1,721,025 research outputs found
Stock prices prediction via tensor decomposition and links forecast
Full text linkMany complex systems display fluctuations between alternative states in correspondence to tipping points. These critical shifts are usually associated with generic empirical phenomena such as strengthening correlations between entities composing the system. In finance, for instance, market crashes are the consequence of herding behaviors that make the units of the system strongly correlated, lowering their distances. Consequently, determining future distances between stocks can be a valuable starting point for predicting market down-turns. This is the scope of the work. It introduces a multi-way procedure for forecasting stock prices by decomposing a distance tensor. This multidimensional method avoids aggregation processes that could lead to the loss of crucial features of the system. The technique is applied to a basket of stocks composing the S&P500 composite index and to the index itself so as to demonstrate its ability to predict the large market shifts that arise in times of turbulence, such as the ongoing financial crisis
Macroeconomic stability and heterogeneous expectations
No full textThe recent macroeconomic literature has been stressing the role of heterogeneous expectations in the formulation of monetary policy and recent laboratory experiments provided more evidence about this phenomenon.
We use a simple model made up by the standard aggregate demand function, the New Keynesian Phillips curve and a Taylor rule to deal with di fferent issues, such as the stabilizing eff ect of diff erent monetary policies in a system populated by heterogeneous agents. The dynamic properties of the system depends crucially on the set of forecasting rules, on agent sensitivity in choosing the best predictor and on Central Bank's reaction to inflation. In particular we investigate whether the policy makers can sharpen macroeconomic stability in the presence of heterogeneous expectations about future in
inflation and output gap and how this framework is able to reduce volatility and distortion in the whole system
The topology of cross-border exposures: beyond the minimal spanning tree approach
Full text linkThe recent financial crisis has stressed the need to understand financial systems as networks of interdependent countries, where crossborder financial linkages play the fundamental role. It has also been emphasized that the relevance of these networks relies on the representation of changes follow on the occurrence of stress events. Here, from series of interbank liabilities and claims over different time periods, we have developed networks of positions (net claims) between countries. Besides the Minimal Spanning Tree analysis of the time-constrained networks, a coefficient of residuality is defined to capture the structural evolution of the network of cross-border financial linkages. Because some structural changes seem to be related to the role that countries play in the financial context, networks of debtor and creditor countries are also developed. Empirical results allows to relate the network structure that emerges in the last years to the globally turbulent period that has characterized financial systems since the latest nineties. The residuality coefficient highlights an important modification acting in the financial linkages across countries in the period 1997-2011, and situates the recent financial crises as replica of a larger structural change going on since 1997
Managing monetary policy in a New Keynesian model with many beliefs types
No full textThis paper considers a standard New Keynesian model with heterogeneous expectations on the future level of inflation and output. A biased perception of the target pursued by the Central Bank may arise due to idiosyncrasies in information processing, leading to heterogeneous beliefs about the target. We consider an arbitrarily large number of agents’ beliefs and apply the concept of Large Type Limit. We find that an increase in the sensitivity of agents in selecting the optimal prediction strategy or in the spread of beliefs is crucial for the extent of the Central Bank to stabilize the economy. When the predictors are largely dispersed around the target, the Taylor principle is a requisite for stability since it prevents the self-fulfilling reinforcement mechanism between the realizations of the relevant macroeconomic variables and the forecasts of the agents. When the set of beliefs is somehow anchored to the target, stability can be achieved with weaker monetary policy
The network origins of Schumpeterian innovation
Full text linkThis 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
Modeling Natural Disaster via Unbalanced Regularized Optimal Transport
No full textNatural disasters have far-reaching consequences that evolve across time and geography, requiring robust methods for analyzing their spatio-temporal impacts. This short paper introduces a novel framework based on unbalanced regularized optimal transport to model the redistribution of quarterly mortality rates from 2000-Q1 to 2024-Q4, applying our proposal to the International Emergency Events Database (EM-DAT). By discretizing disaster-affected regions into a geographic grid, we track how mortality distributions shift between consecutive quarters, accounting for imbalanced datasets where the total mass (e.g., deaths) varies over time. The unbalanced optimal transport formulation enables the modeling of changes in both population distribution and disaster severity, while entropy regularization ensures computational efficiency and robustness to noise. Our results reveal significant spatio-temporal patterns in mortality distributions, identifying regions of heightened vulnerability and potential drivers behind these changes
- …
