Confidence Index Research Articles

Which Is More Helpful in Explaining Commercial Real Estate Return: Fundamentals or Consumer Sentiment?

This paper combines the literature of two schools of studies. It clarifies and introduces a conceptual framework with consumer confidence index and commercial real estate return. The research investigates whether fundamentals or consumer sentiment is more helpful in explaining commercial real estate returns. It proposes that consumer sentiment is exogenous in the conceptual framework. Consumer confidence index is decomposed into two components: fundamentals, as signaled by macroeconomic indicators, and the residual part, representing consumer sentiment. Using data in New Zealand markets, it is found that commercial real estate total return and capital return are more explained by consumer sentiment than by fundamentals. On the contrary, commercial real estate income return is more explained by fundamentals. Consumer sentiment imposes sustained effect on capital return and total return. Findings imply that valuers incorporate market sentiment into their valuation process. On top of that, investors rely on market sentiment during the transaction process. Industrial and Bulk Retail sectors enjoy positive impacts of COVID on capital return. Results imply potential diversification benefits across different commercial real estate sectors during market shocks. The study provides additional insight into consumer confidence and consumer sentiment and its intriguing relationship with commercial real estate return.

Consumer Confidence Index (CCI) for European countries - relation to objective household financial situation

ObjectivesThe Consumer Confidence Index (CCI) has been constructed in order to measure consumer attitudes. Its construction takes into account not only the assessment of the current financial and economic situation in the country and the current level of savings, but also the expectations of consumers with regard to the above-mentioned areas of life and the management of financial resources. The objectives of the article are (1) to test and select the components contributing to the consumer confidence index by proposing a new approach to its calculation and (2) to test the links between consumer sentiment and the level of financial instruments held by European households.Material and methodsThe modelling was carried out using data on the components of the Consumer Confidence Index (CCI) for the countries of the European Union and data on the financial situation of households. The data were taken from the Eurostat database. The analysis period covers the period 2015-2022, depending on data availability.ResultsThe results presented in this study indicate a statistically significant relationship between the subjective assessment of household finances and the actual financial situation of households. The analysis carried out made it possible to select the components that make up the consumer confidence index and to propose a new approach to its calculation. The study of the impact of the individual components of the CCI therefore constitutes a very important area of research crucial for modelling economic phenomena.ConclusionsBased on the available literature, we can conclude that the issue of household financial well-being is a complex, multidimensional phenomenon. Relying solely on objective monetary factors does not allow us to identify the main determinants of household financial behaviour.

Determinants Of Financial Literacy, Digital Literacy, Internet Penetration And Consumer Confidence Level Mediated By Fintech Growth On Retail Industry Growth In Indonesia

This study evaluates how financial literacy, digital literacy, internet penetration, and consumer confidence influence retail industry growth in Indonesia, emphasizing the mediating role of fintech. In the era of industry 4.0, technology, especially fintech, has transformed conventional financial business models into digital ones, accelerating transactions and financial inclusion. The Covid-19 pandemic has further driven digitalization, although it has posed major challenges for the retail industry. This study aims to determine the actual impact of the development of fintech applications including the influence of financial literacy, digital literacy, internet penetration and consumer confidence levels on the development or growth of retail in Indonesia. This study uses a quantitative method with a focus on analyzing the relationship between financial literacy, digital literacy, internet penetration, and consumer confidence levels in the growth of the retail industry, both directly and through the mediation of fintech growth. The data used are secondary data that include information on the financial literacy index, digital literacy index, internet penetration level, consumer confidence index, fintech adoption level, and retail performance indicators. To overcome incomplete data, imputation and proxy variable methods are used. The analysis was conducted using multiple linear regression to identify direct relationships between variables, and structural equation modeling (SEM) to comprehensively evaluate the influence of fintech mediation. This study shows that financial literacy, digital literacy, and consumer confidence levels have a significant influence on retail growth, both with and without fintech growth mediation. Digital literacy and consumer confidence levels have a positive impact, reflecting that understanding of digital technology and consumer confidence contribute to retail development. Conversely, financial literacy shows a negative influence. Internet penetration does not show a significant influence on retail growth, either directly or through fintech mediation.

The Spillover Effects of Market Sentiments on Global Stock Market Volatility: A Multi-Country GJR-GARCH-MIDAS Approach

In behavioral economics, it has widely been documented that there might be a close relationship between overall market sentiment and economic performance, such as GDP per capita. In this paper, we investigate the effects of market sentiment on stock market volatility, which has widely been recognized as an important factor for economic sustainability. In particular, we aim to identify the existence of spillover effects of market sentiments on global stock market volatility. As a first attempt, we chose ten countries from major economic regions over the world (including America, Asia, Europe, and Oceania), and analyzed their interdependence and interconnectedness using a GJR-GARCH-MIDAS model. The results highlight that an individual country’s stock market volatility is significantly influenced not only by its own market sentiment (proxied by the consumer confidence index) but also by the overall market sentiments of other countries across the world. The results also highlight significant country-by-country heterogeneity in the time lags of the global spillover effects, which indicates substantial heterogeneity in the behavioral dynamics of individual countries.

Unique Nucleotide Polymorphism of African Swine Fever Virus Circulating in East Asia and Central Russia.

The lack of data on the whole-genome analysis of genotype II African swine fever virus (ASFV) isolates significantly hinders our understanding of its molecular evolution, and as a result, the range of single nucleotide polymorphisms (SNPs) necessary to describe a more accurate and complete scheme of its circulation. In this regard, this study aimed to identify unique SNPs, conduct phylogenetic analysis, and determine the level of homology of isolates obtained in the period from 2019 to 2022 in the central and eastern regions of Russia. Twenty-one whole-genome sequences of genotype II ASFV isolates were assembled, analyzed, and submitted to GenBank. The isolates in eastern Russia form two clades, "Amur 2022" and "Asia". Within the latter clade, five subclusters can be distinguished, each characterized by a unique set of SNPs and indels. The isolates from the central regions of Russia (2019; 2021) form the "Center of Russia" clade, with two subclusters, "Bryansk 2021" and "Center of Russia 2021" (bootstrap confidence index = 99). The presence of the previously unique genetic variant ASFV for the Kaliningrad region in the wild boar population of the Khabarovsk region (eastern Russia; 2021) has also been confirmed.

Identification of Structural Constituents in Advanced Multiphase High-Strength Steels Using Electron Back-Scattered Diffraction

This study addresses the characterization of the particular microstructural constituents of multiphase transformation-induced plasticity (TRIP)-aided steels belonging to the first and third generations of Advanced High Strength Steels (AHSS) to explore the possibilities of the EBSD method. Complex microstructures composed of ferrite, bainite, retained austenite and martensite were qualitatively and quantitatively assessed. Microstructural constituents with the same crystal structure were distinguished using characteristic EBSD parameters like confidence index (CI), image quality (IQ), kernel average misorientation (KAM) and specific crystallographic orientation relationships. A detailed linear analysis of the IQ parameter and misorientation angles was also performed. These tools are very helpful in linking different symmetric or asymmetric features of metallic alloys with a type of their structure and morphology details. Two types of samples were investigated: thermomechanically processed and subjected to 10% tensile strain to study the microstructural changes caused by plastic deformation.

MODELING AND FORECASTING OF TOURISM INCOME: THE CASE OF TURKEY

This study aims to identify the optimal forecasting model for predicting Türkiye's tourism income, a crucial factor for economic planning and development. This study employs different forecasting techniques, including the seasonal Autoregressive Integrated Moving Average (SARIMA), the additive and multiplicative Holt-Winters methods, the Exponential Smoothing State Space (ETS), Artificial Neural Networks (ANNs) and seasonal-trend decomposition procedure based on the loess (STL)-ANN hybrid model and evaluates their performance. The methodology involves analyzing monthly tourism income data from January 2012 to December 2023, incorporating additional economic indicators such as the economic confidence index, number of visitors, consumer price index, industrial production index, and USD exchange rate, which serve as input for ANN models. The findings reveal that ANNs, particularly the model that incorporates tourism income alongside other economic indicators, outperform traditional models with the lowest Mean Absolute Scaled Error (MASE) and Root Mean Squared Scaled Error (RMSSE). Specifically, the ANN model with additional predictors demonstrates the highest forecasting accuracy. These results suggest that advanced machine learning techniques provide superior predictive capabilities compared to traditional linear models. The study underscores the importance of integrating complex models to achieve more accurate forecasting, offering valuable insights for policymakers and practitioners in the tourism sector.

Assessing business activity of the Russian industry

The relevance of the study is due to the fact that, despite the difficult economic and political situation for the Russian industry and the economy as a whole, there are problems not only with the arrival of foreign raw materials and goods in the domestic market, but also with the smooth operation of industry. It is necessary to develop and form new, more essential approaches to the industrial development strategy in the conditions of forced modernization of the industry. The factors that have the greatest and the least influence on the business activity of enterprises have been identified. The directions for forming new ways of domestic industry effective development through assessing the entrepreneurial confidence index on the part of enterprises of extractive and manufacturing industries have been analyzed. As the index evaluation result, the author concludes that the level of enterprises’ business activity has jumping values, in the course of which the factors that have a negative impact on industrial production development have been identified.

Do low-cost digital tools allow measuring the cladode area?

This study evaluated low-cost digital tools for estimating cladode area by mobile and fixed devices. We conducted the experiment at the Serra Talhada Academic Unit of the Federal Rural University of Pernambuco with pear cactus (Nopalea cochenillifera Salm-Dyck and Opuntia stricta (Haw.)). In this sense, we used four software to measure the cladode area: LAFore (fixed device), ImageJ (fixed device), Easy Leaf App (mobile device), and Petiole App (mobile device). We considered the LAFore software as a reference and used the following statistical parameters: linear regression, correlation coefficient (r), Willmott index (d), confidence index (c), and standard error of estimate (SEE). The software on fixed devices (ImageJ and LAFore) showed similar results, with performance considered excellent, and the Easy Leaf App showed a very good performance among mobile devices.

Investor sentiment and Euro area ETFs: an empirical analysis of consumer behaviour

The goal of this article is to study the relationship between investor sentiment and Euro area Exchange Traded Funds (ETFs), providing insights into the dynamics of market behaviour and the impact of consumer behaviour on investment patterns within the Eurozone. This paper has employed panel data analysis, correlation analysis, regression analysis, and the Granger causality test to investigate the relationship between ETFs and the consumer confidence index (CCI). Results suggest that CCI significantly negatively affects ETF pricing. While a weak positive correlation and regression relationship between iShares MSCI Eurozone ETF prices and CCI 19 Euro area markets have been found, the Granger causality test did not provide evidence of a causal relationship between the two variables. Other factors, such as financial stability, government regulations, and market-specific factors, may also affect ETF prices. The study concludes that, while CCI may be important, it should not be the only factor considered.

DRIVING FORCES OF HOUSING PRICES IN TÜRKİYE: A NONLINEAR ARDL ANALYSIS

The study aims to examine the effects of the industrial production index, consumer price index, consumer confidence index, real effective exchange rate, and loan interest rate variables on the housing price index. Monthly data between 2010:01-2024:05 is used in the study and the NARDL method is used. When the long-term coefficients are examined; it is seen that positive shocks in the industrial production index have a positive effect on the housing price index and negative shocks have a negative effect on the housing price index. It is seen that positive changes in the consumer price index have a positive effect on the housing price index and negative changes have a negative effect. Increases in the consumer confidence index have a positive effect on the housing price index. An increase in the real effective exchange rate causes a decrease in the housing price index in the long term. Finally, both positive and negative changes in housing loan interest rates increase the housing price index.

Global Transmission Shocks of U.S. Monetary Policy on China's Housing Prices

The paper demonstrates that the monetary policy of the United States has important spillover effects to China. Based on the monthly data from January 2016 to April 2024, this paper establishes a VAR model to analyze the influence of U.S. monetary policy on China's real estate prices and its transmission channels, including interest rate, exchange rate, consumer confidence, and asset price. The results show that the impact of the Federal Funds Effective Rate on housing prices is very quick, reaching a maximum in the first month and continuing to have a positive impact over the long term. The Federal Funds Effective Rate and China's Loan Prime Rate have a positive impact on housing prices in the early stage, but then there is a change in impact direction. The Exchange Rate, Consumer Confidence Index and CSI 300 index all had positive spillover effects on housing prices in the early days. The variance decomposition analysis shows that the exchange rate channel plays the most significant role in causing China’s real estate price fluctuations.

Translating mineral systems criteria into a prospectivity model for IOCG deposits in the Kolari region, Finland

The mineral system approach facilitates mapping the fingerprints of geological processes in ore formation using mineral prospectivity modeling (MPM). It aims to reduce the time and cost of exploration as a priority in the mineral exploration industry. Finland, which is covered with thick soil, dense vegetation, and snow in winter and thus has limited outcrops, serves as a suitable testing ground for this method. In this contribution, MPM was employed to predict favorable targets for Fe-Cu-Au (IOCG) deposits in the Kolari region, northwestern Finland. The mineral system components ore metal and sulfur sources, pathways, energy sources/drivers, traps, and the geological factors influencing the ore-forming processes were translated into mappable criteria and were considered for a regional-scale analysis. Knowledge-driven, fuzzy logic overlay, fuzzy inference system, and geometric average methods integrate evidential layers derived from geological (scale-free geology map), geochemical (till and bedrock sample geochemistry), and geophysical data (magnetic, radiometric, electromagnetic measurements, and gravity worms). Subsequently, the data were modeled using a combined conceptual/empirical approach, i.e. fuzzified evidential layers and logistic regression. In the prospectivity models, existing IOCG deposits are consistently placed within high-favorability areas. In addition, new exploration targets were identified. The models were validated against known IOCG deposits using Receiver Operating Characteristic (ROC) analysis and average Area Under the Curve (AUC) with values of final prospectivity models consistently reaching scores > 0.8. This indicates a favorable outcome of the application of MPM, making the approach applicable to other regions and ore deposit types. A majority voting ensemble technique was employed to combine the favorable areas of each prospectivity model. Then a confidence index was adopted reducing uncertainty linked to the models generated. These outputs consistently facilitated the identification of exploration targets more reliably.

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.

Does a correlation exist between delayed vaccination and a decreased vaccine confidence?

ABSTRACT Delay in vaccination schedule may result in increased risk of vaccine-preventable outbreaks. Lack of parental confidence in vaccines and sociodemographic factors could be related to those delays. Vaccine Confidence Index (VCI) is an indicator to estimate confidence in vaccines, composed of simple dimensions about it and evaluated using a 5-point Likert scale. Objectives of this study, conducted in the Region of Murcia, Spain, were to determine which sociodemographic factors were associated with delayed administration in measles, mumps and rubella (MMR) vaccine, both first and second doses, and to evaluate if there were differences in parental vaccine confidence of children who were delayed on MMR vaccine schedule compare to vaccinated on time. The 2019–2020 (first dose) and 2016–2017 (second dose) cohorts were divided into four categories: vaccinated on time, slightly delayed, moderately delayed and highly delayed. Online questionnaire was sent to their parents, followed by a telephone questionnaire for non-responders. Regarding MMR first dose, a significant association was found between delay and birth order and intellectual/motor delay of children. Parental educational status, the main source of vaccine information and the social networks more frequently used by parents were also associated with delay. Suffering motor/intellectual delay and the obstacles to vaccination process derived from caring for other siblings were associated with delay in second MMR dose. The VCI decreased significantly in first dose group as delay increases but did not change in the second one. Confidence in vaccines and sociodemographic factors, respectively, were associated with delaying MMR first and second dose.

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.

Dynamics of persistence in Brazilian economic uncertainty, expectation, and confidence indexes

This study examines the persistence dynamics in Brazilian economic uncertainty, confidence, and expectation indexes, in both business and consumer sides of the economy. Using rolling window estimations of the fractional integration parameter, we assess how economic turbulence influences the persistence of these indexes. We find that economic uncertainty in Brazil is non-stationary but mean-reverting, with its persistence only transitorily affected by economic and political crises. Interestingly, confidence and expectation indexes displayed hysteretic behavior, with multiple persistence regimes linked to crises. These results may be informative for policymakers about the perceptions of the public and businessmen to economic shocks and crises.

Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level

Abstract. Scan-to-BIM is a widely-used approach to generate Building Information Modelling and by extension Digital Twin models in the architecture, engineering, and construction sector. The resulting models need to be as accurate as possible to ensure subsequent activities that make use of them can do so effectively. Quality assessment of point clouds and occlusion assessment of BIM outputted from Scan-to-BIM has been investigated. However, Scan-to-BIM systems currently do not provide proper metrics as to the confidence the user can have in the quality, in particular geometric quality of the outputted model. This paper addresses this gap by introducing a confidence index, for analysing the reliability of the generated 3D models and thereby quantifying the confidence the user can have in them. Index of confidence is itself derived from three more specific indices: index of coverage estimates the portion of the surface of the modelled element that is explained by the input point cloud. Index of distribution estimates how well the points explaining the modelled surfaces are distributed around the overall object’s surface. Index of distance defines the closeness of the generated element models to the input point cloud. The proposed indices are assessed using three real examples, demonstrating their adequacy.

The Relationship Between Sectoral Employment and Real Sector Economic Confidence: Fourier Causation Analysis

Purpose –In order to ensure economic growth in the short term and economic development in the long term, it is important to evaluate the events occurring in the political and economic order correctly and to meet the expectations. This study was created to evaluate the effectiveness of economic confidence on the relevant sectors on a sectoral basisDesign/Methodology/Approach –In this study, the relationship between the real sector economic confidence index and employment rates in industry, construction and services sectors will be analyzed. Turkey is selected as the sample and the data range subject to the analysis is determined as 2009:01-2023:08. In the study, the Fourier analysis method, which is among the new generation analysis methods, was applied, and Fractional Frequency Fourier Augmented Dickey Fuller Unit Root Test and Fourier Toda-Yamamoto Causality Test were applied.Findings –As a result of the application, a one-way causality relationship from the real sector economic confidence index to building and industry sector employment was determined. A recommendation should be made regarding the necessity of making political arrangements that will maintain the stability level of employment in the relevant sector.Discussion –In line with the data obtained in this study, the fact that real economic confidence has an effect on employment in the construction and industrial sectors, in addition to the fact that sectoral employment has no effect on the real economic confidence index, can be linked to the fact that sectoral leaders pursue a policy of contraction or expansion within the framework of expectations

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.