Investor Sentiment and Return Predictability in Agricultural Futures Markets

This study examines the usefulness of trader‐position‐based sentiment index for forecasting future prices in six major agricultural futures markets. It has been found that large speculator sentiment forecasts price continuations. In contrast, large hedger sentiment predicts price reversals. Small trader sentiment hardly forecasts future market movements. An investigation was performed into various sentiment‐based timing strategies, and it was found that the combination of extreme large trader sentiments provides the strongest timing signal. These results are generally consistent with the hedging‐pressure theory, suggesting that hedgers pay risk premiums to transfer nonmarketable risks in futures markets. Moreover, it does not appear that large speculators in the futures markets possess any superior forecasting ability. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:929–952, 2001     

Predicting Future Buyer Behavior with Consumers' Confidence and Sentiment Indexes

Knowledge of consumer attitudes and expectations is an indispensable marketing tool and may be useful for predicting changes in consumer spending and saving patterns. However, most empirical research on the value of consumer confidence (sentiment) indexes for forecasting future buyer behavior has focused on the statistical correlation between these indexes and several measures of consumer spending and business activity. As Huth et al. (1994) correctly pointed out, a more appropriate approach is to focus on the direction of Granger-causality between these variables. Prior to testing for Granger-causality, we investigate the stationarity properties of alternative measures of these variables and we employ a flexible lag structure. Our empirical results support the use of these indexes to forecast business activity but not consumer purchases or overall economic activity.     

Macroeconomic forecasts under the prism of error-correction models

When the error correction term exhibits persistence, its change may convey useful information about short-run economic dynamics which, if not taken sufficiently into account by a forecasting model, may be associated with predictable forecast errors. Such errors are documented in the DRI forecasts for the U.S. consumption, GNP and imports. The strong results, together with the very general assumptions behind the conceptual framework, suggest that similar predictable errors may be pervasive in the forecasts of other large-scale econometric models.     

Is cognitive bias really present in analyst forecasts? The role of investor sentiment

This paper analyses four key markets within the European context. In this context, where the level of analyst coverage is lower than in the US setting, we aim to ascertain whether the origin of optimism in analyst forecasts in these markets is mainly strategic or whether it also contains an element of cognitive bias. Despite the fact that forecast errors lack the explanatory power to account for a significant percentage of the relationship between market sentiment and future stock returns, our new tests based on selection bias (SB1 and SB2), in conjunction with an analysis of abnormal trading volume, confirm the presence of both cognitive bias and strategic behaviour in analyst forecasts. This shows that, although regulation can reduce analyst optimism bias, the benefits are constrained by the fact that optimism bias is partly associated with cognitive bias.     

Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment

Inaccurate forecasts of demand during promotions diminish the already meager profit margins of retailers. No forecasting method described in the literature can accurately account for the combination of seasonal sales variations and promotion-induced sales peaks over forecasting horizons of several weeks or months. We address this research gap by developing a forecasting method for seasonal, frequently promoted products that generates accurate predictions, can handle a large number of sales series, and requires minimal training data. In our method's first stage, we forecast the seasonal sales cycle by fitting a harmonic regression model to a decomposed training set, which excludes promotional and holiday sales, and then extrapolate that model to a testing set. In the second stage, we integrate the resulting seasonal forecast into a multiplicative demand function that accounts for consumer stockpiling and captures promotional and holiday sales uplifts. The final model is then fitted using ridge regression. We use sales data from a grocery retailing chain to compare the forecasting accuracy of our method with popular seasonal and promotion demand forecasting models at multiple aggregation levels for both short and long forecasting horizons. The significantly more accurate forecasts generated by our model attest to the merit of the approach developed here.     

政府支出对居民消费的影响研究--基于空间动态面板模型的分析

本文在居民消费效用最大化的理论背景下,结合统计年鉴相关数据,利用空间动态面板模型,研究了1997-2013年中国的政府支出对全体居民、城镇居民以及农村居民消费的影响。研究结果表明：居民消费存在显著的习惯效应,政府支出和居民收入增加会促进居民消费,各省份之间居民消费既可能存在相互促进的关系,也可能存在相互竞争的关系。因此为了促进居民消费,需要适当地保持较好的消费习惯,改变不良的消费习惯,注意省份之间的密切合作,现阶段可以增加政府支出,来刺激居民消费,特别是农村居民消费,另外还需要提高城镇居民可支配收入和农村居民纯收入。     

The Information Content of Model‐Free Implied Volatility

This study examines the information content of model‐free implied volatility (MFIV) estimates with respect to the options and futures markets in Hong Kong. In this study, the volatility forecasting performance of MFIV is compared, using different prediction horizons, to IV estimates based on Black's futures option pricing model (BIV) and time‐series forecasts based on historical volatility (TS‐HV). The results show that the BIV prediction is unbiased for different horizon forecasts. MFIV outperforms TS‐HV forecasts and, most importantly, BIV subsumes the information content of both MFIV and TS‐HV forecasts. The results are largely maintained for next‐day forecasts but the forecasting quality of the two IV measures declines as expiration day approaches. The information contents of MFIV and TS‐HV forecasts are complementary. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 32:792‐806, 2012     

Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis

Online reviews provide consumers with rich information that may reduce their uncertainty regarding purchases. As such, these reviews have a significant influence on product sales. In this paper, a novel method that combines the Bass/Norton model and sentiment analysis while using historical sales data and online review data is developed for product sales forecasting. A sentiment analysis method, the Naive Bayes algorithm, is used to extract the sentiment index from the content of each online review and integrate it into the imitation coefficient of the Bass/Norton model to improve the forecasting accuracy. We collected real-world automotive industry data and related online reviews. The computational results indicate that the combination of the Bass/Norton model and sentiment analysis has higher forecasting accuracy than the standard Bass/Norton model and some other sales forecasting models.     

Social class: a multivariate analysis of its effect on expenditures for household services

Multivariate statistical techniques were used to explore two major conceptual issues in consumer behaviour research involving social class: (1) the importance of income, as compared to social class, in determining household behaviour, and (2) the effect of the status of wives on the behaviour of their households. A subsample of expenditure data, collected from 5,994 husband-wife families, as part of the 1973 Consumer Expenditure Survey, was used in the analysis. The dependent variable was expenditures for household services. The independent variables included total consumption expenditures (a proxy for income), the status of the wife, the status of the husband, family type, location, and characteristics of the dwelling. The results of an Ordinary Least Squares regression procedure demonstrated that total consumption expenditures were the most important variable in determining expenditures for household services. The size of the effect of the status of the wife was comparable to that of the status of the husband. In both cases, the effect was significant but small.     

The effects of loyalty programs on customer lifetime duration and share of wallet

In the retailing sector, consumers typically patronize multiple outlets, which confronts these outlets with an important issue: determining how to gain a greater part of consumer expenditures. One potential avenue is to increase consumer lifetime duration and repeat purchases through loyalty cards. This research, using BehaviorScan single-source panel data, examines the impact of loyalty programs on customer lifetime duration in grocery stores. The findings suggest that loyalty schemes have positive effects on customer lifetimes and share of consumer expenditures. However, multiple loyalty card memberships of geographically close retailers reduce lifetime duration. Furthermore, the higher the share of consumer expenditures in a store, the longer the lifetime duration will be.     

Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?

User-generated content provides many opportunities for managers and researchers, but insights are hindered by a lack of consensus on how to extract brand-relevant valence and volume. Marketing studies use different sentiment extraction tools (SETs) based on social media volume, top-down language dictionaries and bottom-up machine learning approaches. This paper compares the explanatory and forecasting power of these methods over several years for daily customer mindset metrics obtained from survey data. For 48 brands in diverse industries, vector autoregressive models show that volume metrics explain the most for brand awareness and purchase intent, while bottom-up SETs excel at explaining brand impression, satisfaction and recommendation. Systematic differences yield contingent advice: the most nuanced version of bottom-up SETs (SVM with Neutral) performs best for the search goods for all consumer mind-set metrics but Purchase Intent for which Volume metrics work best. For experienced goods, Volume outperforms SVM with neutral. As processing time and costs increase when moving from volume to top-down to bottom-up sentiment extraction tools, these conditional findings can help managers decide when more detailed analytics are worth the investment.     

“She did what? There is no way I would do that!” The Potential Interpersonal Harm Caused by Mispredicting One’s Behavior

When forecasting their own behavior, people are often inaccurate and tend to predict that they will engage in more socially desirable behavior than they actually do. The problem with inaccurate behavioral forecasts is that they can lead to negative consequences both for the self and for others. One particularly negative consequence may be that such errors can produce overly harsh evaluations and condemnation of others who do not act in a way that most people predict they themselves would act. This paper identifies these common behavioral forecasting errors, why they occur, and the negative interpersonal and unintended, unethical consequences they can have.     

城乡居民收入差距对居民消费影响的实证分析

回归分析证明了改革开放以来城乡居民收入差距过大导致整个消费水平越来越低,增加城市居民的收入不能提高我国居民的整体消费水平;城镇居民收入较高,相对于农村居民来说边际消费倾向要低于农村居民的边际消费倾向,如果能增加农村居民的收入,则会提高全国的消费总量及转变农村的消费结构。     

Comparing consumer-produced product reviews across multiple websites with sentiment classification

ABSTRACT

Online consumer reviews have been extensively studied. However, existing literature analyzing online consumer review data mostly relies on a single data source, resulting in potentially biased analytics conclusions. Many websites encourage consumers to post reviews of their purchased products, so that new consumers can evaluate these reviews for the same product across different websites to help them make purchasing decisions. Confusions often arise in this process, because there often exist substantial discrepancies in customer reviews across different retailers on the same product. Clarifying such confusions can help consumers reduce concerns to make up their mind for their purchases, therefore benefiting both consumers and retailers. Through text analytics and sentiment analysis, we comparatively examine the underlying patterns of online consumer reviews of three large retailers including Sears, Home Depot, and Best Buy for a same product. Afterward, we combine online consumer reviews from these large retailers and conduct an overall text analytics and sentiment analysis. The overall results are further compared with the results from individual retailers. The findings show that the sentiment of the online consumer reviews could vary substantially so relying on a single data source to make purchase decision is not a wise idea. Based on the results, we further devise a framework to comparatively examine and integrate multiple data sources for social media analytics of online consumer reviews. This study offers important managerial implications and identifies several new research directions for social media analytics.     

Wie beeinflusst der demografische Wandel die privaten Verkehrsausgaben?

The level as well as the structure of private consumption is influenced by the age structure of the population. This is shown for Germany by an age-specific analysis of consumption patterns on the basis of household data of the German consumer income and budget survey. According to the gained data and with respect to the changing shares of age cohorts induced by demographic effects, the demand for consumption goods and services in the field of transport will lose significance. This is especially true for private transport expenditures such as the purchase of cars as well as the demand for goods and services related to the use of cars. In contrast, the expenditures for (public) transport services are projected to be unchanged over the lifecycle of a private household.     

Forecasting Real Inventories and the Anomaly of Money Illusion

While the transmission mechanism of inventory behavior in the business cycle has been studied, less effort has been devoted to applied forecasting of inventory change. Inventory fluctuations have accounted for a sizable portion of the changes in U.S. GDP during recessions over the past fifty years. In this paper, we report on out-of-sample forecasts of manufacturing and trade inventories generated by regression and neural network methodology. Our forecasting model is Metzlerian in approach, in that the divergence between actual and targeted sales is hypothesized as the primary cause of inventory imbalance. Our forecasts also rely on the slow adjustment of inventory investment to sales surprises. However, the likely presence of money illusion is a caveat to users, and we address several distortions it introduces to inventory management measures.     

A Comparison of Consensus and BVAR Macroeconomic Forecasts

This paper provides a one-month-ahead, macroeconomic, Bayesian Vector Autoregressive (BVAR) forecasting approach that offers several advantages over conventional short-term forecasting procedures. In particular, it produces more accurate forecasts than the Bloomberg consensus forecasts, on average, for 20 major macroeconomic variables. In addition to a quantitative comparison of BVAR and Bloomberg consensus forecast, the paper focuses on five important areas of macroeconomic forecasting: the role of short-term macroeconomic forecasting, the importance of a robust forecasting approach, the importance of timing of data releases, forecast evaluation criteria, and the importance of changing model specifications as conditions warrant.     

Developing conditions for environmentally sustainable consumption: drawing insight from anti‐smoking policy

This paper starts from the premise that, particularly in industrialized countries, the consumption decisions made by individuals and households are a major source of environmental strain. Several international organizations and national governments have addressed this issue, but, thus far, their efforts have had minimal effect. This paper examines the conditions necessary for the implementation of policy able to effectively reduce the environmental impact of household consumption. It draws from the experience of American tobacco control, a relatively rare example of a public effort that succeeded in reducing the negative consequences of an entitled consumer behaviour. An extensive review of the tobacco control literature informs the conclusion that three fundamental changes brought about conditions conducive to a widespread reduction in smoking, namely, information about its negative effects became common knowledge and was widely believed as true; cigarettes became a target for taxes that raised the price of smoking while placing a penalty on a behaviour increasingly seen as ‘bad’; the public sentiment regarding smoking shifted, deglamourizing the behaviour. The paper further suggests that these changes, which were the result of a concerted public effort, have a similar potential in reducing environmentally unsustainable consumption. A closer tying of consumption to the issue of climate change is suggested as a primary facilitating strategy.     

闻“香”而悦便会因“情”而买?——消费者涉入的调节作用

嗅觉器官是人类接收外界信息的重要器官,已有大量研究证明在顾客的消费过程中利用环境气味刺激个体的嗅觉能够积极影响消费者对于产品的购买意愿或所在环境的评价。文章基于精细加工可能性模型,通过实验研究方法引入消费者涉入作为调节变量,验证环境气味对消费者情绪的影响以及探讨情绪机制的边界条件。实验结果显示:环境气味能够提升消费者购买意愿,情绪在环境气味和购买意愿之间起中介作用;情绪的中介效应被消费者涉入调节,对同一产品而言,涉入度低时,消费者购买意愿显著增强,涉入度高时,消费者购买意愿差异不显著。本研究对于拓展感官营销的适用边界具有一定的理论意义,也对企业有效采取气味营销、提升感官营销效果具有重要的实践意义。     

Forecasting with Monetary Aggregates: Recent Evidence for the United States

We investigate the out-of-sample forecasting performance of various monetary aggregates in four-variate models of real output growth, inflation, changes in an interest rate, and nominal money growth from 1992 to 2004 using vector autoregressive (VAR) and regime-switching (RS) VAR models. We consider both Divisia and simple sum monetary aggregates for M1, M2M, M2, and M3 as well as sweep-adjusted M1 measures. We find little evidence that either aggregation method or level of aggregation has a big impact on the forecasting performance of our model with respect to inflation and real output growth. Our results indicate that VAR models with monetary aggregates appear to produce at best marginal improvements in RMSE over VAR models that omit money growth altogether. We also find that RS-VAR models usually provide better one quarter ahead forecasts than comparable VAR models, but often did worse when forecasting inflation four quarters ahead.