore evaluated and optimized a commercial ELISA kit, the SeroCP IgA test, for the detection of Cpn IgA antibodies.</p> <p>Methods</p> <p>Serum samples from 94 patients with anti-Cpn IgG titers ≥ 256 (study group) and from 100 healthy blood donors (control group) were tested for the presence of IgA antibodies to Cpn, using our in-house MIF test and the SeroCP IgA test. Two graph receiver operating characteristic (TG-ROC) curves were created to optimize the cut off given by the manufacturer.</p> <p>Results</p> <p>The MIF and SeroCP IgA tests detected Cpn IgA antibodies in 72% and 89%, respectively, of sera from the study group, and in 9% and 35%, respectively, of sera from the control group. Using the MIF test as the reference method and the cut-off value of the ELISA test specified by the manufacturer for seropositivity and negativity, the two tests correlated in 76% of the samples, with an agreement of Ƙ = 0.54. When we applied the optimized cut-off value using TG-ROC analysis, 1.65, we observed better concordance (86%) and agreement (0.72) between the MIF and SeroCP IgA tests.</p> <p>Conclusion</p> <p>Use of TG-ROC analysis may help standardize and optimize ELISAs, which are simpler, more objective and less time consuming than the MIF test. Standardization and optimization of commercial ELISA kits may result in better performance.</p

Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias

In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the "superior predictive ability" (SPA) test of Hansen (2005, JBES) to a stepwise SPA test that can identify predictive models without potential data snooping bias. It is shown analytically and by simulations that the stepwise SPA test is more powerful than the stepwise Reality Check test of Romano and Wolf (2005, Econometrica). We then apply the proposed test to examine the predictive ability of technical trading rules based on the data of growth and emerging market indices and their exchange traded funds (ETFs). It is found that technical trading rules have significant predictive power for these markets, yet such evidence weakens after the ETFs are introduced. © 2009.preprin