scholarly journals Firm size, market conditions and takeover likelihood

2019 ◽  
Vol 18 (3) ◽  
pp. 483-507 ◽  
Author(s):  
Abongeh Tunyi
Keyword(s):  
Firm Size ◽  
Prediction Models ◽  
Model Performance ◽  
Model Specification ◽  
Roc Curve Analysis ◽  
Target Firm ◽  
Market Conditions ◽  
Content Type ◽  
Market Growth ◽  
Cost Constraints

Purpose The firm size hypothesis – takeover likelihood (TALI) decreases with target firm size (SIZE) – has enjoyed little traction in the TALI modelling literature; hence, this paper aims to redevelop this hypothesis while taking account of prevailing market conditions – capital liquidity and market performance. Design/methodology/approach The study uses a logit modelling framework to model TALI. Model performance is assessed using receiver operating characteristic (ROC) curve analysis. The empirical analysis is based on a UK sample of 34,661 firm-year observations drawn from 3,105 firms and 1,396 M&A deals over a 30-year period (1987-2016). Findings While acquirers generally seek smaller targets because of transaction cost constraints, the paper shows that the documented negative relation between SIZE and TALI arises from sampling bias. Over a full sample, mid-sized firms are most at risk of takeovers. Additionally, market conditions moderate the SIZE–TALI relationship, with acquirers more inclined to pursue comparatively larger targets when financing costs are low and market growth or sentiment is high. The results are generally robust to endogeneity. Research limitations/implications Sample truncation on the basis of SIZE leads to empirical misspecification of the TALI–SIZE relation. In an unbiased sample, an inverse U-shaped specification between TALI and SIZE sufficiently models the underlying relation and leads to improvements in the predictive ability of TALI models. Originality/value This study advances a new firm size hypothesis which is consistent with classic M&A theories. The study also evidences market conditions as a moderator of the acquirer’s choice of target SIZE. A new model specification which recognises the non-linear relation between TALI and SIZE and accounts for the moderating effect of market conditions on the SIZE-TALI relationship leads to improvements in the performance of TALI prediction models.

Forecasting the risk at infractions: an ensemble comparison of machine learning approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Li ◽  
Desheng Wu
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Model Performance ◽  
Large Data ◽  
Support Vector ◽  
Learning Approaches ◽  
Content Type ◽  
Day To Day Operations ◽  
Prediction Approach

PurposeThe infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve financial problems that are not effective and precise in supervision.Design/methodology/approachThe overall proposed research framework designed for forecasting the infractions (ISRs) include data collection and cleaning, feature engineering, data split, prediction approach application and model performance evaluation. We select Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Network and Long Short-Term Memory Networks (LSTMs) as ISRs prediction models.FindingsThe research results show that prediction performance of proposed models with the prior infractions provides a significant improvement of the ISRs than those without prior, especially for large sample set. The results also indicate when judging whether a company has infractions, we should pay attention to novel artificial intelligence methods, previous infractions of the company, and large data sets.Originality/valueThe findings could be utilized to address the problems of identifying listed corporates' ISRs at hand to a certain degree. Overall, results elucidate the value of the prior infraction of securities regulations (ISRs). This shows the importance of including more data sources when constructing distress models and not only focus on building increasingly more complex models on the same data. This is also beneficial to the regulatory authorities.

The effects of opportunity motivation and environmental contingencies on market growth strategies of African early-stage entrepreneurs

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tianjiao Qiu
Keyword(s):  
Hierarchical Linear Modeling ◽  
Multilevel Models ◽  
Early Stage ◽  
Product Market ◽  
Institutional Environments ◽  
Growth Strategies ◽  
Linear Modeling ◽  
Market Conditions ◽  
Content Type ◽  
Market Growth

PurposeThe purpose of this paper is to examine how early-stage entrepreneurs' opportunity motivation impacts their choice of market growth strategies as well as the contingent roles of institutional environments and product market conditions in Africa.Design/methodology/approachThe study employs hierarchical linear modeling to test multilevel models with nested data empirically.FindingsThe findings show that African early-stage entrepreneurs who are opportunity-driven and from countries with strong institutional environments have a higher tendency to adopt market exploration strategies. African early-stage entrepreneurs from countries with strong product market conditions have a higher tendency to adopt market penetration strategies. Further interaction tests show that both contingency conditions, namely institutional environments and product market conditions, moderate the effects of opportunity motivation on market growth strategies of African early-stage entrepreneurs.Practical implicationsThe study shows that policymakers in Africa need to develop flexible, supportive market-related policies based on entrepreneurs' growth paths, institutional environments and product market conditions.Originality/valueThe study is the first to explore multilevel influences on early-stage entrepreneurs' market growth strategies in Africa. It sheds new insights on the entrepreneurial marketing process of early-stage entrepreneurs in Africa.

scholarly journals Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e044500
Author(s):  
Yauhen Statsenko ◽  
Fatmah Al Zahmi ◽  
Tetiana Habuza ◽  
Klaus Neidl-Van Gorkom ◽  
Nazar Zaki
Keyword(s):  
Lymphocyte Count ◽  
Prediction Models ◽  
Model Performance ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Supervised Machine Learning ◽  
Study Cohort ◽  
Threshold Values ◽  
Laboratory Findings ◽  
Study Results

BackgroundDespite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use.ObjectivesTo identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU).MethodsThe study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening.ResultsWith the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×109/L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL.ConclusionThe performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.

scholarly journals Enabling deeper learning on big data for materials informatics applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dipendra Jha ◽  
Vishu Gupta ◽  
Logan Ward ◽  
Zijiang Yang ◽  
Christopher Wolverton ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Materials Science ◽  
Prediction Models ◽  
Model Performance ◽  
Materials Informatics ◽  
Learning Framework ◽  
Significant Attention

AbstractThe application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

scholarly journals Corporate debt maturity and economic development

2019 ◽  
Vol 15 (5) ◽  
pp. 669-687 ◽  
Author(s):  
Celia Álvarez-Botas ◽  
Víctor M. González-Méndez
Keyword(s):  
Developing Countries ◽  
Economic Development ◽  
Financial Crisis ◽  
Firm Size ◽  
Debt Maturity ◽  
Corporate Debt ◽  
Content Type ◽  
Country Level ◽  
The Relationship ◽  
Bank Concentration

Purpose The purpose of this paper is to analyse the effect of economic development on the influence of country-level determinants on corporate debt maturity, bearing in mind firm size and the period of financial crisis. Design/methodology/approach The authors employ panel data estimation with fixed effects to examine the role of economic development in influencing the relationship between country-level determinants on corporate debt maturity. The paper uses a sample of 30,727 listed firms, belonging to 39 countries, over the period 2005–2012. Findings Corporate debt maturity increases with the efficiency of the legal system and bank concentration and decreases with the weight of banks in the economy. However, the importance of these country determinants is greater in developing than in developed countries. The authors also show that firm size in developed and developing countries influences country determinants of corporate debt maturity. Finally, the results reveal that the financial crisis has affected the debt maturity of firms differently in developed and developing countries, with the effect of bank concentration lengthening debt maturity, this effect being more pronounced in developing countries. Practical implications The findings provide useful insights to guide policy decisions providing access to long-term financing, as corporate debt maturity depends on economic development, institutional environment, banking structure and firm size. Originality/value This study incorporates economic development in explaining the relationship between country-level determinants and corporate debt maturity.

Firm size and workplace learning processes: a study of the restaurant sector

2020 ◽  
Vol 44 (2/3) ◽  
pp. 305-320
Author(s):  
Daniel Bishop
Keyword(s):  
Informal Learning ◽  
Human Resource Development ◽  
Learning Environments ◽  
Workplace Learning ◽  
Firm Size ◽  
Learning Processes ◽  
Future Research ◽  
Structured Learning ◽  
Individual Choice ◽  
Content Type

Purpose The purpose of this paper asks how workplace learning environments change as firm size increases, and how employees respond to this. In doing so, it looks beyond an exclusive focus on formal training and incorporates more informal, work-based learning processes. Design/methodology/approach The study uses a comparative, qualitative research design, using semi-structured interviews with an under-researched group of workers – waiting for staff in restaurants. The data were collected from six restaurants of different sizes. Findings As formally instituted human resource development (HRD) structures expand as firm size increases are more extensive in larger firms, this leaves less room for individual choice and agency in shaping the learning process. This does not inevitably constrain or enhance workplace learning, and can be experienced either negatively or positively by employees, depending on their previous working and learning experiences. Research limitations/implications Future research on HRD and workplace learning should acknowledge both formal and informal learning processes and the interaction between them – particularly in small and growing firms. Insights are drawn from the sociomaterial perspective help the authors to conceptualise this formality and informality. Research is needed in a wider range of sectors. Practical implications There are implications for managers in small, growing firms, in terms of how they maintain space for informal learning as formal HRD structures expand, and how they support learners who may struggle in less structured learning environments. Originality/value The paper extends current understanding of how the workplace learning environment – beyond a narrow focus on “training” – changes as firm size increases.

How does corporate learning orientation enhance industrial brand equity? The roles of firm capabilities and size

2019 ◽  
Vol 35 (2) ◽  
pp. 231-243 ◽  
Author(s):  
Yi Xie ◽  
Xiaoying Zheng
Keyword(s):  
Brand Equity ◽  
Firm Size ◽  
Small Firms ◽  
Learning Orientation ◽  
Innovation Capability ◽  
Marketing Capabilities ◽  
Marketing Capability ◽  
Content Type ◽  
Corporate Learning ◽  
The Impact

Purpose This paper aims to examine the role of learning orientation in building brand equity for B2B firms. The present research proposes that learning orientation contributes to the development of innovation and marketing capabilities and, in turn, leads to enhanced industrial brand equity. Furthermore, the moderating effect of firm size in these processes is investigated. Design/methodology/approach The hypotheses are tested by administering a survey with a set of managers of manufacturing firms in China. Findings Innovation capability and marketing capability serve as the mediators between learning orientation and industrial brand equity. The mediating path through innovation capability is stronger for small firms than for large firms. Research limitations/implications Learning orientation provides a cultural base for B2B firms to cultivate brand equity. Measurement of industrial brand equity and contingency of its effect requires further investigation. Practical implications To transform learning-oriented culture into brand equity, firms need to develop and manage innovation and marketing capabilities. The learning orientation–innovation capability route is more beneficial for small firms. Originality/value While a majority of prior literature ignores the impact of organizational culture in driving industrial brand equity, the present research explores learning orientation as a key cultural antecedent of industrial brand equity. A more refined industrial-brand-equity-building mechanism from learning orientation to corporate capabilities and then to brand equity is proposed and tested. The mechanism varies with firm size.

Merton-type default risk and financial performance: the dynamic panel moderation of firm size

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Mushafiq ◽  
Syed Ahmad Sami ◽  
Muhammad Khalid Sohail ◽  
Muzammal Ilyas Sindhu
Keyword(s):  
Financial Performance ◽  
Firm Size ◽  
Default Risk ◽  
Banking Sector ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Content Type ◽  
Nonfinancial Firms ◽  
The Relationship

PurposeThe main purpose of this study is to evaluate the probability of default and examine the relationship between default risk and financial performance, with dynamic panel moderation of firm size.Design/methodology/approachThis study utilizes a total of 1,500 firm-year observations from 2013 to 2018 using dynamic panel data approach of generalized method of moments to test the relationship between default risk and financial performance with the moderation effect of the firm size.FindingsThis study establishes the findings that default risk significantly impacts the financial performance. The relationship between distance-to-default (DD) and financial performance is positive, which means the relationship of the independent and dependent variable is inverse. Moreover, this study finds that the firm size is a significant positive moderator between DD and financial performance.Practical implicationsThis study provides new and useful insight into the literature on the relationship between default risk and financial performance. The results of this study provide investors and businesses related to nonfinancial firms in the Pakistan Stock Exchange (PSX) with significant default risk's impact on performance. This study finds, on average, the default probability in KSE ALL indexed companies is 6.12%.Originality/valueThe evidence of the default risk and financial performance on samples of nonfinancial firms has been minimal; mainly, it has been limited to the banking sector. Moreover, the existing studies have only catered the direct effect of only. This study fills that gap and evaluates this relationship in nonfinancial firms. This study also helps in the evaluation of Merton model's performance in the nonfinancial firms.

Corporate governance and firm performance: empirical evidence from Pakistan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Farooq ◽  
Amna Noor ◽  
Shoukat Ali
Keyword(s):  
Corporate Governance ◽  
Firm Performance ◽  
Firm Size ◽  
Good Governance ◽  
Positive Impact ◽  
Least Square ◽  
Local Context ◽  
Content Type ◽  
Governance Index ◽  
Governance Quality

Purpose The purpose of this research is to look into the governance–performance relationship in the context of critical firm characteristics, such as firm size. Design/methodology/approach Based on total assets, sample firms were classified as small or large. The governance index, which is based on 29 governance provisions covering the audit committee, board committee, ownership and compensation structure of the respective firm, measures governance quality among sample firms. A higher governance index indicates a higher level of governance quality and vice versa. Accounting and market value measures are used to determine firm profitability. The authors used the two-stage least square (2SLS) method of estimation of the model to eliminate the simultaneous equation bias. Findings Corporate governance (CG) appears to have a positive impact on accounting return and market indices (Tobin’s Q), but it has little impact on return on equity. In terms of firm size, larger companies profited more from better governance implementation than smaller firms that lacked these principles, thus improving CG. The findings indicate that small businesses should improve their governance mechanisms to reap the benefits of CG in terms of increased profitability. Research limitations/implications There are certain drawbacks to this research. First, the authors omitted qualitative aspects of CG from the CG index, such as the board’s decision-making process, directors’ perceptions of the board’s position and directors’ age and qualifications. Such a qualitative component will improve the governance index in the future while building the governance index. Second, as the current study only looks at the nonfinancial sector, caution should be exercised before applying the findings to the entire population. Practical implications The findings show that companies that follow good governance standards have better accounting and market efficiency than those that do not. As a result, good governance practices can help firms in developing countries improve their performance. Academic researchers, regulators, investors, lenders and practitioners can find the findings useful in establishing a true relationship between firm performance and CG practices in Pakistan. Originality/value The relationship between governance and profitability in the context of firm size is examined in this research. Firms with varying resources and ability to implement CG codes have varying effects on profitability. To the authors’ knowledge, there was a gap in the literature that addressed this topic in the local context.

Board Gender Diversity and Cost of Debt: Do Firm Size and Industry type matter?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rabiatu Kamil ◽  
Kingsley Opoku Appiah
Keyword(s):  
Firm Size ◽  
Developing Economies ◽  
Gender Diversity ◽  
Manufacturing Firms ◽  
Least Square ◽  
Cost Of Debt ◽  
Content Type ◽  
Industry Type ◽  
Gender Diverse ◽  
Board Gender Diversity

Purpose This study aims to investigate the nexus between gender-diverse boards and cost of debt in the developing economies context. Specifically, the authors examine whether firm size moderates the relationship between female board representation and cost of debt, regardless of the industry type. Design/methodology/approach The authors use panel data from 17 non-financial listed Ghanaian firms over the period 2007–2017, ordinary least square, two-stage least square and generalised method of moments estimations to test the hypothesis. Findings The authors find that board gender diversity is positively related to cost of debt. Further evidence suggests the interaction of firm size and board gender diversity displays a negative association with cost of debt. Practical implications The study evidence suggests larger non-manufacturing firms with gender-diverse boards attract lower cost of capital in an environment with lax enforcement of rules and regulations in corporate governance. Social implications Lenders consider the size and industry of firms in pricing debt. This has implications on UN Goal 5, highlighting that shareholders of larger non-manufacturing firms benefit immensely from board gender diversity in the context of debt. Originality/value The authors contribute to the board gender diversity and cost of debt literature by demonstrating that firm size and industry type matter in the developing economies context.

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