Optimal Investment in a Levy Market

In this paper we consider the optimal investment problem in a market where the stock price process is modeled by a geometric Levy process (taking into account jumps). Except for the geometric Brownian model and the geometric Poissonian model, the resulting models are incomplete and there are many equivalent martingale measures. However, the model can be completed by the so-called power-jump assets. By doing this we allow investment in these new assets and we can try to maximize the expected utility of these portfolios. As particular cases we obtain the optimal portfolios based in stocks and bonds, showing that the new assets are superfluous for certain martingale measures that depend on the utility function we use.     

Evidence of a worldwide stock market log-periodic anti-bubble since mid-2000

Following our investigation of the USA Standard and Poor index anti-bubble that started in August 2000 (Quant. Finance 2 (2002) 468), we analyze 38 world stock market indices and identify 21 “bearish anti-bubbles” and six “bullish anti-bubbles”. An “anti-bubble” is defined as a self-reinforcing price trajectory with self-similar expanding log-periodic oscillations. Mathematically, a bearish anti-bubble is characterize by a power law decrease of the price (or of the logarithm of the price) as a function of time and by expanding log-periodic oscillations. We propose that bearish anti-bubbles are created by positive price-to-price feedbacks feeding overall pessimism and negative market sentiment further strengthened by inter-personal interactions. Bullish anti-bubbles are here identified for the first time. The most striking discovery is that the majority of European and Western stock market indices as well as other stock indices exhibit practically the same log-periodic power law anti-bubble structure as found for the USA S&P500 index. These anti-bubbles are found to start approximately at the same time, August 2000, in all these markets. This shows a remarkable degree of worldwide synchronization. The descent of the worldwide stock markets since 2000 is thus an international event, suggesting the strengthening of globalization.     

Fitting the control parameters of a genetic algorithm: An application to technical trading systems design

This paper studies the problem of how changes in the design of the genetic algorithm (GA) have an effect on the results obtained in real-life applications. In this study, focused on the application of a GA to the tuning of technical trading rules in the context of financial markets, our tentative thesis is that the GA is robust with respect to design changes. The optimization of technical trading systems is a suitable area for the application of the GA metaheuristic, as the complexity of the problem grows exponentially as new technical rules are added to the system and as the answer time is crucial when applying the system to real-time data. Up to now, most of GAs applications to this subject obviated the question of possible “design dependence” in their results. The data we report, based on our experiments, do not allow us to refute the hypothesis of robustness of the GA to design implementation, when applying to technical trading systems tuning.     

Considerations on modelling the market for softwood lumber in the United States

The analysis in this paper looks at two important elements in modelling the market for timber in the United States. First, the issue of directional causality between price and quantity and its implications in a modelling effort is investigated. Second, the extent of the geographic market for timber is discussed and a method of detecting it is suggested. The method for detecting the extent of the geographical market is tractable and can be applied in a straightforward way. Both considerations are applied to the softwood lumber market in the United States.     

Models of information aggregation in financial markets: a review

This article reviews static and dynamic models of information aggregation in the literature. It highlights the key assumptions these models make, the results they obtain and the issues that still need to be explored to further our understanding of information aggregation in financial markets.     

Complex dynamics in equilibrium asset pricing models with boundedly rational,heterogeneous agents

We study a simple model based upon the Lucas framework where heterogeneous agents behave rationally in a fully intertemporal setting but do not know other investors' personal preferences, wealth or investment portfolios. As a consequence, agents initially do not know the equilibrium asset pricing function and must make guesses, which they update via adaptive learning with constant gain. We demonstrate that even in this simple environment the economy can, depending on parameters, exhibit either stable convergence to equilibrium, or chaotic dynamical behavior of asset prices and trading volume without converging to the rational expectations equilibrium of the Lucas model. This contradicts the assertion that the Lucas model is stable in the face of modest deviations from the strong assumptions required to compute the equilibrium. © 2013 Wiley Periodicals, Inc. Complexity 19: 38–55, 2014     

Recent advances of organometallic complexes in emerging photovoltaics

Organometallic complexes (OMCs) consisting of organic and metal active moieties have shown immense potential for application in solar cells. The diverse structure, rich porosity, and unique charge centers of OMCs enable them to be functional in solar cells. In this review, we introduced four types of OMCs, such as crown organometallic complexes, β-diketone metal complexes, cyclometallic complexes, and main chain metal-containing polymers, providing an in-depth analysis of the structure-performance relationship. OMCs could serve as active or interlayer materials in a variety of solar cell systems such as organic solar cells, perovskite solar cells, and dye-sensitized solar cells, especially some metals to improve the photoelectric performance of the device as dopants. In the end, perspectives on the opportunities and challenges of OMCs are given.     

新型污染物的环境质谱技术研究进展

随着科学技术的飞速发展,质谱检测及其联用技术方法正以前所未有的速度、广度和深度全面渗透到环境分析化学中,其在环境监测中的使用已经或正在日常化.近年来,一些高分辨质谱及其与色谱等的联用技术在目标污染物和非目标污染物的同时甄别鉴定和分析中发挥了重要作用,其对于阐明污染物在环境的归趋具有重要意义.本文对质谱技术及其与气相色谱和液相色谱的联用技术在污染物尤其是新型污染物分析中的进展进行了总结,并对高分辨质谱技术的环境应用研究给于关注,对环境质谱技术的发展方向进行了展望.     

”Illusion of control” in Time-Horizon Minority and Parrondo Games

Human beings like to believe they are in control of their destiny. This ubiquitous trait seems to increase motivation and persistence, and is probably evolutionarily adaptive [J.D. Taylor, S.E. Brown, Psych. Bull. 103, 193 (1988); A. Bandura, Self-efficacy: the exercise of control (WH Freeman, New York, 1997)]. But how good really is our ability to control? How successful is our track record in these areas? There is little understanding of when and under what circumstances we may over-estimate [E. Langer, J. Pers. Soc. Psych. 7, 185 (1975)] or even lose our ability to control and optimize outcomes, especially when they are the result of aggregations of individual optimization processes. Here, we demonstrate analytically using the theory of Markov Chains and by numerical simulations in two classes of games, the Time-Horizon Minority Game [M.L. Hart, P. Jefferies, N.F. Johnson, Phys. A 311, 275 (2002)] and the Parrondo Game [J.M.R. Parrondo, G.P. Harmer, D. Abbott, Phys. Rev. Lett. 85, 5226 (2000); J.M.R. Parrondo, How to cheat a bad mathematician (ISI, Italy, 1996)], that agents who optimize their strategy based on past information may actually perform worse than non-optimizing agents. In other words, low-entropy (more informative) strategies under-perform high-entropy (or random) strategies. This provides a precise definition of the “illusion of control” in certain set-ups a priori defined to emphasize the importance of optimization. An erratum to this article is available at .     

An analysis of cross-correlations in an emerging market

We apply random matrix theory to compare correlation matrix estimators C$C$ obtained from emerging market data. The correlation matrices are constructed from 10 years of daily data for stocks listed on the Johannesburg stock exchange (JSE) from January 1993 to December 2002. We test the spectral properties of C$C$ against random matrix predictions and find some agreement between the distributions of eigenvalues, nearest neighbour spacings, distributions of eigenvector components and the inverse participation ratios for eigenvectors. We show that interpolating both missing data and illiquid trading days with a zero-order hold increases agreement with RMT predictions. For the more realistic estimation of correlations in an emerging market, we suggest a pairwise measured-data correlation matrix. For the data set used, this approach suggests greater temporal stability for the leading eigenvectors. An interpretation of eigenvectors in terms of trading strategies is given, as opposed to classification by economic sectors.