Catálogo de publicaciones - libros

Taking advantage of the oscillatory evolution of stock prices, we analyze the evolution of stock prices in terms of the oscillation theory. We apply the formalisms to Nikkei225 data and compare with the predictions of the random walk theory.

We have shown that the return distributions observed in the S&P500 can be obtained for a random-walk which reacts to moving averages in the technical analysis sense. Characteristic ingredients are mini-trends in accordance with moving averages, which lead to fat tails, delay in trading, which shifts the tails lower in the distributions and a reaction to break-outs of the market (in our case, Bollinger bands) which straighten out the curvature of the tails. Though the chart values of the S&P500 are not Gaussian distributed, it is the minitrends which follow a random walk/ Gaussian distribution with unit variance. This leaves considerable doubts about the actual “efficiency” of the market. It will be interesting to analyze other market data whether the local correlation is universal, the mini-trends η are always standard-normal-distributed and whether the delay is shorter in markets with electronic trading.

We present a novel agent based simulation platform designed for general-purpose modeling in social sciences. Beyond providing convenient environment for modeling, debugging, simulation and analysis, the platform automatically enforces many of the properties inherent to the reality (such as causality and precise timing of events). A unique formalism grants agents with an unprecedented flexibility of actions simultaneously isolating researchers from most of the overhead of the virtual environment maintenance.

We have developed a microscopic model of interacting agents where agents buy or sell shares depending on the information they get from neighbours and a relation of a temporary price to a fundamental price. Depending on the magnitude of the noise present in the system (magnitude of market temperature) prices oscillate between the bull and the bear phases or around a mean fundamental value. The oscillation period can be calculated from a mean field theory. A very influencial investor (market leader) does not get larger profits than a typical one. A crucial role for profits is played by a coupling constant to a fundamental price.

This paper presents the model of the dynamics process of switching the strategies adopted by a large number of agents according to their views of what they deem as the most advantageous strategy in relation to the behavior of other agents and/or exogenous environments. The process of switching the strategies is modeled by the master equation by suitably specifying the transition rates of continuous time Markov chains. The computer simulation explains the effects of demand-supply imbalance created by short-medium term traders in the dollar-yen foreign exchange market.

A so called Zipf analysis portofolio management technique is introduced in order to comprehend the risk and returns. Two portofoios are built each from a well known financial index. The portofolio management is based on two approaches: one called the “equally weighted portofolio”, the other the “confidence parametrized portofolio”. A discussion of the (yearly) expected return, variance, Sharpe ratio and follows. Optimization levels of high returns or low risks are found.

This paper attempts to clarify some time series properties of binarized tick data by investor sentiment and genetic algorithm. For this purpose, first we explore the conditions for genetic algorithm to describe investor sentiment. Then we calculate auto-correlations and conditional probabilities using binarized sample paths generated by estimated models of investor sentiment. The most fitted parameter set of genetic algorithm have the following implications: First, a herd behavior is likely to emerge. Second, traders try to perceive brand-new information even if it is not completely correct.

A model of business scenario simulation is developed by applying game theory to the stochastic agents described by the Langevin equations for enterprise risk management (ERM). Business scenarios of computer-related industries are simulated using the developed model, and are compared with real market data. Economic capital was calculated based on the business scenario, as the most basic requisite of ERM.

We analyze a 19-year time series of North American electric power transmission system blackouts. Contrary to previously reported results we find a fatter than exponential decay in the distribution of inter-occurrence times and evidence of seasonal dependence in the number of events. Our findings question the use of self-organized criticality, and in particular the sandpile model, as a paradigm of blackout dynamics in power transmission systems. Hopefully, though, they will provide guidelines to more accurate models for evaluation of blackout risk.

Portfolio selection has a central role in finance theory and practical applications. The classical approach uses the standard deviation as risk measure, but a couple of alternatives also exist in the literature. Due to its computational advantages, portfolio optimization based on absolute deviation looks particularly interesting and it is widely used in practice. For the practical implementation of any variant, however, one needs to estimate the parameters from finite return series, which inevitably introduces measurement noise that, in turn, affects portfolio selection. Although much research has been devoted to investigating the noise in the classical model, hardly any attention has been paid to the problem in the case of absolute deviation. In this paper, we study the effect of estimation noise in the case of absolute-deviation-based portfolio optimization. We show that the key parameter determining the effect of noise is the ratio of the length of time series to portfolio size and that, other things being equal, the effect of noise is higher than in the classical, variance-based model. This finding points to the importance of checking whether theoretically „better“ portfolio selection models can indeed outperform the classical one in practice.