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| Can High-speed Railway Improve the Accuracy of Analysts' Earnings Forecasts？ Evidence from Listed Companies |
| YANG Qing, JI Yun, WANG Yanan
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School of Economics, Fudan University;
School of Finance, Central South University of Finance, Economics and Law |
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Abstract China's achievements in high-speed rail have attracted worldwide attention. The development of the high-speed rail network has effectively broken the spatial barriers between cities and facilitated the mobility of resources and production factors, especially people and information. As information intermediaries in the financial market, securities analysts actively collect important information on listed companies through various channels, such as company announcements, field investigations, and strategy meetings. They then use this information to make earnings forecasts and guide their investment decisions in the capital market. For security analysts, the high-speed rail network has effectively shortened the space-time distance between cities and reduced the cost of communicating information. The network has also facilitated more field investigations of listed companies and private communications with management, and thus reduced the original information restrictions and helped improve the accuracy of analysts' earnings forecasts.
For our empirical analysis, we collected data on the high-speed rail stations in 201 Chinese prefecture-level cities from 2006 to 2016, and then matched these data with the panel data of 1,244 listed companies in the A share market. Using the operation of high-speed rail as a quasi-natural experiment and using the difference in difference (DID) model, we studied the impact of high-speed rail on the earnings forecasts of security analysts. We found that after the high-speed railway entered service, the accuracy of the analyst earnings forecasts for companies located along the network was significantly improved, and the forecast divergence and optimism of the analysts' earnings were reduced. Further analysis of the internal mechanism showed that the introduction of high-speed rail prompted analysts to conduct more field investigations of the listed companies along the network. Compared with firms not located along the network, the number of analysts participating in investigations and the number of investigations per capita increased for the companies on the network after high-speed rail was introduced. Moreover, we found that the high-speed rail had heterogeneous effects on analysts' earnings forecasts. For enterprises with lower information processing costs, better corporate governance, and lower fund shareholding, the accuracy of the analysts' earnings forecasts clearly improved, and the divergence and optimism of the earnings forecasts were effectively reduced. In addition, from a dynamic point of view, the impact is mainly detected two years after the high-speed rail network begins operating, which indicates that it takes some time for high-speed rail to affect the mobility of the information in the capital market by increasing the research frequency and private information of analysts.
The study makes three main contributions to the literature. First, it extends the research on the economic effects of high-speed rail, and uses data at the micro-company level for the first time to demonstrate the impact of a high-speed rail network on the earnings forecasts of security analysts from the perspective of private information on the capital market. Second, by using the high-speed rail network as a quasi-natural experiment in which the ability of analysts to obtain private information is improved, we verify the impact of the relaxation of the information constraints on analysts' earnings forecasts and effectively alleviate the problems of self-selection and endogeneity in the samples. Third, we confirm the internal mechanism by which the introduction of high-speed rail improves the accuracy of analysts' earnings forecasts through the increased field research and information mobility of the analysts.
The findings of this study have two main implications for policymakers. First, our findings show that high-speed rail helps to accelerate the movement of people and information between adjacent cities, and hence enhances the information disclosure of listed companies and reduces the degree of information asymmetry in the capital market. This shows that the construction of the high-speed rail network produced positive externalities and provides empirical support for the construction of high-speed rail networks. Second, we also add to the literature on analysts' earnings forecasts. The capital market is itself a market for information. Thus, we examine the exogenous impact of high-speed rail on the cost of communicating information. We then study the impact of the acquisition of private information on analysts' forecasting behavior, and find that there is still a serious degree of information asymmetry between analysts and listed companies. Field investigations by analysts provide an effective channel for alleviating this information asymmetry and help to improve the pricing function and resource allocation efficiency of the capital market.
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Received: 03 August 2018
Published: 12 April 2019
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