Consumer Confidence Index Research Articles

Election and Wallet: The Effect of Election Periods and Consumer Expectations on Consumer Confidence with ARDL and QARDL Model Approach

The growing significance of consumer confidence and the explanation of the factors influencing this confidence is an important issue worthy of study. This study aims to examine the effects of election periods and consumer expectations on the Consumer Confidence Index (CCI) in Turkey. To this end, the Autoregressive Distributed Lag (ARDL) and Quantile Autoregressive Distributed Lag (QARDL) models will be employed. Data on the variables in question were collected on a monthly basis from Turkish Statistical Institute (TUIK) over the period spanning 2012:1 to 2024:3. The ARDL model findings indicate that election periods exert a positive and significant influence on the TGE, both in the short run and in the long run. The intention to buy or have a house built and the intention to save have positive and significant effects on the TGE in both the short and long run. Conversely, the intention to buy a car and the cost of living index have a positive and significant effects on the TGE in the short run, but no significant effect was found in the long run. The QARDL model reveals that asymmetry is present, with all variables exhibiting disparate effects across different quartiles. The findings of this study indicate that in order to enhance consumer confidence and guarantee economic stability, it is essential to prioritise the housing market, savings patterns and reducing the cost of living. The development of policies that ensure the sustainability of the confidence and stability created during election periods is crucial. The research offers invaluable insights into ARDL and QARDL models that can inform Turkey's economic policies.

Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.

Are the leading indicators really leading? Evidence from mixed-frequency spillover approach

Leading indicators, usually measured at low frequency (e.g., monthly or quarterly), are widely used as pioneering indices to foresee the future perspective of macro economy and financial markets. However, it is not clear whether these low-frequency leading indicators really lead the actual performance of financial markets, which are usually measured at high frequency (e.g., daily or weekly). This paper uses a novel mixed-frequency spillover method of to examine the spillover effects between monthly leading indicators and weekly financial market prices in China, aiming to assess the pioneering function of these leading indicators. The empirical results suggest that, first, some monthly leading indicators can lead the monthly performance of financial markets, but all of them fail to lead the weekly performance of financial markets. Second, the mixed-frequency approach yields much higher spillover measures than the common-frequency method of Diebold and Yilmaz (2012). Third, China's stock and commodity markets are spillover transmitters to other financial markets in both common and mixed frequency conditions. Finally, the China Monetary Index (CMI) and the Manufacturing Purchasing Managers' Index (CPMI) are more effective leading indicators than the China Consumer Confidence Index (CCCI). These findings help investors and policymakers to adjust risk management strategies in a timely manner and facilitate more accurate financial forecasting.

Performance comparison of alternative volatility models applied to economic indexes: the role of asymmetric effects

ABSTRACT This article examines the empirical relationship between economic indexes – namely the industrial production and the consumer confidence – and their volatility by using the United States data over the periods 1919–2023 and 1960–2023, respectively. To address this topic, several GARCH-family models including volatility feedback and the asymmetric effects are estimated. We find that (i) the industrial production index exhibits asymmetric effects, with significant fitting performance over the standard GARCH model; (ii) the inclusion of t -distributed innovations in the error term improves the fitting performance of the competing models against their counterparts with a normal distribution assumption; (iii) both models tested for the consumer confidence index exhibit similar performance to fit with the data; (iv) by excluding the months during and after the COVID-19 pandemics and the Russia–Ukraine conflict, it leads to the same results and conclusions for both economic indexes. Conditional heteroscedasticity, economic growth, GARCH models, asymmetric effects, volatility feedback

Consumer confidence and cryptocurrency excess returns: A three-factor model

This study examined the relation between consumer confidence and cryptocurrency excess returns using a three-factor model of market, size and momentum. We analysed a dataset comprising 3318 cryptocurrencies from 1 January 2014 to 31 December 2022 based on the CoinMarketCap website. Results indicate a significant negative relation between the United States Consumer Confidence Index and cryptocurrency excess returns. The findings were reinforced based on robustness tests. This study contributes to consumer behaviour research and financial management within the cryptocurrency market. It also provides valuable insights for investors to strengthen their investment portfolios and for relevant authorities seeking to formulate effective policies for monitoring the cryptocurrency market.

Research on China insurance demand forecasting: Based on mixed frequency data model.

In this paper, we introduce the mixed-frequency data model (MIDAS) to China's insurance demand forecasting. We select the monthly indicators Consumer Confidence Index (CCI), China Economic Policy Uncertainty Index (EPU), Consumer Price Index (PPI), and quarterly indicator Depth of Insurance (TID) to construct a Mixed Data Sampling (MIDAS) regression model, which is used to study the impact and forecasting effect of CCI, EPU, and PPI on China's insurance demand. To ensure forecasting accuracy, we investigate the forecasting effects of the MIDAS models with different weighting functions, forecasting windows, and a combination of forecasting methods, and use the selected optimal MIDAS models to forecast the short-term insurance demand in China. The experimental results show that the MIDAS model has good forecasting performance, especially in short-term forecasting. Rolling window and recursive identification prediction can improve the prediction accuracy, and the combination prediction makes the results more robust. Consumer confidence is the main factor influencing the demand for insurance during the COVID-19 period, and the demand for insurance is most sensitive to changes in consumer confidence. Shortly, China's insurance demand is expected to return to the pre-COVID-19 level by 2023Q2, showing positive development. The findings of the study provide new ideas for China's insurance policymaking.

Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm

Based on the CCI index released by China Economic Information Network, combined with the DEGWO difference algorithm and BP neural network regression; Constructed a DEGWO-BP synthesis algorithm under machine learning mode to predict and fit consumer confidence index; The empirical results show that the cumulative error of the consumer confidence index using the DEGWO-BP algorithm is the lowest, only 37.8273; The average absolute error of the model is the lowest, and the model has the minimum level of deviation; The model with the minimum extreme deviation value has the strongest stability.

Can U.S. macroeconomic indicators forecast cryptocurrency volatility?

This research examines the ability of 28 U.S. macroeconomic variables to forecast the volatility of six cryptocurrencies. In- and out-of-sample analyses are performed to validate their forecasting ability. Our analysis shows that during the full-sample period, 15 variables display forecasting ability, while post-COVID-19 period, this number is 17. Among these variables, the most influential include the consumer confidence index, leading economic index, consumer price index, U.S. exports and U.S. imports. Importantly, the predictive ability of these variables appears to have strengthened during the post-COVID-19 period. The out-of-sample results confirm the effectiveness of those macroeconomic variables in the in-sample tests. Furthermore, the robustness test reveals that incorporating these U.S. macroeconomic variables can enhance the performance of the GARCH volatility model. In this study, combination methods are used to enhance forecasting stability and are proven to have good forecasting ability. Our research also indicates that integrating global macroeconomic variables can enhance forecasting ability while recognizing the valuable information provided by U.S. macroeconomic variables. Additionally, we find that variables such as the short-term government bond yield and the M1 money supply emerge as important predictors of cryptocurrency bubbles.

STIMULATING THE DEVELOPMENT OF RESIDENTIAL DEVELOPMENT THROUGH MORTGAGE LENDING

The article is devoted to stimulating the development of housing construction through mortgage lending. The purpose of the article is to determine the role of mortgage lending in the development of the housing sector, in particular through its impact on supply and demand in the real estate market and pricing in this sector. In the course of the research, data analysis, correlation analysis, and methods of forecasting economic trends were used. Graphical methods were also used to provide a clear understanding of how changes in the mortgage market affect the development of the real estate sector. The results of the study show that mortgage lending is a key element in stimulating the development of the housing sector, as it not only directly facilitates access to finance for potential property buyers, but also indirectly affects the pricing and investment attractiveness of the housing sector. The paper shows that fluctuations in mortgage rates have a significant impact on the dynamics of supply and demand in the residential real estate market, as well as on price trends. In particular, rising mortgage rates tend to reduce demand for housing, as households expect better investment opportunities. There is also a strong correlation between mortgage rate increases and slower price growth. The expansion of the mortgage lending market and, consequently, a reduction in mortgage rates boosts supply in the real estate market. Nevertheless, it is found that such changes can have complex and ambiguous consequences, including the risks of market overheating and the formation of price "bubbles" that threaten the stability of the sector in the long run. In addition, the study found that mortgage lending facilitates the implementation of new construction projects and the expansion of the housing stock, which is an important factor in stimulating economic growth. At the same time, the analysis showed that the impact of mortgage rates on the real estate market depends on a wide range of factors, including the economic situation, central bank policy, consumer confidence and other macroeconomic indicators. The practical significance of the publication is to provide recommendations for the development of a balanced policy in the field of mortgage lending aimed at supporting the stable development of the housing sector and preventing potential destabilising factors in the real estate market.

An Empirical Analysis of the European Union's Economy and Politics in the Context of the War in Ukraine

Abstract Crises will always change society. Since 2020, since the outbreak of the COVID-19 pandemic, the world economy has had many trials to overcome. Likewise, the values of states or state alliances prioritizing their citizens' medical, social, and economic well-being have also been tested. The European Union was severely affected by the COVID-19 pandemic, but two years after its appearance, another critical event would accentuate the existing problems and put it in great difficulty. This critical event is the war started by Russia against Ukraine in February 2022. Given the desire Ukraine expressed since 2014 through the EUROMAIDAN movement, the EU could not remain indifferent to what was happening there, and from the very first moments of the war, it showed its support for economic, military, and diplomatic relations towards Ukraine. Thus, the second economic power of the world had to put the decent living and well-being of its citizens to the test in order to save its people. However, it is crucial how much the standard of living of the Europeans suffered compared to the citizens of the states that also contributed to helping Ukraine. Through a difference-in-difference analysis (DID), this article aims to observe how large the discrepancy was between the sacrifice made by Europeans compared to the sacrifice made by other people regarding the values of the consumer price index and consumer confidence index. One thing is sure: regardless of cost, people's freedom, peace, and life are priceless.

Consumer Confidence, Uncertainties, and Risk of the UK Travel and Leisure Industry

This study examines the impact of changes in consumer confidence and uncertainty related to economic policy, financial markets, and the crude oil market, on the returns and risk of the UK travel and leisure stock index. Using monthly data from November 2005 to February 2021 and applying quantile regressions and copula-based conditional value-at-risk (CoVaR) measures, the results are as follows. First, consumer confidence and uncertainties significantly impact the travel and leisure index returns across various quantiles, however, the impact is stronger at the left tail than the right tail; furthermore, the sign and magnitude of the impact differ across the various uncertainty measures. Second, there are significant upside and downside extreme risk spillovers from the extreme movements in the consumer confidence and uncertainty indices to travel and leisure index returns, with evidence of an asymmetric impact. Third, the results are influenced by the COVID-19 pandemic and global financial crisis. These findings have implications for market participants, such as portfolio managers, individual and institutional investors, and policymakers in the UK travel and leisure industry.

Economic Policy Uncertainty and Household Consumer Confidence: Evidence from Indian Household Data

This article aims to analyse how economic policy uncertainty (EPU) affects consumer confidence in India. The study uses unit-level observations on consumer confidence from the Reserve Bank of India’s Consumer Confidence Survey from June 2016 to May 2021. Exploring the variations in consumer confidence across several cities in India, the regression exercise shows how India-specific EPU and global EPU impact consumer confidence. Findings from the study show that EPU has a negative and significant impact on consumers’ perceptions and outlook about the economy. The results also show that the effect of global EPU is stronger, compared to India-specific EPU. A disaggregated analysis is also conducted to analyse the impact of EPU on several consumer confidence indicators, such as the perception and the outlook on household income, spending, employment and general price levels. Contrary to the current literature, this study uses individual-level responses on consumer confidence from survey data and examines the role of EPU on consumer sentiments. Overall, the findings suggest that news-based economic policy-related uncertainty shocks negatively affect households’ sentiments. Moreover, a disaggregated analysis is also conducted for several consumer confidence indicators such as perception and outlook on household income, spending, employment and general price levels. JEL Codes: E66, E71, H31

The Asymmetric Effects of Consumer Confidence on Credit Card Spending in Turkey

This paper analyzes the asymmetric effects of consumer confidence on personal credit card expenditures in Turkey. To this end, the impact of the consumer confidence index on real credit card spending is investigated from January 2013 to June 2023 using the threshold generalized method of moments (GMM). The linear GMM model findings indicate that consumer confidence increases real credit card expenditures. Furthermore, when the threshold GMM model is estimated using the annual percentage change of the consumer confidence index as a threshold variable, the coefficients differ across low- and high-confidence regimes. More specifically, the consumer confidence index has a statistically significant positive impact on credit card expenditure in the low-confidence regime but not in the high-confidence regime. This indicates that the optimistic expectations of economic units in a low-confidence environment may stimulate credit card spending.

Sentiment dynamics and volatility: A study based on GARCH-MIDAS and machine learning

This work investigates the relationship between investor sentiment and volatility of stock indexes. A sentiment proxy is constructed via a machine learning approach from the consumer confidence indexes of four countries. Granger causality tests highlight the influence of sentiment on volatility. This impact is quantified via GARCH-MIDAS models that, retaining variables in their sampling frequency, allow the estimation of the long-run volatility without information loss. Sentiment is finally used to predict long-run volatility. Thus, further insights into the relationship between investor sentiment and return volatility are provided, helping investors to stabilize the former and contain its effect on market uncertainty.

THE MACROECONOMIC DETERMINANTS OF THE STOCK MARKET RETURNS OF TURKISH MANUFACTURING FIRMS: THE COVID-19 PANDEMIC PERIOD

This study explores the impacts of the exchange rate, consumer confidence, oil prices on the stock returns of the Borsa Istanbul (BIST) manufacturing firms for the timeline aftermath of the Covid-19 pandemic (March 2020 – September 2022). As the manufacturing companies comprise the majority of the stock market of the BIST, the composite index of these industrial companies (XUSIN) is selected as the response variable. Implementing the autoregressive distributed lag (ARDL) bounds-testing methodology on the monthly time series data, the cointegration existence is detected among the series. The empirical results also show that oil price is the most significant determinant among these variables affecting manufacturing companies’ returns for the long-run. When considering oil as a vital production input in industries, the decreases in stock prices resulting from oil price rises (i.e. increases in production costs) are inevitable. However, the significant long-run effects of exchange rates and the consumer confidence index on stock returns of this industry cannot be detected for the Covid-19 period.

YATIRIMCI DUYARLILIĞININ GÜVEN VE BEKLENTİ ENDEKSLERİ ARACILIĞIYLA İNCELENMESİ: DAVRANIŞSAL BİR YAKLAŞIM

In this study, the effect of confidence and expectation indices, which are the factors of investor sentiment, on investment decisions was investigated. In the sample, United States was chosen as an example of a developed country and Turkey was chosen as an example of a developing country. The effects of independent variables on investment instruments were examined with multiple linear regression models, and then generalized autoregressive conditional variance models were created for volatility estimates. Stock index returns (BIST100 and S&P500) and the changes in the total deposit amount in the local currencies of both countries were used as dependent variables. As independent variables, the Consumer Confidence Index, Economic Confidence Index, General Confidence Index and VIX Volatility Index were included for both countries in analyses. The monthly frequency dataset between December 2012 - April 2022 was included. As a result of the regression models, it has been observed that the independent variables generally affect the investments in both countries. As a result of the volatility models, while the independent variables did not have an effect on the investments made in the BIST100 index, it was observed that the variables related to expectations and confidence had a significant effect on the other three investment instruments.

Market sentiment and SPACs

ABSTRACT This study investigates how market sentiment affects Special Purpose Acquisition Companies (SPACs). SPACs are blank-check, no-operation companies, which go public and raise money with the intention of acquiring and taking a private company public. Using hand-collected SPAC data, we find that SPAC activities grow significantly in volume and size, especially during the pandemic period of 2020–2021. Based on two market sentiment indexes, the investor sentiment (IS) and consumer confidence index (CCI), we find that SPAC activities are positively associated with market sentiment. The results remain strong and consistent even after controlling for macroeconomic conditions. Overall, we attribute our findings to investors’ willingness to invest in more speculative securities during times of high market sentiment.

The impact of the COVID-19 pandemic on regional inflation in Indonesia

PurposeThis paper aims to examine the impact of COVID-19 on inflation in Indonesia. There are two questions in this study: (1) Is there an impact of COVID-19 on inflation in Indonesia? and (2) whether there are differences in the impact of COVID-19 on regional inflation in Indonesia, considering the different intensities associated with COVID-19.Design/methodology/approachThe estimation technique showing the impact of the COVID-19 pandemic on inflation uses the difference-in-differences (DID) method described by Pischke (2008). The core idea of the estimation above is continuous DID using panel data. No province was affected by COVID-19 before 2020:Q1. Once COVID-19 hits the economy, the effects vary from one district to the other.FindingsThe authors find that the severity of the COVID-19 pandemic negatively affects inflation – the more severe the pandemic, the lower the inflation. This finding conforms with several studies suggesting higher demand pressures than supply during the pandemic. Compared with supply-side indicators such as production index, demand-side indicators – such as consumer confidence index and real sales index – fell more sharply.Research limitations/implicationsIn the Introduction section, the authors have added a discussion that indeed the COVID-19 pandemic affects inflation through both the demand- and supply-side shocks. While factors driving regional differences in inflation rate are important research and policy questions, the analysis of these factors is outside the scope of this study. The study focuses on the COVID-19 impact on inflation and whether the pandemic disproportionately affects some regions than the others.Practical implicationsThis research is important to provide an understanding of the nature of the pandemic on inflation in the context of the Indonesian economy, which is essential to policy formulation, especially for the Central Bank in carrying out the mandate to maintain rupiah stability. This issue is due to the implications of different policy responses between demand- and supply-side shocks.Originality/valueAs a novelty in this study and research gap, the authors use a continuous DID method to account for the varying intensity of COVID-19 across the provinces. In particular, the authors use the number of positive cases of COVID-19 per 1,000 population as opposed to just a binary indicator of before-and-during COVID-19 across provinces.

The Impact of the 2018 Crisis and Covid 19 Pandemic on Macroeconomic Indicators

The aim of this study is to reveal the impact of both the increase in exchange rates and the Covid-19 pandemic on macroeconomic variables. For this purpose, the effects of exchange rate and the dummy variable, which are used to represent the Covid-19 pandemic, on the variables of unemployment rate, general level of prices, credit card expenditures, consumer confidence index and housing price index were analyzed by constructing five different econometric models. Model coefficients were estimated with the two-stage Engle-Granger method and Dynamic Ordinary Squares (DOLS) method. The relationships between the variables were examined by using quarterly data between 2014.I-2023.III. According to the analysis results, due to the increase in the exchange rate, the consumer confidence index has decreased, whereas the unemployment rate, general level of prices, housing prices and credit card expenditures have increased. The same results have been obtained for the period of Covid-19 pandemic. In addition, the response of relevant variables to shocks in the exchange rate was examined with the VAR model. According to the analysis results, while the unemployment rate, general level of prices, housing price index and credit card expenditures react positively to a 1 standard deviation shock in the exchange rate, the consumer confidence index reacts negatively. Therefore, the 2018 crisis and the Covid-19 pandemic have adverse effects on the Turkish economy.

Beyond Accuracy: Building Trustworthy Extreme Events Predictions Through Explainable Machine Learning

Extreme events, despite their rarity, pose a significant threat due to their immense impact. While machine learning has emerged as a game-changer for predicting these events, the crucial challenge lies in trusting these predictions. Existing studies primarily focus on improving accuracy, neglecting the crucial aspect of model explainability. This gap hinders the integration of these solutions into decision-making processes. Addressing this critical issue, this paper investigates the explainability of extreme event forecasting using a hybrid forecasting and classification approach. By focusing on two economic indicators, Business Confidence Index (BCI) and Consumer Confidence Index (CCI), the study aims to understand why and when extreme event predictions can be trusted, especially in the context of imbalanced classes (normal vs. extreme events). Machine learning models are comparatively analysed, exploring their explainability through dedicated tools. Additionally, various class balancing methods are assessed for their effectiveness. This combined approach delves into the factors influencing extreme event prediction accuracy, offering valuable insights for building trustworthy forecasting models.