Recurrence quantification analysis of global stock markets

This study investigates the presence of deterministic dependencies in international stock markets using recurrence plots and recurrence quantification analysis (RQA). The results are based on a large set of free float-adjusted market capitalization stock indices, covering a period of 15 years. The statistical tests suggest that the dynamics of stock prices in emerging markets is characterized by higher values of RQA measures when compared to their developed counterparts. The behavior of stock markets during critical financial events, such as the burst of the technology bubble, the Asian currency crisis, and the recent subprime mortgage crisis, is analyzed by performing RQA in sliding windows. It is shown that during these events stock markets exhibit a distinctive behavior that is characterized by temporary decreases in the fraction of recurrence points contained in diagonal and vertical structures.Recurrence plot, Recurrence quantification analysis, Nonlinear dynamics, International stock markets

Herding and Social Pressure in Trading Tasks: A Behavioural Analysis

We extend the experimental literature on Bayesian herding using evidence from a financial decision-making experiment. We identify significant propensities to herd increasing with the degree of herd-consensus. We test various herding models to capture the differential impacts of Bayesian-style thinking versus behavioural factors. We find statistically significant associations between herding and individual characteristics such as age and personality traits. Overall, our evidence is consistent with explanations of herding as the outcome of social and behavioural factors. Suggestions for further research are outlined and include verifying these findings and identifying the neurological correlates of propensities to herd

Clusters of firms in space and time

The use of the K-functions (Ripley, 1977) has become recently popular in the analysis of the spatial pattern of firms. It was first introduced in the economic literature by Arbia and Espa (1996) and then popularized by Marcon and Puech (2003), Quah and Simpson (2003), Duranton and Overman (2005) and Arbia et al. (2008). In particular in Arbia et al. (2008) we used Ripley’s K-functions as instruments to study the inter-sectoral co-agglomeration pattern of firms in a single moment of time. All this researches have followed a static approach, disregarding the time dimension. Temporal dynamics, on the other hand, play a crucial role in understanding the economic and social phenomena, particularly when referring to the analysis of the individual choices leading to the observed clusters of economic activities. With respect to the contributions previously appeared in the literature, this paper uncovers the process of firm demography by studying the dynamics of localization through space-time K-functions. The empirical part of the paper will focus on the study of the long run localization of firms in the area of Rome (Italy), by concentrating on the ICT sector data collected by the Italian Industrial Union in the period 1920- 2005.Agglomeration, Non-parametric measures; Space-time K-functions, Spatial clusters, Spatial econometrics.

Effectiveness of Social Media Community Using Optimized Clustering Algorithm

Now-a-days social media is used to the introduce new issues and discussion on social media. More number of users participates in the discussion via social media. Different users belong to different kind of groups. Positive and negative comments will be posted by user and they will participate in discussion. Here we proposed system to group different kind of users and system specifies from which category they belong to. For example film industry, politician etc. Once the social media data such as a user messages are parsed and network relationships are identified, data mining techniques can be applied to group of different types of communities. We used K-Means clustering algorithm to cluster data. In this system we detect communities by the clustering messages from large streams of social data. Our proposed algorithm gives better a clustering result and provides a novel use-case of grouping user communities based on their activities. This application is used to the identify group of people who viewed the post and commented on the post. This helps to categorize the users

Turning-point indicators from business surveys: real-time detection for the euro area and its major member countries

We present tools for real-time detection of turning points in the industrial production growth-cycle of the euro area and its four largest economies. In particular, we apply a multivariate hidden Markov model to national survey results – i.e. to the earliest information about current economic developments - in order to estimate the probability of expansionary and recessionary phases. The balances of opinions used as inputs of the model are selected by ranking them according to their degree of commonality with respect to the cyclical fluctuations of the industrial sector, as estimated with the Generalized Dynamic Factor Model. The indicators appear reliable and stable.business cycle, hidden Markov model, business surveys

Crises and collective socio-economic phenomena: simple models and challenges

Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory. In this paper, we review recent efforts to include heterogeneities and interactions in models of decision. We argue that the Random Field Ising model (RFIM) indeed provides a unifying framework to account for many collective socio-economic phenomena that lead to sudden ruptures and crises. We discuss different models that can capture potentially destabilising self-referential feedback loops, induced either by herding, i.e. reference to peers, or trending, i.e. reference to the past, and account for some of the phenomenology missing in the standard models. We discuss some empirically testable predictions of these models, for example robust signatures of RFIM-like herding effects, or the logarithmic decay of spatial correlations of voting patterns. One of the most striking result, inspired by statistical physics methods, is that Adam Smith's invisible hand can badly fail at solving simple coordination problems. We also insist on the issue of time-scales, that can be extremely long in some cases, and prevent socially optimal equilibria to be reached. As a theoretical challenge, the study of so-called "detailed-balance" violating decision rules is needed to decide whether conclusions based on current models (that all assume detailed-balance) are indeed robust and generic.Comment: Review paper accepted for a special issue of J Stat Phys; several minor improvements along reviewers' comment