Covariance forecasting in equity markets

We compare the performance of popular covariance forecasting models in the context of a portfolio of major European equity indices. We find that models based on high-frequency data offer a clear advantage in terms of statistical accuracy. They also yield more theoretically consistent predictions from an empirical asset pricing perspective, and, lead to superior out-of-sample portfolio performance. Overall, a parsimonious Vector Heterogeneous Autoregressive (VHAR) model that involves lagged daily, weekly and monthly realised covariances achieves the best performance out of the competing models. A promising new simple hybrid covariance estimator is developed that exploits option-implied information and high-frequency data while adjusting for the volatility riskpremium. Relative model performance does not change during the global financial crisis, or, if a different forecast horizon, or, intraday sampling frequency is employed. Finally, our evidence remains robust when we consider an alternative sample of U.S. stocks

Flying to Quality: Cultural Influences on Online Reviews

Customers increasingly consult opinions expressed online before making their final decisions. However, inherent factors such as culture may moderate the criteria and the weights individuals use to form their expectations and evaluations. Therefore, not all opinions expressed online match customers’ personal preferences, neither can firms use this information to deduce general conclusions. Our study explores this issue in the context of airline services using Hofstede’s framework as a theoretical anchor. We gauge the effect of each dimension as well as that of cultural distance between the passenger and the airline on the overall satisfaction with the flight as well as specific service factors. Using topic modeling, we also capture the effect of culture on review text and identify factors that are not captured by conventional rating scales. Our results provide significant insights for airline managers about service factors that affect more passengers from specific cultures leading to higher satisfaction/dissatisfaction

Employees’ online reviews and equity prices

We examine the effect of employee satisfaction on corporate performance using employees’ online reviews. Our results indicate that although employee satisfaction positively impacts corporate performance, this is not fully reflected in equity prices

Keyword Portfolio Optimization in Paid Search Advertising

This paper uses investment portfolio theory to determine budget allocation in paid online search advertising. The approach focuses on risk-adjusted performance and favors diversified portfolios of unrelated or negatively correlated keywords. An empirical investigation employs averages, variances and co-variances for keyword popularities, which are estimated using growth rates for 15 major sectors taken from the Google Trends database. In line with portfolio theory, the results show that the average keyword popularity growth is strongly related to the standard deviation of growth for each keyword in the sample (R2 = 74%). Hypothesis testing of differences in Sharpe ratios documents a significantly better performance of the proposed approach compared to that of other strategies currently used by practitioners

Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews

We assess the ability of online employee-generated content in predicting consumption expenditures. In so doing, we aggregate millions of employee expectations for the next six-month business outlook of their employer and build an employee sentiment index. We test whether forward-looking employee sentiment can contribute to baseline models when forecasting aggregate consumption in the United States and compare its performance to well-established, survey-based consumer sentiment indexes. We reveal that online employee opinions have incremental information that can be used to augment the accuracy of consumption forecasting models and inform economic policy decisions

The joint effect of consumer and service providers’ culture on online service evaluations: A response surface analysis

National culture exerts substantial influence on consumers' expectations, satisfaction, and evaluations. Despite that, within a service-based context, two cultures are met, that of the customer and that of the service provider, the existing literature systematically explores the effect of customer culture in isolation neglecting the impact of the provider's culture or their joint effect. We fill this gap by considering the concomitant effect of customer and provider cultural factors on passenger evaluations of airline carriers using a large dataset of reviews that covers the majority of countries. Employing a response surface methodology, our study provides significant advantages over methods based on cultural distance scores in revealing more complex non-linear relationships. This multi-dimensional approach provides new insights for assessing the impact of national culture on customers' service perceptions and evaluations, thus bringing significant implications for researchers and service providers

The informational value of employee online reviews

This paper investigates the informational value of online reviews posted by employees for their employer, a rather untapped source of online information from employees, using a sample of 349,550 reviews from 40,915 UK firms. We explore this novel form of electronic Word-of-Mouth (e-WOM) from different perspectives, namely: (i) its information content as a tool to identify the drivers of job satisfaction/dissatisfaction, (ii) its predictive ability on firm financial performance and (iii) its operational and managerial value. Our approach considers both the rating score as well as the review text through a probabilistic topic modeling method, providing also a roadmap to quantify and exploit employee big data analytics. The novelty of this study lies in the coupling of structured and unstructured data for deriving managerial insights through a battery of econometric, financial and operational research methodologies. Our empirical analyses reveal that employee online reviews have informational value and incremental predictability gains for a firm's internal and external stakeholders. The results indicate that when models integrate structured and unstructured big data there are leveraged opportunities for firms and managers to enhance the informativeness of decision support systems and in turn, gain competitive advantage