Bond Risk Premiums with Machine Learning

Bianchi, Daniele; Büchner, Matthias; Tamoni, Andrea

doi:10.1093/rfs/hhaa062

Cited by 240 publications

(70 citation statements)

References 114 publications

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“…We conclude that combining joint model predictions using multiple clusters with cluster-based weights provides substantial predictive gains, confirming recent evidence that machine learning type of tools are useful for predicting financial markets, see for example Gu et al (2020) and Bianchi et al (2020). Of course, additional gains may be obtained by playing with a more detailed cluster grouping and different performance scoring rules for weights associated with models inside a cluster.…”

Section: Treasury Bill Predictingsupporting

confidence: 82%

A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance

et al. 2020

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A Bayesian dynamic compositional model is introduced that can deal with combining a large set of predictive densities. It extends the mixture of experts and the smoothly mixing regression models by allowing for combination weight dependence across models and time. A compositional model with Logistic-normal noise is specified for the latent weight dynamics and the class-preserving property of the logistic-normal is used to reduce the dimension of the latent space and to build a compositional factor model. The projection used in the dimensionality reduction is based on a dynamic clustering process which partitions the large set of predictive densities into a smaller number of subsets. We exploit the state space form of the model to provide an efficient inference procedure based on Particle MCMC. The approach is applied to track the Standard & Poor 500 index combining 3712 predictive densities, based on 1856 US individual stocks, clustered in relatively small number of model sets. For the period 2007-2009, which included the financial crisis, substantial predictive gains are obtained, in particular, in the tails using Value-at-Risk. Similar predictive gains are obtained for the US Treasury Bill yield using a large set of macroeconomic variables. Evidence obtained on model set incompleteness and dynamic patterns in the financial clusters provide valuable signals for improved modelling and more effective economic and financial decisions.

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Section: Treasury Bill Predictingsupporting

confidence: 82%

A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance

et al. 2020

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“…They have been applied extensively and successfully to various fields, including image classification (Simonyan and Zisserman 2014) as well as speech recognition problems (Graves et al 2013). Gu et al (2020) and Bianchi et al (2020) apply neural networks and other machine learning methods to asset pricing with promising results. We take the off-shelf neural network methodology and apply it to quantify financial risk.…”

Section: Introductionmentioning

confidence: 99%

Modelling systemic risk using neural network quantile regression

Keilbar

Wang

2021

Empir Econ

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We propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building on the estimation results, we model systemic risk spillover effects in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the importance that nonlinearity plays for modelling systemic risk. We then propose three network-based measures from our fitted results. First, we use the Systemic Network Risk Index (SNRI) as a measure for total systemic risk. A comparison to the existing network-based risk measures reveals that our approach offers a new perspective on systemic risk due to the focus on the lower tail and to the allowance for nonlinear effects. We also introduce the Systemic Fragility Index (SFI) and the Systemic Hazard Index (SHI) as firm-specific measures, which allow us to identify systemically relevant firms during the financial crisis.

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“…Overall, the authors find that tree-based models and neural networks with three or four hidden layers perform the best. Bianchi et al (2020) perform a similar comparative study for predicting bond returns. Easley et al (2020) argue that machine leaning can help us better understand market microstructures, and this understanding is profitable.…”

Section: Asset Pricing Modelsmentioning

confidence: 99%

FinTech as a Game Changer: Overview of Research Frontiers

Hendershott

Zhang

Zhao

et al. 2021

Information Systems Research

102

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Technologies have spawned finance innovations since the early days of computer applications in businesses, most recently reaching the stage of disruptive innovations, such as mobile payments, cryptocurrencies, and digitization of business assets. This has led to the emerging field called financial technology or simply FinTech. In this editorial review, we first provide an overview on relevant technological, pedagogical, and managerial issues pertaining to FinTech teaching and research, with a focus on market trading, artificial intelligence, and blockchain in finance. And then we introduce the articles appearing in this special section. We hope that our discussions of potential research directions and topics in FinTech will stimulate future research in the fields of information systems and finance toward making their unique marks in the FinTech evolution and the associated business and societal innovations.

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Bond Risk Premiums with Machine Learning

Cited by 240 publications

References 114 publications

A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance

A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance

Modelling systemic risk using neural network quantile regression

FinTech as a Game Changer: Overview of Research Frontiers

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