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

Seizure characterisation using frequency-dependent multivariate dynamics

The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on recent use of multivariate techniques to study the crosscorrelation dynamics between electroencephalographic (EEG) channels. The Maximum Overlap Discrete Wavelet Transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency dependent changes in the correlation structure between channels which may be indicative of seizure activity. The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures, suggesting that monitoring frequency dependent correlation structure can characterise changes in EEG signals during these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine additional seizure characteristics

Analysing Lyapunov spectra of chaotic dynamical systems

It is shown that the asymptotic spectra of finite-time Lyapunov exponents of a variety of fully chaotic dynamical systems can be understood in terms of a statistical analysis. Using random matrix theory we derive numerical and in particular analytical results which provide insights into the overall behaviour of the Lyapunov exponents particularly for strange attractors. The corresponding distributions for the unstable periodic orbits are investigated for comparison.Comment: 4 pages, 4 figure

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

Unexpected identification of a recurrent mutation in the DLX3 gene causing amelogenesis imperfecta

Objective To identify the molecular genetic aetiology of a family with autosomal dominant amelogenesis imperfecta (AI). Subjects and Methods DNA samples were collected from a six-generation family, and the candidate gene approach was used to screen for the enamelin (ENAM) gene. Whole-exome sequencing and linkage analysis with SNP array data identified linked regions, and candidate gene screening was performed. Results Mutational analysis revealed a mutation (c.561_562delCT and p.Tyr188Glnfs*13) in the DLX3 gene. After finding a recurrent DLX3 mutation, the clinical phenotype of the family members was re-examined. The proband's mother had pulp elongation in the third molars. The proband had not hair phenotype, but her cousin had curly hair at birth. Conclusions In this study, we identified a recurrent 2-bp deletional DLX3 mutation in a new family. The clinical phenotype was the mildest one associated with the DLX3 mutations. These results will advance the understanding of the functional role of DLX3 in developmental processes.OAIID:RECH_ACHV_DSTSH_NO:T201604269RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080446CITE_RATE:2FILENAME:Kim_et_al-2016-Oral_Diseases.pdfDEPT_NM:치의학과EMAIL:[email protected]_YN:YFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/1f942450-fa58-4bd3-8e50-692d90fed3c6/linkCONFIRM:

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