Consumer Sentiment Index Research Articles

Economic Influences on Plantation Sector Stock Prices in Malaysia: A Quantile Regression Approach

This study aims to examine the relationship between microeconomic, sentiment, and macroeconomic variables across three quantiles (q25, q50, and q75) of stock prices in the Malaysian plantation sector. This study used Earnings per share (EPS) and return on equity (ROE) as the proxy for microeconomic variables, consumer sentiment index (CSI), and business condition index (BCI) as the proxy for sentiment variables, while inflation (INF which proxy from consumer price index) and exchange rate (ER, RM/USD) as the proxy for macroeconomic variables. Using a panel dataset covering 32 listed companies in plantation sector from 2008Q3 to 2023Q3. Result of quantile regression indicates that EPS, BCI, INF have a consistently positive and statistically significant relationship with stock prices across all quantiles. ROE has a mixed impact, with a positive relationship at the 25th and 50th quantile, but with a negative relationship at the 75th quantile. Furthermore, CSI and ER have a consistently negative and statistically significant relationship with stock prices across all quantiles.

Development of Supplementary Composite Consumer Sentiment Index based on a Pretrained Language Model

Development of Supplementary Composite Consumer Sentiment Index based on a Pretrained Language Model

Monetary policy and inflation rate in the behavior of consumer sentiment in the us. A fractional integration and cointegration analysis

Consumer sentiment is a relevant tool for experts when it comes to determining the economic situation in a country, making it possible to analyze the spending trend in advance; for this reason, this paper analyzes links between monetary policy and the inflation rate in the behavior of consumer sentiment in the United States. We use methodologies based on fractional integration and fractional cointegration to obtain the stochastic properties of the monthly time series, from December 2019 to August 2022. The results using fractional integration methodologies exhibit a high degree of persistence and the consumer price index (CPI) and consumer sentiment index present a non-mean reversion I(1) behavior. Focusing on the cointegrating part, we conclude from the results that: 1) an increase in the variable “Total Monetary Base” produces an increase in the CPI, 2) the “variation in the total monetary base” in the United States does not affect consumer sentiment, and 3) a positive variation in the consumer sentiment indicator affects the increase in the inflation rate in the United States.

Product Demand Forecasting with Neural Networks and Macroeconomic Indicators: A Comparative Study among Product Categories

In the fiercely competitive global corporate arena, the intricacies of demand forecasting in the retail sector have become a focal point. While previous research has delved into various methodologies, it consistently overlooks the distinct performances of forecasting models within different retail product categories. Understanding these variations in prediction performances is pivotal, enabling firms to fine-tune forecasting models for each category. This study bridges this gap by scrutinizing the prediction performances of models tailored to different product categories. Building on recent research, we incorporate external macroeconomic indicators like the Consumer Price Index, Consumer Sentiment Index, and unemployment rate, alongside time series data of retail sales spanning various categories. This amalgamated dataset is employed to train a Long Short Term Memory model, projecting future demand across product categories. We further extend the analysis by identifying features that contribute most towards explaining product demand and quantifying their strength. The fitted models yield comprehensive insights into their performances and pinpoint the product categories warranting more focused model development.

Consumer sentiments across G7 and BRICS economies: Are they related?

This paper utilizes fractional integration and cointegration techniques to investigate the stochastic properties of the bilateral linkages between the Consumer Sentiment Index (CSI) of eight developed economies, Australia, Canada, France, Germany, Italy, Japan, the UK and the US and five emerging economies comprising Brazil, Russia, India, China and South Africa, for the time period from 15th January 2010 to 15th July 2019. The univariate results support fractional integration with mean reverting behaviour, with many of the series displaying orders of integration in the interval (0, 1), which connotes that shocks to consumer sentiment have significant long-lasting though reverting effects. From the covariate results and testing for cointegration, we found evidence of cointegration for Australia versus Italy, and France versus Italy. For the BRICS, the only evidence of fractional cointegration is found between Russia and India. Some policy implications of the results obtained are also mentioned at the end of the article.

Transparency and trust in the public sector: Target and benchmarks to ensure macroeconomic stability

The article is devoted to the study of the relationship between a country's macroeconomic stability and the level of transparency and public trust in the financial sector and public authorities. Canonical analysis and structural modeling served as methodological tools of the research. The study examined the data from eight EU countries (Austria, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, Hungary, Czech Republic, and Italy) over the 2011-2021period. Eight indicators of public sector transparency and one indicator of the degree of public trust (Consumer Sentiment Index) were chosen to establish the relationship between the components. The results of structural modeling proved that public trust has a much greater impact on macroeconomic stability than indicators of public sector transparency. A 1-point increase in public trust leads the GDP to increase by 0.018% and the stability of the currency exchange rate – by 0.352%. Meanwhile the same effect from a 1-point increase in the level of public sector transparency amounts to 0.061% and 0.021% increases, respectively.

Retail Demand Forecasting: A Comparative Study for Multivariate Time Series

Accurate demand forecasting in the retail industry is a critical determinant of financial performance and supply chain efficiency. As global markets become increasingly interconnected, businesses are turning towards advanced prediction models to gain a competitive edge. However, existing literature mostly focuses on historical sales data and ignores the vital influence of macroeconomic conditions on consumer spending behavior. In this study, we bridge this gap by enriching time series data of customer demand with macroeconomic variables, such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), and unemployment rates. Leveraging this comprehensive dataset, we develop and compare various regression and machine learning models to predict retail demand accurately.

심리적 요인이 지역별 주택가격에 미치는 영향

In order to examine the influence of consumer sentiment on housing prices by region, this study conducted an empirical analysis using the VARX model, including apartment sales price index, consumer sentiment index, apartment construction permit performance, corporate bond yield rate, and consumer price index. As a result of the VARX model estimation, the change rate of the housing consumer sentiment index has a significant positive (+) effect on the change rate of apartment sales prices, the change rate of the corporate bond yield has a negative (-), and the change rate of the consumer price index has a positive (+) effect in all regions. Looking at the coefficient values, it was found that consumer sentiment, corporate bond yield, and consumer price index had a greater impact on the rate of change in apartment sales prices in Seoul than in other regions. As a result of the impulse response analysis, the change rate of apartment sales price showed a positive (+) response to the shock of the change rate of housing consumption sentiment in all regions. The change rate of apartment sales price showed a weakly negative (-) response to the shock of the apartment construction permit performance in Seoul, was converged with a positive (+) response in nationwide and in non-metropolitan areas. It was found that the impact of the change rate of housing consumption sentiment in Seoul has a greater impact on the change rate of apartment sales prices than in other regions.

Factors determining default in P2P lending

PurposeThe research documented in this paper aims to examine multiple factors related to borrowers' default in peer-to-peer (P2P) lending in the USA. This study is motivated by the hypothesis that both P2P loan characteristics and macroeconomic variables have influence on loan performance. The authors define a set of loan characteristics, borrower characteristics and macroeconomic variables that are significant in determining the probability of default and should be taken into consideration when assessing credit risk.Design/methodology/approachThe research question in this study is to find the significant explanatory variables that are essential in determining the probability of default for LendingClub loans. The empirical study is based on a total number of 1,863,491 loan records issued through LendingClub from 2007 to 2020Q3 and a logistic regression model is developed to predict loan defaults.FindingsThe results, in line with prior research, show that a number of borrower and contractual loan characteristics predict loan defaults. The innovation of this study is the introduction of specific macroeconomic indicators. The study indicates that macroeconomic variables assessed alongside loan data can significantly improve the forecasting performance of default model. The general finding demonstrates that higher percentage change in House Price Index, Consumer Sentiment Index and S&P500 Index is associated with a lower probability of delinquency. The empirical results also exhibit significant positive effect of unemployment rate and GDP growth rate on P2P loan default rates.Practical implicationsThe results have important implications for investors for whom it is of great importance to know the determinants of borrowers' creditworthiness and loan performance when estimating the investment in a certain P2P loan. In addition, the forecasting performance of the model could be applied by authorities in order to deal with the credit risk in P2P lending and to prevent the effects of increasing defaults on the economy.Originality/valueThis paper fulfills an identified need to shed light on the association between specific macroeconomic indicators and the default risk from P2P lending within an economy, while the majority of the existing literature investigate loan and borrower information to evaluate credit risk of P2P loans and predict the likelihood of default.

The impact of consumer credit on the stability of the banking system: analysis of economic and psychological factors

The purpose of the research is to determine the impact of consumer lending on bank sustainability and to assess its dependence on economic indicators.Methodology. The research methodology is based on qualitative and quantitative approaches. The methods of regression analysis, transformation of analytical data mapping, analysis and synthesis were used in the article.Originality / value of the research lies in the fact that the authors have determined the dependence of consumer lending on both macroeconomic and psychological factors on the basis of calculations.Findings. The authors, through econometric calculations, obtained the consumer function as the main indicator of aggregate demand, which affects economic growth. They revealed a low dependence of consumer credit on: interest rate, average monthly income of the population and inflation. The result of multiple regression showed that macroeconomic factors explained only 43,3 % of consumer credit growth. Along with this, the authors identified a significant impact of consumer credit on imports and gross value added of trade.Based on qualitative analysis, the authors calculated the Consumer Sentiment Index (CCI), based on a survey of 128 respondents. The survey found that consumers are optimistic and highly susceptible to psychological instruments of influence, such as marketing and promotions. Also, the authors believe that the perception of debt burden and credit availability influenced the dramatic growth of this banking product.Arguing their assumptions by citing contemporary research by economists, the authors determined that changes in demand for consumer credit are determined more by psychological factors than economic ones. The increase in household final expenditures was due to an increase in consumer credit, which stimulated aggregate demand in the short run and thereby influenced economic growth. At the same time, in recent years in Kazakhstan, lending to households has outpaced lending to industries and the ratio of household debt to GDP has been increasing. This trend may lead in the long run to bank defaults and economic recession in the country. Based on this, the authors have proposed some recommendations to address these problems.

Is there a CSI-leverage nexus?

We examine how various measures of consumer sentiment index (CSI) affect firms' debt policy decisions. Using U.S. firm-level quarterly data from 1993 to 2017, we provide a strong positive relationship between CSI measures and corporate debt policy, implying that firms use external borrowing during a positive economic outlook and reap the tax-shield benefit. We also find that improved household optimism over financial and business sentiments leads to future household consumption. The CSI-leverage nexus is moderated by the state of firms' financial condition, reputation, and profitability. Importantly, our results are robust to sub-sample analysis, firm-level and macroeconomic controls, econometric specifications, alternative measures of sentiment including Shiller's cyclically adjusted price-earnings ratio (i.e., CAPE_SH), Baker and Wurgler (2006)’s stock market sentiment index (i.e., SENT_BW) and search-based uncertainty measure such as FEARS (i.e., Financial and Economic Attitudes Revealed by Search) index of Da, Engelberg, and Gao (2015).

Retail Demand Forecasting Using Neural Networks and Macroeconomic Variables

With the growing competition among firms in the globalized corporate environment and considering the complexity of demand forecasting approaches, there has been a large literature on retail demand forecasting utilizing various approaches. However, the current literature largely relies on micro variables as inputs, thereby ignoring the influence of macroeconomic conditions on households’ demand for retail products. In this study, I incorporate external macroeconomic variables such as Consumer Price Index (CPI), Consumer Sentiment Index (ICS), and unemployment rate along with time series data of retail products’ sales to train a Long Short-Term Memory (LSTM) model for predicting future demand. The inclusion of macroeconomic conditions in the predictive model provides greater explanatory power. As anticipated, the developed model, including this external macroeconomic information, outperforms the model developed without this macroeconomic information, thereby demonstrating strong potential for industry application with improved forecasting capability.

Consumer Sentiment in the United States and the Impact of Mental Disorders on Consumer Behavior—Time Trends and Persistence Analysis

This paper analyzes the stochastic properties in clinical disorders to understand how they have manifested in consumer sentiment in the USA since 1990. The results obtained via fractional integration methodologies exhibit a high degree of persistence, finding non-mean reversion behavior in all of the time series analyzed, except for depressive disorder. Using a causality test, we find that mental and substance use disorders, anxiety disorder, schizophrenia, and alcohol use disorder influence consumer sentiment. Focusing on the cointegrating part, we conclude that an increase in the previously cited mental disorders produces a decrease in the Consumer Sentiment Index.

아파트 실거래가를 기반한 전월세전환율의 영향요인 분석

This study examines the theoretical assumptions and previous studies on the factors affecting the conversion rate for Jeonse to Monthly Payment and conducts an empirical analysis of the determinants of the conversion rate for Jeonse to Monthly Payment. The study covers three regions in Seoul of Gangnam (Seocho-gu, Gangnam-gu, and Songpa-gu) and three regions in Seoul (Gangdong-gu, Yangcheon-gu, and Nowon-gu), which are the most active areas for apartment sales, rental, and leasing transactions. In order to empirically analyze the determinants of the conversion rate for Jeonse to Monthly Payment, this study uses the SVAR model based on theoretical constraints between variables, a methodology developed by Lee (2008). The main influencing factors of the conversion rate for Jeonse to Monthly Payment are the 3-year government bond yield, the conversion rate for Jeonse to Monthly Paymentby region, real estate market consumer sentiment index, and mortgage loan amount. The variables except for the real estate market sentiment index had an impact on the sublease conversion rate. By region, the effect was different, with the conversion rate for Jeonse to Monthly Payment in 3 regions (Gangdong-gu, Yangcheon-gu, and Nowon-gu) responding more strongly than in 3 regions (Gangnam (Seocho-gu, Gangnam-gu, and Songpa-gu).

아파트 실거래가를 기반한 전월세전환율의 영향요인 분석

This study examines the theoretical assumptions and previous studies on the factors affecting the conversion rate for Jeonse to Monthly Payment and conducts an empirical analysis of the determinants of the conversion rate for Jeonse to Monthly Payment. The study covers three regions in Seoul of Gangnam (Seocho-gu, Gangnam-gu, and Songpa-gu) and three regions in Seoul (Gangdong-gu, Yangcheon-gu, and Nowon-gu), which are the most active areas for apartment sales, rental, and leasing transactions. In order to empirically analyze the determinants of the conversion rate for Jeonse to Monthly Payment, this study uses the SVAR model based on theoretical constraints between variables, a methodology developed by Lee (2008). The main influencing factors of the conversion rate for Jeonse to Monthly Payment are the 3-year government bond yield, the conversion rate for Jeonse to Monthly Paymentby region, real estate market consumer sentiment index, and mortgage loan amount. The variables except for the real estate market sentiment index had an impact on the sublease conversion rate. By region, the effect was different, with the conversion rate for Jeonse to Monthly Payment in 3 regions (Gangdong-gu, Yangcheon-gu, and Nowon-gu) responding more strongly than in 3 regions (Gangnam (Seocho-gu, Gangnam-gu, and Songpa-gu).

Measuring financial resilience with consumer sentiment data from India

PurposeCovid-19 sparked new interest in consumer financial resilience (CFR) amongst regulatory authorities, financial institutions, policymakers and the academia. No financial and health crisis has been worse than Covid-19, erasing the growth momentum of nations at all development stages. This study measures consumers' current financial resilience and future expectations within India's emerging market and its likely response to policy measures.Design/methodology/approachCFR is investigated using individual household data on economic state, employment, income and savings from the Reserve Bank of India's consumer confidence survey. The empirical approach is based on the temporal time-series data with mixed frequency regression. Consumers' current and future expectation indices appear as the regressand, whereas credit-deposit ratio, credit outstanding, number of bank accounts and digital transactions act as main regressors.FindingsThe response of consumers' current situation is 3.50 times higher than that of their future expectations. This implies that a rise in the credit-deposit ratio and credit line positively affects CFR. In contrast, a higher number of bank accounts, a proxy for financial inclusion, adversely affect consumer's well-being possibly owing to the government's failure to provide financial support through banking networks. Digital payments (value) positively affect consumers' current situation and future expectations.Practical implicationsThe results of this study inform policy formulation for enhancing financial resilience. Consumer sentiment index acts as a proxy for CFR.Originality/valueFinancial resilience is a concern for policymakers. This study is one of the first studies linking CFR with financial inclusion, credit creation and digital financial capability.

ФІНАНСОВА ПОВЕДІНКА ДОМОГОСПОДАРСТВ УКРАЇНИ В УМОВАХ ВІЙСЬКОВОЇ АГРЕСІЇ ТА ЇЇ ВПЛИВ НА ФОРМУВАННЯ ДОВГОСТРОКОВИХ ТРЕНДІВ ПІСЛЯВОЄННОГО РОЗВИТКУ

The article analyzes the financial behavior of households in Ukraine in the conditions of an unstable socio-economic situation caused by military aggression on the part of Russia. Special attention is paid to highlighting the problem of saving behavior, considering that the savings of the population is the internal financial potential of the economy, which has become more relevant in the current conditions. The dynamics of aggregate incomes, the dynamics and structure of aggregate expenditures, growth rates, and the specific weight of household savings in 2013–2021 were analyzed. The dependence of the financial behavior of households in 2022 on the assessment of the level of well-being, the index of consumer sentiment, the index of the current situation and the index of economic expectations was determined. A number of problems that prevent the attraction of household savings into the economy of Ukraine are outlined, and the main ways to overcome them are proposed. The essence of the financial behavior of households has been clarified, the dynamics of savings in their incomes have been analyzed and, on this basis, the modern trends of changes in such behavior and the possibility of increasing the level of savings through the prism of their relationship and role as a financial source of post-war recovery have been identified. The information obtained as a result of surveys of households regarding the level of their well-being was analyzed for the further assessment of the financial behavior of households in Ukraine at the current stage. Operational information about the mood in society, consumer preferences and saving behavior of the population is provided. The problems that prevent the effective formation of financial resources of households and their investment in the country's economy are outlined. The state of financial behavior of households is analyzed in the context of outlined problems related to attracting household savings to the economy of Ukraine, and the most priority directions for overcoming them are given. There are ways to optimize certain aspects of the financial behavior of households and to direct it to the harmonization and positive dynamics of private and national interests.

Stock market volatility predictability in a data-rich world: A new insight

This study develops a shrinkage method, LASSO with a Markov regime-switching model (MRS-LASSO), to predict US stock market volatility. A set of 17 well-known macroeconomic and financial factors are used. The out-of-sample results reveal that the MRS-LASSO model yields statistically and economically significant volatility predictions. We further investigate the predictability of MRS-LASSO with respect to different market conditions, business cycles, and variable selection. Three factors (equity market returns, a short-term reversal factor, and a consumer sentiment index) are the most frequent predictors. To investigate the practical implications, we construct the expected variance risk premium (VRP) by using volatility forecasts generated from the LASSO and MRS-LASSO models to forecast future stock returns and find that those models are also powerful.

The Impact of Dow Jones Sustainability Index, Exchange Rate and Consumer Sentiment Index on Carbon Emissions

The objective of this study is to examine, over the last 20 years, the short-run and long-run effect on global carbon dioxide (CO2) emissions of the stock returns, exchange rates and consumer confidence. Stock markets contribute to environmental degradation; as a result, we employed, for the first time, Dow Jones Sustainability World Index to use stock returns of socially responsible companies. The euro to US dollar exchange rate is used, as the forex market is the largest financial market and considers it as the largest major pair. The Consumer Sentiment Index is used as a proxy to consumer confidence, since consumer behavior is, also, considered as a major factor linked to environmental degradation. The basic testing procedures employed include the Augmented Dickey–Fuller stationarity test, cointegration analysis and Vector Error Correction Model (VECM). The results establish that stock returns of companies listed on the Dow Jones Sustainability World Index exert a significant negative (positive) impact on the global CO2 emissions in the short (long) term. The inverse, i.e., a significant positive (negative) impact on the short (long) run holds for the both other variables, i.e., US consumers’ confidence and euro to US dollar exchange rates. From the outcomes obtained, policy initiatives that could assist companies to mitigate environmental degradation are recommended.

The impact of the COVID-19 pandemic on suicides: A population study

This study examines the factors associated with the change in the number of suicides per month during the COVID-19 pandemic from 2019 to 2021. For economic indicators, employment and unemployment rates, Consumer Price Index, and Consumer Sentiment Index were used. As inverse indicators of social distancing, the numbers of overseas departures, domestic trips, and movie audience were used. The monthly numbers of inpatients and outpatients for depression were included to consider the effect of the prevalence of depression. Pearson's correlation coefficient analysis and a linear regression were conducted. There was a continued decrease in the number of suicides of 1.7% in 2021 from 2020 following the 4.4% decrease in the previous year. The employment rate was positively associated with the number of suicides for males, while the consumer price index was negatively associated with the number of suicides for females. While the inverse social distancing measures were positively correlated with the number of suicides, no significant association was observed in the regression analysis. Commonly shared thoughts that the pandemic would lead to an increase in suicides by its direct negative impact on mental health or indirect impact through the aggravation of economic conditions and social distancing need to be re-examined.