Shared analyst coverage: Unifying momentum spillover effects

Ali, Usman; Hirshleifer, David

doi:10.1016/j.jfineco.2019.10.007

Cited by 170 publications

(89 citation statements)

References 31 publications

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“…In real market, the stock fluctuation is partially affected by its related stocks which is known as momentum spillover effect in finance [Ali and Hirshleifer 2020]. In this section, based upon our newly constructed bi-typed hybrid-relational MKG, we propose a Dual Attention Networks to learn the relational embeddings of stocks that represent their received spillover signals.…”

Section: Learning Stock Relational Embeddings Via Dual Attention Networkmentioning

confidence: 99%

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

Yu¹,

Du²,

Liu³

et al. 2022

Preprint

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Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DANSMP, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DANSMP is capable of improving stock prediction with the constructed MKG.

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Section: Learning Stock Relational Embeddings Via Dual Attention Networkmentioning

confidence: 99%

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

Yu¹,

Du²,

Liu³

et al. 2022

Preprint

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“…This suggests that assets having fundamental similarities will have momentum spillovers, wherein past return of one asset predicts the returns of assets linked to it. Numerous papers have verified such spillovers in the equity market by using a variety of proxies for inter-firm linkages (see, e.g., Moskowitz and Grinblatt, 1999;Cohen and Frazzini, 2008;Menzly and Ozbas, 2010;Lee et al, 2019;Ali and Hirshleifer, 2020;Parsons et al, 2020). Similarly, the estimated communities in the cryptos market are designed to optimally capture the information propagation and technological similarities between cryptos.…”

Section: Momentum Spillovermentioning

confidence: 99%

“…Second, to formally test the information propagation effects within crypto communities, we form a cross-sectional portfolio that implements an inter-crypto momentum trading strategy. Prior literature has documented that, due to information propagation, assets that have fundamental similarities will have momentum spillovers, wherein past return of one asset predicts the returns of assets linked to it (see, e.g., Moskowitz and Grinblatt, 1999;Cohen and Frazzini, 2008;Menzly and Ozbas, 2010;Lee, Sun, Wang, and Zhang, 2019;Ali and Hirshleifer, 2020;Parsons, Sabbatucci, and Titman, 2020). Based on this argument, we construct a trading signal for each crypto using the average returns of the rest of cryptos in the same community.…”

mentioning

confidence: 99%

A Time-Varying Network for Cryptocurrencies

Guo¹,

Härdle²,

Tao³

2021

Preprint

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Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a timevarying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.

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“…In asset pricing, previous studies have identified lead-lag structures in stock returns across various dimensions, such as firm size, industry, and the supply chain. A recent paper by Ali and Hirshleifer (2020) finds that using common analysts to trace a lead-lag structure generates particularly strong momentum strategy profits. Shared analysts offer a potential way to identify related firms for applications such as pairs trading strategies, and analyst connections can be related to information transfers that contribute to co-movement in stock returns.…”

Section: B Potential Value Added In Specific Research Topicsmentioning

confidence: 99%

Common Analysts: Method for Defining Peer Firms

Kaustia

Rantala

2020

J. Financ. Quant. Anal.

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We develop a method for defining groups of peer firms on the basis of joint analyst coverage. Besides industry boundaries, analysts’ coverage choices reflect other aspects of firm relatedness such as business model. We find that the analyst-based method produces substantially more homogeneous groups of firms compared to common industry classifications, and has a number of other desirable properties. The paper has two broader implications. First, it demonstrates the advantages of a self-organizing approach to classification, as opposed to a hierarchical system. Second, it illustrates a new positive information production externality generated by the institution of security market analysis.

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Shared analyst coverage: Unifying momentum spillover effects

Cited by 170 publications

References 31 publications

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

A Time-Varying Network for Cryptocurrencies

Common Analysts: Method for Defining Peer Firms

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