MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH

Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and is asked to put their relationship in words his answer will probably reveal a fuzzy nature like: "IF the inflation rate in the EURO-Zone is low and the GDP growth rate is larger than in the US THEN the EURO will rise against the USD". æLowÆ and ælargerÆ are fuzzy terms which give a gradual linguistic meaning to crisp intervalls in the respective universes of discourse. In order to learn a Fuzzy Fuzzy Rule base from examples we introduce Genetic Algorithms and Artificial Neural Networks as learning operators. These examples can either be empirical data or originate from an economic simulation model. The software GENEFER (GEnetic NEural Fuzzy ExplorER) has been developed for designing such a Fuzzy Rule Base. The design process is modular and comprises Input Identification, Fuzzification, Rule-Base Generating and Rule-Base Tuning. The two latter steps make use of genetic and neural learning algorithms for optimizing the Fuzzy Rule-Base.

Productivity growth and competition in spanish manufacturing firms: What has happened in recent years?

This paper addresses the issue of the relationship between productivity and market competition. In comparison to the economies of other European countries, the Spanish economy has been growing, while productivity growth has stagnated. Here we provide empirical evidence about the relationship between productivity and market competition from Spanish manufacturing firms at firm level between 1994 and 2004. Correcting for selection bias, our study pays special attention to the patterns of productivity growth between openness and non-openness firms. When market competition increases the effect on firms operating in domestic markets is positive but when the level of competition is high incentives to invest in innovation and productivity gains disappear. The empirical relationship between competition and productivity is an inverted U-shape, where productivity growth is highest at intermediate levels of competition. The productivity growth of firms operating in international markets is higher than that of non-openness firms, but when market competition rises they moderate their productivity growth. Our empirical results suggest that the correct competition policy in the Spanish economy should remove the barriers to competition in internal markets in order to increase the incentives for manufacturing firms to invest in innovation and productivity growth.Manufacturing industries, innovation, competitiveness, international trade, Heckman equation

The Essential Role of Securities Regulation

This Article posits that the essential role of securities regulation is to create a competitive market for sophisticated professional investors and analysts (information traders). The Article advances two related theses-one descriptive and the other normative. Descriptively, the Article demonstrates that securities regulation is specifically designed to facilitate and protect the work of information traders. Securities regulation may be divided into three broad categories: (i) disclosure duties; (ii) restrictions on fraud and manipulation; and (iii) restrictions on insider trading-each of which contributes to the creation of a vibrant market for information traders. Disclosure duties reduce information traders\u27 costs of searching and gathering information. Restrictions on fraud and manipulation lower information traders\u27 cost of verifying the credibility of information, and thus enhance information traders\u27 ability to make accurate predictions. Finally, restrictions on insider trading protect information traders from competition from insiders that would undermine information traders\u27 ability to recoup their investment in information. Normatively, the Article shows that information traders can best underwrite efficient and liquid capital markets, and, hence, it is this group that securities regulation should strive to protect. Our account has important implications for several policy debates. First, our account supports the system of mandatory disclosure. We show that, although market forces may provide management with an adequate incentive to disclose at the initial public offering (IPO) stage, they cannot be relied on to effect optimal disclosure thereafter. Second, our analysis categorically rejects calls to limit disclosure duties to hard information and self-dealing by management. Third, our analysis supports the use of the fraud-on-the-market presumption in all fraud cases even when markets are inefficient. Fourth, our analysis suggests that in cases involving corporate misstatements, the appropriate standard of care should, in principle, be negligence, not fraud

The Essential Role of Securities Regulation

This Article posits that the essential role of securities regulation is to create a competitive market for sophisticated professional investors and analysts (information traders). The Article advances two related theses-one descriptive and the other normative. Descriptively, the Article demonstrates that securities regulation is specifically designed to facilitate and protect the work of information traders. Securities regulation may be divided into three broad categories: (i) disclosure duties; (ii) restrictions on fraud and manipulation; and (iii) restrictions on insider trading-each of which contributes to the creation of a vibrant market for information traders. Disclosure duties reduce information traders\u27 costs of searching and gathering information. Restrictions on fraud and manipulation lower information traders\u27 cost of verifying the credibility of information, and thus enhance information traders\u27 ability to make accurate predictions. Finally, restrictions on insider trading protect information traders from competition from insiders that would undermine information traders\u27 ability to recoup their investment in information. Normatively, the Article shows that information traders can best underwrite efficient and liquid capital markets, and, hence, it is this group that securities regulation should strive to protect. Our account has important implications for several policy debates. First, our account supports the system of mandatory disclosure. We show that, although market forces may provide management with an adequate incentive to disclose at the initial public offering (IPO) stage, they cannot be relied on to effect optimal disclosure thereafter. Second, our analysis categorically rejects calls to limit disclosure duties to hard information and self-dealing by management. Third, our analysis supports the use of the fraud-on-the-market presumption in all fraud cases even when markets are inefficient. Fourth, our analysis suggests that in cases involving corporate misstatements, the appropriate standard of care should, in principle, be negligence, not fraud

Asymmetric Conditional Volatility Models: Empirical Estimation and Comparison of Forecasting Accuracy

This paper compares several statistical models for daily stock return volatility in terms of sample fit and out-of-sample forecast ability. The focus is on U.S. and Romanian daily stock return data corresponding to the 2002-2010 time interval. We investigate the presence of leverage effects in empirical time series and estimate different asymmetric GARCH-family models (EGACH, PGARCH and TGARCH) specifying successively a Normal, Student's t and GED error distribution. We find that GARCH family models with normal errors are not capable to capture fully the leptokurtosis in empirical time series, while GED and Student’s t errors provide a better description for the conditional volatility. In addition, we outline some stylized facts about volatility that are not captured by conventional ARCH or GARCH models, but are considered by the asymmetric models and document their presence in empirical time series. Finally, we report that volatility estimates given by the EGARCH model exhibit generally lower forecast errors and are therefore more accurate than the estimates given by the other asymmetric GARCH models.stylized facts, leverage effects, asymmetric GARCH, volatility modeling, volatility forecasting

Fron Neo-classical Entrepreneur to Socio-economic Organization

Despite the growing role that business has played in the development of capitalism, the neo-classical paradigm has largely ignored the concept of organization. This paper illustrates the neo-classical concept of the firm and the entrepreneur. Analyzing both, the moral and economic thought of Adam Smith, this paper explains why, in the heart of Industrial Revolution, the paradigm elects an unrealistic and quasi-medieval concept of the firm. The paper argues that it is not by chance that the collective actions and thinking were neglected, rather it is necessary in order to maintain the core-values of the paradigm. Finally, the paper discusses if a firm could be a good subject for institutionalizing of socioeconomics.

Forecasting inflation with thick models and neural networks

This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing linear models for “real-time” and “bootstrap” forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31bootstrap, Neural Networks, Phillips Curves, real-time forecasting, Thick Models