Consumer Sentiment Index Research Articles

US consumers' confidence and responses to COVID-19 shock

PurposeThis research's main objective is to investigate the relationship between consumption expenditure and consumer confidence in the USA and to study their effects on US economic revivalism during and after the coronavirus disease 2019 (COVID-19) shock.Design/methodology/approachThe authors use Michigan's monthly Consumer Sentiment Index and its five components from January 1978 to April 2020. The study is unique in quantifying the potential variations in US consumer confidence due to COVID-19 under different scenarios, by providing a projection until December 2021. It also estimates the time needed for recovery and offers guidance to policymakers on ways to contain the negative impacts of COVID-19 on the economy by restoring consumer confidence.FindingsAll scenarios show a gradual recovery of consumer confidence and consumption expenditure. This study recommends expansionary policies to encourage consumption expenditure to generate additional demand and boost economic growth and job creation.Practical implicationsThough this study is limited to the US consumer confidence index, it offers significant implications for marketers, customers and policymakers of other developed economies. The authors recommend expansionary economic policies to boost consumer confidence, raise economic growth and result in job creation.Originality/valueThe study is unique in quantifying the potential variations in US consumer confidence due to COVID-19 under different scenarios; by providing a projection until December 2021. It also estimates the time needed for recovery and guidance for policymakers on ways to contain the COVID-19 shock negative impacts on the economy by restoring consumer confidence.

The impact of oil supply shocks on real economic activity: new evidence based on the proxy SVARs

ABSTRACT The existing literature on the impact of oil supply shocks on real economic activity after the mid-1980s still fails to reach an agreement. This may be partly due to different identification assumptions and model specifications corresponding to the oil supply shock, and partly due to the oil supply shock identified previously is endogenous to real economic activity. In this paper, based on the proxy structural vector autoregressions (SAVRs), we identify an oil supply shock that is exogenous to real economic activity, and then study the impact of this shock on U.S. real economic activity. We find exogenous oil supply shocks have a very large and persistent impact on U.S. real economic activity after the mid-1980s. Besides, several new possible transmission channels through which exogenous oil supply shocks can influence U.S. real GDP are detected. Specifically, unfavorable exogenous oil supply shocks can lower the consumer sentiment index (CSI) and consumer confidence index (CCI), decrease real net exports of goods and services (NEGS) through the terms of trade (TOT) effect, and reduce real government consumption expenditures and gross investment of state and local (GCEGISL), thus leading to a decline in real GDP. These newly detected transmission channels are very important for policymakers.

Economic Indicators Selection for Property Crime Rates using Grey Relational Analysis and Support Vector Regression

Features selection is very important in the multivariate models because the accuracy of forecasting results produced by the model are highly dependent on these selected features. The purpose of this study is to propose grey relational analysis and support vector regression for features selection. The features are economic indicators that are used to forecast property crime rate. Grey relational analysis selects the best data series to represent each economic indicator and rank the economic indicators according to its importance to the property crime rate. Next, the support vector regression is used to select the significant economic indicators where particle swarm optimization estimates the parameters of support vector regression. In this study, we use unemployment rate, consumer price index, gross domestic product and consumer sentiment index as the economic indicators, as well as property crime rate for the United States. From our experiments, we found that the gross domestic product, unemployment rate and consumer price index are the most influential economic indicators. The proposed method is also found to produce better forecasting accuracy as compared to multiple linear regressions.

Https://francis-press.com/uploads/papers/4WucANTyoUTaV2PPu7fmMIOpu6X3DfTKFr2t2G5Q.pdf

With the further development of economic globalization, the economic strength of various countries has increased, the confidence of consumers has increased, and the consumption level of rural residents has improved. The index of Consumer Sentiment is a leading indicator to quantify consumers' subjective feelings about the current economic situation and consumer psychological state. The consumption expenditure level of Chinese rural residents is affected by many economic factors, but the influence of the index of Consumer Sentiment cannot be ignored. This paper mainly studies the relationship between per capita consumption expenditure level of Rural residents and per capita disposable income level, per capita GDP, rural consumer price index, gross output value index of agriculture, forestry, animal husbandry and fishery, and index of Consumer Sentiment.

An Interpretable Model for Real-time Tracking of Economic Indicators Using Social Media Data

Measures of public opinion on economic matters are vital in creating accurate official statistics needed to design appropriate policy interventions and shape private investment decisions. This is traditionally done using public opinion polls and representative surveys. However, this process is time and money intensive and currently suffers from reduced public participation. As a result, official statistics are usually delayed and are not frequently available at the resolution needed for better policy interventions. Hence, researchers have looked into anonymized digital data to continuously sense information about public behaviors, especially those related to consumer sentiment index and unemployment insurance claims. However, past studies relied on linear models with simplistic assumptions and thus provided limited extrapolatory power and no insights as to why these predictive methods work. Worryingly, the strong correlations reported in these studies disappeared when the original models were tested with newer social media data. We propose a novel interpretable machine learning model, called Group Additive Gaussian Processes, to provide accurate and near real-time estimates of economic indicators about public behaviors using social media data, along with insights into the model behavior. Our model exploits the underlying structure in data and encodes interpretability in the modeling framework. It is based on Gaussian Process regression, which provides a robust non-parametric Bayesian learning framework that produces calibrated uncertainty measures along with its predictions. A key challenge in the learning task is learning from limited training data. We demonstrate how our model not only learns but also generalizes well in these scenarios. Through extensive evaluation we show how our model performs on two important indicators of economic health-consumer confidence index and unemployment insurance claims data. Further, we demonstrate how our model can reduce the need to conduct surveys by producing highly accurate and frequent estimates in between the surveying periods.

On the factors of Bitcoin\u2019s value at risk

This study investigates the factors of Bitcoin’s tail risk, quantified by Value at Risk (VaR). Extending the conditional autoregressive VaR model proposed by Engle and Manganelli (2004), I examine 30 potential drivers of Bitcoin’s 5% and 1% VaR. For the 5% VaR, quantity variables, such as Bitcoin trading volume and monetary policy rate, were positively significant, but these effects were attenuated when new samples were added. The 5% VaR responds positively to the Internet search index and negatively to the fluctuation of returns on commodity variables and the Chinese stock market index. For the 1% VaR, variables related to the macroeconomy play a key role. The consumer sentiment index exerts a strong positive effect on the 1% VaR. I also find that the 1% VaR has positive relationships with the US economic policy uncertainty index and the fluctuation of returns on the corporate bond index.

Optimistic and pessimistic economic sentiments and US Dollar exchange rate

This study investigates the affiliation of various proxies of economic sentiments and the US Dollar exchange rate, mainly focusing on the real effective exchange rate of USD pairing with three other major currencies (USDEUR, USDGBP, and USDCAD). The study has employed Google Trends data of economy optimistic and pessimistic sentiments index and survey-based economy sentiments data on monthly basis from January 2004 to December 2018. The study engaged Ordinary Least Squares (OLS) and Auto-Regressive Distributed Lag (ARDL) estimation techniques to evaluate the short-run and long-run effects of economy-related sentiments and macroeconomic variables on the exchange rate. The results from the study found that Economy Optimistic Sentiments Index (EOSI) and Economy Pessimistic Sentiments Index (EPSI) appreciate and depreciate the US Dollar exchange rate in the short-run, respectively. Our sentiment measures are robust to survey-based Michigan Consumer Sentiment Index (MSCI), Consumer Confidence Index (CCI), and various macroeconomic factors. The MSCI and CCI sentiments show a long-term impact on the foreign exchange market. This study implies that economic sentiments play a vital role in the foreign exchange market and it is essential to consider behavioral aspects when modeling the exchange rate movements.

The role of consumer confidence in forecasting consumption, evidence from Florida

AbstractConsumers' attitudes about the direction of the economy influence their decisions about discretionary purchases, saving, and investment. This paper uses data from Florida's consumer sentiment index to study the role and accuracy of consumer confidence in forecasting consumption, as well as the mechanism behind such a relationship. Spending on durable goods tends to be more discretionary in nature and it is frequently done using credit, thus potentially more sensitive to changes in consumer attitudes. Our results indicate that the in‐sample predictive power of the index and its questions is limited to predicting spending on durable goods, particularly, on autos. Furthermore, consumer confidence does not improve the out‐of‐sample forecast beyond the forecast from a baseline model, which considers economic fundamentals. Finally, the evidence shows that the relationship between shocks in consumer confidence and economic activity arises because confidence measures contain information about the state of the economy, thus rejecting animal spirits.

The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model

Previous studies have shown that the treasury yield curve, T, forecasts upcoming recessions when it obtains a negative value. In this paper, we try to improve the yield curve model while keeping its parsimony. First, we show that adding the federal funds rate, FF, to the model, GDP = f(T, FF), gives seven months vs. five months warning time, and it gives a higher prediction skill for the recessions in the out-of-sample test set. Second, we find that including the quadratic term of the yield curve and the federal funds rate improves the prediction of the 1990 recession, but not the other recessions in the period 1977 to 2019. Third, the T caused a pronounced false peak in GDP for the test set. Restricting the learning set to periods where T and FF were leading the GDP in the learning set did not improve the forecast. In general, recessions are predicted better than the general movement in the economy. A “horse race” between GDP = f(T, FF) and the Michigan consumer sentiment index suggests that the first beats the latter by being a leading index for the observed GDP for more months (50% vs. 6%) during the first test year.

Dynamic connectedness between uncertainty and energy markets: Do investor sentiments matter?

This paper examines the dynamics between the energy markets and uncertainty indices from January 1st, 2001 till July 1st, 2020, using the time-domain TVP-VAR-based connectedness approach of Antonakakis and Gabauer (2017). In particular, we examine whether Economic Policy Uncertainty (EPU), Geopolitical Risk (GPR), World Trade Uncertainty (WTU), and Equity Market Uncertainty (EMU) have an impact on the dynamics of returns of oil, gas, and coal markets. Results suggest that the average influence of market uncertainty on energy markets is approximately 53%. Second, we find that the EPU contributes the most to the energy markets, followed by the World Trade Uncertainty index. Third, considering the energy markets, we find that the oil markets contribute the most to other markets. Fourth, we find that the EMU receives the most contribution from other markets, followed by the EPU. Finally, we observe that the total connectedness index is relatively high, coinciding with the Global Financial Crisis of 2008 and the COVID-19 pandemic outbreak. Further analysis of the possible effect of the investor sentiment on the dynamic connectedness shows that the consumer sentiment index (CSI) has a negative (positive) effect on the uncertainty (energy) net connectedness. Our findings have important implications for risk management in energy markets and entail some policy implications for regulators.

Does Sentiment or Anxiety Drive Consumer Demand

The University of Michigan's Consumer Sentiment Index has pre- occupied politicians, journalists, and Wall Street for decades (Uchitelle, 2002). This American economic indicator is now co-published with Thomson Reuters in London. The international reach of this index cries out for another look at George Katona's consumer sentiment construct as a predictor of consumer demand. Regressions from the British Household Panel Survey (BHPS) show that consumer sentiment is ineffectual in predicting micro variation in discretionary spending between consumers, within consumers over time, or between and within consumers overall. Moreover, consumer sentiment bears no relationship whatsoever to national consumer demand over annual BHPS surveys from 1997 to 2008. In contrast, an indicator of economic anxiety accounts for all three types of variation in micro demand, as well as variation in macro demand over time.

Property investor decisions using income and rental ratio signals

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

Economic policy uncertainty and hotel occupancy: the mediating effect of consumer sentiment

PurposeThe purpose of this paper is twofold. First, the study examines the prolonged effect of policy-related economic uncertainty on hotel operating performance, particularly the room demand (occupancy). Second, the study attempts to explain why occupancy drops when the perceived economic uncertainty is high by studying the mediating effect of consumer sentiment in the relationship between economic policy uncertainty and hotel demand.Design/methodology/approachThis quantitative study uses secondary data – US economic policy uncertainty (EPU) index, University of Michigan's index of consumer sentiment (ICS), and property-level hotel operating data from three states of the US – California, Florida and New York. Data were analyzed using random effect regression and structural equation modeling. Robustness tests were conducted to enhance the reliability of the research findings.FindingsRandom-effects regression analysis reveals that policy-related economic uncertainty has a negative and lead-lag effect on hotel occupancy, average daily rate and revenue per available room (RevPAR). Structural equation modeling results show that the relationship between economic policy uncertainty and hotel occupancy is significantly mediated by consumer sentiment. Robustness test results support the findings from the main analysis.Practical implicationsThis study offers valuable implications for the hotel professionals in regard to anticipating the economic impact of policy-related uncertainty on hotel industry and understanding how consumer sentiment affects demand at such crises times. Moreover, the study suggests potential course of actions to deal with declining room demand at times of uncertainty.Originality/valueThis empirical study explores how economic policy uncertainty affects hotel performance at the property level and explains the mediating effect of consumer sentiment on hotel room demand. The study provides a first-hand evidence of how consumer sentiment relates to the perception of economic uncertainty and leads to decline in consumer demand. In that regard, findings of the study have valuable implications for hospitality industry practitioners and relevant policymakers.

Sentiment, Confidence and Uncertainty: The Case of Tourism and Leisure Stocks

This study tests the possible effect of sentiment on the performance of the tourism and leisure (T&L) industry. Using a portfolio consisting of U.S. publicly traded firms, we reveal that the Consumer Sentiment Index (CSI) and the Consumer Confidence Index (CCI) are perhaps better candidates for capturing a contemporaneous relationship or making predictions about the performance of the T&L stocks than the Economic Policy Uncertainty Index (EPU) or the Volatility Index (VIX). Macroeconomic variables have a limited contribution to the establishment of contemporaneous relationships and none of them is suitable for making predictions, which underscores the importance of addressing and paying attention to the issue of sentiment. Our results are robust using different empirical procedures such as ordinary least squares (OLS), GARCH, and quantile regressions. The findings may have important implications for policy makers at both the state and firm levels in light of increased uncertainty periods such as the ongoing coronavirus epidemic.

Latent Dirichlet Analysis of Categorical Survey Responses

Beliefs are important determinants of an individual’s choices and economic outcomes, so understanding how they comove and differ across individuals is of considerable interest. Researchers often rely on surveys that report individual beliefs as qualitative data. We propose using a Bayesian hierarchical latent class model to analyze the comovements and observed heterogeneity in categorical survey responses. We show that the statistical model corresponds to an economic structural model of information acquisition, which guides interpretation and estimation of the model parameters. An algorithm based on stochastic optimization is proposed to estimate a model for repeated surveys when responses follow a dynamic structure and conjugate priors are not appropriate. Guidance on selecting the number of belief types is also provided. Two examples are considered. The first shows that there is information in the Michigan survey responses beyond the consumer sentiment index that is officially published. The second shows that belief types constructed from survey responses can be used in a subsequent analysis to estimate heterogeneous returns to education.

Does the source of oil supply shock matter in explaining the behavior of U.S. consumer spending and sentiment?

In light of the U.S. shale oil boom and given that commercial shale oil production has been mostly confined to the USA, this paper disentangles the oil supply determinant into its U.S. and non-U.S. production components, and studies their impact on U.S. real personal consumption expenditures (PCE) and on U.S. index of consumer sentiment (ICS). First, I estimate a structural VAR model to identify and study the effect of structural shocks in the crude oil market on PCE and ICS. The results show that while PCE and ICS respond negatively to oil demand shocks, they respond differently to oil supply shocks. While ICS experiences transitory negative response to both oil supply shocks, PCE shows that the response of inflation and output differs depending on whether the oil supply shock is domestic or foreign. Second, I compute the forecast-error-variance decompositions to quantify the contribution of oil supply and demand shocks to the historical fluctuations in PCE and ICS. My findings reveal that most of the variation in PCE is explained by oil supply shocks—unlike ICS where most of its variation is explained by aggregate demand shocks. Third, I conduct a historical decomposition exercise to examine the contributions of structural oil shocks in explaining historical changes in PCE and ICS. My results indicate a strong presence of U.S. oil supply shock in boosting ICS during the 2014 period of the oil price drop. Finally, the results that not all oil supply shocks are alike are robust to alternative measures of real economic activity.

Потребление vs сбережения: выбор россиян в условиях экономической нестабильности 2015–2017 гг.

<p class="_1Vvodka">В статье на основе данных официальной статистики и национальных социологических исследований рассматриваются процессы потребления и сбережения денежных средств населением в период нестабильной экономической конъюнктуры 2015–2017 гг. Обращение к данной тематике актуализируется тем, что именно потребительское и сберегательное поведение являются первичными маркерами адаптации населения к изменяющимся внешним условиям. За 2015–2017 гг. структура формирования и распределения доходов, соотношение доходных групп существенно не изменились. Не наблюдается кардинального сокращения сбережений в форме банковских вкладов и объемов потребления, рассматриваемых через розничный товарооборот. Неизменными остаются как самооценки населением материального положения, так и общественные настроения относительно реализации потребительских предпочтений. Несмотря на затянувшийся спад реальных доходов, население стало менее негативно оценивать свою жизнь в целом; сократилось число тех, кто испытывает острую нехватку денег или экономит на текущем потреблении.</p><p class="_2Keys"> </p>

Asymmetric relationship of investor sentiment with stock return and volatility: evidence from India

PurposeThe study examines the cross-sectional and asymmetric relationship of investor sentiment with the stock returns and volatility in India.Design/methodology/approachThe investor sentiment is captured using a market-based measure Market Mood Index (MMI) and a survey-based measure Consumer Sentiment Index (CSI). The asymmetric effect of the relationship is examined using quantile causality approach and cross-sectional effect is examined by considering indices such as the BSE Sensex, and the various size indices such as BSE Large cap, BSE Mid cap and BSE Small cap.FindingsThe result of the study found that investor sentiment (MMI) cause stock returns at extreme quantiles. Lower sentiment induces fear-induced selling, thereby lowers the returns and high sentiment is followed by lower future returns as market reverts to fundamentals. On the other hand, bullish shifts in sentiment lower the volatility. There exists a positive feedback effect of stock return and volatility in the formation of investor sentiment.Originality/valueThe study captures both asymmetric and cross-sectional relationship of investor sentiment and stock market in an emerging economy, India. The study uses a novel data set (i.e.) MMI which captures the sentiment based on market indicators and are widely disseminated to the public.

ANALYSIS OF THE MAIN DETERMINANTS OF THE RUSSIAN CONSUMERS’ CURRENT MOOD

The socio-psychological phenomenon “consumer mood”, which is interpreted as an emotional and evaluative summary of the subjective understanding of the individual of various information elements that form the semantic structure of his implicit theory of consumption, has been investigated. A hypothesis has been put forward that the formation of the semantic field of the implicit theory of consumption of an individual is based on two system-forming factors: 1) the real purchasing power of the consumer, 2) the dynamics of his socio-economic status. In the event that both factors have negative actual indicators, the mood of consumer will be characterized by a negative emotional tone and high stability over time. The proposed hypothesis is confirmed by the revealing and analysis of the main determinants of the current mood of Russian consumers, and their subsequent comparison with the consumer sentiment index, identificated empirically.

INVESTOR SENTIMENT AND STOCK RETURNS: EVIDENCE IN MALAYSIA

The objective of the research is to investigate the role of investor sentiment in Malaysia’s stock returns. The state of investor rationality and efficiency of the stock market is debated in theoretical lenses of the behavioural finance paradigm. The behavioural factor, namely, closed-end fund discount, advance-decline ratio, trading volume/turnover, consumer sentiment index (CSI) and business condition index (BCI) that act as the sentiment proxies. These variables are utilized to analyse the relationship with stock returns by implementing a statistical method including Ordinary Least Square (OLS) regression model. The research findings are suggested consistently with theoretical perspectives on the view of behavioural finance and existing evidence on revealing the relationship between sentiment and stock market return index that is statistically significant. However, the risk proxies’ relationship with the stock market is heterogeneous that is in line with the view of the stock market in a complex state. In summary, this study offers important finance body of knowledge (academic), practice (investor) and policy implications through a new insight of theoretical and empirical evidence on the role of sentiment towards the stock market in Malaysia. Briefly, behavioural factors in particular sentiments can be taken into consideration instead only relying on a fundamental factor for stock investment decision making.