1,721,015 research outputs found
Financial market predictability with tensor decomposition and links forecast
No full textAbstract Inspecting financial markets from a complex network perspective means to extract relationships and interdependencies from stock price time series. Correlation networks have been shown to adequately capture such dependence structures between financial assets. Moreover, researchers have observed modifications in the correlation structure between stock prices in the face of a market turbulence. This happens because financial markets experience sudden regime shifts near phase transitions such as a financial crisis. These abrupt and irregular fluctuations from one state to another lead to an increase of the correlation between the units of the system, lowering the distances between the stocks in a correlation network. The aim of this paper is to predict such abrupt changes by inferring the forthcoming dynamic of stock prices through the prediction of future distances between them. By introducing a tensor decomposition technique to empirically extract complex relationships from prices’ time series and using them in a portfolio maximization application, this work first illustrates that, near critical transitions, there exit spatial signals such as an increasing spatial correlation. Secondly using this information in a portfolio optimization context it shows the ability of the methodology in forecasting future stock prices through these spatial signals. The results demonstrate that an optimization approach aiming at minimizing the interconnectedness risk of a portfolio by maximizing the signals produced by tensor decomposition induces investment plans superior to simpler strategies. Trivially speaking portfolios made up of strongly connected assets are more vulnerable to shock events than portfolios of low interconnected assets since heavily connected assets, being close to a transition point, carry a significant amount of interconnectedness risk, i.e. tail events propagate more quickly to these assets
An empirical analysis of the global input–output network and its evolution
No full textThis paper studies the global production network using a general equilibrium model calibrated on world input–output data. The analysis of propagation of idiosyncratic productivity shocks in the calibrated model allows to define a model-based network centrality measure. Such measure is used to investigate the topology of the global input–output network in 2014 and its evolution from 2000 to 2014. We find that new influential sectors have emerged over time. Moreover, we show that the global production system has evolved to become more sensitive to idiosyncratic productivity shocks and that this result is related to the increase of the intermediate input intensity of production
Mobility-based real-time economic monitoring amid the COVID-19 pandemic
No full textMobility restrictions have been identified as key non-pharmaceutical interventions to limit the spread of the SARS-COV-2 epidemics. However, these interventions present significant drawbacks to the social fabric and negative outcomes for the real economy. In this paper we propose a real-time monitoring framework for tracking the economic consequences of various forms of mobility reductions involving European countries. We adopt a granular representation of mobility patterns during both the first and second waves of SARS-COV-2 in Italy, Germany, France and Spain to provide an analytical characterization of the rate of losses of industrial production by means of a nowcasting methodology. Our approach exploits the information encoded in massive datasets of human mobility provided by Facebook and Google, which are published at higher frequencies than the target economic variables, in order to obtain an early estimate before the official data becomes available. Our results show, in first place, the ability of mobility-related policies to induce a contraction of mobility patterns across jurisdictions. Besides this contraction, we observe a substitution effect which increases mobility within jurisdictions. Secondly, we show how industrial production strictly follows the dynamics of population commuting patterns and of human mobility trends, which thus provide information on the day-by-day variations in countries’ economic activities. Our work, besides shedding light on how policy interventions targeted to induce a mobility contraction impact the real economy, constitutes a practical toolbox for helping governments to design appropriate and balanced policy actions aimed at containing the SARS-COV-2 spread, while mitigating the detrimental effect on the economy. Our study reveals how complex mobility patterns can have unequal consequences to economic losses across countries and call for a more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic activities
The motifs of risk transmission in multivariate time series: Application to commodity prices
No full textOn the fragility of the Italian economic territories under SARS-COV2 lockdown policies
Full text linkWe leverage a granular representation of mobility patterns before and during the first wave of SARS-COV2 in Italy to investigate the economic consequences of various forms of lockdown policies when accounting for mobility restrictions between and within local jurisdictions, i.e. municipalities, provinces and regions. We provide an analytical characterization of the rate of economic losses using a network-based spectral method. The latter treats the spread of contagion of economic losses due to commuting restrictions as a dynamical system stability problem. Our results indicate that the interplay between lower level of smartworking and the polarization of commuting flows to fewer local labor hubs in the South of Italy makes Southern territories extremely important in spreading economic losses. We estimate an economic contraction of total income derived from commuting restrictions in the range of 10-30% depending on the economic assumptions. However, alternative policies proposed during the second wave of SARS-COV2 can pose a greater risk to Northern areas due to their higher degree of mobility between jurisdictions than Southern ones. The direction of economic losses tend to propagate from large to medium-small jurisdictions across all alternative lockdown policies we tested. Our study shows how complex mobility patterns can have unequal consequences to economic losses across the country and call for more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic output
The network origins of Schumpeterian innovation
No full textThis paper investigates the key driving features of the evolving long-term division of innovative labor in biotechnology and pharmaceuticals from 1981 to 2012. Our main goal is to find if technological trajectories and mechanisms discovered by Orsenigo et al. (Res Policy 30(3): 485–508, 2001) as the main drivers of the structural configuration of the network of collaborative alliances have been at work in the long-term evolution of the industry. We extensively analyze the evolving dynamics of the degree distribution and the higher order properties of the R&D network. As in Orsenigo et al. (Res Policy 30(3): 485–508, 2001), we find that polarization through preferential attachment driven by large pharmaceutical companies as Developers and by the entry of new specialized biotechnology companies acting as Originators of new R&D opportunities dominated the early stages of the biotechnology revolution. Later on the evolution of the collaborative network has been shaped by roles’ transitions between Originators and Developers of innovative ideas. Against this background, we introduce parsimonious model of network formation and evolution is introduced, to account for some essential features of the data generating processes underlying the evolution of the network
Network models to improve robot advisory portfolios
Full text linkRobot advisory services are rapidly expanding, responding to a growing interest people have in directly managing their savings. Robot-advisors may reduce costs and improve the quality of asset allocation services, making user’s involvement more transparent. Against this background, there exists the possibility that robot advisors underestimate market risks, especially during crisis times, when high order interconnections arise. This may lead to a mismatch between investors’ expected and actual risk. The aim of this paper is to overcome this issue, taking into account not only investors’ risk preference but also their attitude towards interconnectdness. To achieve this aim, we combine random matrix theory with correlation networks and extend the Markowitz’ optimisation problem to a third dimension. To demonstrate the practical advantage of our proposed approach we employ daily returns of a large set of Exchange Traded Funds, which are representative of the financial products employed by robot-advisors
Use of polyoxyethylenic compounds as vulcanization co-adjuvant for chloroacrylic rubbers
No full textCommodity prices co-movements and financial stability: A multidimensional visibility nexus with climate conditions
Full text linkThis paper investigates the nexus between climate-related variables, commodity price co-movements and financial stability. First, we project the commodity price time series onto a multilayer network. Centrality measures computed on the network are used to detect the existence of common trends between the series and to characterize the role of different nodes during phases of market downturns and upturns, unveiling the onset of financial instability. Then, an econometric analysis is introduced to show how climate-related variables affect financial stability by influencing co-movements of commodity prices. Overall, the paper reveals how synthetic indicators of commodity price co-movements generate valuable signals to study the nexus between climate-related conditions and the dynamics of financial systems
Network based credit risk models
No full textPeer-to-Peer lending platforms may lead to cost reduction, and to an improved user experience. These improvements may come at the price of inaccurate credit risk measurements, which can hamper lenders and endanger the stability of a financial system. In the article, we propose how to improve credit risk accuracy of peer to peer platforms and, specifically, of those who lend to small and medium enterprises. To achieve this goal, we propose to augment traditional credit scoring methods with “alternative data” that consist of centrality measures derived from similarity networks among borrowers, deduced from their financial ratios. Our empirical findings suggest that the proposed approach improves predictive accuracy as well as model explainability
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