TY - GEN
T1 - Human-Centered Financial Signal Processing
T2 - 20th International Forum on Digital TV and Wireless Multimedia Communications, IFTC 2023
AU - Zhang, Kaixun
AU - Chen, Yuzhen
AU - Luo, Ji Feng
AU - Hu, Menghan
AU - An, Xudong
AU - Zhai, Guangtao
AU - Zhang, Xiao Ping
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - In this paper, we explore the “human-centered” financial model. To illustrate this idea, we conducted a case study on the stock chart, referring to stock price chart plus stock volume chart. We first construct the stock chart with professional stock traders’ visual attention (SPSTV) dataset, which contains 150 stock charts images associated with eye-movement data from 10 professional stock traders. Based on the SPSTV dataset, the transfer learning and human attention inspired morphological operation are leveraged to develop the stock chart attention model. In validation experiments, compared to other models, SamVgg optimized by transfer learning and human visual attention function performs best with the AUC_Judd, CC, SIM, and NSS of 96.11%, 82.74%, 69.84%, and 2.84, respectively. Through visual comparative analysis, we can find that the visual attention map area after the double optimization strategy is more focused overall and has less excess attention at the edges. This visual optimization will enhance people’s observation experience. The proposed model has great potential for two application scenarios: (1) instruct amateur traders how to observe stock charts; and (2) evaluate stock analysis ability of investors. In the future, we will continue to iterate the model and try to apply it in real economic activities to generate benefits.
AB - In this paper, we explore the “human-centered” financial model. To illustrate this idea, we conducted a case study on the stock chart, referring to stock price chart plus stock volume chart. We first construct the stock chart with professional stock traders’ visual attention (SPSTV) dataset, which contains 150 stock charts images associated with eye-movement data from 10 professional stock traders. Based on the SPSTV dataset, the transfer learning and human attention inspired morphological operation are leveraged to develop the stock chart attention model. In validation experiments, compared to other models, SamVgg optimized by transfer learning and human visual attention function performs best with the AUC_Judd, CC, SIM, and NSS of 96.11%, 82.74%, 69.84%, and 2.84, respectively. Through visual comparative analysis, we can find that the visual attention map area after the double optimization strategy is more focused overall and has less excess attention at the edges. This visual optimization will enhance people’s observation experience. The proposed model has great potential for two application scenarios: (1) instruct amateur traders how to observe stock charts; and (2) evaluate stock analysis ability of investors. In the future, we will continue to iterate the model and try to apply it in real economic activities to generate benefits.
KW - Financial Computer Vision
KW - Financial Signal Processing
KW - Human Attention
KW - Saliency Prediction
KW - Stock Analysis
UR - https://www.scopus.com/pages/publications/85200440427
U2 - 10.1007/978-981-97-3626-3_14
DO - 10.1007/978-981-97-3626-3_14
M3 - 会议稿件
AN - SCOPUS:85200440427
SN - 9789819736256
T3 - Communications in Computer and Information Science
SP - 187
EP - 198
BT - Digital Multimedia Communications - 20th International Forum on Digital TV and Wireless Multimedia Communications, IFTC 2023, Revised Selected Papers
A2 - Zhai, Guangtao
A2 - Zhou, Jun
A2 - Yang, Hua
A2 - Ye, Long
A2 - An, Ping
A2 - Yang, Xiaokang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 21 December 2023 through 22 December 2023
ER -