Strategic groups and product differentiation: Evidence from the Spanish airline market deregulation

Ruiz‐Moreno, Felipe; Mas‐Ruiz, Francisco; Esper, Franco Sancho

doi:10.1016/j.retrec.2021.101030

Cited by 4 publications

(2 citation statements)

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“…When an industry grows slowly, many companies of similar size tend to perform highly competitively. Among the most common strategies are: product differentiation, low-cost production, better location or specification, efficient advertising, price discrimination, or any other process that creates consumer loyalty or gives the company market power [6][7][8].…”

Section: Economic Moatmentioning

confidence: 99%

Stock Portfolio Optimization with Competitive Advantages (MOAT): A Machine Learning Approach

2022

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This paper aimed to develop a useful Machine Learning (ML) model for detecting companies with lasting competitive advantages (companies’ moats) according to their financial ratios in order to improve the performance of investment portfolios. First, we computed the financial ratios of companies belonging to the S&P 500. Subsequently, we assessed the stocks’ moats according to an evaluation defined between 0 and 5 for each financial ratio. The sum of all the ratios provided a score between 0 and 100 to classify the companies as wide, narrow or null moats. Finally, several ML models were applied for classification to obtain an efficient, faster and less expensive method to select companies with lasting competitive advantages. The main findings are: (1) the model with the highest precision is the Random Forest; and (2) the most important financial ratios for detecting competitive advantages are a long-term debt-to-net income, Depreciation and Amortization (D&A)-to-gross profit, interest expense-to-Earnings Before Interest and Taxes (EBIT), and Earnings Per Share (EPS) trend. This research provides a new combination of ML tools and information that can improve the performance of investment portfolios; to the authors’ knowledge, this has not been done before. The algorithm developed in this paper has a limitation in the calculation of the stocks’ moats since it does not consider its cost, price-to-earnings ratio (PE), or valuation. Due to this limitation, this algorithm does not represent a strategy for short-term or intraday trading.

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Section: Economic Moatmentioning

confidence: 99%

Stock Portfolio Optimization with Competitive Advantages (MOAT): A Machine Learning Approach

2022

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“…In this approach, a strategic group refers to the set of firms in a specific industry that follow a similar strategic recipe. Several studies provide empirical support for the existence of strategic groups across different industries including banking (DeSarbo et al, 2009;Ebbes et al, 2010;G omez et al, 2021;Penagos-Londoño and Ruiz-Moreno, 2019), Evolution of competitive market structure insurance (Fiegenbaum et al, 2001), pharmaceuticals (Duan and Jin, 2014;Tejero-Martos et al, 2021) , mobile phone (García-Ochoa Mayor et al, 2016), furniture (Aboutorab et al, 2018;Brege et al, 2021), higher education (Adam, 2016) and airlines (Carroll, 2018;Cheng, 2017;Murthi et al, 2013;Ruiz-Moreno et al, 2021). More recently, researchers have focused on practical insights gained by strategic groups identification.…”

Section: Sources Of Intra-industry Competitive Heterogeneitymentioning

confidence: 99%

Modeling the evolution of competitive market structure via competitive group dynamics

Kani

Fong

DeSarbo

2022

JM2

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Purpose This paper aims to examine the evolution of a competitive market structure over time through the lens of competitive group membership dynamics. Design/methodology/approach A new hidden Markov modeling approach is devised that accounts for the three sources of competitive heterogeneity involving managerial strategy, corporate performance and the impact of strategy on performance. In addition, some observed “entry” and “exit” states are considered to model firms’ entry into and exit from the market. The proposed model is illustrated with an investigation of the US banking industry based on a data set created from the COMPUSTAT database. This paper estimated the model within the Bayesian framework and devised a reversible jump Markov chain Monte Carlo estimation procedure to determine the number of latent competitive groups and uncover the characteristics of each group. Findings This paper shows that the US banking industry, contrary to the prior findings of having a relatively stable structure, has, in fact, gone through dramatic changes in the past number of decades. Originality/value Contrary to prior work that has primarily focused on managerial strategy to study market evolutions, the competitive groups perspective accounts for all three sources of intra-industry competitive heterogeneity. In addition, unlike prior research, the analysis is not limited to firms remaining in the panel of study for the entire observation period. Such limitation results in missing the various changes that occur in the competitive market structure because of the new entrants or the struggling firms that do not survive in the market.

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Competitive positioning and market share of selected insurance companies: case of Nigeria

Ololade A.,

Adefulu,

Grace Olubisi

et al. 2024

Insurance Markets and Companies

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The insurance industry contributes to the smooth running of the economy. Unfortunately, not every country develops insurance at a high level; that is, the level of insurance penetration is low. This is happening in Nigeria. Therefore, this study aims to establish the effect of competitive positioning on the market share of selected insurance companies in Lagos State, Nigeria. The study adopts a survey research design. A structured instrument was used for data collection using the modified six-point Likert-type scale. The questionnaire was divided into three sections (about respondents’ demographic factors, competitive positioning, and market share). The population was 2,183 management-level employees from the 20 selected insurance companies. The sample size of 507 was determined using stratified random sampling and proportionate method. Test-retest method was used to validate the instrument, while the reliability was determined through internal consistency method. The Cronbach Alpha coefficients ranged from 0.71 to 0.88. Response rate of 86.7% was achieved. Descriptive and inferential statistics were used for analysis. Findings reveal that competitive positioning components have a significant effect on market share (Adj.R2 = 0.194, F(5. 434) = 22.097, p &lt; 0.05). The results demonstrate that if all the competitive positioning components were set to zero, the market share of selected insurance companies in Lagos State would be 6.537, which is a positive value. Thus, competitive positioning components significantly affect market share. As a result, this study found that insurers would benefit from increasing their focus on developing the strategic asset and research and development aspects of competitive positioning to expand their market share. Acknowledgments The researchers acknowledge the contributions of the anonymous reviewers for the useful comments in making the article better. We acknowledge the management of the University that made it mandatory for the supervisee to publish two articles from their thesis, one in the name of the supervisor as the lead author, while another in the name of the supervisee as the lead author. The research assistants are acknowledged for the cooperation during the field work and the management of the insurance companies engaged for the study. The inputs of the supervisors are greatly acknowledged.

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Strategic groups and product differentiation: Evidence from the Spanish airline market deregulation

Cited by 4 publications

References 64 publications

Stock Portfolio Optimization with Competitive Advantages (MOAT): A Machine Learning Approach

Stock Portfolio Optimization with Competitive Advantages (MOAT): A Machine Learning Approach

Modeling the evolution of competitive market structure via competitive group dynamics

Competitive positioning and market share of selected insurance companies: case of Nigeria

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