The Impact of Information Dissemination (Measured by Information Discontinuity and Noise) on the Effect of Industry Time-Series Momentum under Economic Boom and Recession Conditions

Authors

    Fatemeh Ahmadi Nezamabadi PhD student, Department of Accounting, Khom.C., Islamic Azad University, Khomein, Iran.
    Seyed Rasoul Hosayni * Assistant Professor, Department of Accounting, Faculty of Humanities, University of Zanjan, Zanjan, Iran. Rasoulhosayni@znu.ac.ir
    Azar Moslemi Assistant Professor, Department of Accounting, Khom.C., Islamic Azad University, Khomein, Iran.
    Abolfazl Saeidifar Assistant Professor, Department of Mathematics and Statistics, Ar.C., Islamic Azad University, Arak, Iran.

Keywords:

Information discontinuity, noise level, frog in the frying pan hypothesis, momentum, time series

Abstract

Among the anomalies observed in contrast to classical finance is momentum, a concept derived from physics that represents the persistence in past performance and examines the existence of inertia and the root of continuity in the future outperformance of previous winning stocks and the underperformance of previous losing stocks. On the other hand, industry time-series momentum focuses solely on absolute performance, and its strategies are dependent on temporally varying net long positions. Hence, time-series momentum has outpaced other strategies. In examining the role of information dissemination on the effect of industry time-series momentum, indicators with valid meanings were selected under both positive and negative signals in order to incorporate the dimension of the informational environment. In this regard, information discontinuity and noise level were used as proxies and measured across 120 listed companies using a systematic elimination method and within four decile-based portfolios structured under 3-month strategies over holding periods ranging from 1 to 36 months, during the years 2021 to 2023. Ultimately, across all long-term portfolio formation strategies, including two short-term and nine short-term holding periods, the effect of industry time-series momentum was found to be stronger during boom periods; thus, the research hypothesis is confirmed.

Downloads

Download data is not yet available.

References

Andrei, D., & Cujean, J. (2017). Information percolation, momentum and reversal. Journal of Financial Economics, 123(3), 617-645. https://doi.org/10.1016/j.jfineco.2016.05.012

Bird, R., Gao, X., & Yeung, D. (2017). 'Time-series and cross-sectional momentum strategies under alternative implementation strategies'. Australian Journal of Management, 42(2), 230-251. https://doi.org/10.1177/0312896215619965

Borgards, O. (2021). Dynamic time series momentum of cryptocurrencies. The North American Journal of Economics and Finance, 57, 101428. https://doi.org/10.1016/j.najef.2021.101428

Cheema, M. A., Chiah, M., & Man, Y. (2020). Cross-sectional and time-series momentum returns: Is China different? Pacific-Basin Finance Journal, 64, 101458. https://doi.org/10.1016/j.pacfin.2020.101458

Chen, C. D., Cheng, C. M., & Demirer, R. (2017). Oil and stock market momentum. Energy Econ, 68, 151-159. https://doi.org/10.1016/j.eneco.2017.09.025

Da, Z., Gurun, U. G., & Warachka, M. (2014). Frog in the pan: Continuous information and momentum. The Review of Financial Studies, 27(7), 2171-2218. https://doi.org/10.1093/rfs/hhu003

Fallahi, F., Panahi, H., & Karimi Kandoleh, M. (2018). Examining the Correlation Between Stock Market Returns, Currency, and Gold in Iran's Economy: An Application of the Hilbert-Huang Transform.

Fang, Y. (2021). The time series momentum effect: the impact of information diffusion and time-varying risk Loughborough University].

Gorji, A., Hosseini, M., & Hoorieh, S. (2022). A Comprehensive Review of Investment and Risk Management. Negah Danesh Publishing.

Goyal, A., & Jegadeesh, N. (2018). Cross-sec0tional and time-series tests of return predictability: What is the difference? The Review of Financial Studies, 31(5), 1784-1824. https://doi.org/10.1093/rfs/hhx131

Hou, K., Xue, C., & Zhang, L. (2020). Replicating anomalies. Review of Financial Studies, 33(5), 2019-2133. https://doi.org/10.1093/rfs/hhy131

Huang, S., Lee, C. M., Song, Y., & Xiang, H. (2022). A frog in every pan: Information discreteness and the lead-lag returns puzzle. Journal of Financial Economics, 145(2), 83-102. https://doi.org/10.1016/j.jfineco.2021.10.011

Hutchinson, M. C., & O'Brien, J. (2020). Time series momentum and macroeconomic risk. International Review of Financial Analysis, 69, 101469. https://doi.org/10.1016/j.irfa.2020.101469

Kim, A. Y., Tse, Y., & Wald, J. K. (2016). Time series momentum and volatility scaling. Journal of Financial Markets, 30, 103-124. https://doi.org/10.1016/j.finmar.2016.05.003

Kumar, R., & Kumar, D. (2023). Blockchain-Based Smart Dairy Supply Chain: Catching The momentum for Digital Transformation. Journal of Agribusiness in Developing and Emerging Economies. https://doi.org/10.1108/jadee-07-2022-0141

Lim, B. Y., Wang, J., & Yao, Y. (2018). 'Time-series momentum in nearly 100 years of stock returns'. Journal of Banking & Finance, 97, 283-296. https://doi.org/10.1016/j.jbankfin.2018.10.010

Lin, C. A. U. K. K. C., Chen, Y. L., & Chu, H. H. (2016). Information discreteness, price limits and earnings momentum. Pacific-Basin Finance Journal, 37, 1-22. https://doi.org/10.1016/j.pacfin.2016.02.003

Ma, Y.-Q., Ventre, C., & Polukarov, M. (2021). Denoised Labels for Financial Time-Series Data via Self-Supervised Learning. https://doi.org/10.48550/arxiv.2112.10139

Mohammadi, S., & Mansourfar, G. (2022). The Effect of Financial Data Noise on the Long-Term Co-Movement of Stock Markets. Transactions on Data Analysis in Social Science, 4(1), 9-21. https://doi.org/10.47176/tdass/2022.9

Mostafavi, S. M., & Mostafavi, S. M. (2022). A Study on the Performance of the Momentum Strategy in the Tehran Stock Exchange. Transactions on Data Analysis in Social Science, 4(2), 78-87. https://doi.org/10.47176/tdass/2022.78

Pitkäjärvi, A., Suominen, M., & Vaittinen, L. (2020). Cross-asset signals and time series momentum. Journal of Financial Economics, 136(1), 63-85. https://doi.org/10.1016/j.jfineco.2019.02.011

Rameshini, M. (2018). Analyzing Boom and Bust Markets in Iran's Stock Market Using a Nonparametric Approach University of TehranER -].

Singh, B., & Kaunert, C. (2024). Vertical Assimilation of Artificial Intelligence and Machine Learning in Safeguarding Financial Data. 173-200. https://doi.org/10.4018/979-8-3693-3633-5.ch010

Tan, Y. M., & Cheng, F. F. (2019). Industry-and liquidity-based momentum in Australian equities. Financial Innovation, 5(1), 43. https://doi.org/10.1186/s40854-019-0155-z

Xu, K., Wu, Y., Li, Z., Zhang, R., & Feng, Z. (2024). Investigating financial risk behavior prediction using deep learning and big data. International Journal of Innovative Research in Engineering and Management, 11(3), 77-81. https://doi.org/10.55524/ijirem.2024.11.3.12

Zamani Sabzi, M., Saeedi, A., & Mohamad, H. (2020). The Speed of Capital Structure Adjustment and the Impact of Boom and Bust Cycles: Evidence from Companies Listed on the Tehran Stock Exchange. Scientific-Research Quarterly Journal of Financial Research, 22(2).

Downloads

Published

2025-01-31

Submitted

2024-12-05

Revised

2025-01-10

Accepted

2025-01-21

How to Cite

Ahmadi Nezamabadi, F. ., Hosayni, S. R., Moslemi, A. ., & Saeidifar, A. . (1403). The Impact of Information Dissemination (Measured by Information Discontinuity and Noise) on the Effect of Industry Time-Series Momentum under Economic Boom and Recession Conditions. Accounting, Finance and Computational Intelligence, 2(4), 194-210. https://jafci.com/index.php/jafci/article/view/78

Similar Articles

1-10 of 30

You may also start an advanced similarity search for this article.