Hyper-Growth Among European SMEs: An Explorative Study

Cassia, Lucio; Cogliati, Giordano Maria; Paleari, Stefano

doi:10.2139/ssrn.1389521

Cited by 16 publications

(14 citation statements)

References 56 publications

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“…Factors influencing the growth potential of an enterprise are usually viewed in through three main categories: the entrepreneur, the company itself and company strategy [29]. Education, risk acceptance, aspiration to grow, mid-management experience have been shown as important growth factors at the entrepreneur level [3]; [17]. At the company and strategy level, age and size of a company, strategic orientation, level of R&D, innovation are found to be relevant growth determinants [1], [10], [11], and [22].…”

Section: Review Of Previous Researchmentioning

confidence: 99%

Predicting company growth using logistic regression and neural networks

Zekić–Sušac

Šarlija

Has

et al. 2016

CRORR

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Abstract. The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre-processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non-parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.

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Section: Review Of Previous Researchmentioning

confidence: 99%

Predicting company growth using logistic regression and neural networks

Zekić–Sušac

Šarlija

Has

et al. 2016

CRORR

View full text Add to dashboard Cite

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“…This view is also shared by Cassia et al (2009), although the authors observe an empirically higher leverage relationship and a similar level of solvency of rapid-growth firms relative to the other firms.…”

Section: How To Measure Hgf: Literature Reviewmentioning

confidence: 75%

Predicting rapid-growth SMEs through a reversal of credit-scoring principles

Sampagnaro

2013

IJESB

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The purpose of this paper is to analyse the main variables distinguishing between high-growth firms and non-high-growth firms in the Italian manufacturing market. Specifically, we aim to establish which balance-sheet ratios enable us to distinguish between high-growth and non-high-growth firms. For this purpose, we employed a discriminant analysis on the financial data of two groups of firms selected from a population of approximately 22,000 firms. The results of the analysis indicate the roles of firm size, non-financial debt and internal cash flows in the growth and success of a firm. We adopt an innovative approach that considers financial statements issued the year prior to the observation of accelerated growth as predictive of this growth (as is used by credit-scoring models, i.e., the Z-Score model, to measure the probability of default).

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“…In that context, factors influencing growth potential are usually identified at three levels: the entrepreneur, the company and the environment. With respect to entrepreneur's characteristics, previous studies have singled out a willingness to become involved in situations with uncertain outcomes, midmanagement experience, education and the entrepreneur's aspiration to grow to be relevant growth factors (Cassia et al, 2009;Kolvereid and Bullvag, 1996). From a company perspective, a positive relationship exists between the growth potential of a company and R&D investments, innovation capacity and productivity.…”

Section: Previous Research On Growth Predictionmentioning

confidence: 99%

Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

Šarlija

Bilandžić

Stanić

2017

Croat. Oper. Res. Rev.

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Abstract. This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1) to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2) to discuss common pitfalls and methodological errors in developing a model, and 3) to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

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Hyper-Growth Among European SMEs: An Explorative Study

Cited by 16 publications

References 56 publications

Predicting company growth using logistic regression and neural networks

Predicting company growth using logistic regression and neural networks

Predicting rapid-growth SMEs through a reversal of credit-scoring principles

Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

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