Silvano Cincotti

Agent-based simulation of a financial market

This paper introduces an agent-based artificial financial market in which heterogeneous agents trade one single asset through a realistic trading mechanism for price formation. Agents are initially endowed with a finite amount of cash and a given finite portfolio of assets. There is no money-creation process; the total available cash is conserved in time. In each period, agents make random buy and sell decisions that are constrained by available resources, subject to clustering, and dependent on the volatility of previous periods. The model proposed herein is able to reproduce the leptokurtic shape of the probability density of log price returns and the clustering of volatility. Implemented using extreme programming and object-oriented technology, the simulator is a flexible computational experimental facility that can find applications in both academic and industrial research projects.

Hyperchaotic behaviour of two bi-directionally coupled Chua's circuits

In this paper, a non-linear bi-directional coupling of two Chuas circuits is presented. The coupling is obtained by using polynomial functions that are symmetric with respect to the state variables of the two Chuas circuits. Both a transverse and a tangent system are studied to ensure a global validity of the results in the state space. First, it is shown that the transverse system is an autonomous Chuas circuit, which directly allows the evaluation of the conditions on its chaotic behaviour, i.e. the absence of synchronization between the coupled circuits. Moreover, it is demonstrated that the tangent system is also a Chuas circuit, forced by the transverse system; therefore, its dynamics is ruled by a time-dependent equation. Thus, the calculus of conditional Lyapunov exponents is necessary in order to exclude antisynchronization along the tangent manifold. The properties of the transverse and tangent systems simplify the study of the coupled Chuas circuits and the determination of the conditions on their hyperchaotic behaviour. In particular, it is shown that hyperchaotic behaviour occurs for proper values of the coupling strength between the two Chuas circuits. Finally, numerical examples are given and discussed. Copyright

Agent-based Modeling and Simulation of Competitive Wholesale Electricity Markets

This paper sheds light on a promising and very active research area for electricity market modeling, that is, agent-based computational economics. The intriguing perspective of such research methodology is to succeed in tackling the complexity of the electricity market structure, thus the fast-growing literature appeared in the last decade on this field. This paper aims to present the state-of-the-art in this field by studying the evolution and by characterizing the heterogeneity of the research issues, of the modeling assumptions and of the computational techniques adopted by the several research publications reviewed.

Self-assembled alkane monolayers on MoSe2 and MoS2

Highly ordered monolayers of a long‐chain molecule, dotriacontane (C32H66), have been obtained by self‐assembly at the interface between an organic solution and the basal planes of two semiconductors, MoSe2 and MoS2. Scanning tunneling microscopy in situ at the solid–fluid interfaces revealed that the adsorbate lattices are close packed and oriented relative to the substrate lattices, but that they are not simply commensurate. The results indicate that for flexible chain molecules, which interact only weakly with the surface of a solid substrate, a high degree of order in the adsorbed monolayers is induced by the atomical flatness of the substrate, while the coincidence of lattice parameters and the specific surface chemistry play only a minor role. Details of the packing, including the symmetry of the adsorbate unit cell, depend on the particular substrate. The results imply that atomical flatness is a key factor for the fabrication of highly ordered organic/inorganic heterostructures.

Learning of Chua's circuit attractors by locally recurrent neural networks

Abstract Many practical applications of neural networks require the identification of strongly non-linear (e.g., chaotic) systems. In this paper, locally recurrent neural networks (LRNNs) are used to learn the attractors of Chuas circuit, a paradigm for studying chaos. LRNNs are characterized by a feed-forward structure whose synapses between adjacent layers have taps and feedback connections. In general, the learning procedures of LRNNs are computationally simpler than those of globally recurrent networks. Results show that LRNNs can be trained to identify the underlying link among Chuas circuit state variables, and exhibit chaotic attractors under autonomous working conditions.

Who wins? Study of long-run trader survival in an artificial stock market

We introduce a multi-asset artificial financial market with finite amount of cash and number of stocks. The background trading is characterized by a random trading strategy constrained by the finiteness of resources and by market volatility. Stock price processes exhibit volatility clustering, fat-tailed distribution of returns and reversion to the mean. Three active trading strategies have been introduced and studied in two different market conditions: steady market and growing market with asset inflation. We show that the profitability of each strategy depends both on the periodicity of portfolio reallocation and on the market condition. The best performing strategy is the one that exploits the mean reversion characteristic of asset price processes.

A PWL ladder circuit which exhibits hysteresis

A circuit model for static hysteresis is presented. the circuit has a ladder structure with linear capacitors and piece wise linear resistors. the model behaviour shows all basic characteristics of static hysteresis phenomena. the model is put into relation with others proposed in the literature. an analysis of the model properties and a detailed comparison with the static Preisach model are made.

Modeling and forecasting of electricity spot-prices: Computational intelligence vs classical econometrics

In European countries, the last decade has been characterized by a deregulation of power production and electricity became a commodity exchanged in proper markets. This resulted in an increasing interest of the scientific community on electricity exchanges for modeling both market activity and price process. This paper analyzes electricity spot-prices of the Italian Power Exchange IPEX and proposes three different methods to model prices time series: a discrete-time univariate econometric model ARMA-GARCH and two computational-intelligence techniques Neural Network and Support Vector Machine. Price series exhibit a strong daily seasonality, addressed by analyzing separately a series for each of the 24 hours. One-day ahead forecasts of hourly prices have been considered so to compare the prediction performances of three different methods, with respect to the canonical benchmark model based on the random walk hypothesis. Results point out that Support Vector Machine methodology gives better forecasting accuracy for price time series, closely followed by the econometric technique.

A multi-assets artificial stock market with zero-intelligence traders

In this paper, a multi-assets artificial financial market populated by zero-intelligence traders with finite financial resources is presented. The market is characterized by different types of stocks representing firms operating in different sectors of the economy. Zero-intelligence traders follow a random allocation strategy which is constrained by finite resources, past market volatility and allocation universe. Within this framework, stock price processes exhibit volatility clustering, fat-tailed distribution of returns and reversion to the mean. Moreover, the cross-correlations between returns of different stocks are studied using methods of random matrix theory. The probability distribution of eigenvalues of the cross-correlation matrix shows the presence of outliers, similar to those recently observed on real data for business sectors. It is worth noting that business sectors have been recovered in our framework without dividends as only consequence of random restrictions on the allocation universe of zero-intelligence traders. Furthermore, in the presence of dividend-paying stocks and in the case of cash inflow added to the market, the artificial stock market points out the same structural results obtained in the simulation without dividends. These results suggest a significative structural influence on statistical properties of multi-assets stock market.

The Genoa Artificial Power-Exchange

The paper presents the Genoa Artificial Power Exchange, an agent-based framework for modeling and simulating power exchanges implemented in MATLAB. GAPEX allows creation of artificial power exchanges reproducing exact market clearing procedures of the most important European power-exchanges. In this paper we present results from a simulation performed on the Italian PEX where we have reproduced the Locational Marginal Price Algorithm based on the Italian high-voltage transmission network with its zonal subdivisions and we considered the Gencos in direct correspondence with the real ones. An enhanced version of the Roth-Erev algorithm is presented so to be able to consider the presence of affine total cost functions for the Gencos which results in payoff either positive, negative and null. A close agreement with historical real market data during both peak- and off-peak load hours of prices reproduced by GAPEX confirm its direct applicability to model and to simulate power exchanges.