Composite Leading Indicators Research Articles

Business Cycle and Early Warning Indicators for the Economy of Hong Kong– Challenges of Forecasting Work amid the COVID-19 Pandemic

This paper discusses the use of the OECD’s framework to identify early warning indicators for building a Composite Leading Indicator (CLI) and the use of the Vector Autoregressive Model (VAR) for constructing a short-term forecasting model of economic growth of Hong Kong. With the onset of the COVID-19 pandemic, this paper further evaluates the performance of the CLI and forecasting model which were built based on pre-COVID-19 parameters and identify further adjustments to enhance the model performance.

Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends

Since December 2019 we have been living with a virus called SARS-CoV-2 which has led to health policies being given prevalence over economic ones, causing serious consequences with regard to China's economic growth. For this purpose, we have built a Real Time Leading Economic Indicator based on Google Trends that improves the performance of Composite Leading Indicators (CLIs) to anticipate GDP trends and turning points for the Chinese economy. First, we assess the effectiveness of this new leading indicator relative to China's GDP by analyzing its statistical properties. We use fractional integration techniques to show the high degree of persistence of the new Real Time Leading Economic Indicator (RT-LEI) for China. Second, we observe the same relationship between GDP and RT-LEI in the long term using a Fractional Cointegration VAR (FCVAR) model. Third, we use a multivariate Continuous Wavelet Transform analysis to show which leading indicator best fits GDP and to identify when a structural change occurs. Finally, we forecast, using Artificial Neural Networks and a KNN model based on Machine Learning, our RT-LEI predicting the conclusion of a bearish scenario, after which the recovery begins in mid-2022.

Forecasting turning points of the Russian economic cycle with leading indicators

In recent decades, predicting turning points of economic cycles (their peaks and troughs) using leading indicators has established itself as a fairly simple, clear and at the same time reliable method. But the world experience has shown that any system of leading indicators requires revision and clarification from time to time. The recent non-economic shocks experienced by the Russian economy have made urgent a full-scale revision of existing leading indicators. The article proposes a methodology for constructing a new index, which, one may hope, will not have the shortcomings of its predecessors. In the process of its construction, the dating of the peaks and troughs associated with previous recessions in the Russian economy was specified; the peculiarities of the cyclical dynamics of several dozens of time series were re-examined. The eight most promising indicators were aggregated into a composite leading indicator (CLI) using two alternative methods; we have chosen the one which is not only easier to use and update, but, apparently, with better predictive properties.

How do stock prices respond to the leading economic indicators? Analysis of large and small shocks

Leading economic indicators provide a glimpse into the future economic scenario, which help predict future business conditions. Do changes in leading economic indicators help predict future stock volatility? Do different shock sizes of this indicator caused by an uncertain financial environment provide valid information for forecasting stock market volatility? To answer this question, this paper investigates the predictive performance of composite leading indicator (CLI) shock sizes on stock price volatility under the framework of the GARCH-MIDAS model from an innovative perspective. Interestingly, we find that the asymmetric shock sizes of the CLI perform best both in a statistical and economic sense.

Macroeconomics matter: Leading economic indicators and the cross-section of global stock returns

Leading economic indicators assist in forecasting future business conditions. Can they also predict aggregate stock returns? To answer this question, we examine six decades of data from 39 countries. Short-term changes in the composite leading indicator (CLI) positively correlate with future stock returns in the cross-section. The quintile of markets with the highest CLI increase outperforms the quintile with the lowest CLI change by 1.43% per month. The predictive power of the CLI survives multiple robustness checks and cannot be absorbed by established risk factors. Our findings imply an exploitable investment strategy that can be pursued with exchange-traded funds.

EXAMINING THE CORRELATION BETWEEN ISLAMIC BANKS PROFITABILITY AND THE BUSINESS CYCLE IN INDONESIA

This study aims to examine the correlation between Islamic banks' profitability in Indonesia and the business cycle, which measured by composite leading Indicator (CLI).�This study used several annual data covering the period from 2004-2018. This study utilizes Islamic banks profitability represent by Return on Asset (ROA) data as the dependent variable, CLI data as the dependent variable, and six control variables including Gross Domestic Product, Inflation, Financial to Deposit Ratio, Capital Adequacy Ratio, Non-Performing Finance, and Operating Costs Operating Income. This study adopted multiple regression analysis by using EViews 9.0 software. This study reveals that CLI has a positive and significant impact on ROA, which indicated that the expansion of business cycle activities would increase Islamic banks' profitability. Thus, this study suggests that Islamic bank should engage with the companies that relied on their business activities in the rill sector to boost their profitability. The limitation of this study is this study adopted Indonesian Islamic banks in general. Therefore, this study does not capture the correlation amongst the variables in the specific Islamic banks and region, which might be different from this result due to the differences in banks' internal conditions, culture, and the business cycle in the various region.

The Science and Art of Communicating Fan Chart Uncertainty: The Case of Inflation Outcome in Sierra Leone

The use of macro-econometric modelling technique has become a norm for policy decisions in central banks and in particular, the Bank of Sierra Leone. This study has leveraged on the technicalities of scientific and artistic approaches of assessing risks around point / baseline forecast; this in general makes it more convincing for probability confidence bands to be used in explaining uncertainty that surround point forecast in particular. In the case of this study, the use of the Box-Jenkins ARIMAX model has made it possible to highlight the relevance of Composite Leading Indicator (CLI) like Exchange Rate in alerting signals about early turning point of inflation outcome, both in terms of the uncertainty and risks surrounding its projections. With the derived (scientific) probability distribution of risks (30%, 60% and 90%), it was possible for the study outcome to unearth vast amount of information from the Inflation Fan Chart, particularly with respect to the art of providing balanced assessment of policy framework needed to communicate the BSL’s price stability objective. While the use of Fan Chart is hailed as a very relevant tool, the paper also recommend the use of other model approaches like Scenario and Sensitivity analysis, also considered relevant in providing leading evidence of balancing risks surrounding macroeconomic outlook.

Macroeconomics Matter: Leading Economic Indicators and the Cross-Section of Global Stock Returns

Leading economic indicators assist in forecasting future business conditions, but can they predict aggregate stock returns? To answer this, we examine six decades of data from 39 countries. Past short-term changes in the composite leading indicator (CLI) positively correlate with future stock returns in the cross-section. The quintile of markets with top CLI increases outperforms the quintile with the lowest CLI change by 1.43% per month. The effect survives extensive robustness checks and cannot be explained by known predictors of country equity returns. Furthermore, it may be directly translated into exploitable investment strategies and be harvested with exchange-traded funds.

The Science and Art of Communicating Fan Chart Uncertainty: The case of Inflation Outcome in Sierra Leone

The use of macro-econometric modelling technique has become a norm for policy decisions in central banks and in particular, the Bank of Sierra Leone. This study has leveraged on the technicalities of scientific and artistic approaches of assessing risks around point / baseline forecast; this in general makes it more convincing for probability confidence bands to be used in explaining uncertainty that surround point forecast in particular. In the case of this study, the use of the Box-Jenkins ARIMAX model has made it possible to highlight the relevance of Composite Leading Indicator (CLI) like Exchange Rate in alerting signals about early turning point of inflation outcome, both in terms of the uncertainty and risks surrounding its projections. With the derived (scientific) probability distribution of risks (30%, 60% and 90%), it was possible for the study outcome to unearth vast amount of information from the Inflation Fan Chart, particularly with respect to the art of providing balanced assessment of policy framework needed to communicate the BSL’s price stability objective. While the use of Fan Chart is hailed as a very relevant tool, the paper also recommend the use of other model approaches like Scenario and Sensitivity analysis, also considered relevant in providing leading evidence of balancing risks surrounding macroeconomic outlook.

Predicting Turning Points of the Russian Economic Cycle Using Composite Leading Indicators

Thearticleprovidesa brief overview of the background of constructing composite leading indicators (CLI) for Russia; the paper defines key indicators which currently are calculated and published monthly; they can be put in practice to monitor the Russianeconomy. The underlying methodological approaches are analyzed, along with their advantages and disadvantages. The importance of accounting for a factor of regular or irregular updates and revisions of methods for calculating the CLI is emphasized. Due to them not only the current but also the “historical” dynamics of the indices occasionally change. The paper compares various CLI components and reveals both similarities and differences in the notion of the cyclical dynamics of individual components, reaching the point that the same indicators depending on the methodology are considered as leading, or as lagging. A set of “core” indicators included in the calculation of almost all Russian CLIs is determined; the author also noted those indicators that are widely used in other countries but not yet in Russia, for various reasons.Specialattentionispaidtotheproblemsofdatingeconomiccycleturningpoints,inparticular,thosearisingfromdifferentnotionsofthevery concept of the economic cycle. Dating using formal statistical methods, firstly, is mostly determined by purely technical (and not substantive) nuances, and secondly, it often changes retroactively when revising historical time series. Analysis of global experience indicates that the way out of this impasse can be the detecting of cyclical turning points based on the decisions of the special expert council, whose sole tasks include dating cyclical peaks and troughs. The article describes methodological approaches that the Economic Cycle Dating Committee (Russian Dating Committee, RDC) under the Association of Russian Economic Think Tanks (ARETT), is supposed to follow.The final part of the article analyzes the ability of various Russian CLIs to timely warn when a new phase of the economic cycle is approaching, especially the impending recession. It is shown that expert opinions on the future dynamics of the Russian economy, contained in monthly press releases, are often more accurate than the conclusions that can be obtained based on the CLI trajectory using purely formal decision rules. On this basis, it is concluded that the existing Russian CLIs can be improved; this calls for clarification and finally fixing the dating of cyclical turning points (peaks and troughs), as well as for conducting additional research to identify various economic and financial indicators as the leading, synchronous or lagging indicators of the Russian economic cycle.

A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis

The main aim of this paper is to build a Real Time Leading Economic Indicator (RT-LEI) that improves Composite Leading Indicators (CLI)’s performance to anticipate GDP trends and turning points for the Spanish economy. The indicator has been constructed using a Factor Analysis and is composed of 21 variables concerning motor vehicle activity, financial activity, real estate activity, economic sentiment, and industrial sector. The data sources used are Google Trends and Thomson Reuters Eikon-Datastream. This work contributes to the literature, studying the dynamics of GDP, CLI and RT-LEI using Fractional Cointegration VAR (FCVAR model) and Continuous Wavelet Transform (CWT) for its resolution. The results show that the model does not present mean reversion and it is expected the RT-LEI reveals a bear trend in the next two years, alike IMF and Consensus FUNCAS′ forecasts. The reasons are mostly associated with escalating global protectionism, uncertainty related to Catalonia and faster monetary policy normalization.

Forecasting Stock Market Indices with the Composite Leading Indicators: Evidence from Turkey

The objective of this study is to evaluate the empirical performance of composite leading indicators (CLIs) in forecasting stock market indices for Turkey in the period from 2007:03 through 2019:07. After examining the stationary of the series by using Narayan and Popp (2010) and Enders and Lee (2012) Fourier ADF unit root tests, the causality relationship from the composite leading indicators to stock market indices are tested by employing Enders and Jones (2016) Fourier Granger causality test. The results support the evidence of a causality relationship from composite leading indicators to BIST100, BIST Financial and BIST Industrial under structural breaks.

A suggestion for financial condition index for Turkish tourism sector

Purpose- When the economic structures and growth behaviors of the countries are examined, it is seen that the countries with the advantage in the tourism sector have achieved significant role if they give the necessary importance to this sector. It is insufficient and incomplete to consider the issue only in terms of macroeconomic indicators. In our study, we think that this sector should take place in the plans made by considering this sector and should be followed carefully. When the world literature is examined, it is seen that tourism financial conditions index has been developed and it has taken place in finance literature. However, we believe that we need to be taking into consideration of terrorism for this index in countries like Turkey. In our study, we introduce a new index by extension with terror variable to the tourism financial conditions index which is used and developed in the literature. Methodology– In this study, Consumer Confidence Index (CCI), Composite Leading Indicator (CLI) Consumer Price Index (CPI), Real Effective Exchange Rate (REE), number of tourist arrivals (ARV) and number of terrorist attacks (TRR) for the period between 2004:01–2017:12 are used. The Factor Analysis is used to determine the direction of the coefficients of variables. The main purpose of data normalization is to guarantee the quality of the data before it is subjected to any analysis. In the study, min-max and z score methods, which are the most commonly used statistical normalization methods, were used in the normalization of variables. Findings- According to factor analysis findings, only terror variable affects negatively on the TFCI. Other variables affect positively on the TFCITwo composite indicators were formed from the combination of the variables obtained as a result of standardization. These two variables are different in structure. Both composite indicators are made primarily from seasonal effects of the removal process. Conclusion- As a result, Factor analysis is used to determine the direction of the coefficients of the variables that form the index. According to factor analysis findings, only terror variable affects negatively on the TFCI. And then two separate standardization methods were used to form the composite indicator. These are Z-standardization and Min-Max method. The variables obtained with both methods have seasonal effects, also the seasonally adjusted indices are reported.

Analysis of Indonesia Business Cycle through Composite Leading Indicator Data Processing for Banking Industry

This study aimed to analyze the business cycle of Indonesia through data processing CLI (Composite Leading Indicator) and to obtain an investment leading indicator which a composite of several indicators. This study used the OECD method and used time series data, i.e. quarterly data from 2001-2013. The result of the analysis of this study was the formed CLI was well functioned (significant), although the correlation coefficient both not overlapped (low). Because the IDX Composite movement was more volatile than GDP movement. However, the formed CLI was capable in following the cyclical movement of the reference series (significant). The result of hypotheses in this study was assumed that there were some variables that met the category as a leading indicator in GDP reference series that were: CPI, exchange rate, property credit, and housing loan. While the variables in the reference series IDX composite namely: import capital, Pi_paper, export manufacture, export agriculture, housing loan, and property credit. In this study, especially for the government and central bank (Bank of Indonesia), they should be able to work together in making policies that pay attention to the economic variables classified in leading indicators. DOI : https://doi.org/10.26905/jkdp.v21i4.1553

Zmeny zloženia kompozitného predstihového indikátora Slovenska v čase

Cyclical behavior of the economy can be monitored and predicted with the help of composite leading indicator (CLI) which is an aggregate index of several individual indicators and is statistical relevant for analyzing and forecasting of reference series. This article is focus on construction of CLI for Slovakia and its changes over time. For its construction is used combination of methodologies of OECD and Institute of Informatics and Statistics (Infostat). These methodologies are based on chosen reference series and 124 economic indicators, filtering of time series with Hodrick-Prescott filter (HP filter), normalization of time series, cross correlation and creation of CLI with system of same and different weights. On the base of economic indicator analysis are selected different groups of leading cyclical indicators in seven different time periods. It means that exist changes of CLI composition over time. The best predictable abilities of Slovak business cycle has CLI constructed from time period 2010-2015. On this base was created short-term forecast of Slovak business cycle which supposed economic grow in second and third quarter 2017. Positive GDP growth expectations create space for new investment opportunities as well as government restrictions on public expenditures. The result of our analysis indicate that in the future we can expect new changes in composition of CLI for Slovak economy.

Examining relationship between composite leading indicators and Borsa Istanbul sector indices

The prediction of fluctuations in economic activity has become even more important, especially after the crises experienced in recent years. In order to make such a prediction, economic and financial indicators are needed. The Composite Leading Indicators (BONC) published by Central Bank of the Republic of Turkey is useful in predicting the macroeconomic contraction or expansion. In this study, it is tested whether there is a relationship between Composite Leading Indicators Index and 12 Borsa Istanbul sector indices returns. Unit root test results show that sector indices are stationary at the first differences and the BONC is at level. Thus long term relationship between each sector indices and the BONC is examined by Boundary Test. The findings show that there is a long term and relationship between BONC and all the sector indices in the study. Also it has been determined that positive change in the BONC has a statistically significant and positive effect on XUTEK and XGMYO in the long term. The short term relationship is also found between all sector indices and BONC. In addition, it is determined that increase in BONC affects all sector indices positively in the short term. These results indicated that investors take into account the BONC without distinguishing the sector when investing to stocks and that BONC is an important indicator for stock returns.

Developing Composite Leading Indicators to Forecast Industrial Business Cycles in Iran

Economic cycles are referred as repeatable movement of economic indicators with different domain and duration. Detection of these cycles may help in forecasting the contraction period or expansions of important parts of the economy specifically industry. In this regard, many economist and researchers have focused on composite leading indicators which are growing in terms of diversity of econometric methods. This paper studies 13 macro economical time series in order to develop the best composite indicator, reflecting business cycles of Iran’s industry. Number of established licenses, production index of large industrial units, producer price index and import value index are the chosen variables that make the proposed composite leading indicator. The result of comparison between composite leading indicator and value added time series in the studied period (1997 up to 2012) showed a good correlation between the fluctuations of composite indicators and value added of industry. The result also showed that the proposed leading indicator has the ability to predict the business cycle for maximum 4 and minimum 1 period ahead and in average the forecasted period is equal to 3.2 seasons.

New composite leading indicator of the Hungarian business cycle

The objective of the paper is to create a composite leading indicator (CLI) for monitoring and predicting Hungarian business cycles. We compare the existing CLI applied by the OECD and Eurostat with our own CLI. According to our findings, our CLI forecasts the evolution of a referential series more precisely than the CLIs developed by the OECD and Eurostat. Nevertheless, from our point of view, the application of all existing CLIs at the same time can be appropriate. Consequently, the number of false signals should be reduced. The CLIs allow us to receive the first rough preliminary estimations of an economic cycle, in our case, the Hungarian one.

Making leading indicators more leading

This paper proposes a novel wavelet-based approach for constructing composite indicators. The wavelet-based methodology exploits the ability of wavelet analysis to analyse the relationships between variables on a scale-by-scale, rather than aggregate, basis. A wavelet-based index which combines several scale-based subindexes is constructed by using a scale-by-scale selection of the components included in the OECD composite leading indicator (CLI) for the US. The comparison with the CLI and its derived measures indicate that the wavelet-based composite index tends to provide early signals of business cycle turning points well in advance of the OECD CLI. Moreover we find that the reliability of the signals tends to increase considerably when the sub-index obtained from the time scale components corresponding to minor cycles, that is, 2-4 years, is removed from the overall wavelet-based index. Keywords: wavelets; composite leading indicators; early warning signals JEL classification: C1; C3; C5; E3

Kompozitný súbežný indikátor hospodárskeho cyklu českej ekonomiky

This article’s objective is to create the Composite Coincident Indicator - CCI for the Czech economy, which is useful for monitoring of the Czech business cycle and its comparison with the GDP and the Index of Industrial Production. In the theoretical part of this article, we describe three indexes which currently represent options for monitoring of business cycles. The most useful indexes are the GDP, the Index of Industrial Production and the CCI. We also compare advantages and disadvantages of these indexes for the Czech business cycle. In the empirical part of the article, we analyse 127 indicators from different parts of economics and select coincident cyclic indicators that are the most appropriate for the creation of the CCI. The most useful methods in our analysis are Hodrick- Prescott fi lter and cross-correlations. Besides the creation of our own CCI, we compare this composite indicator with the GDP and the Index of Industrial Production. Then we analyse prediction abilities of Composite Leading Indicators (CLI) in a relationship with the CCI, GDP and Index of Industrial Production for the Czech economy.