Assessing the historical role of credit: Business cycles,financial crises and the legacy of Charles S. Peirce

This paper provides a historical overview of financial crises and their origins. The objective is to discuss a few of the modern statistical methods that can be used to evaluate predictors of these rare events. The problem involves the prediction of binary events, and therefore fits modern statistical learning, signal processing theory, and classification methods. The discussion also emphasizes the need for statistics and computational techniques to be supplemented with economics. The success of a forecast in this environment hinges on the economic consequences of the actions taken as a result of the forecast, rather than on typical statistical metrics of prediction accuracy.     

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.     

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.     

Waste of money or growth opportunity: The causal effect of EU subsidies on Hungarian SMEs

Although EU subsidies aiming at economic development play a pivotal role not only for Hungary but for the entire European Union as well, there is a debate regarding their effectiveness in the literature. This paper investigates the impact of direct economic development subsidies extended in the context of Structural Funds and the Cohesion Fund as part of the 2007–2013 programming period of the European Union on Hungarian micro, small and medium-sized enterprises. Based on a micro database, we evaluate the impact of corporates’ first subsidies on various performance indicators, using a combination of propensity score matching and fixed effects panel regression. According to our results, economic development funds had a significant positive effect on the number of employees, sales revenue, gross value added and, in some cases, operating profit. However, the labour productivity of enterprises was not significantly affected by any of the support schemes. Furthermore, by explicitly comparing non-refundable subsidies (grants) and refundable assistance (financial instruments), we find that there is no significant difference in the effectiveness of the two types of subsidy.     

Semitractability of optimal stopping problems via a weighted stochastic mesh algorithm

In this paper, we propose a Weighted Stochastic Mesh (WSM) algorithm for approximating the value of discrete‐ and continuous‐time optimal stopping problems. In this context, we consider tractability of such problems via a useful notion of semitractability and the introduction of a tractability index for a particular numerical solution algorithm. It is shown that in the discrete‐time case the WSM algorithm leads to semitractability of the corresponding optimal stopping problem in the sense that its complexity is bounded in order by with being the dimension of the underlying Markov chain. Furthermore, we study the WSM approach in the context of continuous‐time optimal stopping problems and derive the corresponding complexity bounds. Although we cannot prove semitractability in this case, our bounds turn out to be the tightest ones among the complexity bounds known in the literature. We illustrate our theoretical findings by a numerical example.     

Social media bots and stock markets

This study examines the link between information spread by social media bots and stock trading. Based on a large sample of tweets mentioning 55 companies in the FTSE 100 composites, we find significant relations between bot tweets and stock returns, volatility, and trading volume at both daily and intraday levels. These results are also confirmed by an event study of stock response following abnormal increases in the volume of tweets. The findings are robust to various specifications, including controlling for traditional news channel, alternative measures of volatility, information flows in pretrading hours, and different measures of sentiment.     

基于GIS的皖江流域耕地地力评价研究

[目的]为了整体掌握皖江流域耕地地力水平、特征和土壤肥力状况等,充分利用耕地资源,提升皖江流域的耕地质量。[方法]文章基于农业部耕地资源管理信息系统软件,分别构建了皖江流域平原区和皖江流域山地丘陵区的评价指标体系,并通过现在较为成熟的特尔菲法、层次分析法、模糊数学评价等方法相结合,开展皖江流域耕地地力评价。[结果]皖江流域平原区耕地划分为9个等级, 1～9等地耕地面积所占比例分别为8.7%,18.4%,22.5%,22.7%,13.5%,17.2%,2.8%,2.9%和1.3%,其中2等地、3等地以及4等地分布区域较广,所占面积较大;皖江流域山地丘陵区耕地划分为9个等级, 1～9等地耕地面积所占比例分别为2.2%,8.6%,14.2%,26.0%,21.3%,10.6%,9.0%,5.0%和3.2%,该区域主要有3等地、4等地、5等地分布。[结论]该评价结果对皖江流域耕地资源的科学管理和可持续利用以及农业现代化发展具有非常重要的意义。     

The Home Bias in Equities and Distribution Costs

We show that incorporating distribution costs into a general equilibrium model of international portfolio choice helps to explain the home bias in international equity investment. Our model is able to replicate observed investment positions for a wide range of parameter values, even if agents have an incentive to hedge labor income risk by purchasing foreign equity. This is because the existence of a retail sector affects both the correlation of domestic returns with the domestic price level and the correlation between financial and non‐financial income.     

Risks Matrixes Risks Assessment Tools of Small and Medium-Sized Enterprises： The Case of Latvia

The enterprises need to assess the risk dynamic of financial instability and its impact on small and medium-sized enterprises＇ development, because it is important for enterprises to extend commercial activity and to open a new structural subdivision. We have researched types of risks, their identification, classification, and assessment possibilities in activities of small and medium-sized enterprises. We have used our own algorithm of identification, classification, and assessment of enterprises＇ risks. The goal of this research is to study the economic and financial risks＇ impact on small and medium-sized enterprises＇ development in Latvia. For study purpose, we have carried out the questionnaire of representative small and medium-sized enterprises about the economic and financial risks＇ impact on enterprises＇ development in Latvia. We have created classification of Latvian services sectors＇ economic and financial risks in the period from 2011 to 2012. Those risks have been included in the questionnaire. The risks matrix is a quantitative assessment tool of risks. We have created Latvian service sector economic and financial risks matrix. We have arranged risks by their sizes of possible losses for enterprises. For each risk has been assessed its probability of realization. We have created Latvian accommodations （hotel） and food services technological process risks map. Several parts of the risk map （segments） make it possible to assess each type of the risk separately in its segment. Risks matrix can be used to choose enterprises＇ strategy of risk management. Enterprise＇s strategy of risk management is developed by analysing zones of risk level.     

Scale effects of R&D as reflected in earnings and returns

Using returns to scale as a conceptual foundation, we explore how R&D-related earnings performance and earnings variability depend upon firm size. We find that the positive association between the level of future earnings and R&D intensity increases with firm size, and that the positive association between the volatility of future earnings and R&D intensity decreases with firm size, consistent with R&D productivity increasing with scale. We also show that R&D scale is associated with lower market returns, consistent with the idea that R&D investment risk declines with scale.