Review Article: Perspectives on the Future of Asset Pricing

Brunnermeier, Markus K.; Farhi, Emmanuel; Koijen, Ralph S. J.; Ludvigson, Sydney C.; Lustig, Hanno; Nagel, Stefan; Piazzesi, Monika

doi:10.1093/rfs/hhaa129

Cited by 69 publications

(13 citation statements)

References 99 publications

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“…A policy of monetary easing increased bond prices—bonds paying a fixed coupon become more attractive as interest rates fall, thereby driving up demand and bond prices. Near-zero interest rates and quantitative easing have stimulated economic activity, resulting in booming asset prices, for example, stocks and CBs [ 63 – 65 ]. In particular, the information flow from bonds to CBs suggests that an increase in CB prices reflects the bond floor well, i.e., the sum of the present value of coupon and par resulting from an increase in bond price [ 66 ].…”

Section: Resultsmentioning

confidence: 99%

Information flow among stocks, bonds, and convertible bonds

Choi

Jeong

et al. 2023

PLoS ONE

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This study examines the information flow between convertible bonds (CBs) and other investment assets, such as stocks and bonds. In particular, we employ transfer entropy (TE) as a proxy for the causal effect between the two assets considering that one of the most widely used methods, Granger causality, requires strict assumptions. When adopting TE, we find that asymmetric information flow arising between assets depends on macroeconomic phases. The stock and bond markets affected the CB market prior to and during the global financial crisis, respectively. In the post-crisis period, we find no meaningful information exchange between CBs and other investment assets concerning their return series. However, we observe a significant cause–effect relationship between CBs and stocks in the rise–fall patterns of their price series. The findings suggest that the appearance of one-directional information flow depends on macroeconomic conditions and the level of data, for example, return series or price fluctuations. Accordingly, investors could exploit this pattern predictability in their portfolio management. In addition, policymakers must closely monitor the information flow among the three markets. When any two markets exchange information in a state of strong market integration, unbalanced regulation between them could lead to market distortions and regulatory arbitrage.

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Section: Resultsmentioning

confidence: 99%

Information flow among stocks, bonds, and convertible bonds

Choi

Jeong

et al. 2023

PLoS ONE

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“…); thus, the search was as comprehensive as WoS permits. The search was conducted over the period January 2011-August 2022; documents published prior to 2011 are extensively covered in previous review articles (e.g., Nagel 2021;Brunnermeier et al 2021;Fama and French 2004;Koumou 2020). The documents generated by WoS were exported in BibTeX format using the "Full Record and Cited References" option.…”

Section: Methodsmentioning

confidence: 99%

“…Advances and applications discussed in this review article provide a guide to future research, and, given the suggestions in the top papers reviewed, ingenious model specification approaches that are supported by economic theory and improved computational methods offer promising opportunities for future research on both the theory and practice of machine learning econometrics. Gu et al, for example, suggested that future work may focus on the use of economic theories of equilibrium to better understand the causes of improved prediction with ML econometric models, an emphasis which is discussed with Bayesian perspectives in Nagel (2021) and illustrated via future research perspectives in Brunnermeier et al (2021).…”

Section: Future Researchmentioning

confidence: 99%

“…A challenge in ML research is the development of economic theory-data consistent models that improve out-of-sample predictions but also help to identify the factors that contribute to it (e.g., Israel et al 2020;Nagel 2021) 2 . While the history of asset pricing research is rich, the perspectives for continued research look promising (Brunnermeier et al 2021), and recent modeling and advances in computational methods contribute to an enhanced understanding of their applicability and interpretation in asset pricing. New neural networks and deep learning algorithms are efficient, flexible, and more accurate in modeling nonlinearities, parsimony, and out-of-sample forecasting.…”

Section: Introductionmentioning

confidence: 99%

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A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing

Zapata

Mukhopadhyay

2022

JRFM

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Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better suited for, problems of prediction. While some ML methods have been applied in econometrics for decades, their success in prediction has been limited, and examples of this abound in the asset pricing literature. In recent years, the ML literature has advanced new, more efficient, computation methods for regularization, modeling nonlinearity, and improved out-of-sample prediction. This article conducted a comprehensive, objective, and quantitative bibliometric analysis of this growing literature using Web of Science (WoS) data. We identified trends in the literature over the past decade, the geographical distribution of articles, authorship, and institutional contributions worldwide. The paper also identifies the dominant literature using citations in WoS and discusses computational algorithms that are expanding the econometric frontiers in asset pricing. The top cited papers were reviewed, highlighting their contribution. The limitations of ML learning methods and recent advances in ML were used to provide a conic view to future ML econometric practice.

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“…social media sentiments). Recent developing in assets pricing found prominent the investigation of the formation of investors' expectation (Brunnermeier et al;2021). Indeed, the information released by several factors become crucial in explaining price behavior: the "anchoring effect" (see Furnham and Boo;2011) of traders forecasting on the basis of the information (sentiment) they receive (perceive) and the related herding-like behavior (Kumar and Goyal; are all ingredients remarking how the convergence of beliefs to the prevailing market reaction might explain price dynamics.…”

Section: Introductionmentioning

confidence: 99%

The logic of markets: From Micro to Macro Dynamics

Caferra¹

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This paper aims at investigating the relationship between news-driven sentiments and the convergence of behavior in cryptocurrencies market, contributing to the existing literature in the field. The novelty stands in the relation set between the tone of news and returns dispersion. The average daily sentiment score deriving from a worldwide online news dataset has been exploited as a proxy of market humor, in the attempt to identify how emotions spread by the press are related to traders' actions. By employing both Cross-sectional standard (CSSD) and absolute (CSAD) deviation, it is found that the rises and falls of optimism shape returns variability. Indeed, the paper evidences how an increase of news positivity is associated with a lower returns dispersion, evidencing the convergence of beliefs among investors.

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Review Article: Perspectives on the Future of Asset Pricing

Cited by 69 publications

References 99 publications

Information flow among stocks, bonds, and convertible bonds

Information flow among stocks, bonds, and convertible bonds

A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing

The logic of markets: From Micro to Macro Dynamics

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