Portfolio risk, introduced by Markowitz in 1952, and defined as the standard deviation of the portfolio return, is an important metric in the Modern Portfolio Theory (MPT). A popular method for portfolio selection is to manage the risk and return of a portfolio according to the cross-correlations of returns for various financial assets. In a real world scenario, estimated empirical financial correlation matrix contains significant level of intrinsic noise that needs to be filtered prior to risk calculations. In this paper, we present basic concepts of risk engineering in finance applications. Then, we extend our discussion to the eigenfiltering of measurement noise for hedged portfolios. Moreover, we extend risk measurement methods for trading in multiple frequencies. Finally, three novel risk management methods are proposed as an independent overlay of the underlying investment decision mechanism, i.e. the trading strategy. We highlight performance and merit of the risk engineering techniques introduced by presenting the back-testing results of an investment strategy for the stocks listed in the NASDAQ 100 index. It is shown in the paper that managing portfolio risk more intelligently may offer advantages for improved return on investment.
Signal dependent Karhunen-Loève transform (KLT), also called factor analysis or principal component analysis (PCA), has been of great interest in applied mathematics and various engineering disciplines due to optimal performance. However, implementation of KLT has always been the main concern. Therefore, fixed transforms like discrete Fourier (DFT) and discrete cosine (DCT) with efficient algorithms have been successfully used as good approximations to KLT for popular applications spanning from source coding to digital communications. In this paper, we propose a simple method to derive explicit KLT kernel, or to perform PCA, in closed-form for first-order autoregressive, AR (1), discrete process. It is a widely used approximation to many real world signals. The merit of the proposed technique is shown. The novel method introduced in this paper is expected to make real-time and data-intensive applications of KLT, and PCA, more feasible.Index Terms-Covariance analysis, eigenanalysis, explicit Karhunen-Loève transform (KLT) kernel, factor analysis, first-order autoregressive process, principal component analysis (PCA), signal dependent transform.
Resistance of gram-negative aerobic bacteria to aminoglycoside antibiotics differs by region and country. It is known that 54% of gram-negative bacilli in Turkey are resistant to gentamicin, 32% to netilmicin, 35% to tobramycin, and only 0.9% to amikacin. Resistance to these antibiotics is generally caused by aminoglycoside-modifying enzymes. The resistance mechanisms of 300 aminoglycoside-resistant gram-negative bacteria were evaluated by determination of susceptibility to selected aminoglycosides. Comparison of strains isolated from community acquired infections and hospital acquired infections was made. Of the strains from community, 45.4% had an aminoglycoside resistance pattern indicative of 2''-adenyltransferase [ANT(2'')]. This was found in 44.4% of the hospital isolates. In both groups the second common enzyme was the 3-acetyltransferase [AAC(3)-II], in 20.8% and 23.3% respectively. Overall, most strains had an aminoglycoside resistance pattern indicative of ANT(2''), followed by AAC(3)-II and AAC(3)-I. Among bacteria tested, AAC(3)-II was the most common enzyme in Pseudomonas aeruginosa. The results of this study suggest that local antibiotic prescribing patterns play an important role in regional resistance mechanisms.
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