Haibing Shu

485 total citations · 1 hit paper
9 papers, 333 citations indexed

About

Haibing Shu is a scholar working on Strategy and Management, Accounting and Finance. According to data from OpenAlex, Haibing Shu has authored 9 papers receiving a total of 333 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Strategy and Management, 5 papers in Accounting and 4 papers in Finance. Recurrent topics in Haibing Shu's work include Corporate Finance and Governance (5 papers), Financial Markets and Investment Strategies (4 papers) and Market Dynamics and Volatility (3 papers). Haibing Shu is often cited by papers focused on Corporate Finance and Governance (5 papers), Financial Markets and Investment Strategies (4 papers) and Market Dynamics and Volatility (3 papers). Haibing Shu collaborates with scholars based in China, Hong Kong and United States. Haibing Shu's co-authors include K.C. John Wei, Sonia M.L. Wong, Zhenyu Gao, Ran Duchin, V. Brian Viard, Hui Ou‐Yang and Abhinav Goyal and has published in prestigious journals such as Journal of Financial Economics, Review of Financial Studies and Journal of Management Studies.

In The Last Decade

Haibing Shu

7 papers receiving 323 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The impacts of political uncertainty on asset prices: Evidence from the Bo scandal in China

2017 266 citations Haibing Shu, K.C. John Wei et al. Journal of Financial Economics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Haibing Shu China	4	173	164	121	112	51	9	333
Alfredo Schclarek Argentina	8	162 0.9×	179 1.1×	212 1.8×	52 0.5×	23 0.5×	10	348
Masami Imai United States	12	197 1.1×	137 0.8×	237 2.0×	89 0.8×	45 0.9×	33	398
Stefan Lewellen United States	8	165 1.0×	205 1.3×	224 1.9×	100 0.9×	34 0.7×	13	409
Jiacai Xiong China	10	206 1.2×	82 0.5×	64 0.5×	80 0.7×	22 0.4×	22	282
Yoshirō Miwa Japan	14	308 1.8×	108 0.7×	155 1.3×	116 1.0×	28 0.5×	48	444
Qinyuan Chen China	4	230 1.3×	138 0.8×	62 0.5×	138 1.2×	34 0.7×	8	344
Tyler J. Brough United States	7	207 1.2×	112 0.7×	170 1.4×	146 1.3×	36 0.7×	18	352
Shweta Srinivasan United States	3	250 1.4×	139 0.8×	81 0.7×	93 0.8×	29 0.6×	4	323
Mohamed Eskandar Shah Mohd Rasid Malaysia	12	222 1.3×	128 0.8×	110 0.9×	52 0.5×	48 0.9×	34	303
Guoqian Tu China	5	230 1.3×	122 0.7×	204 1.7×	73 0.7×	14 0.3×	9	361

Countries citing papers authored by Haibing Shu

Since Specialization

Citations

This map shows the geographic impact of Haibing Shu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Haibing Shu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haibing Shu more than expected).

Fields of papers citing papers by Haibing Shu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Haibing Shu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Haibing Shu. The network helps show where Haibing Shu may publish in the future.

Co-authorship network of co-authors of Haibing Shu

This figure shows the co-authorship network connecting the top 25 collaborators of Haibing Shu. A scholar is included among the top collaborators of Haibing Shu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Haibing Shu. Haibing Shu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Goyal, Abhinav, et al.. (2024). Neighborhood Awareness and IPO Underpricing: Evidence from Satellite Nightlight. SSRN Electronic Journal. 1 indexed citations

2.

Shu, Haibing, et al.. (2024). Neighborhood Awareness and Stock Return Synchronicity: Evidence from Satellite Nighttime Light Data. China & World Economy. 32(6). 187–216.

3.

Shu, Haibing, et al.. (2024). Return seasonalities in the Chinese stock market. Pacific-Basin Finance Journal. 85. 102391–102391.

4.

Shu, Haibing & V. Brian Viard. (2024). The Dynamic Role of Subnational Regions in Firm Performance. Journal of Management Studies. 62(2). 632–665. 1 indexed citations

5.

Duchin, Ran, Zhenyu Gao, & Haibing Shu. (2020). The Role of Government in Firm Outcomes. Review of Financial Studies. 33(12). 5555–5593. 34 indexed citations

6.

Shu, Haibing, et al.. (2017). The impacts of political uncertainty on asset prices: Evidence from the Bo scandal in China. Journal of Financial Economics. 125(2). 286–310. 266 indexed citations breakdown →

7.

Shu, Haibing & Sonia M.L. Wong. (2017). When a Sinner Does a Good Deed: The Path‐Dependence of Reputation Repair. Journal of Management Studies. 55(5). 770–808. 26 indexed citations

8.

Shu, Haibing, et al.. (2016). The Impacts of Political Uncertainty on Asset Prices: Evidence from the Bo Scandal in China. RePEc: Research Papers in Economics. 2 indexed citations

9.

Ou‐Yang, Hui, Haibing Shu, & Sonia M.L. Wong. (2015). Does it Pay for entertaining your stakeholders. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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