The impact of sentiment and attention measures on stock market volatility

We analyze the impact of sentiment and attention variables on the stock market volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. We apply a state-of-the-art sentiment classification technique in order to investigate the question of whether sentiment and attention measures contain additional predictive power for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized regression framework, we identify the most relevant variables to be investors’ attention, as measured by the number of Google searches on financial keywords (e.g. “financial market” and “stock market”), and the daily volume of company-specific short messages posted on StockTwits. In addition, our study shows that attention and sentiment variables are able to improve volatility forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of view.     

货币政策和财政政策、市场情绪与大类资产收益——基于隐性知识传播视角的解读

以货币政策和财政政策为主的宏观经济政策松紧程度不仅对大类资产收益产生直接效应,还会通过市场情绪对大类资产收益产生间接效应。通过构建基于隐性知识传播的概念模型,探究了宏观经济政策、市场情绪和大类资产收益之间的内在逻辑关系,并提出三个研究假说：宏观经济政策紧缩程度的提高会降低大类资产收益;宏观经济政策紧缩程度的提高会促进市场情绪高涨;宏观经济政策紧缩程度的提高会通过市场情绪高涨,进一步降低大类资产收益。在采用多种计量检验方法并进行稳健性检验后,检验结果都能很好地验证所提出的三个研究假说,研究结论能为我国政府制定相关宏观经济政策提供决策参考。     

Multiple capital inputs, Q, and investment spending

Despite their explicit treatment of dynamics and solid theoretical basis, investment models based on the Brainard-Tobin Q have recorded a generally disappointing empirical performance. When the Q model is expanded to recognize the possibility that the value of the firm depends on two or more capital inputs with differing adjustment cost technologies, the econometric equation following from optimizing behavior includes Q as well as a set of additional explanatory variables. The importance of these omitted variables is assessed, and the capital homogeneity assumption for equipment and structures implicit in Conventional Q models is rejected. The Multi-Capital Q model is then extended in two ways: (i) adding inventory, research and development, and labor as quasi-fixed factors and (ii) exploring the sensitivity of the instrumental variables estimates to normalization. We conclude that the Multi-Capital Q model is a useful extension that overcomes an important omitted variables problem in the Conventional Q framework.     

How accurate are government forecasts of economic fundamentals? The case of Taiwan

A government’s ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate.In this paper, we develop a methodology for evaluating non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that the undocumented knowledge of the Taiwanese government reduces forecast errors substantially.     

TESTING ASSUMPTIONS MADE IN THE CONSTRUCTION OF INPUT–OUTPUT TABLES

Product input–output (IO) tables are mainly constructed on the basis of product and/or industry technology assumptions. The choice is not trivial and deserves empirical analysis using input and output data at the level of establishments. This paper offers input–output compilers econometric tests to facilitate the construction of tailored hybrid technology-based product IO tables. We provide weighted likelihood ratios of the product and industry technology assumptions. Although the proposed econometric tests are aimed to be used ex ante, we construct four variants of hybrid technology-based product IO tables using establishment data from Andalusia (Spain) and contrast them to the official product IO table and the pure product and industry technology-based tables. Our econometric tests are not valid for industry IO tables.     

A note on equity ownership and corporate value in Greece

This study attempts to investigate whether corporate performance is affected by the ownership structure, using data from companies quoted on the Athens Stock Exchange for the period 1996–1998. Given such an objective, the basic hypothesis examined, is that corporate performance as measured by Tobin's Q ratio is a function of ownership and other control variables. Our econometric approach relies on the use of a combination of time series and cross section data (panel‐data analysis), a procedure that avoids many statistical problems. After examining the role of each identifiable shareholder, we find a positive relationship between institutional investors and corporate performance. Copyright © 2004 John Wiley & Sons, Ltd.     

Uncertainty in Integrated Regional Models

This paper examines the nature of uncertainty in integrated econometric+input–output (ECIO) regional models. We focus on three sources of uncertainty: (a) econometric model parameter uncertainty; (b) econometric disturbance term uncertainty; and (c) input–output coefficient uncertainty. Through a series of Monte Carlo simulations we analyse the relative importance of each component as well as the question of how their interaction may propagate through the integrated model to affect the distributions of the endogenous variables. Our results suggest that there is no simple answer to the question of which source of uncertainty is most important in an integrated model. Instead, that answer is conditioned upon the focus of the analysis and whether the industry specific or macro level variables are of central concerns.     

Is econometrics useful for private policy making? A case study of replacement policy at an auto rental company

The goal of this paper is to illustrate the potential usefulness of econometrics as a tool to assist private policy makers. We provide a case study and detailed econometric analysis of the automobile replacement policy adopted by a large car rental company. Unlike public policy making–where the benefits from using econometric models and “science-based” approaches to policy making are hard to quantify because the outcomes of interest are typically subjective quantities such as “social welfare”–in the case of firms there is an objective, easily quantifiable criterion for judging whether policy A is better than policy B: profits. We introduce and estimate an econometric model of the rental histories of individual cars in the company’s fleet. Via stochastic simulations, we show that the model provides a good approximation to the company’s actual operations. In particular, the econometric model is able to reproduce the extraordinarily high rates of return that the company obtains on its rental cars, with average internal rates of return between purchase and sale of approximately 50%. However, the econometric model can simulate outcomes under a range of counterfactual vehicle replacement policies. We use the econometric model to simulate the profitability of an alternative replacement policy under pessimistic assumptions about the rate maintenance costs would increase and rental rates would have to be decreased if the company were to keep its rental cars longer than it does under the status quo. Depending on the vehicle type, we find that the company’s expected discounted profits would be between 6% to over 140% higher under the suggested alternative operating strategy where vehicles are kept longer and rental rates of older vehicles are discounted to induce customers to rent them. The company found this analysis to be sufficiently convincing that it undertook an experiment to verify the predictions of the econometric model.     

存在遗漏变量时的内生性研究

存在遗漏变量时回归系数的估计是计量经济学的一个重要内容。本文讨论单方程计量经济模型中随机解释变量的内生性,指出了目前的计量经济理论所存在的问题,提出了普通最小二乘估计一致性判别的新方法,并证明了存在遗漏变量情况下的普通最小二乘估计仍是一致估计。     

An update on EMU sovereign yield spread drivers in times of crisis: A panel data analysis

We empirically investigate the determinants of EMU sovereign bond yield spreads with respect to the German bund. Using panel data techniques, we examine the role of a wide set of potential drivers. To our knowledge, this paper presents one of the most exhaustive compilations of the variables used in the literature to study the behaviour of sovereign yield spreads and, in particular, to gauge the effect on these spreads of changes in market sentiment and risk aversion. We use a sample of both central and peripheral countries from January 1999 to December 2012 and assess whether there were significant changes after the outbreak of the euro area debt crisis. Our results suggest that the rise in sovereign risk in central countries can only be partially explained by the evolution of local macroeconomic variables in those countries. Besides, without exception, the marginal effects of sovereign spread drivers (specifically, the variables that measure global market sentiment) increased during the crisis compared to the pre-crisis period, especially in peripheral countries. Moreover, the increase in the significance of the banking level of indebtedness and foreign bank's claims in the public sector (mainly in peripheral countries) along with the crisis unfolding seems to highlight the interconnection between private and public debt and thus, between banking and sovereign crises.     

Econometric analysis of linearized singular dynamic stochastic general equilibrium models

In this paper I propose an alternative to calibration of linearized singular dynamic stochastic general equilibrium models. Given an a-theoretical econometric model as a representative of the data generating process, I will construct an information measure which compares the conditional distribution of the econometric model variables with the corresponding singular conditional distribution of the theoretical model variables. The singularity problem will be solved by using convolutions of both distributions with a non-singular distribution. This information measure will then be maximized to the deep parameters of the theoretical model, which links these parameters to the parameters of the econometric model and provides an alternative to calibration. This approach will be illustrated by an application to a linearized version of the stochastic growth model of King, Plosser and Rebelo.     

Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values

The modern calculation of textual sentiment involves a myriad of choices as to the actual calibration. We introduce a general sentiment engineering framework that optimizes the design for forecasting purposes. It includes the use of the elastic net for sparse data-driven selection and the weighting of thousands of sentiment values. These values are obtained by pooling the textual sentiment values across publication venues, article topics, sentiment construction methods, and time. We apply the framework to the investigation of the value added by textual analysis-based sentiment indices for forecasting economic growth in the US. We find that the additional use of optimized news-based sentiment values yields significant accuracy gains for forecasting the nine-month and annual growth rates of the US industrial production, compared to the use of high-dimensional forecasting techniques based on only economic and financial indicators.     

CONSTRUCTING OPTIMAL DENSITY FORECASTS FROM POINT FORECAST COMBINATIONS

Decision makers often observe point forecasts of the same variable computed, for instance, by commercial banks, IMF and the World Bank, but the econometric models used by such institutions are frequently unknown. This paper shows how to use the information available on point forecasts to compute optimal density forecasts. Our idea builds upon the combination of point forecasts under general loss functions and unknown forecast error distributions. We use real‐time data to forecast the density of US inflation. The results indicate that the proposed method materially improves the real‐time accuracy of density forecasts vis‐à‐vis those from the (unknown) individual econometric models. Copyright © 2013 John Wiley & Sons, Ltd.     

What are the macroeconomic effects of high‐frequency uncertainty shocks?

This paper evaluates the effects of high‐frequency uncertainty shocks on a set of low‐frequency macroeconomic variables representative of the US economy. Rather than estimating models at the same common low frequency, we use recently developed econometric models, which allow us to deal with data of different sampling frequencies. We find that credit and labor market variables react the most to uncertainty shocks in that they exhibit a prolonged negative response to such shocks. When looking at detailed investment subcategories, our estimates suggest that the most irreversible investment projects are the most affected by uncertainty shocks. We also find that the responses of macroeconomic variables to uncertainty shocks are relatively similar across single‐frequency and mixed‐frequency data models, suggesting that the temporal aggregation bias is not acute in this context.     

Forecasting and forecast narratives: The Bank of England Inflation Reports

We analyze the narratives that accompany the numerical forecasts in the Bank of England’s Quarterly Inflation Reports, 1997–2018. We focus on whether the narratives contain useful information about the future course of key macro variables over and above the point predictions, in terms of whether the narratives can be used to enhance the accuracy of the numerical forecasts. We also consider whether the narratives are able to predict future changes in the numerical forecasts. We find that a measure of sentiment derived from the narratives can predict the errors in the numerical forecasts of output growth, but not of inflation. We find no evidence that past changes in sentiment predict subsequent changes in the point forecasts of output growth or of inflation, but do find that the adjustments to the numerical output growth forecasts have a systematic element.     

Cultural Diversity and Cultural Distance as Choice Determinants of Migration Destination

This study analyses the impact of cultural composition on regional attractiveness from the perspective of international migrant sorting behaviour on a European regional NUTS1 level. We use an attitudinal survey to quantify cultural distances between natives and immigrants in the region concerned, and estimate the migrants’ varying preferences for both cultural diversity and cultural distance. To account for regional unobserved heterogeneity, our econometric analysis employs artificial instrumental variables, as developed by Bayer et al., [2004a. An equilibrium model of sorting in an urban housing market. NBER no. 10865]. The main conclusions are twofold. On the one hand, cultural diversity increases regional attractiveness. On the other hand, average cultural distance greatly weakens regional attractiveness.     

WHU Invitation and Call for Applications

Abstract

The discussion reinforces and expands on some of the fundamental issues about endogeneity raised by Chenhall and Moers (European Accounting Review, this issue, pp. 173–195). We focus on the econometric problems researchers encounter when investigating the performance effects of some endogenous firm choice. Our points are illustrated using the classic research question about the relation between managerial equity ownership and firm value. We consider cases where ownership is treated as an exogenous, endogenous and ‘partially’ endogenous variable. We argue treating ownership as an exogenous variable is seriously flawed. Unfortunately, when ownership is at least partially endogenous, it is necessary for empirical researchers to identify exogenous variables that are the determinants of the ownership choice. This calls for better theory to guide the empirical work.     

Forecasting residential burglary

Following the work of Dhiri et al. [Modelling and predicting property crime trends. Home Office Research Study 198 (1999). London: HMSO] at the Home Office predicting recorded burglary and theft for England and Wales to the year 2001, econometric and time series models were constructed for predicting recorded residential burglary to the same date. A comparison between the Home Office econometric predictions and the less alarming econometric predictions made in this paper identified the differences as stemming from the particular set of variables used in the models. However, the Home Office and one of our econometric models adopted an error correction form which appeared to be the main reason why these models predicted increases in burglary. To identify the role of error correction in these models, time series models were built for the purpose of comparison, all of which predicted substantially lower numbers of residential burglaries. The years 1998–2001 appeared to offer an opportunity to test the utility of error correction models in the analysis of criminal behaviour. Subsequent to the forecasting exercise carried out in 1999, recorded outcomes have materialised, which point to the superiority of time series models compared to error correction models for the short-run forecasting of property crime. This result calls into question the concept of a long-run equilibrium relationship for crime.     

Bayesian VARs: Specification Choices and Forecast Accuracy

In this paper we discuss how the point and density forecasting performance of Bayesian vector autoregressions (BVARs) is affected by a number of specification choices. We adopt as a benchmark a common specification in the literature, a BVAR with variables entering in levels and a prior modeled along the lines of Sims and Zha (International Economic Review 1998; 39 : 949–968). We then consider optimal choice of the tightness, of the lag length and of both; evaluate the relative merits of modeling in levels or growth rates; compare alternative approaches to h‐step‐ahead forecasting (direct, iterated and pseudo‐iterated); discuss the treatment of the error variance and of cross‐variable shrinkage; and assess rolling versus recursive estimation. Finally, we analyze the robustness of the results to the VAR size and composition (using also data for France, Canada and the UK, while the main analysis is for the USA). We obtain a large set of empirical results, but the overall message is that we find very small losses (and sometimes even gains) from the adoption of specification choices that make BVAR modeling quick and easy, in particular for point forecasting. This finding could therefore further enhance the diffusion of the BVAR as an econometric tool for a vast range of applications. Copyright © 2013 John Wiley & Sons, Ltd.