Optimal Investment Under Transaction Costs: A Threshold Rebalanced Portfolio Approach

We study optimal investment in a financial market having a finite number of assets from a signal processing perspective. We investigate how an investor should distribute capital over these assets and when he should reallocate the distribution of the funds over these assets to maximize the cumulative wealth over any investment period. In particular, we introduce a portfolio selection algorithm that maximizes the expected cumulative wealth in i.i.d. two-asset discrete-time markets where the market levies proportional transaction costs in buying and selling stocks. We achieve this using "threshold rebalanced portfolios", where trading occurs only if the portfolio breaches certain thresholds. Under the assumption that the relative price sequences have log-normal distribution from the Black-Scholes model, we evaluate the expected wealth under proportional transaction costs and find the threshold rebalanced portfolio that achieves the maximal expected cumulative wealth over any investment period. Our derivations can be readily extended to markets having more than two stocks, where these extensions are pointed out in the paper. As predicted from our derivations, we significantly improve the achieved wealth over portfolio selection algorithms from the literature on historical data sets.Comment: Submitted to IEEE Transactions on Signal Processin

Influence of osmotic and metal stresses on nitrogenase activity of cyanobacteria ýsolated from paddy fields

Samples were collected from paddy fields in Corum-Turk.ye. Nitrogen-free BG-11 medium was used for isolation of nitrogen fixing cyanobacteria. Acetylene reduction technique was used to determine the effects of different chemical agents on the nitrogenase activities of the cyanobacteria, which were identified at the genus level. Nostoc showed the highest nitrogenase activity (0.09 ethylene ƒÊl/mg.h) at 50 mM salt concentration. At 60 mM sucrose concentration, Nostoc showed the highest nitrogenase activity (0.08 ethylene ƒÊl/mg.h). The highest tolerances for the metals were present in Anabaena (0.006 ethylene ƒÊl/mg.h) for iron (20 ppm), Nodularia 0.1 ethylene ƒÊl/mg.h (for manganese 20 ppm) and Nostoc 0.96 ethylene ƒÊl/mg.h (for zinc 5 ppm)

Quasi Periodic Oscillations (QPOs) and frequencies in an accretion disk and comparison with the numerical results from non-rotating black hole computed by the GRH code

The shocked wave created on the accretion disk after different physical phenomena (accretion flows with pressure gradients, star-disk interaction etc.) may be responsible observed Quasi Periodic Oscillations (QPOs) in $X-$ray binaries. We present the set of characteristics frequencies associated with accretion disk around the rotating and non-rotating black holes for one particle case. These persistent frequencies are results of the rotating pattern in an accretion disk. We compare the frequency's from two different numerical results for fluid flow around the non-rotating black hole with one particle case. The numerical results are taken from our papers Refs.\refcite{Donmez2} and \refcite{Donmez3} using fully general relativistic hydrodynamical code with non-selfgravitating disk. While the first numerical result has a relativistic tori around the black hole, the second one includes one-armed spiral shock wave produced from star-disk interaction. Some physical modes presented in the QPOs can be excited in numerical simulation of relativistic tori and spiral waves on the accretion disk. The results of these different dynamical structures on the accretion disk responsible for QPOs are discussed in detail.Comment: 13 figures, added reference, accepted for publication in Modern Physics Letters

Robust Least Squares Methods Under Bounded Data Uncertainties

Cataloged from PDF version of article.We study the problem of estimating an unknown deterministic signal that is observed through an unknown deterministic data matrix under additive noise. In particular, we present a minimax optimization framework to the least squares problems, where the estimator has imperfect data matrix and output vector information. We define the performance of an estimator relative to the performance of the optimal least squares (LS) estimator tuned to the underlying unknown data matrix and output vector, which is defined as the regret of the estimator. We then introduce an efficient robust LS estimation approach that minimizes this regret for the worst possible data matrix and output vector, where we refrain from any structural assumptions on the data. We demonstrate that minimizing this worst-case regret can be cast as a semi-definite programming (SDP) problem. We then consider the regularized and structured LS problems and present novel robust estimation methods by demonstrating that these problems can also be cast as SDP problems. We illustrate the merits of the proposed algorithms with respect to the well-known alternatives in the literature through our simulations

Robust estimation in flat fading channels under bounded channel uncertainties

Cataloged from PDF version of article.We investigate channel equalization problem for time-varying flat fading channels under bounded channel uncertainties. We analyze three robust methods to estimate an unknown signal transmitted through a time-varying flat fading channel. These methods are based on minimizing certain meansquare error criteria that incorporate the channel uncertainties into their problem formulations instead of directly using the inaccurate channel information that is available. We present closed-form solutions to the channel equalization problems for each method and for both zero mean and nonzero mean signals. We illustrate the performances of the equalization methods through simulations. © 2013 Elsevier Inc. All rights reserved

A Deterministic Analysis of an Online Convex Mixture of Expert Algorithms

Cataloged from PDF version of article.We analyze an online learning algorithm that adaptively combines outputs of two constituent algorithms (or the experts) running in parallel to model an unknown desired signal. This online learning algorithm is shown to achieve (and in some cases outperform) the mean-square error (MSE) performance of the best constituent algorithm in the mixture in the steady-state. However, the MSE analysis of this algorithm in the literature uses approximations and relies on statistical models on the underlying signals and systems. Hence, such an analysis may not be useful or valid for signals generated by various real life systems that show high degrees of nonstationarity, limit cycles and, in many cases, that are even chaotic. In this paper, we produce results in an individual sequence manner. In particular, we relate the time-accumulated squared estimation error of this online algorithm at any time over any interval to the time-accumulated squared estimation error of the optimal convex mixture of the constituent algorithms directly tuned to the underlying signal in a deterministic sense without any statistical assumptions. In this sense, our analysis provides the transient, steady-state and tracking behavior of this algorithm in a strong sense without any approximations in the derivations or statistical assumptions on the underlying signals such that our results are guaranteed to hold. We illustrate the introduced results through examples. © 2012 IEEE