Statistical correlation properties of the SHIBOR interbank lending market

2015 ◽  
Vol 5 (2) ◽  
pp. 91-102 ◽  
Author(s):  
Yong Luo ◽  
Jie Xiong ◽  
Lie Gang Dong ◽  
Yong Tang
Keyword(s):  
Correlation Analysis ◽  
Matrix Theory ◽  
Statistical Correlation ◽  
Largest Eigenvalue ◽  
Content Type ◽  
Crisis Period ◽  
Good Estimator ◽  
Interbank Lending ◽  
Correlation Properties ◽  
Market Mode

Purpose – The purpose of this paper is to investigate the statistical correlation properties of the Shanghai Interbank Offered Rate (SHIBOR) interbank lending market. Design/methodology/approach – The authors apply methods of correlation analysis, random matrix theory (RMT) and minimum spanning tree (MST) to investigate the correlation properties of Chinese interbank lending market and analyze how the SHIBOR panel banks behave in different market periods. Findings – First, the largest eigenvalue λ 1 is the index to describe the market mode of the whole market when all banks behavior collectively and λ 1/N is a good estimator of the average correlation <C> of the correlation matrix. Second, notably, the authors find the “market mode” is weakened in two crises periods of 2008 stock market crash and 2009 Global Financial Crisis. This is significantly different from other market where the “market mode” is normally strengthened in crises periods. Third, the authors subtract the contribution of λ 1, the second and third eigenvalue, λ 2 and λ 3, will fall outside of the predicted interval. And both λ 2 and λ 3 are getting times larger in the crises periods than in “Non-Crisis” period. Fourth, and in the MST analysis, the authors find again that the average distances of the MST are the times larger in crises periods than in “Non-Crisis” period and the second largest eigenvalue is a good estimator of the average distance of the MST. Originality/value – According to the best knowledge, this paper is the first work on the study of the statistical properties of an interbank lending market using quotation level data of panel banks, which allows us to analyze the properties of the interest rate formation and how all panel banks behavior in different periods. This work is also the first study on the SHIBOR market using econophysics methods of correlation analysis, RMT and MST.

Distribution and Correlation of the Coding Sequence Lengths in Bacterial Genomes

2008 ◽  
Vol 59 (11) ◽  
Author(s):  
Vasile V. Morariu
Keyword(s):  
Correlation Analysis ◽  
Exponential Function ◽  
Short Range ◽  
Statistical Physics ◽  
Bacterial Genomes ◽  
Coding Sequence ◽  
Correlation Properties ◽  
Short Range Correlations ◽  
Fluctuating System

The length of coding sequence (CDS) series in bacterial genomes were regarded as a fluctuating system and characterized by the methods of statistical physics. The distribution and the correlation properties of CDS for 47 genomes were investigated. The distribution was found to be approximated by an exponential function while the correlation analysis revealed short range correlations.

Antecedents of growth-oriented entrepreneurship before and during the Greek economic crisis

2017 ◽  
Vol 24 (3) ◽  
pp. 528-544 ◽  
Author(s):  
Ioannis Giotopoulos ◽  
Alexandra Kontolaimou ◽  
Aggelos Tsakanikas
Keyword(s):  
Work Experience ◽  
Early Stage ◽  
New Ventures ◽  
High Growth ◽  
Content Type ◽  
Crisis Period ◽  
Individual Level ◽  
Level Data ◽  
The Social ◽  
Before And After

Purpose The purpose of this paper is to explore potential drivers of high-growth intentions of early-stage entrepreneurs in Greece before and after the onset of the financial crisis of 2008. Design/methodology/approach To this end, the authors use individual-level data retrieved from Global Entrepreneurship Monitor annual surveys (2003-2015). Findings The results show that high-growth intentions of Greek entrepreneurs are driven by different factors in the crisis compared to the non-crisis period. Male entrepreneurs and entrepreneurs with significant work experience seem to be more likely to be engaged in growth-oriented new ventures during the crisis period. The same appears to hold for entrepreneurs who are motivated by an opportunity and also perceive future business opportunities in adverse economic conditions. On the other hand, the educational level and the social contacts of founders with other entrepreneurs are found to drive ambitious Greek entrepreneurship in the years before the crisis, while they were insignificant after the crisis outbreak. Originality/value Based on the concept of ambitious entrepreneurship, this study contributes to the literature by investigating the determinants of entrepreneurial high-growth expectations in the Greek context emphasizing the crisis period in comparison to the pre-crisis years.

Managers ability and investment dynamics during the global financial crisis: an emerging market context

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rehana Naheed ◽  
Bushra Sarwar ◽  
Rukhsana Naheed
Keyword(s):  
Global Financial Crisis ◽  
Stock Exchange ◽  
Emerging Market ◽  
Investment Policy ◽  
Managerial Ability ◽  
Content Type ◽  
Crisis Period ◽  
Managerial Talent ◽  
The Impact

Purpose Many scholars have developed several theories and empirics to study issues related to investment policy. However, there are still some unexplored issues in the field of finance that require further analysis and investigation, particularly in the corporate governance literature such as the role of managerial talent in the firms. This study investigated the impact of managerial ability on investment decisions of the firms. Design/methodology/approach The study first uses firm efficiency and managerial ability by using data envelope analysis (DEA) proposed by Demerjian, Lev and McVay, 2012. Data is collected for the firms listed in Shenzhen and Shanghai stock exchange for an emerging market of China during the crisis period with 1,640 number of observations. Findings The study reveals that the presence of more managerial talent in a firm is significant for the strategic decisions of the firms. Findings follow a resource-based view and identify that more talented managers help the firms in the acquisition of resources specifically during financial distress. The study subdivides the firms based on: ownership structures and financial constraints. Results generated from propensity score matching imply that the role of high-talented managers is significantly different from that of low-talented managers. Originality/value The study reveals managerial ability as a determinant of investment policy. To the researchers’ best knowledge, none of the previous studies have been conducted in emerging market literature during the crisis period.

Global financial crisis, ownership structure and firm financial performance

2017 ◽  
Vol 34 (4) ◽  
pp. 447-465 ◽  
Author(s):  
Ali Salman Saleh ◽  
Enver Halili ◽  
Rami Zeitun ◽  
Ruhul Salim
Keyword(s):  
Financial Crisis ◽  
Financial Performance ◽  
Ownership Structure ◽  
Family Firms ◽  
Global Financial Crisis ◽  
Ownership Concentration ◽  
Financial Leverage ◽  
Content Type ◽  
Crisis Period ◽  
The Global Financial Crisis

Purpose This paper aims to investigate the financial performance of listed firms on the Australian Securities Exchange (ASX) over two sample periods (1998-2007 and 2008-2010) before and during the global financial crisis periods. Design/methodology/approach The generalized method of moments (GMM) has been used to examine the relationship between family ownership and a firm’s performance during the financial crisis period, reflecting on the higher risk exposure associated with capital markets. Findings Applying firm-based measures of financial performance (ROA and ROE), the empirical results show that family firms with ownership concentration performed better than nonfamily firms with dispersed ownership structures. The results also show that ownership concentration has a positive and significant impact on family- and nonfamily-owned firms during the crisis period. In addition, financial leverage had a positive and significant effect on the performance of Australian family-owned firms during both periods. However, if the impact of the crisis by sector is taking into account, the financial leverage only becomes significant for the nonmining family firms during the pre-crisis period. The results also reveal that family businesses are risk-averse business organizations. These findings are consistent with the underlying economic theories. Originality/value This paper contributes to the debate whether the ownership structure affects firms’ financial performance such as ROE and ROA during the global financial crisis by investigating family and nonfamily firms listed on the Australian capital market. It also identifies several influential drivers of financial performance in both normal and crisis periods. Given the paucity of studies in the area of family business, the empirical results of this research provide useful information for researchers, practitioners and investors, who are operating in capital markets for family and nonfamily businesses.

Effect of electrolysis parameters on corrosion resistance of extra-low carbon high silicon iron-based alloy

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yue Zhang ◽  
Jun Xiao ◽  
Shaoguang Yang ◽  
Aimin Zhao
Keyword(s):  
Corrosion Resistance ◽  
Correlation Analysis ◽  
Corrosion Rate ◽  
Low Carbon ◽  
Silicon Iron ◽  
Content Type ◽  
Grey Correlation ◽  
Grey Correlation Analysis ◽  
Iron Based ◽  
High Silicon

Purpose High silicon iron-based alloys possess excellent corrosion resistance in certain specific media, but the effects of electrolysis parameters on corrosion resistance remain unknown. This study aims to guide the development and application of an extra-low carbon high silicon iron-based alloy (ECHSIA) in electrode plates. Design/methodology/approach The corrosion resistance of ECHSIA and a conventional high-silicon cast iron (CHSCI) was analyzed through experimental characterizations. The morphology was observed by scanning electron microscopy. The influence of electrolysis parameters on the corrosion resistance of ECHSIA was investigated through corrosion experiments. The relationship between the electrolysis parameters and the corrosion resistance of ECHSIA was statistically investigated using the grey correlation analysis method. Findings The corrosion resistance of the ECHSIA is better than that of the CHSCI. The corrosion rate showed an increasing tendency with the increase in the nitric acid concentration (CHNO3), electrolyte temperature and current density. The grey correlation analysis results showed that the CHNO3 was the main factor affecting the corrosion rate of the ECHSIA. Originality/value An ECHSIA with a single ferrite microstructure was prepared. This study provides a guideline for the future development and application of ECHSIAs as electrode plates.

Do altmetrics correlate with citations? A study based on the 1,000 most-cited articles

2019 ◽  
Vol 47 (4) ◽  
pp. 192-202 ◽  
Author(s):  
Ali Ouchi ◽  
Mohammad Karim Saberi ◽  
Nasim Ansari ◽  
Leila Hashempour ◽  
Alireza Isfandyari-Moghaddam
Keyword(s):  
Biological Sciences ◽  
Social Media ◽  
Statistical Correlation ◽  
Microsoft Excel ◽  
Content Type ◽  
News Stories ◽  
Editorial Boards ◽  
The Usa ◽  
Highly Cited Articles ◽  
Highly Cited

Purpose The purpose of this paper is to study the presence of highly cited papers of Nature in social media websites and tools. It also tries to examine the correlation between altmetric and bibliometric indicators. Design/methodology/approach This descriptive study was carried out using altmetric indicators. The research sample consisted of 1,000 most-cited articles in Nature. In February 2019, the bibliographic information of these articles was extracted from the Scopus database. Then, the titles of all articles were manually searched on Google, and by referring to the article in the journal website and altmetric institution, the data related to social media presence and altmetric score of articles were collected. The data were analyzed using Microsoft Excel and SPSS. Findings According to the results of the study, from 1,000 articles, 989 of them (98.9 per cent) were mentioned at least once in different social media websites and tools. The most used altmetric source in highly cited articles was Mendeley (98.9 per cent), followed by Citeulike (79.8 per cent) and Wikipedia (69.4 per cent). Most Tweets, blog posts, Facebook posts, news stories, readers in Mendeley, Citeulike and Connotea and Wikipedia citations belonged to the article titled “Mastering the game of Go with deep neural networks and tree search”. The highest altmetric score was 3,135 which belonged to this paper. Most tweeters and articles’ readers were from the USA. The membership type of the tweeters was public membership. In terms of fields of study, most readers were PhD students in Agricultural and Biological Sciences. Finally, the results of Spearman’s Correlation revealed positive significant statistical correlation between all altmetric indicators and received citations of highly cited articles (p-value = 0.0001). Practical implications The results of this study can help researchers, editors and editorial boards of journals better understand the importance and benefits of using social media and tools to publish articles. Originality/value Altmetrics is a relatively new field, and in particular, there are not many studies related to the presence of articles in various social media until now. Accordingly, in this study, a comprehensive altmetric analysis was carried out on 1000 most-cited articles of one of the world's most reliable journals.

Debt rating model based on default identification

2019 ◽  
Vol 57 (9) ◽  
pp. 2239-2260 ◽  
Author(s):  
Guotai Chi ◽  
Bin Meng
Keyword(s):  
Correlation Analysis ◽  
Index System ◽  
Rating System ◽  
Content Type ◽  
Industrial Enterprises ◽  
Homogeneity Of Variance ◽  
Small Industry ◽  
Correlation Analysis Method ◽  
Default State ◽  
Rating Model

Purpose The purpose of this paper is to propose a debt rating index system for small industrial enterprises that significantly distinguishes the default state. This debt rating system is constructed using the F-test and correlation analysis method, with the small industrial enterprise loans of a Chinese commercial bank as the data sample. This study establishes the weighting principle for the debt scoring model: “the more significant the default state, the larger is the weight.” The debt rating system for small industrial enterprises is constructed based on the standard “the higher the debt rating, the lower is the loss given default.” Design/methodology/approach In this study, the authors selected indexes that pass the homogeneity of variance test based on the principle that a greater deviation of the default sample’s mean from the whole sample’s mean leads to greater significance in distinguishing the default samples from the non-default samples. The authors removed correlated indexes based on the results of the correlation analysis and constructed a debt rating index system for small industrial enterprises that included 23 indexes. Findings Among the 23 indexes, the weights of 12 quantitative indexes add up to 0.547, while the weights of the remaining 11 qualitative indexes add up to 0.453. That is, in the debt rating of the small industry enterprises, the financial indexes are not capable of reflecting all the debt situations, and the qualitative indexes play a more important role in debt rating. The weights of indexes “X17 Outstanding loans to all assets ratio” and “X59 Date of the enterprise establishment” are 0.146 and 0.133, respectively; both these are greater than 0.1, and the indexes are ranked first and second, respectively. The weights of indexes “X6 EBIT-to- current liabilities ratio,” “X13 Ratio of capital to fixed” and “X78 Legal dispute number” are between 0.07 and 0.09, these indexes are ranked third to fifth. The weights of indexes “X3 Quick ratio” and “X50 Per capital year-end savings balance of Urban and rural residents” are both 0.013, and these are the lowest ranked indexes. Originality/value The data of index i are divided into two categories: default and non-default. A greater deviation in the mean of the default sample from that of the whole sample leads to greater deviation from the non-default sample’s mean as well; thus, the index can easily distinguish the default and the non-default samples. Following this line of thought, the authors select indexes that pass the F-test for the debt rating system that identifies whether or not the sample is default. This avoids the disadvantages of the existing research in which the standard for selecting the index has nothing to do with the default state; further, this presents a new way of debt rating. When the correlation coefficient of two indexes is greater than 0.8, the index with the smaller F-value is removed because of its weaker prediction capacity. This avoids the mistake of eliminating an index that has strong ability to distinguish default and non-default samples. The greater the deviation of the default sample’s mean from the whole sample’s mean, the greater is the capability of the index to distinguish the default state. According to this rule, the authors assign a larger weight to the index that exhibits the ability to identify the default state. This is different from the existing index system, which does not take into account the ability to identify the default state.

scholarly journals Quality of work, economic crisis, and temporary employment

2019 ◽  
Vol 41 (1) ◽  
pp. 37-51 ◽  
Author(s):  
Xavier Bartoll ◽  
Raul Ramos
Keyword(s):  
Economic Crisis ◽  
Temporary Employment ◽  
Structural Equations ◽  
Work Quality ◽  
Content Type ◽  
Crisis Period ◽  
Quality Of Work ◽  
Before And After ◽  
Different Dimensions

Purpose The purpose of this paper is to analyse the association between the type of contract (temporary vs permanent) and the quality of work and its different dimensions before and after the economic crisis among Spanish employees. Design/methodology/approach Structural equations techniques are used to analyse the association between the type of contract and the work quality and its different dimensions before and after the crisis. Data are drawn from the 2006/2007 and 2009/2010 waves of the Encuesta de Calidad de Vida en el Trabajo. Findings The results show that in the two considered periods there are no differences in quality of work among male involuntary temporary workers and those with permanent contracts. However, there is an adverse widening gap across all dimensions of work quality for women in involuntary temporary employment during the economic crisis. There is also a shift among men and women in involuntary temporary employment from valuing intrinsic job quality dimension in the pre-crisis period to valuing more the work environment dimension during the crisis period. Research limitations/implications The analysis is limited by the continuity of variables across years and the high proportion of missing values in some variables. The obtained results cannot be interpreted in terms of causality. Originality/value This is the first study to consider whether the deterioration in the Spanish labour market during the crisis has affected the relationship between the type of contract and the different dimensions of the quality of work.

Neural network models for actual duration of Greek highway projects

2019 ◽  
Vol 17 (6) ◽  
pp. 1323-1339
Author(s):  
Magdalini Titirla ◽  
Georgios Aretoulis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Analysis ◽  
Network Models ◽  
Neural Network Models ◽  
Tender Offer ◽  
Highway Projects ◽  
Content Type ◽  
Initial Cost ◽  
Actual Duration

Purpose This paper aims to examine selected similar Greek highway projects to create artificial neural network-based models to predict their actual construction duration based on data available at the bidding stage. Design/methodology/approach Relevant literature review is presented that highlights similar research approaches. Thirty-seven highway projects, constructed in Greece, with similar type of available data, were examined. Considering each project’s characteristics and the actual construction duration, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most significant project variables toward predicting actual duration. Furthermore, the WEKA application, through its attribute selection function, highlighted the most important subset of variables. The selected variables through correlation analysis and/or WEKA and appropriate combinations of these are used as input neurons for a neural network. Fast Artificial Neural Network (FANN) Tool is used to construct neural network models in an effort to predict projects’ actual duration. Findings Variables that significantly correlate with actual time at completion include initial cost, initial duration, length, lanes, technical projects, bridges, tunnels, geotechnical projects, embankment, landfill, land requirement (expropriation) and tender offer. Neural networks’ models succeeded in predicting actual completion time with significant accuracy. The optimum neural network model produced a mean squared error with a value of 6.96E-06 and was based on initial cost, initial duration, length, lanes, technical projects, tender offer, embankment, existence of bridges, geotechnical projects and landfills. Research limitations/implications The sample size is limited to 37 projects. These are extensive highway projects with similar work packages, constructed in Greece. Practical implications The proposed models could early in the planning stage predict the actual project duration. Originality/value The originality of the current study focuses both on the methodology applied (combination of Correlation Analysis, WEKA, FannTool) and on the resulting models and their potential application for future projects.

scholarly journals Online learning behavior analysis based on machine learning

2019 ◽  
Vol 14 (2) ◽  
pp. 97-106
Author(s):  
Ning Yan ◽  
Oliver Tat-Sheung Au
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Correlation Analysis ◽  
Prediction Accuracy ◽  
Classification Models ◽  
Limited Data ◽  
Learning Models ◽  
Learning Behavior ◽  
Content Type ◽  
Machine Learning Models

Purpose The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data. Design/methodology/approach The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues. Findings Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper. Originality/value This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

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