Working Papers

Resolving Indeterminacy with Neural Network Learning: Sinks become Sources

This paper uses neural network learning to identify learnable rational expectations equilibria in environments where equilibrium behaviour is indeterminate under rational expectations in some regions of the state space. The identified rational expectations equilibria acts as a source in locally indeterminate regions, meaning that endogenous variables are repelled and spend very little time in their neighbourhood. These results contrast sharply with the perfect-foresight behaviour in these environments, in which locally indeterminate regions act as a sink, attracting endogenous variables to their neighbourhood. Previous work has analysed such systems under perfect foresight or perturbation around steady states, discussing behaviour in the locally indeterminate region as acting as a sink. Such emphasis would appear to be misplaced, since under rational expectations the locally indeterminate region is a source not a sink. It is also shown that more familiar learning algorithms, such as recursive least square will converge to qualitatively similar equilibria, but the flexibility of a neural network is necessary for this equilibrium to be consistent with rational expectations. These results have potentially important implications in a wide range of contexts, as demonstrated by applying neural network learning to a simple model in which monetary policy is constrained by a Zero Lower Bound. If the indeterminacy due to this constraint on policy is bounded, agents can learn a fully-stochastic equilibrium with multiple steady states where transitory shocks can have permanent effects.

An Adaptive Dynamical Model of Default Cascades in Financial Networks with Damian Smug, Peter Ashwin and Didier Sornette (in submission)

We present a model of the dynamics of the contagion in financial networks. We assume that the health of a financial institution is described by a single variable represent net worth as a proportion of asset holdings, that becomes zero at default. We argue that differences in the growth of assets and liabilities can give a stable defaulted as well as a stable healthy state. Stochastic balance sheet shocks can push an institution to the bankruptcy state and lead to further bankruptcy cascades. We introduce contagion between institutions by adapting the shape of the potential landscape so as to make it easier to default given others defaulted shortly beforehand, motivated by links between institutions' balance sheets. The introduced model provides a microscopic dynamical description of the default process, since the default events are constructed via a stochastic dynamical process, rather than just point event modelled by a point process. The correspondence that we find provides a stochastic micro-foundation of the models of defaults' intensity.

Multimodal Bayesian Topic Regression with Maximillian Ahrens, Jan-Peter Calliess and Vu Nguyen (in submission)

Text data is an important source of information that can supplement numerical features to improve prediction performance and help to identify causal effects. This paper presents a Multimodal Bayesian Topic Regression (MBTR) model that uses both text and numerical information to predict a response variable. To this end, we combine a supervised Bayesian topic model with a Bayesian regression framework and perform supervised representation learning for the text features jointly with the training of the regression parameters. Our model makes two main contributions. First, we provide a regression framework that allows sound statistical inference in settings when both text and numerical features are of relevance. We show with a synthetic dataset that our joint approach is necessary to recover true parameters when text and numerical features are correlated. Second, experiments on two public real-world datasets demonstrate that our joint and supervised learning strategy yields superior prediction performance in comparison to competing approaches - including deep neural networks - whilst not coming at the cost of higher perplexity scores on document modelling tasks.

Work in Progress

Financial News Media and Volatility: is there more to Newspapers than News?

It is an open question what role the media plays in financial markets, whether it is a causal one, and if so whether this effect is of aggregate importance. Exploiting the publication time of articles in the Financial Times print newspaper and controlling for realised returns and expected volatility implied by option prices, a potentially causal effect of media coverage on firm-level volatility is identified. This suggests that the media does not play a purely intermediary role in financial markets. While this effect of media coverage is important at a firm-level, analysis of spillovers suggests it has limited aggregate implications.

The Shifting Focus of Central Bankers

This paper quantifies the focus of central bank communication and news media, offers an explanation for its variation over time, and shows a robust co-movement in this focus. A model of multidimensional uncertainty and limited attention is proposed to explain the shifting focus of central bank communication. Evidence from the Survey of Professional Forecasters is used to support this explanation, suggesting that not only does central bank communication contain information that can improve private sector “nowcasts”, but its focus shifts to cover variables about which there is greater uncertainty. An event study approach is used to show a potentially causal influence of Federal Reserve communication on the focus of US news media, implying that central banks have some power even if their own communication does not reach agents directly. Finally, I show that the communication of three different central banks (Federal Reserve, Bank of England and European Central Bank from 1997 to 2014) co-move, and that the focus of the Federal Reserve’s communication appears to lead that of other central banks.

NuCamp Virtual PhD Workshop Call for Papers.