The sharp peak-flat trough pattern and critical speculation

We find empirically a characteristic sharp peak-flat trough pattern in a large set of commodity prices. We argue that the sharp peak structure reflects an endogenous inter-market organization, and that peaks may be seen as local ``singularities'' resulting from imitation and herding. These findings impose a novel stringent constraint on the construction of models. Intermittent amplification is not sufficient and nonlinear effects seem necessary to account for the observations.Comment: 20 pages, 6 figures (only fig.4 and 6 available in ps format), 3 tables, European Physical Journal B (in press

The Impact of Promotions on Store Visits: A Counterfactual Approach

This thesis empirically quantifies the impact of promotions on store visits in the Swedish grocery retailing sector with nationally representative panel data on household purchases of ground coffee. Using the potential outcomes framework, the impact is calculated as the difference between outcomes with promotion and their counterfactuals estimated with two regression models. The first model is an OLS fixed effects model used by market research firm GfK and the second is a Poisson fixed effects model. The Poisson model’s identification of the promotion effect is shown to be superior by accounting for that the dependent variable is discrete, the heterogenous time effects in the cross-section, and possible brand-switching behaviour. Standard errors robust to heteroscedasticity and cross-sectional and serial correlation are estimated for inference of the promotion effect under spatio-temporal dependence. A procedure for obtaining counterfactuals with regression models under multiple concurrent and continuous treatments is presented and an estimator of the cumulative treatment effect with adjustment for spatio-temporal dependence is derived and used to estimate the promotion impact on store visits. The findings are valuable for companies in market research, retailing and consumer packaged goods. The contributions of the thesis are methods for estimating promotion impact and an improvement of GfK’s methodology

Non Linear Modelling of Financial Data Using Topologically Evolved Neural Network Committees

Most of artificial neural network modelling methods are difficult to use as maximising or minimising an objective function in a non-linear context involves complex optimisation algorithms. Problems related to the efficiency of these algorithms are often mixed with the difficulty of the a priori estimation of a network's fixed topology for a specific problem making it even harder to appreciate the real power of neural networks. In this thesis, we propose a method that overcomes these issues by using genetic algorithms to optimise a network's weights and topology, simultaneously. The proposed method searches for virtually any kind of network whether it is a simple feed forward, recurrent, or even an adaptive network. When the data is high dimensional, modelling its often sophisticated behaviour is a very complex task that requires the optimisation of thousands of parameters. To enable optimisation techniques to overpass their limitations or failure, practitioners use methods to reduce the dimensionality of the data space. However, some of these methods are forced to make unrealistic assumptions when applied to non-linear data while others are very complex and require a priori knowledge of the intrinsic dimension of the system which is usually unknown and very difficult to estimate. The proposed method is non-linear and reduces the dimensionality of the input space without any information on the system's intrinsic dimension. This is achieved by first searching in a low dimensional space of simple networks, and gradually making them more complex as the search progresses by elaborating on existing solutions. The high dimensional space of the final solution is only encountered at the very end of the search. This increases the system's efficiency by guaranteeing that the network becomes no more complex than necessary. The modelling performance of the system is further improved by searching not only for one network as the ideal solution to a specific problem, but a combination of networks. These committces of networks are formed by combining a diverse selection of network species from a population of networks derived by the proposed method. This approach automatically exploits the strengths and weaknesses of each member of the committee while avoiding having all members giving the same bad judgements at the same time. In this thesis, the proposed method is used in the context of non-linear modelling of high-dimensional financial data. Experimental results are'encouraging as both robustness and complexity are concerned.Imperial Users onl

Applications of statistical physics in finance and economics

This chapter reviews recent research adopting methods from statistical physics in theoretical or empirical work in economics and nance. The bulk of what has recently become known as 'econophysics' in broader circles draws its motivation from observed scaling laws in nancial markets and the abundance of data available from the economy's nancial sphere. The rst part of this review presents the robust power laws encountered in nancial economics and discusses potential explanations for scaling in nance derived from models of stochastic interactions of traders. Sec. 3 provides an overview over other applications of statistical physics methodology in nance and attempts to evaluate the impact they have had so far on nancial economies. With the following section, the review turns to recent work on the emergence of wealth and income heterogeneity and the recent inception of new strands of research on this topic both within econophysics and the neoclassical economics tradition. The third part reviews the new stylized facts that have been identi ed in cross-sectional data of rm characteristics and agent-based approaches to industrial organization and macroeconomic dynamics that have been motivated by these ndings. We conclude with an assessment of the major methodological contributions of this new strand of research. --

Complexity in financial market. Modeling psychological behavior in agent-based models and order book models

The fundamental idea developed throughout this work is the introduction of new metrics in Social Sciences (Economics, Finance, opinion dynamics, etc). The concept of metric, that is the concept of measure, is usually neglected by mainstream theories of Economics and Finance. Financial Markets are the natural starting point of such an approach to Social Sciences because a systematic approach can be undertaken and the methods of Physics has shown to be very effective. In fact since a decade there exists a very huge amount of high frequency data from stock exchanges which permit to perform experimental procedures as in Natural Sciences. Financial markets appear as a perfect playground where models can be tested and where repeatability of empirical evidences are well-established features differently from, for instance, Macro-Economy and Micro-Economy. Thus Finance has been the first point of contact for the interdisciplinary application of methods and tools deriving from Physics and it has been also the starting point of this work. We investigated the origin of the so-called Stylized Facts of financial markets (i.e. the statistical properties of financial time series) in the framework of agent-based models. We found that Stylized Facts can be interpreted as a finite size effect in terms of the number of effectively independent agents (i.e. strategy) which results to be a key variable to understand the self-organization of financial markets. As a second issue we focused our attention on the order book dynamics both from a theoretical and a data oriented point of view. We developed a zero intelligence model in order to investigate the role of vanishing liquidity in the price response to incoming orders. Within the framework of this model we have analyzed the effect of the introduction of strategies pointing out that simple strategic behaviors can explain bursts of intermittency and long memory effects. On the other hand we quantitatively showed that there exists a feedback effect in markets called self-fulfilling prophecy which is the mechanism through which technical trading can exist and work. This feature is a very interesting quantitative evidence of a self-reinforcement of agents’ belief. Last but not least nowadays we live in a computerized and networked society where many of our actions leave a digital trace and affect other people’s actions. This has lead to the emergence of a new data-driven research field. In this work we highlighted how non financial data can be used to track financial activity, in detail we investigate query log volumes, i.e. the volumes of searches for a specific query done by users in a search engine, as a proxy for trading volumes and we find that users’ activity on Yahoo! search engine anticipates trading volume by one-two days. Differently from Finance, Economics is far from being an ideal candidate to export the methodology of Natural Sciences because of the lack of empirical data since controlled (and repeatable) experiments are totally artificial while real experiments are almost incontrollable and non repeatable due to a high degree of non stationarity of economical systems. However, the application of method deriving from complexity to the Economics of Growth is one of the more important achievement of the work here developed. The basic idea is to study the network defined by international trade flows and introduce a (non-monetary) metric to measure the complexity and the competitiveness of countries’ productive system. In addition we are able to define a metric for products’ quality which overcomes traditional economic measure for the quality of products given in terms of hours of qualified labour needed to produce a good. The method developed provides some impressive results in predicting economical growth of countries and offers many opportunities of improvements and generalizations

AN EVALUATION OF AN ENTROPY BASED INDEX OF SEGREGATION

This paper reviews the properties suggested in the methodological literature on the measurement of gender segregation by occupation. It is found that an index of segregation based on the entropy concept satisfies twelve basic axioms previously proposed in the single-dimensional case. This index can be expressed as the sum of a between-group and a within-group term in the two-dimensional case. In pair-wise comparisons, it can be meaningfully decomposed into three terms, one of which is independent of both the gender composition of the population and the population’s distribution across occupations. Finally, it can be motivated as two different loglikelihood tests in nonparametric econometric models. Other existing measures of segregation either fail to satisfy one or more of the basic axioms, do not admit a between/within decomposition, have not been motivated from a statistical approach, or are based on more restricted econometric models.