An integrated macroprudential stress test of bank liquidity and solvency

We propose a new measure of systemic financial distress that incorporates idiosyncratic and systemic risks in the financial system network. Using this measure, we develop an integrated stress test of bank liquidity and solvency risks based on the dynamics of financial distress within the banking system network. We apply this stress test framework to the US banking system and identify systemic vulnerability of individual banks as well as the resilience of the system as a whole to an economic shock. The framework helps us identify and monitor systemic interdependencies between banks. The proposed stress testing framework is useful for practical macroprudential monitoring and is informative for policy making.     

Daily peak electrical load forecasting with a multi-resolution approach

In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders in the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are (a) a general and formal introduction to the multi-resolution modelling approach, (b) a discussion of modelling approaches at different resolutions implemented via generalised additive models and neural networks, and (c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.     

Moving beyond the transfer dyad: Exploring network influences on transfer effectiveness

There is a wealth of research analyzing sender-receiver transfers within multinational corporations focusing on the characteristics of (a) the sender, (b) the receiver, (c) the knowledge subject to transfer, and (d) the immediate transfer context. However, less is known about how networks external to the sender-receiver transfer dyad influence the outcomes of a transfer project. In this paper, we focus on the receiving subunits' internal and external networks and how embedded actors in these networks influence transfer effectiveness. More specifically, by means of an inductive multiple-case study, we explore how internal and external networks of subunits influence the effectiveness of capability transfers from headquarters to subunits. We study 18 transfers of the same capability from headquarters to subunits’ innovation projects. We theorize about how the capacity and configuration of receiving subunits’ networks can have a unique and detrimental influence on transfer effectiveness. The results of our study suggest that the receiver in a transfer project is not so much a specific unit as a network.     

Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data

We propose an out-of-sample prediction approach that combines unrestricted mixed-data sampling with machine learning (mixed-frequency machine learning, MFML). We use the MFML approach to generate a sequence of nowcasts and backcasts of weekly unemployment insurance initial claims based on a rich trove of daily Google Trends search volume data for terms related to unemployment. The predictions are based on linear models estimated via the LASSO and elastic net, nonlinear models based on artificial neural networks, and ensembles of linear and nonlinear models. Nowcasts and backcasts of weekly initial claims based on models that incorporate the information in the daily Google Trends search volume data substantially outperform those based on models that ignore the information. Predictive accuracy increases as the nowcasts and backcasts include more recent daily Google Trends data. The relevance of daily Google Trends data for predicting weekly initial claims is strongly linked to the COVID-19 crisis.     

Towards an early warning system for sovereign defaults leveraging on machine learning methodologies

In this study, we address the topic of credit risk stemming from central governments from a technical point of view. First, we explore various econometric and machine learning techniques to build an enhanced sovereign rating system that effectively differentiates the risk of default among countries. Our empirical results indicate that the machine learning method of XGBOOST has a superior out-of-sample and out-of-time predictive performance. Then, we use the models developed to calibrate a sovereign rating system and provide useful insights into the set-up of a parsimonious early warning system. Our results provide a more concise view of the most robust method for classifying countries’ default risk with significant regulatory implications, given that the efficient assessment of sovereign debt is crucial for effective proactive risk measurement.     

Machine learning and the cross-section of emerging market stock returns

This paper compares various machine learning models to predict the cross-section of emerging market stock returns. We document that allowing for non-linearities and interactions leads to economically and statistically superior out-of-sample returns compared to traditional linear models. Although we find that both linear and machine learning models show higher predictability for stocks associated with higher limits to arbitrage, we also show that this effect is less pronounced for non-linear models. Furthermore, significant net returns can be achieved when accounting for transaction costs, short-selling constraints, and limiting our investment universe to big stocks only.     

Detecting the hidden asymmetric relationship between crude oil and the US dollar: A novel neural Granger causality method

The relationship between financial series is not always easy to detect due to their underlying asymmetry and nonlinearity. Both characteristics are not usually considered simultaneously, which may lead to many drawbacks in financial analysis. Hence, we develop a novel neural Granger causality method from both asymmetric and nonlinear perspectives and further revisit the response and impact of crude oil on the exchange rate. Our findings reveal the unidirectional nonlinear and asymmetric effect of crude oil on the exchange rate; that is, positive and negative oil prices can have a substantial impact on exchange rate shocks. Interestingly, this influence seems to strengthen after the Russia–Ukraine conflict. Besides, we also use simulation technology to evaluate the rationality and effectiveness of our proposed methods. Investors, policymakers, and scholars may be interested in our findings regarding the oil-dollar relationships; as well as interested in applying our methodology to other contexts.     

Shaking hands with common foes: Clique premium and information diffusion in private equity networks

The fastest growing segment of private equity deals is secondary buyouts (SBOs) sales from one private equity (PE) firm to another. We operationalize a novel FactSet database to map the network structures of secondary buyouts between PE firms. We offer three contributions. First, after controlling for economic covariates, we find that PE firms are almost three times more likely to transact if they share a partner, that is both firms belong to the same clique. Second, we find that the profitability of such transactions is unambiguously higher relative to the baseline only if these are the result of repeated interaction between firms belonging to the same cliques. In other words, a clique premium exists under repeated interaction. Third, we provide evidence that the economic incentive at the core of clique premium may be related to access to information. In fact, we show that information related to transactions diffuses through the network, with 23% and 16% of the information going one and two steps beyond transacting parties, respectively.     

Neural networks for estimating Macro Asset Pricing model in football clubs

The recent crisis caused by COVID-19 directly affected consumption habits and the stability sof financial markets. In particular, the football industry has been hit hard by this pandemic and therefore has more volatile stock prices. Given this new scenario, further research is needed to accurately estimate the value of the shares of football clubs. In this paper, we estimate an asset pricing model in football clubs with different compositions of risk nature using non-linear techniques of artificial neural networks. Usually, asset pricing models have been estimated with linear methods such as ordinary least squares. Our results show a precision higher than 90% for all the estimated models, which far exceeds those shown by linear methods in the previous literature. We find that the residual represents about 40% of the variance of the price-dividend ratio. Long-term risks follow in importance, and above all, the habit component and its behaviour in the face of changes. The importance of the residual component exists due to a low correlation between the asset price and consumer behaviour, but to a much lesser extent than that shown in previous studies. The estimation carried out with artificial neural networks, both the Deep Learning methods and especially the Quantum Neural Network, opens up new possibilities to estimate more efficiently the pricing of financial assets in the football industry.     

Data-driven forecasting for operational planning of emergency medical services

Emergency medical services (EMS) play a vital role in delivering pre-hospital care. The operational efficiency of such services is critical and adequate demand forecasts can contribute to such a goal. But for that, the available data need to be well characterized before being used. Previous studies have failed to address some important aspects of this need, such as exploring a comprehensive list of contextual data to decide which are relevant to explain the EMS demand behavior. Moreover, modern forecasting techniques have been explored in the EMS context, including neural networks, but the computational complexity inherent to the methods and their use was not discussed. Finally, it is also unclear how different demand patterns can be when predicting the volume of emergency calls considering the priority level and the number of dispatches according to vehicle type. This study proposes a generic data-driven forecasting method to address these shortcomings and to support operational decisions. The results obtained with the proposed method indicate that each priority call and vehicle type shows different patterns, which suggests that such differentiation should contribute to better resource allocation. At the same time, the operational impact of the demand shared by neighboring zones proved to be significant at bases near the border. The models developed resulted in important decision tools that can be used to predict the dynamic demand of EMS on an hourly or shift basis. Additionally, the method adds value for decision-makers that want to plan not only when and how many but also where resources are demanded, avoiding assumptions that impact the operational performance.