Media-Based Sentiment Indices as an Alternative Measure of Consumer Confidence

In this paper, we consider the feasibility of constructing online sentiment indices, using large amounts of media data, as an alternative to the conventional survey method used to create the consumer confidence index in South Africa. A clustering framework is adopted to provide an indication of possible candidate sentiment indices constructed from a combination of different text sources and dictionaries that best mimic the traditional survey-based consumer confidence index from the South African Bureau for Economic Research (BER). The results conclude that it is possible to create an index using sentiment analysis using online editorial data that does resemble the BER’s consumer confidence index. The different media-based sentiment indices (MSI) show a significant level of correlation and co-movement with the BER’s CCI. Impulse responses and cross-correlation functions indicate that the MSI could potentially lead the survey-based method up to two quarters. Furthermore, Granger-causality tests show that the media-based indices are good predictors of future consumer confidence index values. The results provide motivation for further study on the use of sentiment-based techniques and online media data sources to track consumer confidence within an emerging market such as South Africa.     

Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA

Pareto-Koopmans efficiency in Data Envelopment Analysis (DEA) is extended to stochastic inputs and outputs via probabilistic input-output vector comparisons in a given empirical production (possibility) set. In contrast to other approaches which have used Chance Constrained Programming formulations in DEA, the emphasis here is on joint chance constraints. An assumption of arbitrary but known probability distributions leads to the P-Model of chance constrained programming. A necessary condition for a DMU to be stochastically efficient and a sufficient condition for a DMU to be non-stochastically efficient are provided. Deterministic equivalents using the zero order decision rules of chance constrained programming and multivariate normal distributions take the form of an extended version of the additive model of DEA. Contacts are also maintained with all of the other presently available deterministic DEA models in the form of easily identified extensions which can be used to formalize the treatment of efficiency when stochastic elements are present.     

Asymptotic confidence bands for the estimated autocovariance and autocorrelation functions of vector autoregressive models

This paper provides closed-form formulae for computing the asymptotic covariance matrices of the estimated autocovariance and autocorrelation functions of stable VAR models by means of the delta method. These covariance matrices can be used to construct asymptotic confidence bands for the estimated autocovariance and autocorrelation functions to assess the underlying estimation uncertainty. The usefulness of the formulae for empirical work is illustrated by an application to inflation and output gap data for the U.S. economy indicating the existence of a significant short-run Phillips-curve tradeoff.First version received: November 2002/Final version received: September 2003     

Impact of Divergent Consumer Confidence on Option Prices

This paper investigates the impact of divergent consumer confidence on option prices. To model this, we assume that consumers disagree on the expected growth rate of aggregate consumption. With other conditions unchanged in the discrete-time Black–Scholes option-pricing model, we show that the representative consumer will have declining relative risk aversion instead of the assumed constant relative risk aversion. In this case all options will be underpriced by the Black–Scholes model under the assumption of bivariate lognormality. This revised version was published online in June 2006 with corrections to the Cover Date.     

Inference with Dependent Data in Accounting and Finance Applications

We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include the standard clustered and multiway clustered estimators, and discuss alternative sample‐splitting inference procedures, such as the Fama–Macbeth procedure, within this context. We outline pros and cons of the different procedures. We then illustrate the properties of the discussed procedures within a simulation experiment designed to mimic the type of firm‐level panel data that might be encountered in accounting and finance applications. Our conclusion, based on theoretical properties and simulation performance, is that sample‐splitting procedures with suitably chosen splits are the most likely to deliver robust inferential statements with approximately correct coverage properties in the types of large, heterogeneous panels many researchers are likely to face.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Experts,firms, consumers or even hard data? Forecasting employment in Germany

In this article, we forecast employment growth for Germany with data for the period from November 2008 to November 2015. Hutter and Weber (2015) introduced an innovative unemployment indicator and evaluated the performance of several leading indicators, including the Ifo Employment Barometer (IEB), to predict unemployment changes. Since the IEB focuses on employment growth instead of unemployment developments, we mirror the study by Hutter and Weber (2015). It turns out that in our case, and in contrast to their article, the IEB outperforms their newly developed indicator. Additionally, consumers’ unemployment expectations and hard data such as new orders exhibit a high forecasting accuracy.     

Restoring tourist confidence and travel intentions after disasters: some insights from a rejoinder to a series of unfortunate events in Malaysian tourism

Many countries face a decline in tourist confidence and reduced travel intentions after disasters. However, Malaysia – a country that experienced a series of aviation tragedies throughout 2014 – encountered an overall increase in inbound tourists and monetary receipts. This article more closely examines the effects of these unfortunate events on Malaysian tourism and identifies several strategic avenues that can contribute to restoring tourist confidence and reigniting tourist interest in travelling to disaster-stricken countries.     

Consumer confidence and consumption expenditure in Indonesia

We estimate several competing regressions and find that confidence predicts consumption expenditure in Indonesia. Our estimations employ data on two measures of confidence, namely consumer and business confidence indexes, consumption and three standard predictors of consumption, namely labour income, stock price, and interest rate. We show that there are economic and statistical gains from consumption growth frameworks that account for consumer and business sentiments. Specifically, we show that policymakers can improve their forecast accuracy by between 4% and 13% by incorporating consumer and business sentiments into their forecasting frameworks.     

Forecasting value at risk and expected shortfall with mixed data sampling

I propose applying the Mixed Data Sampling (MIDAS) framework to forecast Value at Risk (VaR) and Expected shortfall (ES). The new methods exploit the serial dependence on short-horizon returns to directly forecast the tail dynamics of the desired horizon. I perform a comprehensive comparison of out-of-sample VaR and ES forecasts with established models for a wide range of financial assets and backtests. The MIDAS-based models significantly outperform traditional GARCH-based forecasts and alternative conditional quantile specifications, especially in terms of multi-day forecast horizons. My analysis advocates models that feature asymmetric conditional quantiles and the use of the Asymmetric Laplace density to jointly estimate VaR and ES.