基于耗散理论的企业创新网络配置机理研究

研究与实践表明,企业创新网络能增强企业创新能力。创新网络配置的问题是企业创新网络研究的热点问题之一。本文从企业创新网络结构的角度,运用耗散理论和联结熵,对企业创新网络配置及其动态性的机理进行了分析,为企业创新网络配置提供了理论依据,对企业创新网络配置的实践具有一定的指导意义。     

Identifying unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting approach

This study considers the review reliability problem by identifying biased user-given ratings through rating prediction on the basis of the textual content. Deep learning approaches were introduced to investigate the textual review and validate the effect of rating prediction using a dataset collected from Yelp. The definition of “biased rating” was clarified and influenced the matching rules. The approach obtains high performance on a total of 1,000,000 reviews for prediction, with user-given ratings as the benchmark. Using the revealed biased ratings, unreliable reviews were detected by combining the results of several deep learning kernels. Findings shed light on understanding review quality by distinguishing biased ratings and unreliable reviews that may cause inconsistency and ambiguity to readers. Hence, theoretical and managerial areas for social media analytics are enriched on the basis of online review meta-data in hospitality and tourism.     

Bayesian hierarchical statistics for traffic safety modelling and forecasting

Abstract

Road traffic accidents (RTAs) represent a serious problem globally causing losses in many ways. Gulf Cooperation Council (GCC) countries have a high rate of RTAs compared to other high-income countries. In this study, a Bayesian hierarchical model was utilized for accident counts forecasting in Abu Dhabi, United Arab Emirates. This work will help traffic planners and decision makers to enhance road safety levels and decrease accident fatality rate. Accidents data along 5 years from 2008 to 2012 at 143 road sites in Abu Dhabi with 5,511 accidents were used. The proposed model considered a number of covariates; speed limit, type of road, number of lanes, type of area, weather, time, surface condition and seat belt usage. Five sites with the highest numbers of accidents were studied. Year 2012 was used to validate predictions. The model prediction accuracy was 72%.     

Does audit report information improve financial distress prediction over Altman's traditional Z‐Score model?

We analyze empirically the usefulness of combining accounting and auditing data in order to predict corporate financial distress. Concretely, we examine whether audit report information incrementally predicts distress over a traditional accounting model: the Altman's Z‐Score model. Although the audit report seems to play a critical part in financial distress prediction because auditors should warn investors about any default risks, this is the first study that uses audit report disclosures for predicting purposes. From a dataset of 1,821 Spanish distressed private firms, we analyze a sample of distressed and non‐distressed firms and develop logit prediction models. Our results show that while the only accounting model registers a classification accuracy of 77%, combined models of accounting and auditing data exhibit considerably higher accuracy (about 87%). Specifically, our findings indicate that the number of disclosures included in the audit report, as well as disclosures related to a firm's going concern status, firms’ assets, and firms’ recognition of revenues and expenses contribute the most to the prediction. Our empirical evidence has implications for financial distress practice. For managers, our study highlights the importance of audit report disclosures for anticipating a financial distress situation. For regulators and auditors, our study underscores the importance of recent changes in regulation worldwide intended to increase auditor's transparency through a more informative audit report.     

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.     

Targeted growth rates for long-horizon crude oil price forecasts

This paper proposes growth rate transformations with targeted lag selection in order to improve the long-horizon forecast accuracy. The method targets lower frequencies of the data that correspond to particular forecast horizons, and is applied to models of the real price of crude oil. Targeted growth rates can improve the forecast precision significantly at horizons of up to five years. For the real price of crude oil, the method can achieve a degree of accuracy up to five years ahead that previously has been achieved only at shorter horizons.     

Predicting unlisted SMEs' default: Incorporating market information on accounting-based models for improved accuracy

The risk associated with lending to small businesses has become more important since regulations started obliging banks to use separate procedures in assessing SMEs' credit worthiness. However, current accounting-based models for SMEs do not account for the impact of market information on default prediction. We fill this gap in the literature by introducing a hybrid default prediction model for unlisted SMEs that uses market information of listed SMEs (comparable approach) alongside existing accounting information of unlisted SMEs. Our results suggest that the accuracy of this default prediction modelling approach in the hold-out sample, during the period of the financial crisis 2007-09 and for the entire sample-period, improves considerably. We conclude that the proposed hybrid model is a good replacement for existing standard accounting-based methods on SMEs' default prediction.     

Do stock bulletin board systems (BBS) contain useful information? A viewpoint of interaction between BBS quality and predicting ability

This study explores whether information on internet stock bulletin board systems (BBS) is valuable for stock return prediction, taking advantage of data derived from the biggest stock BBS in China. Using a text classification algorithm, we find the online messages significantly predict stock return with negligible R‐squared. However, we find that accuracy of individual BBS posts is below 50 percent and there is no distinction at prediction accuracy between high‐ and low‐quality stock BBS. Due to the autocorrelation of stock returns, we argue that BBS predicts stock returns because of its reflection on the simultaneous stock return rather than revelation on valuable information.     

Forecasting corporate default risk in China

Default risk prediction can not only provide forward-looking and timely risk measures for regulators and investors, but also improve the stability of the financial system. However, the determinants of corporate default risk in China have not been well-identified. An empirical analysis was conducted using a unique dataset of default events in the Chinese market to fill this gap. First, we demonstrated that the default probability estimated by a structural model, which is widely used in the literature, do not fully reveal the default risk of firms in China. Second, we classified default events into minor and major defaults for empirical analysis. We found that firms that survive minor defaults behave differently from other bankrupt firms. Our results suggest that the determinants of corporate default risk in China and the United States differ. We also found that a firm’s continued increase in cash holdings is one of the most important signs of default. Overall, our study significantly improves the accuracy of forecasting corporate default risk in China.