The Effect of Financial Data Noise on the Long-Term Co-Movement of Stock Markets

Document Type : Original Article


1 Department of Management, Faculty of Social Sciences & Economics, Alzahra University, Iran

2 Department of Accounting, Faculty of Economic & Management, Urmia University, Iran


Due to advances in information technology and development in economies, the linkage among international markets becomes more significant especially for portfolio managers. Meanwhile, the noise component of time series data observes to be imperfect in doing financial analysis such that testing the theories becomes difficult. Applying the wavelet de-nosing method to the co-integration model, this paper investigate the effect of financial time series noise on long term behavior of 16 capital market indices. The weekly closing index price of the selected markets is used and findings revealed that the de-noised time series are more co-integrated compared to the noisy data. Moreover, using de-noised time series would give profound view in the long-term co-movement analysis. Further research studies in this direction might include testing robustness of many financial theories and models using de-noised data (i.e. efficient market hypotheses, Capital Asset pricing model, Arbitrage pricing theory, etc.) in order to divulge the behavior of actual parts of the time series.


Black, F. (1986). Noise. Journal of Finance, 41(3), 529-543.
Trueman, B. (1988). A theory of noise trading in securities markets. The Journal of Finance, 43(1), 83–95. doi:10.1111/j.1540-6261.1988.tb02590.x
De Long, J. Bradford, Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. The journal of political economy, 98(4), 703–738. doi:10.1086/261703
De Long, J. B., Shleifer, A., Summers, H. L., & Waldmann, R. J. (1991). The Survival of Noise Trades in Financial Markets. Journal of Business.
Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(432), 1200–1224. doi:10.1080/01621459.1995.10476626
Campbell, J. Y., & Kyle, A. S. (1993). Smart money, noise trading and stock price behaviour. The Review of Economic Studies, 60(1), 1. doi:10.2307/2297810
Leontitsis, A., & Siriopoulos, C. (2006). Nonlinear forecast of financial time series through dynamical calendar correction. Applied financial economics letters, 2(5), 337–340. doi:10.1080/17446540500461786
Bandi, F. M., & Russell, J. R. (2006). Separating microstructure noise from volatility. Journal of Financial Economics, 79(3), 655–692. doi:10.1016/j.jfineco.2005.01.005
Hu, S.-Y. (2006). A simple estimate of noise and its determinant in a call auction market. International Review of Financial Analysis, 15(4–5), 348–362. doi:10.1016/j.irfa.2006.02.004
Berkman, H., & Koch, P. D. (2008). Noise trading and the price formation process. Journal of Empirical Finance, 15(2), 232–250. doi:10.1016/j.jempfin.2006.10.005
Kurov, A. (2008). Information and noise in financial markets: Evidence from the e-mini index futures. Journal of Financial Research, 31(3), 247–270. doi:10.1111/j.1475-6803.2008.00239.x
Bloomfield, R., O’Hara, M., & Saar, G. (2009). How noise trading affects markets: An experimental analysis. The Review of Financial Studies, 22(6), 2275–2302. doi:10.1093/rfs/hhn102
Chai, J., Du, J., Lai, K. K., & Lee, Y. P. (2015). A hybrid least square support vector machine model with parameters optimization for stock forecasting. Mathematical problems in engineering, 2015, 1–7. doi:10.1155/2015/231394
Ramsey, J. B. (2002). Wavelets in economics and finance: Past and future. Studies in Nonlinear Dynamics & Econometrics, 6(3). doi:10.2202/1558-3708.1090
Fodor, I. K. (2003). Denoising through wavelet shrinkage: an empirical study. Journal of Electronic Imaging, 12(1), 151. doi:10.1117/1.1525793
Karthikeyan, P., Murugappan, M., & Yaacob, S. (2012). ECG signal denoising using wavelet thresholding techniques in human stress assessment. International Journal on Electrical Engineering and Informatics, 4(2), 306–319. doi:10.15676/ijeei.2012.4.2.9
Gençay, R., Selçuk, F., & Whitcher, B. (2002). An introduction to wavelets and other filtering methods in finance and economics. Waves in Random Media, 12(3), 399-399.
Anderson, N., & Noss, J. (2013). The Fractal Market Hypothesis and its implications for the stability of financial markets.
Verma, N., & Verma, A. K. (2012). Performance analysis of wavelet thresholding methods in denoising of audio signals of some Indian Musical Instruments. International Journal of Engineering Science and Technology, 4(5), 2040–2045.
Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). The usefulness of wavelet transform to reduce noise in the SEMG signal. Στο EMG Methods for Evaluating Muscle and Nerve Function. doi:10.5772/25757
Mehr, N. B. (2011). Portfolio allocation using wavelet transform. Proquest, Umi Dissertation Publishing.
Ibrahim, M. H. (2006). Financial integration and international portfolio diversification: US, japan and ASEAN equity markets. Journal of Asia-Pacific Business, 7(1), 5–23. doi:10.1300/j098v07n01_02
Johansen, Søren. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics & Control, 12(2–3), 231–254. doi:10.1016/0165-1889(88)90041-3
Johansen, Soren. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica: journal of the Econometric Society, 59(6), 1551. doi:10.2307/2938278
Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration-with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169–210.
Johansen, Søren. (1992). Testing weak exogeneity and the order of cointegration in UK money demand data. Journal of Policy Modeling, 14(3), 313–334. doi:10.1016/0161-8938(92)90003-u
Mansourfar, G., Mohamad, S., & Hassan, T. (2010). The behavior of MENA oil and non-oil producing countries in international portfolio optimization. The Quarterly Review of Economics and Finance: Journal of the Midwest Economics Association, 50(4), 415–423. doi:10.1016/j.qref.2010.06.007
Imen, G. M., & Rim, A. (2012). A dynamic analysis of financial contagion: the case of the subprime crisis. Journal of Business Studies Quarterly, 4(2).
Lean, H. H., & Teng, K. T. (2013). Integration of world leaders and emerging powers into the Malaysian stock market: A DCC-MGARCH approach. Economic Modelling, 32, 333–342. doi:10.1016/j.econmod.2013.02.013
Chen, M.-P., Chen, P.-F., & Lee, C.-C. (2014). Frontier stock market integration and the global financial crisis. The North American journal of economics and finance, 29, 84–103. doi:10.1016/j.najef.2014.05.004