Economics (E) Session 3
Time and Date: 16:15 - 18:00 on 19th Sep 2016
Room: A - Administratiezaal
Chair: Marco Alberto Javarone
|202|| Which past should be used to forecast the future of China?
Abstract: Forecasting the evolution of economic systems is one of the key challenges for Economics and it is a natural consequence of the ultimate mission of this discipline: explain why an economic system grows. A natural way to tackle this forecast is the use of the past but the state of an economic system is typically specified by a very huge set of indicators and variables. This fact prevents to provide reliable forecast especially on the long term. We will discuss how Economic Complexity, a data-driven approach to growth, can provide a scientific grounding to this challenge. The starting point will be a recent empirical framework, the Selective Predictability Scheme, hereinafter SPS, which provides a practical guide on how to use the past to forecast the future and in particular which past should be used to forecast which future. It also provides an estimate of the goodness of this strategy, introducing the concept of heterogeneity of the degree of predictability which challenges regressive approaches. In practice, the SPS acts as a feature selector in a suitable space which allows as to find events which are candidates to be predictors for the growth of a country and to simultaneously estimate their goodness. This space is defined by GDP per capita and by a synthetic measure of the competitiveness of a country named Fitness. The dynamics in this plane permits to interpret several facts concerning country growth developments as well, including the the exit from the poverty trap and why some emerging countries catch up with developed economies. We will also discuss how this search for analogues to forecast country’s future can be refined and extended even at a micro level by measuring the distance between countries’ productive systems at product and sector level. Close
|Matthieu Cristelli, Andrea Tacchella and Luciano Pietronero|
|89|| Can Twitter sentiment predict Earning Announcements returns?
Abstract: Social media are increasingly reflecting and influencing behavior of other complex systems. We investigate the relations between Twitter and stock market, Dow Jones 30 in particular. In our previous work we adapted the well-known "event study" from economics  to the analysis of Twitter data. We defined "events" as peaks of Twitter activity, and automatically classified sentiment in Twitter posts. During the Twitter peaks, we found significant dependence between the Twitter sentiment and stock returns . Can these results be used to devise a trading strategy? We focus on the Earning Announcements (EA) only, for which the dates are well known in advance. We compare the effects of Twitter sentiment on Cumulative Abnormal Returns (CAR), on the day of EA to the day before. Twitter sentiment is an indicator of the direction of CAR movement, when measured on the day of EA, consistent with our previous results . Somehow surprisingly, the same relation holds even if we consider the Twitter sentiment on the day immediately _before_ the EA. The amount of CAR is lower (about 1%), but the dependence is still statistically significant at the 5% level. These results suggest a simple trading strategy, ignoring the transaction costs. Classify the Twitter sentiment before the EA, take a long position for positive sentiment, and short for negative sentiment. Currently we consider the Twitter and trading data during a period of 15 months. We plan to extend the study to the period of the last three years. We will also investigate if there are higher abnormal returns for specific classes of stocks.  MacKinlay. Event studies in economics and finance. Journal of economic literature:13--39, 1997.  Ranco, Aleksovski, Caldarelli, Grcar, Mozetic. The effects of Twitter sentiment on stock price returns. PLoS ONE 10(9):e0138441, 2015. Close
|Igor Mozetic, Peter Gabrovsek, Darko Aleksovski and Miha Grcar|
|147|| Architectures of Power: The Evolution of the Global Ownership Network
Abstract: The global financial crisis has had a profound disruptive effect on the world economy. Indeed, the repercussions can be observed to this day. However, how has this upheaval impacted the structure of economic power worldwide? To answer this question, we present the first exhaustive analysis of the evolution of the entire global ownership network, comprised of tens of millions of nodes and links. By utilizing an efficient algorithm to detect the influence of individual (or groups of) economic actors in the network and analyzing the network's topology, the transformation of the global economic system is tracked in time. We observe resilience both in the network structure overall and specifically in the emergent power-structures harboring a few highly influential actors. In detail, the network approximately doubles in size from 2007 to 2012, starting with 16,636,351 nodes and 14,724,489 links. The network forms one dominant, largest connected component (LCC) displaying a bow-tie topology. The main feature in this structure is comprised of 715,629 nodes (+/-8.6%) and represents 70.5% (+/-2.1%) of the total economic value in the system (proxied by operating revenue). Our algorithm, for the first time, is able to compute the monetary value of economic influence of shareholders in USD, meaning the value of the portion of the network an actor can recursively influence. Not only can we now rank all the shareholders in the network but crucially compute the cumulative influence value for a set of economics actors. We uncover that the tiny core of the LCC is comprised of the most influential actors. Nested within this core is a "super-entity" made up of approximately 100 to 160 nodes, capable of wielding disproportionate influence corresponding to 16 to 20 trillion USD. Tracking the evolution of the cumulative influence value of the various power-structures in the network reveals their resilience. Close
|James Glattfelder and Stefano Battiston|
|503|| Higher-order correlations of consumption patterns in social-economic networks
Abstract: We analyze a multi-modal dataset collecting the mobile phone communication and bank transactions of a large number of individuals living in Mexico . This corpus allows for an innovative global analysis both in term of social network and its relation to the economical status and merchant habits of individuals. We introduce several measures to estimate the socioeconomic status of each individual together with their purchasing habits. Using these information we identify distinct socioeconomic classes, which reflect strongly imbalanced distribution of purchasing power in the population. After mapping the social network of egos from mobile phone interactions, we show that typical consumption patterns are strongly correlated with the socioeconomic classes and the social network behind. We observe these correlations on the individual and social class level. In the second half of our study we detected correlations between merchant categories commonly purchased together and introduced a correlation network which in turn emerged with communities grouping homogeneous sets of purchase categories. We further analyze some multivariate relations between merchant categories and average demographic and socioeconomic features, and find meaningful patterns of correlations giving insights into higher-order correlations in purchasing habits of individuals. We identify several new directions to explore in the future. One possible track would be to better understand the role of the social structure and interpersonal influence on individual purchasing habits, while the exploration of correlated patterns between commonly purchased brands assigns another promising directions. Beyond our over goal to better understand the relation between social and consuming behaviour we believe that these results may enhance applications to better design marketing, advertising, and recommendation strategies.  C. Sarraute, P. Blanc, and J. Burroni. A study of age and gender seen through mobile phone usage patterns in mexico. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM, 836–843 (2014). Close
|Yannick Léo, Márton Karsai, Carlos Sarraute and Eric Fleury|
|223|| Credit Risk Valuation in Financial Networks
Abstract: In this paper, we develop a new valuation model to carry out an ex ante valuation of the claims, in a network context, in the presence of uncertainty deriving from shocks on the external assets of banks, and, at the same time, providing an endogenous and consistent recovery rate. To our knowledge, our valuation model is the first to provide such a comprehensive and consistent way to carry out asset valuation in a network of liabilities. The new valuation model encompasses both the ex-post approaches of Eisenberg and Noe and Rogers and Veraart and the ex-ante approach of Merton and DebtRank in the sense that each of these models can be recovered with the appropriate parameter set. We characterize the existence and maximality of the solution of the valuation problem. Further, we define an algorithm to carry out the network-adjusted claim valuation and provide sufficient conditions for convergence to the maximal solution to a given precision in finite time. Finally we show that the valuation given by the new model converges towards Eisenberg and Noe valuation as maturity approaches, thus providing a consistent valuation procedure. Close
|Paolo Barucca, Marco Bardoscia, Fabio Caccioli, Gabriele Visentin, Marco D'Errico, Guido Caldarelli and Stefano Battiston|
|61|| Foods, fuels or finances: Which prices matter for biofuels?
Abstract: We examine co-movements between biofuels and a wide range of commodities and assets in the US, Europe, and Brazil. We analyze a unique dataset of 32 commodities and relevant assets (between 2003 and 2015) which is unprecedented in the biofuels literature. We combine the minimum spanning trees correlation filtration to detect the most important connections of the broad analyzed system with continuous wavelet analysis which allows for studying dynamic connections between biofuels and relevant commodities and assets and their frequency characteristics as well. We confirm that for the Brazilian and US ethanol, their respective feedstock commodities lead the prices of biofuels, and not vice versa. This dynamics remains qualitatively unchanged when controlling for the influence of crude oil prices. As opposed to the Brazilian and US ethanol, the European biodiesel exhibits only moderate ties to its production factors. We show that financial factors do not significantly interact with biofuel prices. Close
|Ondrej Filip, Karel Janda, Ladislav Kristoufek and David Zilberman|