Maximum Entropy Production Principle for Stock Returns

In our previous studies we have investigated the structural complexity of time series describing stock returns on New York's and Warsaw's stock exchanges, by employing two estimators of Shannon's entropy rate based on Lempel-Ziv and Context Tree Weighting algorithms, which were originally used for data compression. Such structural complexity of the time series describing logarithmic stock returns can be used as a measure of the inherent (model-free) predictability of the underlying price formation processes, testing the Efficient-Market Hypothesis in practice. We have also correlated the estimated predictability with the profitability of standard trading algorithms, and found that these do not use the structure inherent in the stock returns to any significant degree. To find a way to use the structural complexity of the stock returns for the purpose of predictions we propose the Maximum Entropy Production Principle as applied to stock returns, and test it on the two mentioned markets, inquiring into whether it is possible to enhance prediction of stock returns based on the structural complexity of these and the mentioned principle.Comment: 14 pages, 5 figure

Wealth, income, earnings and the statistical mechanics of flow systems

This paper looks at empirical data from economics regarding wealth, earnings and income, alongside a flow model for an economy based on the general Lotka-Volterra models of Levy & Solomon. The data and modelling suggest that a simple economic system might provide a tractable model for giving an exact statistical mechanical solution for an 'out of equilibrium' flow model. This might also include an exact mathematical definition of a 'dissipative structure' derived from maximum entropy considerations. This paper is primarily a qualitative discussion of how such a mathematical proof might be achieved

Wealth, income, earnings and the statistical mechanics of flow systems

This paper looks at empirical data from economics regarding wealth, earnings and income, alongside a flow model for an economy based on the general Lotka-Volterra models of Levy & Solomon. The data and modelling suggest that a simple economic system might provide a tractable model for giving an exact statistical mechanical solution for an 'out of equilibrium' flow model. This might also include an exact mathematical definition of a 'dissipative structure' derived from maximum entropy considerations. This paper is primarily a qualitative discussion of how such a mathematical proof might be achieved.wealth; earnings; income; entropy; lotka; volterra; dissipative

Why Money Trickles Up - Wealth & Income Distributions

This paper combines ideas from classical economics and modern finance with the general Lotka-Volterra models of Levy & Solomon to provide straightforward explanations of wealth and income distributions. Using a simple and realistic economic formulation, the distributions of both wealth and income are fully explained. Both the power tail and the log-normal like body are fully captured. It is of note that the full distribution, including the power law tail, is created via the use of absolutely identical agents. It is further demonstrated that a simple scheme of compulsory saving could eliminate poverty at little cost to the taxpayer.Comment: 45 pages of text, 36 figure

Parameter estimation for a computable general equilibrium model: a maximum entropy approach

We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints. Second, it permits incorporation of prior information on parameter values. Third, it can be applied in the absence of copious data. Finally, it supplies measures of the capacity of the model to reproduce the historical record and the statistical significance of parameter estimates. The method is applied to estimating a CGE model of Mozambique.Estimation theory., Equilibrium (Economics) Models., Mozambique.,

Volatility models with innovations from new maximum entropy densities at work

Generalized autoregressive conditional heteroskedasticity (GARCH) processes have become very popular as models for financial return data because they are able to capture volatility clustering as well as leptokurtic unconditional distributions which result from the assumption of conditionally normal error distributions. In contrast, Bollerslev (1987) and several follow-ups provided evidence that starting with leptokurtic and possibly skewed (conditional) error distributions will achieve better results. Parallel to these exible but to some extend arbitrary chosen parametric distributions, recent years saw a rise in suggestions for maximum entropy distributions (e.g. Rockinger and Jondeau, 2002, Park and Bera, 2009 or Fischer and Herrmann, 2010). Within this contribution we provide a comprehensive comparison between both different ME densities and their parametric competitors within different generalized GARCH models such as APARCH and GJR-GARCH. --GARCH,APARCH,Entropy density,Skewness,Kurtosis

A stochastic production frontier model with a translog specification using the generalized maximum entropy estimator

In this paper, an empirical application of the generalized maximum entropy estimator in a stochastic production frontier model with a translog specification is discussed to investigate technical efficiency in a wine region of Portugal. The empirical results indicate technical progress over the time period of the sample and an increasing technical inefficiency over time. All production units are technically inefficient, although wine cooperatives are less inefficient than private firms.stochastic frontier analysis, generalized maximum entropy, technical efficiency, translog

Energy and the State of Nations

The mathematical conditions for the existence of macroeconomic production functions that are state functions of the economic system are pointed out. The output elasticities and the elasticities of substitution of energy-dependent Cobb-Douglas, CES and LinEx production functions are calculated. The output elasticities, which measure the productive powers of production factors and whose numerical values have been obtained for Germany, Japan, and the USA, are for energy much larger and for labor much smaller than the cost shares of these factors. Energy and its conversion into physical work accounts for most of the growth that mainstream economics attributes to “technological progress” and related concepts. It decisively determines the economic state of nations. Consequences for automation and globalization and perspectives on growth are discussed.energy; economic growth; macroeconomic production functions; output elasticities