The purpose of this paper is to introduce an approximation of the kernel-based logoptimal investment strategy that guarantees an almost optimal rate of growth of the capital under minimal assumptions on the behavior of the market. The new strategy uses much less knowledge on the distribution of the market process. It is analyzed both theoretically and empirically. The theoretical results show that the asymptotic rate of growth well approximates the optimal one that one could achieve with a full knowledge of the statistical properties of the underlying process generating the market, under the only assumption that the market is stationary and ergodic. The empirical results show that the proposed semi-log-optimal and the log-optimal strategies have essentially the same performance measured on past nyse data.
The spray from an airblast atomizer was investigated by the Phase-Doppler technique. The drop size-velocity data determined the properties of the gas and droplet phases. Formulae to estimate mean diameters and size distributions of sprays were evaluated. The Gamma PDF described most accurately the size distribution of the spray.
This paper provides a survey of discrete time, multi period, sequential investment strategies for financial markets. Under memoryless assumption on the underlying process generating the asset prices the Best Constantly Rebalanced Portfolio is studied, called log-optimal portfolio, which achieves the maximal asymptotic average growth rate. Semi-log optimal portfolio selection as a small computational complexity alternative of the log-optimal portfolio selection is studied both theoretically and empirically. For generalized dynamic portfolio selection, when asset prices are generated by a stationary and ergodic process, universally consistent empirical methods are shown. Empirical portfolio selection methods are proposed to handle the proportional transaction cost. The empirical performance of the methods illustrated for NYSE data with and without transaction costs.
Airblast atomization is a suitable model platform to understand atomization physics since the atomizer geometry has an insignificant influence on the spray formation. Besides its theoretical relevance, this configuration is used in several practical applications ranging from healthcare to combustion. Presently, a plain-jet airblast atomizer has been investigated experimentally under atmospheric conditions at various atomizing pressures and liquid preheating temperatures. To cover a wide range of liquids by viscosity and surface tension, water, diesel oil, light heating oil, and crude rapeseed oil were atomized to evaluate the droplet size-velocity correlations when the spray is fully developed. Increasing the temperature of high-viscosity liquids prior to atomization improves the spray characteristics until their kinematic viscosity decreases to a certain value that is newly introduced as a limiting viscosity. Further preheating has a marginal effect on droplet size-velocity plots, and the spray becomes more homogeneous. Several SMDestimating formulae were analyzed and improved to consider the effect of liquid preheating and to extend their range of validity. When the kinematic viscosity exceeded the limiting viscosity, the part containing the Weber number was corrected linearly by the preheating temperature. The coefficient of the Ohnesorge number was corrected by the inverse of the kinematic viscosity, without considering the limiting viscosity. The above results help to correct the SMD of atmospheric measurements to elevated liquid temperatures and to contribute to advanced atomization models for numerical software.
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