Yuping Song

481 total citations
37 papers, 294 citations indexed

About

Yuping Song is a scholar working on Finance, Economics and Econometrics and Management Science and Operations Research. According to data from OpenAlex, Yuping Song has authored 37 papers receiving a total of 294 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Finance, 21 papers in Economics and Econometrics and 14 papers in Management Science and Operations Research. Recurrent topics in Yuping Song's work include Financial Risk and Volatility Modeling (22 papers), Market Dynamics and Volatility (16 papers) and Stock Market Forecasting Methods (14 papers). Yuping Song is often cited by papers focused on Financial Risk and Volatility Modeling (22 papers), Market Dynamics and Volatility (16 papers) and Stock Market Forecasting Methods (14 papers). Yuping Song collaborates with scholars based in China and Hong Kong. Yuping Song's co-authors include Zhenwei Li, Jing Han, Xiaolong Tang, Qi Yang, Boyu Zhang, Jing Ni, Yankun Sun, Min Zhu, Zhengyan Lin and Yun Feng and has published in prestigious journals such as Expert Systems with Applications, Sustainability and Applied Soft Computing.

In The Last Decade

Yuping Song

32 papers receiving 282 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yuping Song China	9	143	123	95	77	23	37	294
Peter Nystrup Denmark	12	125 0.9×	124 1.0×	128 1.3×	133 1.7×	4 0.2×	25	370
Hans-Jakob Lüthi Switzerland	10	95 0.7×	59 0.5×	63 0.7×	36 0.5×	10 0.4×	15	278
Carlos Maté Spain	7	205 1.4×	119 1.0×	46 0.5×	152 2.0×	5 0.2×	10	408
André Luis Santiago Maia Brazil	5	161 1.1×	66 0.5×	28 0.3×	92 1.2×	5 0.2×	10	296
Ke Yu China	7	119 0.8×	125 1.0×	230 2.4×	47 0.6×	10 0.4×	13	395
Silvano Bordignon Italy	12	84 0.6×	181 1.5×	116 1.2×	139 1.8×	4 0.2×	26	407
Francesco Battaglia Italy	10	51 0.4×	72 0.6×	86 0.9×	16 0.2×	15 0.7×	45	292
Haeran Cho United Kingdom	10	50 0.3×	92 0.7×	63 0.7×	65 0.8×	30 1.3×	24	438
Rui Ren China	7	133 0.9×	71 0.6×	62 0.7×	49 0.6×	2 0.1×	27	297
S. Al Wadi Jordan	9	172 1.2×	81 0.7×	34 0.4×	88 1.1×	2 0.1×	30	293

Countries citing papers authored by Yuping Song

Since Specialization

Citations

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

Fields of papers citing papers by Yuping Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuping Song

This figure shows the co-authorship network connecting the top 25 collaborators of Yuping Song. A scholar is included among the top collaborators of Yuping Song 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 Yuping Song. Yuping Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Song, Yuping, et al.. (2025). A Study of the Factors Contributing to the Impact of Climate Risks on Corporate Performance in China’s Energy Sector. Sustainability. 17(11). 5139–5139. 1 indexed citations

Song, Yuping, et al.. (2025). Volatility and value-at-risk forecasting using BERT and transformer models incorporating investors' textual sentiments. Finance research letters. 85. 108210–108210.

Song, Yuping, et al.. (2024). Multi-decomposition in deep learning models for futures price prediction. Expert Systems with Applications. 246. 123171–123171. 9 indexed citations

Zhu, Min, et al.. (2024). Energy price prediction based on decomposed price dynamics: A parallel neural network approach. Applied Soft Computing. 164. 111972–111972. 2 indexed citations

Song, Yuping, et al.. (2024). On the impact of provincial digital economy on high-quality economic development in China from a regional heterogeneity perspective. International Journal of Financial Engineering. 11(3). 2 indexed citations

Song, Yuping, et al.. (2024). On Stock Volatility Forecasting under Mixed-Frequency Data Based on Hybrid RR-MIDAS and CNN-LSTM Models. Mathematics. 12(10). 1538–1538. 8 indexed citations

Feng, Yun, et al.. (2023). Tail risk in the Chinese stock market: An AEV model on the maximal drawdowns. Finance research letters. 58. 104294–104294. 3 indexed citations

Song, Yuping, et al.. (2023). Modelling and forecasting high-frequency data with jumps based on a hybrid nonparametric regression and LSTM model. Expert Systems with Applications. 237. 121527–121527. 34 indexed citations

Tang, Xiaolong, et al.. (2023). On Forecasting Realized Volatility for Bitcoin Based on Deep Learning PSO–GRU Model. Computational Economics. 63(5). 2011–2033. 15 indexed citations

10.

Song, Yuping, et al.. (2023). Volatility forecasting for stock market index based on complex network and hybrid deep learning model. Journal of Forecasting. 43(3). 544–566. 4 indexed citations

11.

Song, Yuping, et al.. (2022). Threshold reweighted Nadaraya–Watson estimation of jump-diffusion models. 7(1). 31–31.

12.

Song, Yuping, et al.. (2022). Dynamic forecasting for nonstationary high‐frequency financial data with jumps based on series decomposition and reconstruction. Journal of Forecasting. 42(5). 1055–1068. 4 indexed citations

13.

Song, Yuping, et al.. (2021). Volatility forecasting for crude oil based on text information and deep learning PSO‐LSTM model. Journal of Forecasting. 41(5). 933–944. 27 indexed citations

14.

Zhang, Boyu, et al.. (2021). Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model. Mathematics. 9(4). 320–320. 16 indexed citations

15.

Li, Zhenwei, Jing Han, & Yuping Song. (2020). On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning. Journal of Forecasting. 39(7). 1081–1097. 60 indexed citations

16.

Song, Yuping, et al.. (2019). Variance reduction estimation for return models with jumps using gamma asymmetric kernels. Studies in Nonlinear Dynamics and Econometrics. 23(5). 2 indexed citations

17.

Yang, Qi & Yuping Song. (2018). Data driven confidence intervals for diffusion process using double smoothing empirical likelihood. Journal of Computational and Applied Mathematics. 348. 282–297. 22 indexed citations

18.

Song, Yuping, et al.. (2018). Central Limit Theorems of Local Polynomial Threshold Estimator for Diffusion Processes with Jumps. Scandinavian Journal of Statistics. 45(3). 644–681. 3 indexed citations

19.

Song, Yuping & Jing Ni. (2016). Effect of variable network clustering on the accuracy of node centrality. Acta Physica Sinica. 65(2). 28901–28901. 8 indexed citations

20.

Song, Yuping, et al.. (2012). Re-weighted Nadaraya–Watson estimation of second-order jump-diffusion model. Journal of Statistical Planning and Inference. 143(4). 730–744. 4 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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