Debt contract strictness and auditor specialization

The conflicts of interest among managers, shareholders and creditors resulting in agency costs, can be mitigated by restricting managers’ adverse behavior, through financial covenants to better align the various stakeholder interests. Thus, debt contract strictness represents an important aspect of agency costs between creditors, shareholders, and management that is not always captured by interest rates. The contract setting provides a unique opportunity to investigate how creditors may rely on auditors to alleviate information uncertainty stemming from reliance on management's financial reporting and thus alleviate the creditor's potential loss of invested capital. After controlling for borrower risks, loan characteristics, and audit factors, we show that auditor industry specialization is significantly associated with a reduction in the strictness of debt contracts, consistent with creditors viewing certain industry expert auditors as effective monitors against financial reporting manipulation aimed at the avoidance of debt covenant triggers that protect creditors against potential loss. Further, we find that the association between loan strictness and auditor specialization is attenuated by stronger corporate governance systems, external monitors, and prior lender relationships.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

中国旅游景区功能演变与用地分类构想

研究目的:基于中国旅游景区功能演变、用地特征及问题分析,构建旅游景区用地分类体系,以期为旅游景区用地纳入区域土地利用提供理论基础,为旅游景区规划的深度编制提供实践依据。研究方法:通过调研和问卷厘清现状景区用地情况,对比借鉴相关用地分类体系,基于此构建旅游景区用地分类方案。研究结果:分析并阐明了旅游景区的功能演变、用地特征和现状问题,构建了2大类、9中类、28小类的景区用地分类体系,并与《土地利用现状分类》进行衔接。研究结论:建立可衔接且具可操作性的旅游景区用地分类体系,是实现旅游景区健康可持续发展与用地规范化管控的关键。     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Ownership discrimination and private firms financing in China

This study examines the financing/funding of private firms in China. Our results show that private firms are significantly less funded through formal financing channels such as bank loans than state-owned firms, and hence have to resort to alternative financing such as trade credit. Consistent with the theoretical expectation and literature, there is a substitution effect between trade credit and bank loans for private firms, but this effect is much weaker compared to that of state-owned firms. Moreover, while the univariate comparisons indicate that private firms obtain more notes payable than state-owned firms, the multivariate regression analyses show that the relation between bank loan and notes payable is positive and indifferent between private and state-owned firms.     

Extendible stock loan

In this paper, the model of extendible stock loan with forbearance is proposed. The loan is extendible, so as to prevent immediate losses or to prevent subsequent price drop; while the forbearance is granted only when the pledged share’s value is above threshold, so as to mitigate the risk-taking behavior induced by the extension. The non-synchronization of the liquidation of insolvent stock loans also alleviates the downward leverage spiral in a market downturn. Numerical analysis shows that fair extendible stock loan rates increase with the forbearance level as well as extension period, and loan rates are quite sensitive to the change of asset volatility and debt ratio. For lenders waiving the interest rates during extension period, their burden grows with extension rapidly when they grant looser forbearance and when asset volatility or loan-to-value is higher. Some suggestions are made accordingly. First, lenders offering uniform extendible loan rate can let borrowers choose between looser forbearance with shorter extension, or tighter forbearance with longer extension. Second, if the loan rate is priced fairly, lower margin requirement can only be accomplished with tighter forbearance. More looser forbearance worth higher rates.     

Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach

It is commonly observed that high grade loans with better ratings are often associated with low recoveries if they default (i.e. with relatively high loss-given-default (LGD)). To address the mismatch problem, this paper proposes a credit risk approach by minimizing LGD for higher rated loans as a risk-rating matching standard in the sense that the decreasing LGD from creditors’ perspective is associated with higher credit rating for the borrower. This standard forces customers’ credit rating of each grade to be optimally determined in correspondence to its LGD, which means the LGD of high grade loans tends to be low. The approach is then tested using three credit datasets from China, i.e. credit data from 2044 farmers, 2157 small private businesses and 3111 SMEs. The empirical results show that the proposed approach indeed guides the way to solve the mismatch phenomenon between credit ratings and LGDs in the existing credit rating literature. By optimally determining credit ratings, the findings derived from this paper help provide a valuable reference for bankers, and bond investors to manage their credit risk.     

Research on a diagnostic system of rural vitalization based on development elements in China

In 2017, the Chinese government implemented a national strategy of "Rural Vitalization" that sought to realize full-scale rural vitalization. However, is it possible to achieve vitalization for all the villages in China? How should their development potential be determined? This paper identified and analyzed the "element-composite" messages of rural development based on 99 exemplary sites of “Beautiful Villages” in China. Combined with the projection pursuit classification method, a diagnostic system of rural vitalization was established; then, Dehua County was taken as a case study for an in-depth analysis. Based on national data analysis, the final results indicated that livelihood resources (LR), agglomeration effects (AE), location and transportation (LT), cultural/natural landscapes (CN), and economic circumstance (EC) are essential elements for successful rural development. Additionally, EC was the only exogenous element, while the remaining elements were endogenous. Furthermore, the villages with better EC presented urbanization rates of 38∼82 % and Engel coefficients of 29∼41 % in their counties; exemplary sites lacking LR, CN, LT, and AE account for 13.13 %, 19.19 %, 26.26 %, and 60.61 % respectively, so the indispensability of these elements decreases progressively in sequence. Only 2 % of villages rely on single element for success, therefore, the composite pattern of development element was also critical; 10 out of 16 types were found to successfully facilitate village development, among which, the type of R-a-L-C (32.32 %) and R-A-L-C (15.15 %) were considered as the greatest potential patterns for vitalization. Finally, by means of the diagnostic system, the ratio of representative villages for high-low potential in Dehua County is evenly split; then, development paths, and land use policies that match with paths were proposed, on the basis of development potential and “element-composite” condition of themselves.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.