业绩预告下的股份减持与内幕交易——会计稳健性是遏制还是助力？

选取2010—2018年沪深A股上市公司为样本,考察业绩预告发布前后大股东是否存在以及如何利用自身信息优势进行股份减持交易,会计稳健性是否以及如何对该内幕交易产生影响。研究结果表明:与强制性业绩预告相比,自愿性业绩预告披露前后发生大股东减持的概率更高,并且会计稳健性会显著抑制自愿性业绩预告披露前后的大股东减持行为。进一步将业绩预告消息区分为好消息和坏消息之后研究发现:坏消息的利空程度越高,大股东在业绩预告之前进行股份减持的规模越大;或者好消息的利好程度越高,大股东在业绩预告之后减持的规模也越大;稳健的财务信息能够抑制公司在隐匿坏消息方面进行的内幕交易,但会加剧公司在隐匿好消息方面进行的内幕交易。     

How quickly do forecasters incorporate news? Evidence from cross‐country surveys

Using forecasts from Consensus Economics Inc., we provide evidence on the efficiency of real GDP growth forecasts by testing whether forecast revisions are uncorrelated. As the forecast data used are multi‐dimensional—18 countries, 24 monthly forecasts for the current and the following year and 16 target years—the panel estimation takes into account the complex structure of the variance–covariance matrix due to propagation of shocks across countries and economic linkages among them. Efficiency is rejected for all 18 countries: forecast revisions show a high degree of serial correlation. We then develop a framework for characterizing the nature of the inefficiency in forecasts. For a smaller set of countries, the G‐7, we estimate a VAR model on forecast revisions. The degree of inefficiency, as manifested in the serial correlation of forecast revisions, tends to be smaller in forecasts of the USA than in forecasts for European countries. Our framework also shows that one of the sources of the inefficiency in a country's forecasts is resistance to utilizing foreign news. Thus the quality of forecasts for many of these countries can be significantly improved if forecasters pay more attention to news originating from outside their respective countries. This is particularly the case for Canadian and French forecasts, which would gain by paying greater attention than they do to news from the USA and Germany, respectively. Copyright © 2006 John Wiley & Sons, Ltd.     

Temperature anomaly detection for electric load forecasting

Since temperature variables are used in many load forecasting models, the quality of historical temperature data is crucial to the forecast accuracy. The raw data collected by local weather stations and archived by government agencies often include many missing values and incorrect readings, and thus cannot be used directly by load forecasters. As a result, many power companies today purchase data from commercial weather service vendors. Such quality-controlled data may still have many defects, but many load forecasters have been using them in full faith. This paper proposes a novel temperature anomaly detection methodology that makes use of the local load information collected by power companies. The effectiveness of the proposed method is demonstrated through two public datasets: one from the Global Energy Forecasting Competition 2014 and the other from ISO New England. The results show that the accuracy of the final load forecasts can be enhanced by removing the detected observations from the original input data.     

News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar

The paper proposes a novel approach to predict intraday directional-movements of currency-pairs in the foreign exchange market based on news story events in the economy calendar. Prior work on using textual data for forecasting foreign exchange market developments does not consider economy calendar events. We consider a rich set of text analytics methods to extract information from news story events and propose a novel sentiment dictionary for the foreign exchange market. The paper shows how news events and corresponding news stories provide valuable information to increase forecast accuracy and inform trading decisions. More specifically, using textual data together with technical indicators as inputs to different machine learning models reveals that the accuracy of market predictions shortly after the release of news is substantially higher than in other periods, which suggests the feasibility of news-based trading. Furthermore, empirical results identify a combination of a gradient boosting algorithm, our new sentiment dictionary, and text-features based-on term frequency weighting to offer the most accurate forecasts. These findings are valuable for traders, risk managers and other consumers of foreign exchange market forecasts and offer guidance how to design accurate prediction systems.     

Voluntary Disclosure and Analyst Forecast

Empiricists document that firms more often voluntarily disclose bad news than good news and link this pessimism to managers’ increased incentives not to fall short of earnings expectations. This paper analyzes the voluntary disclosure of a manager’s private information by explicitly considering her incentives to meet or beat an analyst’s earnings forecast. The model predicts that managers who face strong incentives to meet or beat these forecasts more frequently disclose bad news than good news in order to guide analysts’ expectations about future earnings downward. This pessimism is higher in markets with less informed managers and may hold even if the manager has strong incentives for high stock prices and meet-or-beat incentives are comparably low.     

如何做好民族地区广播电视民生新闻的报道

新时期民族地区广播电视新闻宣传工作中,民生新闻占有突出的位置。做好民生新闻,需要深入研究、了解民生新闻的涵义、党性原则、价值取向以及民族地区民生新闻的理论实践、新闻报道的突破,为做好民族地区广播电视民生新闻宣传找好点、定好位。     

Media activity and public spending

Politicians seeking reelection need voters to know what they have done for them. Thus, incentives may arise to spend more money where media coverage is higher. We present a simple model to explain the allocation of public spending across jurisdictions contingent on media activity. A politician seeking to maximize the probability of reelection will shift more money to jurisdictions where an extra dollar raises more votes because a larger share of the electorate is informed about his policy. The main prediction of the model is that media activity is higher in the core areas of media markets. This implies higher spending levels there and lower spending levels in remote jurisdictions. Empirical support for this prediction is found using United States data on county-level federal grant allocation, Designated Market Areas and the location of licensed television stations.     

A financial accelerator in the business sector of a macroeconometric model of a small open economy

We have incorporated a financial accelerator mechanism operating through investments in the business sector in a dynamic macroeconometric model of the Norwegian economy. In this new and amended model aggregated credit and equity prices are determined simultaneously in a system characterized by a two-directional contemporaneous causal link, which has been designed and estimated by a new procedure for simultaneous structural model design. Combined with a mechanism where credit and asset prices are mutually influenced by real investments, this creates a financial accelerator amplified by a credit-asset price spiral. Simulations illustrate how the introduction of a financial accelerator significantly reinforces and extends the economic cycles in projections and forecasts, in particular when confronted by a severe shock. Furthermore, monetary policy has a markedly stronger effect in the short and medium term, while the impact of fiscal policy is affected to a relatively small degree as it is more remotely linked to financial markets.     

Does the Capitalization of Development Costs Improve Analyst Forecast Accuracy? Evidence from the UK

It has been documented that investments in Research and Development (R&D) are associated with increased errors and inaccuracy in earnings forecasts made by financial analysts. These deficiencies have been generally attributed to information complexity and the uncertainty of the future benefits of R&D. This paper examines whether the capitalization of development costs can reduce analyst uncertainty about the future economic outcome of R&D investments, provide outsiders with a better matching of future R&D‐related revenues and costs, and therefore promote accuracy in analyst forecasts. UK data is used, because accounting rules in the United Kingdom permitted firms to conditionally capitalize development costs even before the introduction of the International Financial Reporting Standards. The choice to expense R&D rather than conditionally capitalize development costs is found to relate positively to signed analyst forecast errors. This finding is robust to controlling for the influence of other factors that may affect errors, as well as for the influence of R&D investments on forecast errors. The decision to capitalize versus expense is not observed to have a significant influence on analyst forecast revisions. The findings are interpreted as evidence that the choice to capitalize as opposed to expense may help to reduce deficiencies in analyst forecasts; hence, is informative for users of financial statements. Increased informativeness is expected to have repercussions for the effectiveness with which analysts produce earnings forecasts, and, as a result, market efficiency.     

Impacts of news media coverage on Canadian medical crowdfunding campaigns

While donation-based crowdfunding for health-related purposes raises hundreds of millions of dollars yearly, most campaigns fail to meet their fundraising goals. Crowdfunding campaigners are advised to seek traditional news media coverage of their campaigns to increase donor interest and fundraising success. In this study, we seek to better understand what happens to donor behavior via donation-based crowdfunding campaigns after they receive news media coverage. While research has focused on the impact of social media sharing on donation-based crowdfunding, academic analyses of the impact of news media coverage is largely speculative. We searched the Newsstream and Factiva databases for Canadian news coverage of domestic donation-based health-related crowdfunding campaigns. This news coverage was paired with the crowdfunding campaign reported on in the story. Campaign text and daily fundraising totals and donor amounts were recorded for the 7 days before and after publication of the news article. The authors identified emergent patterns in this data around the amplification of personal information from the crowdfunding campaign to a wider audience and inclusion of new personal details. This process identified 17 relevant pairs of news stories and crowdfunding campaigns over a review period of just under 5 months in 2021–22. These campaigns raised a total of CAD$443,134 (median CAD$20,030) out of a total goal of CAD$772,500 (median CAD$40,000) or 57.4% of the requested funds. Median campaign donations and donor numbers increased for the 3 days following publication of the news article. Our exploratory analysis shows a relationship between crowdfunding campaigns that receive news media coverage and the numbers of donations and total amount donated shortly after this coverage. Campaigners may feel pressure to participate in news media coverage in order to reach their fundraising goals. Media coverage has implications for campaign recipient privacy and the equitable distribution of health-related funding. This exploratory analysis establishes the need for additional research on this topic.     

Downside risk management and VaR-based optimal portfolios for precious metals,oil and stocks

Value-at-Risk (VaR) is used to analyze the market downside risk associated with investments in six key individual assets including four precious metals, oil and the S&P 500 index, and three diversified portfolios. Using combinations of these assets, three optimal portfolios and their efficient frontiers within a VaR framework are constructed and the returns and downside risks for these portfolios are also analyzed. One-day-ahead VaR forecasts are computed with nine risk models including calibrated RiskMetrics, asymmetric GARCH type models, the filtered Historical Simulation approach, methodologies from statistics of extremes and a risk management strategy involving combinations of models. These risk models are evaluated and compared based on the unconditional coverage, independence and conditional coverage criteria. The economic importance of the results is also highlighted by assessing the daily capital charges under the Basel Accord rule. The best approaches for estimating the VaR for the individual assets under study and for the three VaR-based optimal portfolios and efficient frontiers are discussed. The VaR-based performance measure ranks the most diversified optimal portfolio (Portfolio #2) as the most efficient and the pure precious metals (Portfolio #1) as the least efficient.     

Evidence on the motivation for management forecasts of corporate earnings

A multivariate model of the process by which managers decide to release public forecasts of their firms' earnings is developed, based on factors that are hypothesized to affect the demand for and the willingness to supply such forecasts. We test the model on data from a comprehensive sample of earnings forecasts, and find support for our hypotheses about the likely joint influence of those factors. Larger firm size, greater leverage, higher and more stable earnings rates and less rapid growth rates are found to be associated with an increased propensity for management to provide earnings forecasts for their firms.     

Using weather ensemble predictions in electricity demand forecasting

Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electricity demand. The results show that the average of the demand scenarios is a more accurate demand forecast than that produced using traditional weather forecasts. We use the distribution of the demand scenarios to estimate the demand forecast uncertainty. This compares favourably with estimates produced using univariate volatility forecasting methods.     

News on inflation and the epidemiology of inflation expectations in China

Inflation expectations are important elements in monetary policy analysis. This paper examines how inflation expectations of Chinese consumers and professional forecasters are affected by media sentiments based on the epidemiological foundations of the sticky information model. Rather than assuming professional forecasts are identical to newspaper forecasts, we assume news media are a common source for the transmission of typical people's inflation expectations. We collect media data from 30 leading newspapers and magazines in China and code news reports into three types of inflation: rising, falling, and unchanged. More importantly, we categorize the media pool into comprehensive, economic, and politically oriented media sources. We find a fundamental connection between news media and inflation expectations. However, there are significantly different impacts of news reports in different media sources on expectations. The difference is mainly concentrated in politically oriented media sources, and may be a reflection of China's unique media administration system.     

Information rigidity in growth forecasts: Some cross-country evidence

We document information rigidity in forecasts of real GDP growth in 46 countries over the past two decades. We also investigate: (i) whether rigidities differ across countries, particularly between advanced countries and emerging markets; (ii) whether rigidities are lower around turning points in the economy, such as in times of recessions and crises; and (iii) how quickly forecasters incorporate news about growth in other countries into their growth forecasts, with a focus on the way in which advanced countries’ growth forecasts incorporate news about emerging market growth, and vice versa.     

Introducing the Bank of Canada staff economic projections database

We present a new, publicly available database of real-time data and forecasts from the Bank of Canada's staff economic projections, which will be updated on an annual basis. We describe the data construct, its variables, coverage, and frequency. We then provide a forecast evaluation for gross domestic product (GDP) growth, consumer price index (CPI) inflation and the policy rate since 1982: We compare the staff's forecasts with those from commonly used time series models estimated with the real-time data, and with forecasts from other professional forecasters, and provide standard bias tests. Finally, we study changes in predictability of the Canadian economy following the announcement of the inflation-targeting regime in 1991. Our data set is unprecedented outside the USA, and our evidence is particularly interesting, as it covers over 30 years of staff forecasts, two severe recessions, and different monetary policy regimes.     

Professional forecasters and real-time forecasting with a DSGE model

This paper analyses the real-time forecasting performance of the New Keynesian DSGE model of Galí, Smets and Wouters (2012), estimated on euro area data. It investigates the extent to which the inclusion of forecasts of inflation, GDP growth and unemployment by professional forecasters improve the forecasting performance. We consider two approaches for conditioning on such information. Under the “noise” approach, the mean professional forecasts are assumed to be noisy indicators of the rational expectations forecasts implied by the DSGE model. Under the “news” approach, it is assumed that the forecasts reveal the presence of expected future structural shocks in line with those estimated in the past. The forecasts of the DSGE model are compared with those from a Bayesian VAR model, an AR(1) model, a sample mean and a random walk.     

Taking a Second Look in a Warped Crystal Ball: Explaining the Accuracy of Revised Forecasts

The fundamental questions we address are whether firms with a higher initial forecasting ability are able to accurately revise the exit forecasts of their investments; and how co‐investment partners and value‐adding commitment with their investment influence the main effect. We explore these questions with novel and unique data collected via mixed research methods on venture capital firms’ forecasts of 114 portfolio companies. We find that venture capital firms that are better at making initial forecasts are less effective in revising their forecasts. In addition, while the number of co‐investment partners positively moderate this relationship, venture capital firms’ value‐adding commitment moderates it negatively. Our findings contribute to the literature on organizational forecasting as well as inter‐organizational knowledge transfer and knowledge creation. They also provide novel insights into venture capital literature and practice.     

Innovation adoption and training activities in SMEs

This article adopts the resource-based view and the complementarities approach to examine how small and medium enterprises (SMEs) combine the adoption of organisational and technological innovation with investments in training activities. The results of econometric analysis on a panel data-set of about 118 Italian manufacturing SMEs furnish a quite complex picture of the effects of innovation on training. On the one hand, organisational innovation seems to be related to higher investments in (formal and informal) internal training; specifically, it is the adoption of autonomous teams and multi-skilling practices that is associated with the coverage and the intensity of internal training, whereas job rotation is negatively associated with the coverage of external training. On the other hand, the general index of technological innovation does not show any significant relationship with training activities, whereas the individual technological innovation variables are associated with internal training. Specifically, the coverage of internal training is positively affected by ICT innovation and negatively affected by process innovation. These results demonstrate that SMEs have limited awareness of the risks associated with underinvesting in training during the implementation phase of the innovation process. The implications of such findings for research and practice are discussed.     

Calibration of deterministic NWP forecasts and its impact on verification

Deterministic forecasts (as opposed to ensemble or probabilistic forecasts) issued by numerical weather prediction (NWP) models require post-processing. Such corrective procedure can be viewed as a form of calibration. It is well known that, based on different objective functions, e.g., minimizing the mean square error or the mean absolute error, the calibrated forecasts have different impacts on verification. In this regard, this paper investigates how a calibration directive can affect various aspects of forecast quality outlined in the Murphy–Winkler distribution-oriented verification framework. It is argued that the correlation coefficient is the best measure for the potential performance of NWP forecast verification when linear calibration is involved, because (1) it is not affected by the directive of linear calibration, (2) it can be used to compute the skill score of the linearly calibrated forecasts, and (3) it can avoid the potential deficiency of using squared error to rank forecasts. Since no single error metric can fully represent all aspects of forecast quality, forecasters need to understand the trade-offs between different calibration strategies. To echo the increasing need to bridge atmospheric sciences, renewable energy engineering, and power system engineering, as to move toward the grand goal of carbon neutrality, this paper first provides a brief introduction to solar forecasting, and then revolves its discussion around a solar forecasting case study, such that the readers of this journal can gain further understanding on the subject and thus potentially contribute to it.