Forecast accuracy for residential property prices in China improves significantly when we use financial big data for forecasting in relation to the results we obtain without these data. We can see from table 4 that all forecast accuracy scores prove the validity of this statement. For the credit to private non-financial sector time series forecast accuracy when using financial big data improves as well (on a smaller scale when compared to the forecast accuracy for the residential property prices). The same result holds for the credit share in the GDP series with forecast accuracy using financial big data (MSSA) outperform the forecast produces by (SSA) without financial big data. 
Forecast accuracy tests and scores show on the sample for the UK, USA, Japan and China (four growth potential economies quite different in structure and size) that financial big data can significantly improve forecasting of the financial cycle components (residential property prices, credit to private non-financial sector and credit share in the GDP). 

Conclusion

This study has demonstrated that using financial big data significantly improve the forecast accuracy for financial cycle components (residential property prices, credit 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 the first study to examine the role of the financial big data in the study of financial cycles. Inclusion of the financial big data in the various model (time series, frequency domain, turning point, multiple cycles) aiming at measuring the exact length of financial cycles. Such a knowledge (financial big data) will help to better understand the mechanism behind financial cycles, methods and tools for their monitoring and forecasting. Policymakers and central banks motivated to mitigate the risks of the financial cycles will find this knowledge useful in building new models for financial cycles detection and forecasting. 
The lack of studies using financial big data for measuring and forecasting financial cycles may indicate that policymakers, practitioners and academics do not find the link between financial big data and financial cycles important or promising. Another plausible explanation is that financial big data from their point of view serve the purpose of forecasting financial conditions \cite{subrahmanyam2019big}, market portfolio selection \cite{Fan_2012}, equity prices forecasting, mitigating risks and volatility on the financial markets. Although all these issues are important for financial studies \cite{alessi2009forecasting}, no previous study on the link between financial cycles and big data exists. 
Our analysis suggests research on financial cycles can be significantly improved if the financial big data are used in the research. The Diebold - Mariano test result confirms the validity of the hypothesis that financial big data are important for measurement and forecasting of the financial cycles. Our findings bring important insight to the policymakers and financial practitioners and academic community as well as interested in monitoring and studying the nature and consequence of the financial cycles and their role in financial crisis and thus business cycles. 
An important strength of our study is that we use several financial big data (supporting the research of \cite{TETLOCK_2007} for a sample of four countries reporting several forecast accuracy scores and Diebold-Mariano test results. However, since financial big data are not available in a long time series form, we use a limited-time series sample. Furthermore, research results should be validated for a larger sample of countries that could bring additional insight into the research question. 
In our sample, all forecast accuracy statistics and Diebold-Mariano test results put forward a single, statistically validated conclusion - financial big data are an important element for studying financial cycles. We encourage further research to clearly distinguish whether financial cycles research is based on financial big data; identify the most important sources of such data (statistical robustness), and become familiar with developing new study models on financial cycles using financial big data.