Pangjing Wu

427 total citations · 1 hit paper
8 papers, 265 citations indexed

About

Pangjing Wu is a scholar working on Finance, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Pangjing Wu has authored 8 papers receiving a total of 265 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Finance, 4 papers in Artificial Intelligence and 4 papers in Management Science and Operations Research. Recurrent topics in Pangjing Wu's work include Financial Markets and Investment Strategies (4 papers), Stock Market Forecasting Methods (4 papers) and Natural Language Processing Techniques (3 papers). Pangjing Wu is often cited by papers focused on Financial Markets and Investment Strategies (4 papers), Stock Market Forecasting Methods (4 papers) and Natural Language Processing Techniques (3 papers). Pangjing Wu collaborates with scholars based in China and Hong Kong. Pangjing Wu's co-authors include Xiaodong Li, Wenpeng Wang, Fu Lee Wang, Qing Li, Haoran Xie, Chen Li, Qing Li, Yujuan Ding, Peter H. F. Ng and Liangbo Ning and has published in prestigious journals such as Knowledge-Based Systems, Information Processing & Management and IEEE Transactions on Affective Computing.

In The Last Decade

Pangjing Wu

7 papers receiving 255 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.

Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong

2020 229 citations Xiaodong Li, Pangjing Wu et al. Information Processing & Management profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pangjing Wu China	4	211	91	81	80	73	8	265
Yumo Xu United Kingdom	5	199 0.9×	153 1.7×	70 0.9×	75 0.9×	78 1.1×	11	309
Sudeepa Roy Dey India	4	210 1.0×	31 0.3×	84 1.0×	96 1.2×	85 1.2×	12	275
Luckyson Khaidem United States	3	210 1.0×	29 0.3×	85 1.0×	97 1.2×	85 1.2×	4	266
Michael Hagenau Germany	5	204 1.0×	110 1.2×	35 0.4×	101 1.3×	99 1.4×	7	293
Anna Pomeranets United States	4	121 0.6×	134 1.5×	35 0.4×	48 0.6×	45 0.6×	6	260
Pyry Takala Finland	4	153 0.7×	210 2.3×	21 0.3×	29 0.4×	41 0.6×	5	309
Robert Ślepaczuk Poland	9	150 0.7×	15 0.2×	46 0.6×	146 1.8×	128 1.8×	45	268
Hans Buehler United States	10	145 0.7×	48 0.5×	29 0.4×	113 1.4×	289 4.0×	17	384
Ruizhu Han China	4	210 1.0×	20 0.2×	96 1.2×	68 0.8×	116 1.6×	5	253
Irene Aldridge United States	7	124 0.6×	29 0.3×	15 0.2×	140 1.8×	158 2.2×	20	270

Countries citing papers authored by Pangjing Wu

Since Specialization

Citations

This map shows the geographic impact of Pangjing Wu'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 Pangjing Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pangjing Wu more than expected).

Fields of papers citing papers by Pangjing Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pangjing Wu. 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 Pangjing Wu. The network helps show where Pangjing Wu may publish in the future.

Co-authorship network of co-authors of Pangjing Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Pangjing Wu. A scholar is included among the top collaborators of Pangjing Wu 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 Pangjing Wu. Pangjing Wu 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:

8 of 8 papers shown

1.

Wu, Pangjing, et al.. (2025). HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoning. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 5505–5515.

2.

Wu, Pangjing, et al.. (2025). Towards Retrieval-Augmented Large Language Models: Data Management and System Design. 4509–4512. 1 indexed citations

3.

Li, Xiaodong, et al.. (2023). Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization. IEEE Transactions on Big Data. 10(3). 288–300. 2 indexed citations

4.

Li, Xiaodong, et al.. (2022). TSSRD: A Topic Sentiment Summarization Framework Based on Reaching Definition. IEEE Transactions on Affective Computing. 14(3). 1716–1730. 3 indexed citations

5.

Wu, Pangjing & Xiaodong Li. (2022). Market Style Discrimination via Ensemble Learning. 170–173. 1 indexed citations

6.

Li, Xiaodong & Pangjing Wu. (2021). Stock Price Prediction Incorporating Market Style Clustering. Cognitive Computation. 14(1). 149–166. 22 indexed citations

7.

Li, Xiaodong, et al.. (2021). Sentiment Lossless Summarization. Knowledge-Based Systems. 227. 107170–107170. 7 indexed citations

8.

Li, Xiaodong, Pangjing Wu, & Wenpeng Wang. (2020). Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong. Information Processing & Management. 57(5). 102212–102212. 229 indexed citations breakdown →

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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