Catálogo de publicaciones - libros

As complex networks in economics, we consider Japanese shareholding networks as they existed in 1985, 1990, 1995, 2000, 2002, and 2003. In this study, we use as data lists of shareholders for companies listed on the stock market or on the over-the-counter market. The lengths of the shareholder lists vary with the companies, and we use lists for the top 20 shareholders. We represent these shareholding networks as a directed graph by drawing arrows from shareholders to stock corporations. Consequently, the distribution of incoming edges has an upper bound, while that of outgoing edges has no bound. This representation shows that for all years the distributions of outgoing degrees can be well explained by the power law function with an exponential tail. The exponent depends on the year and the country, while the power law shape is maintained universally. We show that the exponent strongly correlates with the long-term shareholding rate and the cross-shareholding rate.

We analyze the trades and quotes database of the TSE (Tokyo Stock Exchange) to derive the average price response to transaction volumes. Through the analysis, we point out that the assumption of the independence of the amplitude of returns on the size of transactions cannot fully explain the profile of the average price response.

Commodity prices act as leading indicators and have important implications for output and business fluctuations, but their dynamics are not well understood. We used some econophysic tools to evaluate five agricultural commodities traded at the NYBOT (cocoa, coffee, cotton, frozen orange juice and sugar), both in price and volume. Results show important differences between price and volume fluctuations and among the commodities. All commodities have high volatile but non-random dynamic, the less so the larger their market.

This study investigates that a characteristic time scale on an exchange rate market (USD/JPY) is examined for the period of 1998 to 2000. Calculating power spectrum densities for the number of tick quotes per minute and averaging them over the year yield that the mean power spectrum density has a peak at high frequencies. Consequently it means that there exist the characteristic scales which dealers act in the market. A simple agent model to explain this phenomenon is proposed. This phenomena may be a result of stochastic resonance with exogenous periodic information and physiological fluctuations of the agents. This may be attributed to the traders’ behavior on the market. The potential application is both quantitative characterization and classification of foreign currency markets.

The price impact function describes how prices change if stocks are bought or sold. Using order book data, we explain the shape of the average price impact function by a feedback mechanism due to a strong anticorrelation between price changes and limit order flow. We find that the average price impact function has only weak explanatory power for large price changes. Hence, we study the time dependence of liquidity and find it to be a necessary prerequisite for the explanation of extreme price fluctuation.

A phenomenon of the financial log-periodicity is discussed and the characteristics that amplify its predictive potential are elaborated. The principal one is self-similarity that obeys across all the time scales. Furthermore the same preferred scaling factor appears to provide the most consistent description of the market dynamics on all these scales both in the bull as well as in the bear market phases and is common to all the major markets. These ingredients set very desirable and useful constraints for understanding the past market behavior as well as in designing forecasting scenarios. One novel speculative example of a more detailed S&P500 development until 2010 is presented.

Considering the effect of economic agents’ preferences on their actions, relationships between conventional summary statistics and forecasts’ profit are investigated. Analytical examination demonstrates that investors’ utility maximization is determined by their risk attitude. The computational experiment rejects the claims that the accuracy of the forecast does not depend upon which error-criteria are used. Profitability of networks trained with loss function appeared to be statistically significant and stable.

The features of the time series for the overlap of two Cantor sets when one set moves with uniform relative velocity over the other looks somewhat similar to the time series of stock prices. We analyze both and explore the possibilities of anticipating a large (change in Cantor set) overlap or a large change in stock price. An anticipation method for some of the crashes has been proposed here, based on these observations.

Empirically, correlations are seen to exist between market action in specific, short market periods such as the AM, PM and overnight (ON) periods for different days of the week on the one hand and market trends (on various time scales) on the other hand. We use real-time spline fits with tunable smoothness parameters and their signs to obtain signals for these market periods and show that they are stationary (and tradable) for S&P 500 futures.

Basing on the perceived stationary internal structure of market movements on appropriate time scales, a series of interrelated pattern recognition programs was designed to compare specific features of current cycle “legs” with a selected universe of analogous prior market features periods which are then queried to obtain a prediction as to the future of the current cycle leg. Similarities are determined by a K-Nearest-Neighbor (KNN) method. This procedure yields good results in simulated S&P futures trading and demonstrates the hypothesized stationary of market responses to stimuli.