Physicists in the last few years have started applying concepts and methods of statistical physics to understand economic phenomena. The word "econophysics" is sometimes used to refer to this work. One reason for this interest is the fact that Economic systems such as financial markets are examples of complex interacting systems for which a huge amount of data exist and it is possible that econ...

This paper describes an application of genetic programming to forecasting financial markets that allowed the authors to rank first in a competition organized within the CEC2000 on “Dow Jones Prediction”. The approach is substantially driven by the rules of that competition, and is characterized by individuals being made up of multiple GP expressions and specific genetic operators.

We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done throu...

We present a exactly soluble model for financial time series that mimics the long range volatility correlations known to be present in financial data. Although our model is asymptotically ‘monofractal’ by construction, it shows apparent multiscaling as a result of a slow crossover phenomenon on finite time scales. Our results suggest that it might be hard to distinguish apparent and true multif...

This paper draws attention to the limitations of the standard unit root/cointegration approach to economic and financial modelling, and to some of the alternatives based on the idea of fractional integration, long memory models, and the random field regression approach to nonlinearity. Following brief explanations of fractional integration and random field regression, and the methods of applyin...

In this paper we work with nonparametric methods in modeling and analyzing the financial times series. We use the concept of fractal dimension for measuring the complexity of time series of observed financial data. The aim of this paper is to distinguish between the randomness and determinism of the financial information. We will compare the fractal analysis of the selected forward exchange rat...

This paper draws attention to the limitations of the standard unit root/cointegration approach to economic and financial modelling, and to some of the alternatives based on the idea of fractional integration, long memory models, and the random field regression approach to nonlinearity. Following brief explanations of fractional integration and random field regression, and the methods of applyin...

We analyze the financial statements of 58,653 firm-years from 34 countries for the period 1985-1998 to construct a panel data set measuring three dimensions of reported accounting earnings for each country – earnings aggressiveness, loss avoidance, and earnings smoothing. We hypothesize that these three dimensions are associated with uninformative or opaque earnings, and so we combine these thr...