391
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A long-short dual-mode knowledge distillation framework for empirical asset pricing models in digital financial networks

, , , , , , & show all
Article: 2306970 | Received 24 Oct 2023, Accepted 13 Jan 2024, Published online: 25 Jan 2024

References

  • Ai, Y., Sun, G., & Kong, T. (2023). Digital finance and stock price crash risk. International Review of Economics & Finance, 88, 607–619. https://doi.org/10.1016/j.iref.2023.07.003
  • Butaru, F., Chen, Q., Clark, B., Das, S., Lo, A. W., & Siddique, A. (2016). Risk and risk management in the credit card industry. Journal of Banking & Finance, 72, 218–239. https://doi.org/10.1016/j.jbankfin.2016.07.015
  • Chen, C., Zhang, P., Liu, Y., & Liu, J. (2020). Financial quantitative investment using convolutional neural network and deep learning technology. Neurocomputing, 390, 384–390. https://doi.org/10.1016/j.neucom.2019.09.092
  • Cooper, M., Gulen, H., & Ion, M. (2024). The use of asset growth in empirical asset pricing models. Journal of Financial Economics, 151, 103746. https://doi.org/10.1016/j.jfineco.2023.103746
  • Dai, H., Xu, Y., Chen, G., Dou, W., Tian, C., Wu, X., & He, T. (2022). Rose: Robustly safe charging for wireless power transfer. IEEE Transactions on Mobile Computing, 21(6), 2180–2197. https://doi.org/10.1109/TMC.2020.3032591
  • Deng, X., Jiang, Y., Yang, L. T., Lin, M., Yi, L., & Wang, M. (2019). Data fusion based coverage optimization in heterogeneous sensor networks: A survey. Information Fusion, 52, 90–105. https://doi.org/10.1016/j.inffus.2018.11.020
  • Deng, X., Wang, B., Liu, W., & Yang, L. T. (2015). Sensor scheduling for multi-modal confident information coverage in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(3), 902–913. https://doi.org/10.1109/TPDS.2014.2315193
  • Fama, E. F., & French, K. R. (2008). Dissecting anomalies. The Journal of Finance, 63(4), 1653–1678. https://doi.org/10.1111/jofi.2008.63.issue-4
  • Goh, J. C., Jiang, F., Tu, J., & Wang, Y. (2013). Can us economic variables predict the Chinese stock market?. Pacific-Basin Finance Journal, 22, 69–87. https://doi.org/10.1016/j.pacfin.2012.10.002
  • Gou, J., Sun, L., Yu, B., Wan, S., Ou, W., & Yi, Z. (2022). Multilevel attention-based sample correlations for knowledge distillation. IEEE Transactions on Industrial Informatics, 19(5), 7099–7109. https://doi.org/10.1109/TII.2022.3209672
  • Green, J., Hand, J. R., & Zhang, X. F. (2017). The characteristics that provide independent information about average us monthly stock returns. The Review of Financial Studies, 30(12), 4389–4436. https://doi.org/10.1093/rfs/hhx019
  • Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009
  • Guo, L., Chen, J., Li, S., Li, Y., & Lu, J. (2022). A blockchain and IoT-based lightweight framework for enabling information transparency in supply chain finance. Digital Communications and Networks, 8(4), 576–587. https://doi.org/10.1016/j.dcan.2022.03.020
  • Han, X., Song, X., Yao, Y., Xu, X.-S., & Nie, L. (2019). Neural compatibility modeling with probabilistic knowledge distillation. IEEE Transactions on Image Processing, 29, 871–882. https://doi.org/10.1109/TIP.83
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
  • He, S., Shi, K., Liu, C., Guo, B., Chen, J., & Shi, Z. (2022). Collaborative sensing in internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 24(3), 1435–1474. https://doi.org/10.1109/COMST.2022.3187138
  • Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
  • Huang, W., Ye, M., & Du, B. (2022). Learn from others and be yourself in heterogeneous federated learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 10143–10153).
  • Huang, W., Ye, M., Shi, Z., Li, H., & Du, B. (2023). Rethinking federated learning with domain shift: A prototype view. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 16312–16322). IEEE.
  • Islam, M. M. (2022). A privacy-preserving transparent central bank digital currency system based on consortium blockchain and unspent transaction outputs. IEEE Transactions on Services Computing, 16(4), 2372–2386. https://doi.org/10.1109/TSC.2022.3226120
  • Islam, M. M., Islam, M. K., Shahjalal, M., Chowdhury, M. Z., & Jang, Y. M. (2022). A low-cost cross-border payment system based on auditable cryptocurrency with consortium blockchain: Joint digital currency. IEEE Transactions on Services Computing, 16(3), 1616–1629. https://doi.org/10.1109/TSC.2022.3207224
  • Jing, P., Cui, K., Guan, W., Nie, L., & Su, Y. (2023). Category-aware multimodal attention network for fashion compatibility modeling. IEEE Transactions on Multimedia. https://doi.org/10.1109/TMM.2023.3246796
  • Jing, P., Cui, K., Zhang, J., Li, Y., & Su, Y. (2023). Multimodal high-order relationship inference network for fashion compatibility modeling in internet of multimedia things. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2023.3285601
  • Kelly, B., & Pruitt, S. (2013). Market expectations in the cross-section of present values. The Journal of Finance, 68(5), 1721–1756. https://doi.org/10.1111/jofi.2013.68.issue-5
  • Li, S., Lin, M., Wang, Y., Wu, Y., Tian, Y., Shao, L., & Ji, R. (2022). Distilling a powerful student model via online knowledge distillation. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2022.3152732
  • Liang, W., Li, Y., Xie, K., Zhang, D., Li, K.-C., Souri, A., & Li, K. (2023). Spatial-temporal aware inductive graph neural network for C-ITS data recovery. IEEE Transactions on Intelligent Transportation Systems, 24(8), 8431–8442. https://doi.org/10.1109/TITS.2022.3156266
  • Liang, W., Tang, M., Long, J., Peng, X., Xu, J., & Li, K.-C. (2019). A secure fabric blockchain-based data transmission technique for industrial Internet-of-Things. IEEE Transactions on Industrial Informatics, 15(6), 3582–3592. https://doi.org/10.1109/TII.9424
  • Light, N., Maslov, D., & Rytchkov, O. (2017). Aggregation of information about the cross section of stock returns: A latent variable approach. The Review of Financial Studies, 30(4), 1339–1381. https://doi.org/10.1093/rfs/hhw102
  • Ma, W., Chen, Q., Zhou, T., Zhao, S., & Cai, Z. (2023). Using multimodal contrastive knowledge distillation for video-text retrieval. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/TCSVT.2023.3257193
  • Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384. https://doi.org/10.1016/j.asoc.2020.106384
  • Pukthuanthong, K., & Roll, R. (2009). Global market integration: An alternative measure and its application. Journal of Financial Economics, 94(2), 214–232. https://doi.org/10.1016/j.jfineco.2008.12.004
  • Rapach, D., & Zhou, G. (2013). Forecasting stock returns. In Handbook of economic forecasting (Vol. 2, pp. 328–383). Elsevier.
  • Rezaei, H., Faaljou, H., & Mansourfar, G. (2021). Stock price prediction using deep learning and frequency decomposition. Expert Systems with Applications, 169, 114332. https://doi.org/10.1016/j.eswa.2020.114332
  • Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Müller, K.-R. (2016). Evaluating the visualization of what a deep neural network has learned. IEEE Transactions on Neural Networks and Learning Systems, 28(11), 2660–2673. https://doi.org/10.1109/TNNLS.5962385
  • Shi, N., Tan, L., Li, W., Qi, X., & Yu, K. (2021). A blockchain-empowered AAA scheme in the large-scale HetNet. Digital Communications and Networks, 7(3), 308–316. https://doi.org/10.1016/j.dcan.2020.10.002
  • Teng, H. W., Li, Y.-H., & Chang, S.-W. (2020). Machine learning in empirical asset pricing models. In 2020 international conference on pervasive artificial intelligence (ICPAI) (pp. 123–129). IEEE.
  • Wang, L., & Yoon, K.-J. (2021). Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(6), 3048–3068. https://doi.org/10.1109/TPAMI.2021.3055564
  • Wang, W., Wang, Y., Duan, P., Liu, T., Tong, X., & Cai, Z. (2023). A triple real-time trajectory privacy protection mechanism based on edge computing and blockchain in mobile crowdsourcing. IEEE Transactions on Mobile Computing, 22(10), 5625–5642. https://doi.org/10.1109/TMC.2022.3187047
  • Wang, Z., Liu, K., Hu, J., Ren, J., Guo, H., & Yuan, W. (2023). Attrleaks on the edge: Exploiting information leakage from privacy-preserving co-inference. Chinese Journal of Electronics, 32(1), 1–12. https://doi.org/10.23919/cje.2022.00.031
  • Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. The Review of Financial Studies, 21(4), 1455–1508. https://doi.org/10.1093/rfs/hhm014
  • Yao, J., Li, Y., & Tan, C. L. (2000). Option price forecasting using neural networks. Omega, 28(4), 455–466. https://doi.org/10.1016/S0305-0483(99)00066-3
  • Yuan, Y.-P., Tan, G. W.-H., & Ooi, K.-B. (2022). Does COVID-19 pandemic motivate privacy self-disclosure in mobile fintech transactions? A privacy-calculus-based dual-stage SEM-ANN analysis. IEEE Transactions on Engineering Management, 71, 2986–3000. https://doi.org/10.1109/TEM.2022.3204285
  • Zhang, P., Kang, Z., Yang, T., Zhang, X., Zheng, N., & Sun, J. (2022). Lgd: Label-guided self-distillation for object detection. In Proceedings of the AAAI conference on artificial intelligence (Vol. 36(3), pp. 3309–3317).
  • Zhao, B., Cui, Q., Song, R., Qiu, Y., & Liang, J. (2022). Decoupled knowledge distillation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11953–11962).