Forecasting financial cycles: can big data help?
Financial cycles as a source of financial crisis and business cycles that was demonstrated during the financial crisis of 2008, so it is important to understand proper methods of measuring and forecasting them to unravel their true nature. We searched financial big data for the UK, USA, Japan and China for a period 2004Q1 to 2019Q1 to find important data corresponding to the research and determine their importance for the financial cycle studies. We use singular spectral analysis (SSA without financial big data) and multichannel singular spectral analysis (MSSA with financial big data) to identify significant deterministic cycles in the residential property prices, credits to private non-financial sector and credit share in the GDP. The forecast test results show on the data for the UK, USA, Japan and China that inclusion of the financial big data significantly (on the level from 30% to four times) improves forecast accuracy for financial cycle components. This is a first study on the importance of the link between financial cycles and financial big data. Policymakers, practitioners and financial cycles research should take into the account the importance of financial big data for the studies of financial cycles for a better understanding of their true nature and improving their forecast accuracy.
First published online 22 June 2020
This work is licensed under a Creative Commons Attribution 4.0 International License.
Altissimo, F., Cristadoro, R., Forni, M., Lippi, M., & Veronese, G. (2010). New Eurocoin: Tracking economic growth in real time. Review of Economics and Statistics, 92(4), 1024–1034. https://doi.org/10.1162/REST_a_00045
Bartlett, M. S. (1954). A note on the multiplying factors for various $ 2 approximations. Journal of the Royal Statistical Society. Series B (Methodological), 16(2), 296–298. https://doi.org/10.1111/j.2517-6161.1954.tb00174.x
Bańbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29(1), 133–160. https://doi.org/10.1002/jae.2306
Borio, C., & Drehmann, M. (2011). Financial instability and macroeconomics: Bridging the Gulf. In The International Financial Crisis (pp. 237–268). World Scientific Publishing Co. https://doi.org/10.1142/9789814322096_0017
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88(1), 2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
Chordia, T., Green, T. C., & Kottimukkalur, B. (2018). Rent seeking by low-latency traders: Evidence from trading on macroeconomic announcements. The Review of Financial Studies, 31(12), 4650– 4687. https://doi.org/10.1093/rfs/hhy025
Diebold, F. X. (2015). Comparing predictive accuracy, twenty years later: A personal perspective on the use and abuse of Diebold–Mariano Tests. Journal of Business and Economic Statistics. https://doi.org/10.1080/07350015.2014.983236
Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics. https://doi.org/10.1080/07350015.1995.10524599
Drehmann, M., Borio, C., & Tsatsaronis, K. (2012). Characterising the financial cycle: Don’t lose sight of the medium term! (BIS Working Papers No. 380, pp. 1–38).
Drehmann, M., Borio, C., & Tsatsaronis, K. (2013). Can we identify the financial cycle? In The role of central banks in financial stability (pp. 131–156). World Scientific Publishing Co. https://doi.org/10.1142/9789814449922_0007
Fan, J., Li, Y., & Yu, K. (2012). Vast volatility matrix estimation using high-frequency data for portfolio selection. Journal of the American Statistical Association, 107(497), 412–428. https://doi.org/10.1080/01621459.2012.656041
Fisher, R. A. (1929). Tests of significance in harmonic analysis. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 125(796), 54–59. https://doi.org/10.1098/rspa.1929.0151
Ghil, M., Allen, M. R., Dettinger, M. D., Ide, K., Kondrashov, D., Mann, M. E., Robertson, A. W., Saunders, A., Tian, Y., Varadi, F., & Yiou, P. (2002). Advanced spectral methods for climatic time series, Reviews of Geophysics, 40(1), 3-1–3-41. https://doi.org/10.1029/2000RG000092
Gupta, R., Kabundi, A., Miller, S. M., & Uwilingiye, J. (2014). Using large data sets to forecast sectoral employment. Statistical Methods & Applications, 23(2), 229–264. https://doi.org/10.1007/s10260-013-0243-6
Hassani, H., & Silva, E. S. (2015). Forecasting with Big Data: A review. Annals of Data Science, 2, 5–19. https://doi.org/10.1007/s40745-015-0029-9
Hiebert, P., Klaus, B., Peltonen, T. A., Schüler, Y. S., & Welz, P. (2014). Capturing the financial cycle in euro area countries. ECB Financial Stability Review, (November), 109–117.
Huang, J. (2018). The customer knows best: The investment value of consumer opinions. Journal of Financial Economics, 128(1), 164–182. https://doi.org/10.1016/j.jfineco.2018.02.001
Huarng, K. H., Yu, T. H.-K., Rodriguez-Garcia, M. (2019). Qualitative analysis of housing demand using Google trends data, Economic Research-Ekonomska Istraživanja. https://doi.org/10.1080/1331677X.2018.1547205
Investing.com. (2019). China 10-year bond yield. https://www.investing.com/rates-bonds/china-10-year-bond-yield-historical-data
Keynes, J. M. (1936). The general theory of employment, interest and money. Macmillan. https://search.library.wisc.edu/catalog/999623618402121
Milburn, O. (2007). The book of the young master of accountancy: An ancient Chinese economics text. Journal of the Economic and Social History of the Orient, 50(1), 19–40. https://doi.org/10.1163/156852007780324002
Minsky, H. P. (1963). Can ’it’ happen aggain? In D. Carson (Ed.), Banking and monetary studies. Homewood.
Minsky, H. P. (1975). John Maynard Keynes. Columbia University Press. https://doi.org/10.1007/978-1-349-02679-1
OECD. (2019). Main economic indicators. https://www.oecd-ilibrary.org/economics/data/main-economic-indicators_mei-data-en
Phillips, A. W. (1962). Employment, inflation and growth. Economica, 29(113), 1–16. https://doi.org/10.1111/j.1468-0335.1962.tb00001.x
Phillips, F. (2019). Does complexity belong inside the firm, or out? Economic Research-Ekonomska Istraživanja. https://doi.org/10.1080/1331677X.2019.1625796
Ronderos, N. (2014). Spectral analysis using EViews. https://www.eviews.com/Addins/SpectralAnalysis.aipz.
Rünstler, G., & Vlekke, M. (2018). Business and financial cycles: an unobserved components models perspective. Journal of Applied Econometrics, 33(2), 212–226. https://doi.org/10.1002/jae.2604
Schumpeter, J. A. (1939). Business cycles: a theoretical, historical, and statistical analysis of the capitalist process. McGraw-Hill.
Schüler, Y. S., Hiebert, P. P., & Peltonen, T. A. (2015). Characterising the financial cycle: a multivariate and time-varying approach (ECB Working Paper No. 1846). European Central Bank.
Silver, N. (2012). The signal and the noise: Why so many predictions fail-but some don’t. Penguin Publishing Group.
Skare, M., & Porada-Rochoń, M. (2020). Multi-channel Singular-Spectrum Analysis (MSSA) of financial cycles in ten developed economies 1970–2018. Journal of Business Research, 112, 567–575. https://doi.org/10.1016/j.jbusres.2019.10.047
Strohsal, T., Proaño, C. R., & Wolters, J. (2019). Characterizing the financial cycle: Evidence from a frequency domain analysis. Journal of Banking and Finance, 106, 568–591. https://doi.org/10.1016/j.jbankfin.2019.06.010
Subrahmanyam, A. (2019). Big data in finance: Evidence and challenges. Borsa Istanbul Review, 19(4), 283–287. https://doi.org/10.1016/j.bir.2019.07.007
Šestanović, T., Arnerić, J., & Aljinović, Z. (2018). Non-structural approach to implied moments extraction. Economic Research-Ekonomska Istraživanja, 31(1), 1923–1939. https://doi.org/10.1080/1331677X.2018.1530607
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139–1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x
Tetlock, P. C., Saar-Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms’ fundamentals. Journal of Finance, 63(3), 1437–1467. https://doi.org/10.1111/j.1540-6261.2008.01362.x
Tissot, B. (2019). Financial big data and policy work: opportunities and challenges (pp. 1–21). Eurostat.
Vautard, R., Yiou, P., & Ghil, M. (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D: Nonlinear Phenomena, 58(1–4), 95–126. https://doi.org/10.1016/0167-2789(92)90103-T
Vychytilová, J., Pavelková, D., Pham, H., & Urbánek, T. (2019). Macroeconomic factors explaining stock volatility: Multi-country empirical evidence from the auto industry. Economic Research-Ekonomska Istraživanja, 32(1), 3327–3341. https://doi.org/10.1080/1331677X.2019.1661003
Whittle, P. (1952). Tests of fit in time series. Biometrika, 39(3/4), 309–318. https://doi.org/10.2307/2334027
Wibisono, O., Ari, H. D., Widjanarti, A., Zulen, A. A., & Tissot, B. (2019). The use of big data analytics and artificial intelligence in central banking (IFC Bulletin No. 50). Irving Fisher Committee on Central Bank Statistics.
Wei, W. W. S. (1994). Time series analysis. Univariate and multivariate methods. Addison Wesley.