Ahmet Duran

Mobile ad hoc P2P file sharing

Wc propose two efficient search schemes that use query messages filtering/gossiping and adaptive hop-limited search, respectively, for peer-to-peer (P2P) file sharing over mobile ad hoc networks. While the former does network-wide flooding of query messages and is suitable for the applications requiring file update, the latter uses local broadcast transmission and exploits the degree of replication. We compare the first scheme with a Gnutella-like P2P file sharing system [T. Klingberg and R. Manfredi, 2002] in a MANET environment via simulation, and show that the proposed schemes offers less transmission overhead and better scalability. We also qualitatively compare both proposed schemes with ORION [A. Klemm et al., Mar. 2003].

Overreaction Diamonds: Precursors and Aftershocks for Significant Price Changes

Overreactions and other behavioral effects in stock prices can best be examined by adjusting for the changes in fundamentals. We perform this by subtracting the relative price changes in the net asset value (NAV) from that of market price (MP) daily for 134 406 data points of closed-end funds trading in US markets. We examine the days before and after a significant rise or fall in price deviation and MP return and find evidence of overreaction in the days after the change. Prior to a spike in deviation we find a gradual two- or three-day decline (and analogously in the other direction). Overall, there is a characteristic diamond pattern, revealing a symmetry in deviations before and after the significant change. Much of the statistical significance and the patterns disappear when the subtraction of NAV return is eliminated, suggesting that the frequent changes in fundamentals mask behavioral effects. A second study subdivides the data depending on whether the NAV or market price is responsible for the spike in the relative difference. In a majority of spikes, it is the change in market price rather than NAV that is dominant. Among those spikes for which there is little or no change in NAV, the results are similar to the overall study. Furthermore, the upward spikes are preceded by one or two days of declining market price while NAV rises slightly or is relatively unchanged. This suggests that a cause of the spike may be due to over-positioning of traders in the opposite direction in anticipation.

Parameter optimization for differential equations in asset price forecasting

A system of nonlinear asset flow differential equations (AFDE) gives rise to an inverse problem involving optimization of parameters that characterize an investor population. The optimization procedure is used in conjunction with daily market prices (MPs) and net asset values to determine the parameters for which the AFDE yield the best fit for the previous n days. Using these optimal parameters, the equations are computed and solved to render a forecast for MPs for the following days. For a number of closed-end funds, the results are statistically closer to the ensuing MPs than the default prediction of random walk (RW). In particular, we perform this optimization by a nonlinear computational algorithm that combines a quasi-Newton weak line search with the Broyden–Fletcher–Goldfarb–Shanno formula. We develop a nonlinear least-square technique with an initial value problem (IVP) approach for arbitrary stream data by focusing on the MP variable P since any real data for the other three variables B, ζ1, and ζ2 in the dynamical system is not available explicitly. We minimize the sum of exponentially weighted squared differences F[[Ktilde]] between the true trading prices from Day i to Day i+n−1, and the corresponding computed MPs obtained from the first row vector of the numerical solution U of the IVP with AFDE for ith optimal parameter vector, where [Ktilde] is an initial parameter vector. Here, the gradient (∇ F(x)) is approximated by using the central difference formula, and step length s is determined by the backtracking line search. One of the novel components of the proposed asset flow optimization forecast algorithm is a dynamic initial parameter pool that contains most recently used successful parameters, besides the various fixed parameters from a set of grid points in a hyper-box.

A profitable trading and risk management strategy despite transaction costs

We present a new profitable trading and risk management strategy with transaction cost for an adaptive equally weighted portfolio. Moreover, we implement a rule-based expert system for the daily financial decision-making process using the power of spectral analysis. We use several key components such as principal component analysis, partitioning, memory in stock markets, percentile for relative standing, the first four normalized central moments, learning algorithm, and switching among several investment positions consisting of short stock market, long stock market and money market with real risk-free rates. We find that it is possible to beat the proxy for the equity market without short selling for 168 S&P 500-listed stocks during the 1998–2008 period and 213 Russell 2000-listed stocks during the 1995–2007 period. Our Monte Carlo simulation for both the various set of stocks and the interval of time confirms our findings.

Investigating the antioxidant potential of Turkish herbs and spices

Herbs and spices have been used for many purposes including medicine, flavourings, and preservatives, etc. Constituents of herbs and spices can function as natural antioxidants and thus improve human nutrition and health. The main objective of this study was to evaluate the total phenolic and flavonoid contents of 35 different Turkish herbs and spices and to determine their antioxidant activity. Total phenolics, flavonoids, and total antioxidant capacities were analysed by four different methods: 2,2’-azinobis(3-ethylbenzothiazoline-6-sulphonic acid) diammonium salt (ABTS), 1,1-diphenyl-2-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP) and cupric ion reducing antioxidant capacity (CUPRAC) assay. The correlation coefficients (R2) between spectrophotometric assays were calculated. Wide variation in total phenolics (TP; 0.36-104 mg GAE/g), flavonoids (TF; 0.44-53.7 mg CE/g) and antioxidant capacity (TAC; 0.88-1007 mg trolox equivalent/g) was observed. Clove (Eugenia caryophyllata), yarrow (Ac...

Stability analysis of asset flow differential equations

I study the stability analysis of the solutions for the dynamical system of nonlinear asset flow differential equations (AFDEs) in three versions. I show that the previous two versions are not structurally stable mathematically because there are infinitely many critical points. It is important to reformulate a problem in order to eliminate any hypersensitivity in the mathematical model. I find that there is no critical point in the new version unless the chronic discount over the past finite time interval is zero.

APACHE III Score on ICU Admission Predicts Hospital Mortality After Open Thoracoabdominal and Open Abdominal Aortic Aneurysm Repair

BACKGROUND No prior studies, to our knowledge, have examined the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) III score in predicting mortality of patients undergoing open thoracoabdominal aortic aneurysm (TAAA) or open abdominal aortic aneurysm (AAA) repair. We sought to evaluate APACHE III scores in the prediction of postoperative mortality in elective TAAA and AAA repairs. METHODS Over a 9-year period (July 1998 through June 2007), prospective data (demographics, admitting diagnosis, APACHE III score, intensive care unit [ICU] and hospital length of stay, ICU and hospital mortality) were collected by a dedicated APACHE III coordinator for all patients admitted to a tertiary academic surgical ICU (20 beds). Observational and comparative analyses were performed. Emergent repairs for ruptured aneurysms were excluded from the study. RESULTS Forty-one patients underwent open elective repair of TAAA and 404 underwent open elective repair of AAA. Mean age of the TAAA group was 63.4 ± 9.8 years and the AAA group was 70.3 ± 8.3 years. Mean APACHE III score was 54 (range: 10-103) for the TAAA group and 45 (range: 11-103) for the AAA group. The in-hospital mortality rate for TAAA patients was 4.9% (n = 2) and for AAA patients was 2.0% (n = 8). Mean APACHE III scores on ICU admission were significantly greater in nonsurvivors versus survivors (79 vs. 45, p < 0.0001). For the entire patient cohort, the APACHE III score on ICU admission was an excellent discriminator of hospital mortality (receiver operating characteristic and area under the curve 0.92 [standard error of 0.05, 95% CI: 0.83-1.0]). CONCLUSIONS APACHE III is an accurate predictor of survival to hospital discharge in both open elective TAAA and AAA repairs.

3D extreme value analysis for stock return, interest rate and speed of mean reversion

It is important to analyze extreme cases of stock return, interest rate and speed of mean reversion together. While we explore strengths and limitations of Heston stochastic volatility model based on behavior of its numerical solutions using Milstein method simulations, we suggest model improvements in the light of real data applications. First, we perform high peak and fat-tail analysis for the impact of Heston model parameters on the simulations of the extreme situations by using the first four standardized moments and extreme value tools such as quantile-quantile (QQ), mean excess (ME) and Hill plots to examine the fat-tailness of the distributions. Later, we illustrate high peak and fat-tail analysis for BIST-100 index between 02.01.2004 and 17.06.2013 . Moreover, we investigate 3D dynamics of the average logarithmic stock return, interest rate and speed of mean reversion variables, together. Furthermore, we believe that polarization and the transitions between polarizations and comovements are important part of extreme situation picture. We investigate comovement and polarization of interest rates and daily returns of BIST-100 index between 2010 and 2013 in order to understand the corresponding behavioral dynamics. Heston stochastic volatility model predicts that the average logarithmic stock return increases as interest rate rises. Actually, we observe that there are also sufficiently large time intervals where interest rates were decreased and stock prices increased gradually in US stock markets and Borsa Istanbul, unlike the Heston stochastic volatility model suggests.

Spectral Analysis of Large Sparse Matrices for Scalable Direct Solvers

It is significant to perform structural analysis of large sparse matrices in order to obtain scalable direct solvers. In this paper, we focus on spectral analysis of large sparse matrices. We believe that the approach for exception handling of challenging matrices via Gerschgorin circles and using tuned parameters is beneficial and practical to stabilize the performance of sparse direct solvers. Nearly defective matrices are among challenging matrices for the performance of solver. Such matrices should be handled separately in order to get rid of potential performance bottleneck. Clustered eigenvalues observed via Gerschgorin circles may be used to detect nearly defective matrix. We observe that the usage of super-nodal storage parameters affects the number of fill-ins and memory usage accordingly.

Application of the Heston stochastic volatility model for Borsa Istanbul using impression matrix norm

We study the behavior of solutions for stochastic differential equations such as the Heston stochastic volatility model. We examine the numerical solutions using Euler-Maruyama, Milstein and stochastic Runge-Kutta methods to investigate whether there is a role of the methods for different volatility cases or not, related to the impact of cumulative errors on this application. We perform simulations for different stock market conditions by using the large data set from Borsa Istanbul-100 (BIST-100) between 04.01.2007 and 31.12.2012. We use volatilities in terms of extreme values at the overlapping case when we examine initial and long term volatilities for the application of the Heston model. We also apply unit volatility based on extreme values to approximate volatilities in our analysis. We examine the advantages and limitations of the model. Moreover, we introduce 3-dimensional matrix norms. Furthermore, we define market impression matrix norm as an application to the 3-dimensional matrix norms. We can benefit from it to quantify market impression approximately by means of the numerical solutions for the stochastic differential equations. Finally, we analyze the simulation results for various parameters.