Introduction
The global financial crisis of 2008 after decades of the great moderation policy governance opened the discussion of the financial stability importance for economic activity. Identification of the earning warning signals to predict business cycles and avoid instability in the economy was always the primary goal of the policymakers. Our paper is the first to combine financial big data to forecast financial cycles.
First to mention the importance of the big data for macroeconomic management was \cite{Phillips1962}. Phillips (1962) put forward the assumption that policymakers can not design effective economic policy without quantitative knowledge behind the main economic forces governing the economic system. Main economic forces consist of employment, inflation and growth as instruments and policy goals at the same time. Therefore, it is not possible to design an optimal economic policy without knowing the quantitative relations that hold between these economic forces. To be able to measure and identify the correct quantitive relationship among employment, inflation and growth, a big data approach is essential.
Fluctuations in financial variables always had a significant impact on real economic activities. That is the case today but was all the same 3000 years ago. Oscillatory behaviour in savings and investments dynamics and price targeting articulate economic cycle, see Ji Ran \cite{Milburn2007}. Consequently, policymakers must always monitor the saving-investments cycle keeping prices under control to ensure the smooth running of the economy. The same concept highlighting the importance of financial conditions for business cycles is extended in \cite{Keynes1883-19461936}, \cite{Schumpeter1939}, \cite{Minsky1963,Minsky1975}.
Inherent dynamics in financial conditions articulate the dynamics of the business cycles driving economic booms and downturns. Financial markets offer a large number of financial data reflecting financial conditions still the issue of financial cycles definition, measurement and forecasting remain undisclosed. We can understand the complexity of the problem from the fact that today we still miss a generalised definition of financial cycles. General literature uses Minsky's financial instability hypothesis to define financial cycles in the form of the Minsky's cycle. Pioneering works of \cite{Borio2011,Drehmann2012,Drehmann_2013} define financial cycles as long swings in financial conditions (credit and assets price). A large body of research focuses on the issue of finding an optimal method for measuring financial cycles \cite{Schuler2015b}, \cite{Strohsal2019b}, \cite{Hiebert2014}, \cite{Runstler2018}, \cite{Strohsal2019}, \citep*{marinko}.
In this paper, we use quarterly big data for the United Kingdom, the United States, Japan and China from Q1 2004 Q1 2019. We use web-based indicators from the Google trends, financial market data (house price index and credit volume, share in the GDP), long- interest rates on 10-year government treasury bond, consumer and business confidence index and share price index. Using frequency domain time-series methods singular (SSA) and multichannel singular spectrum analysis (MSSA), we test the impact of big data statistics on financial cycles forecasting. Our results show that policymakers can obtain better macro-aggregates to use in financial cycles forecasting and thus better forecasting accuracy and validity. Assuring better information support and prognosis on financial cycles can help economic agents in the decision-making process on financial markets. In the same time, using relevant micro-financial data improve the relevance of macro-financial aggregates. Financial cycles reflect fluctuations in macro-financial aggregates. Thus, more precise measurement and identification of financial cycles support central bank policies meeting information needs for price and macro-financial stability. Comparing the forecasting results in this study, we can see that the results of the \cite{Diebold1995} test support the hypothesis that more accurate forecasting of the financial cycles can be obtained using financial big data. Big data financial cycles can serve the purpose of developing early warning signals of financial distress as financial crisis predictor. Since more contemporary studies show financial conditions articulate real economic activity dynamics and thus business cycles, understanding the true nature of the financial cycles is of crucial importance for unravelling business cycles. Our study results in financial big data can have an essential role in this attempt.
The rest of the papers is structured as follows: after the introduction on financial cycles and big data link, literature with interest in financial big data is presented. In section three, we describe data used in the study and methodology we apply to obtain the results on tested hypothesis. Empirical results discussion and implications are presented in section four, while part five bring up conclusions on the role and importance of financial big data for financial and economic stability analysis.
'Financial Big Data' Implication for Financial Cycles: A Review
The literature on the importance of financial big data for financial cycles measurement is novel and scarce. \citet*{Hassani2015} offer a review of the pros and cons of using big data for the purpose of forecasting. \citet{alessi2009forecasting} study show large macroeconomic and financial data improve forecast performance in finance and economics. \citet{Altissimo2010} provide evidence of using big data to improve the forecast accuracy when using band-pass filters. Using data from the google trends in the study of \citet{Choi2012} improve the forecasting prediction for economic indicators. \citet{gupta2014using} forecast employment using classical and Bayesian methods applied to eight sectors for the US economy with 143 monthly series. Forecast model using big data information outperform other models in forecast accuracy.
Large data sets (big data) availability is essential but not sufficient condition for improving forecast accuracy or model predictions \cite{silver2012signal}. \citet{Banbura2014} find forecasting with large datasets more complex for the purpose of signal extraction.
To fully understand financial cycles we need big data on the micro and macro level. \citet{wibisonouse}
define big data as a large volume of information resulting from the web, financial, administrative and commercial records. They find the financial big data phenomenon as a new opportunity for decision-making process improvement (complete, immediate and granural information adding new value to 'traditional' macroeconomic indicators). Therefore, big data analytics and artificial intelligence offer new opportunities to central banks and economic agents on the financial markets. Opportunities but challenges as well (resources availability, unresolved changing decision-making processes, best policy introduction). \citet*{subrahmanyam2019big} review the body of literature on big data in the finance application. Big data application in finance ranges from the basic application as in \cite{TETLOCK_2007} to complex \citep{Tetlock2008}. According to their study, text analysis in the financial press capture hard-to-quantify aspects of firms' fundamental supporting robust low firm' earning forecasting. Big data based on quantifying language application can help to improve methods for measuring firms' fundamentals or forecast market trends. Big data in finance also help to fill in the gap between qualitative and quantitative data application \cite{Huang_2018}. His study results show big data on consumers opinions (from Amazon. com) when used provide a more robust prediction about firms' cash flow and stock pricing. \citet{Chordia_2018} show that using newsfeed data on macroeconomic conditions only minorly increase profits from fast trading. According to the study of \citep{Fan_2012}, using high-frequency financial data improve the efficiency and stability of portfolio selection. The literature on big data application for policy purpose is much more limited \citep[see][]{Tissot2019}. He lists the advantages and disadvantages of using financial big data to obtain macro-relevant micro information, better macro aggregates to improve policy design and assessment.
Financial big data opened its way to portfolio and decision making processes on the micro-level and it is just moving (slowly) on the macro-level (central banks and financial authorities).
Dataset and Stylized Facts on Financial Cycles in the UK, USA, Japan and China 2005 - 2019
Our paper is first to combine financial big data for the purpose of financial cycles predictions. We follow \citet{wibisonouse} definition of financial big data and use the following variables:
- HPI = Residential property prices selected - Real - Index, 2010 = 100, Bank for International Settlements
- CRS = Credit to Private non-financial sector from all sectors at Market value - Percentage of GDP - Adjusted for breaks, Bank for International Settlements
- CRT = Credit to Private non-financial sector from all sectors at Market value - US dollar - Adjusted for breaks, Bank for International Settlements
- SHARE = OECD (2019), Share prices (indicator). doi: 10.1787/6ad82f42-en (Accessed on 29 October 2019), 2015=100
- CCI = OECD (2019), Consumer confidence index (CCI) (indicator). doi: 10.1787/46434d78-en (Accessed on 30 October 2019), Long-term average=100
- BCI = OECD (2019), Business confidence index (BCI) (indicator). doi: 10.1787/3092dc4f-en (Accessed on 30 October 2019), Long-average=100
- WEB = Google trends data on a financial crisis search term (Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means there was not enough data for this term)
- OECD (2019), Long-term interest rates (indicator). doi: 10.1787/662d712c-en (Accessed on 29 October 2019), Long-term interest rates refer to government bonds maturing in ten years. For China we use China 10-Year Bond Yield government bonds maturing in ten years, investing com, https://www.investing.com/rates-bonds/china-10-year-bond-yield-historical-data.
We use data for the United Kingdom (UK), United States (US), Japan (Jap) and China (CHI) from Q1 2004 to Q1 2019 (quarterly data). Previous studies on financial cycles use data on residential property prices, credits to private non-financial sectors and credits to private non-financial sectors share in the GDP (gross domestic product). Facing limited data availability on financial conditions and web we had to choose between longer time series and a variety of data sources. For the purpose of testing the importance of financial big data in financial cycles prediction, we use data from various sources: web-based indicators - social networks (financial crisis search term importance), financial market data (share prices, residential property prices, credits), administrative data (consumer confidence index, business confidence index, long-term interest rates), commercial data sets (government bonds yields).
To measure financial cycle components, we use spectral analysis techniques - Eviews 10.0 adds-in for Spectral analysis \citet*{Ronderos2014}. Prior to analysis, we normalize and centre all the series for the purpose of eliminating noise in the series. Since normalization procedure increases the possibility of spurious cycles existence in the series, we apply \citet*{Ronderos2014} significant pass filter (SPF) method of the form:
\(F_{t}=\sum_{k=0}^{n / 2}\left[A_{k} \cos \left(\omega_{k} t\right)+B_{k} \sin \left(\omega_{k} t\right)\right]\)(1)
with Ft = filtered series, \((A_{k}=a_{k}), (B_{k}=b_{k}) ordinate (I\left(\omega_{k}\right))\) to test statistical significance of the deterministic component in the spectrum.
To test if the isolated deterministic cycles are statistically significant we employ the following set of tests \cite{Ronderos2014}, \cite{william1994time}, \cite{Whittle_1952}, \cite{fisher1929tests}, \cite{bartlett1954note}:
F test \(F=\frac{\left(a_{k}^{2}+b_{k}^{2}\right) / \nu_{1}}{\sum_{j=1, j \neq k}^{n / 2}\left(a_{j}^{2}+b_{j}^{2}\right) / \nu_{2}} \sim F\left(\nu_{1}, \nu_{2}\right)\) (2)
Fisher test \(T=\frac{\max \left\{I\left(\omega_{k}\right)\right\}}{\sum_{k=1}^{n / 2} I\left(\omega_{k}\right)}\) (3)
Whittle test \( T_{2}=\frac{I^{(2)}\left(\omega_{(2)}\right)}{\sum_{k=1}^{n / 2} I\left(\omega_{k}\right)-I^{(1)}\left(\omega_{(1)}\right)}\) (4)