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Financial ratios are often used to classify firms into different clusters of financial performance. This study aims to classify firms using financial ratios with advanced techniques and identify the transition matrix of firms moving clusters during the COVID-19 period.

This study uses compositional data (CoDa) analysis based on existing clustering methods with transformed data by weighted logarithms of financial ratios. The data include 66 listed firms in Vietnam’s food and beverage and fishery sectors over a three-year period from 2019 to 2021, including the COVID-19 period.

These firms can be classified into three clusters of distinctive characteristics, which can serve as benchmarks for solvency and profitability. The results also show the migration from one cluster to another during the COVID-19 pandemic, allowing for the calculation of the transition probability or the transition matrix.

The findings indicate three distinct clusters (good, average and below-average firm performance) that can help financial analysts, accountants, investors and other strategic decision-makers in making informed choices.

Clustering firms with their financial ratios often suffer from various limitations, such as ratio choices, skewed distributions, outliers and redundancy. This study is motivated by a weighted CoDa approach that addresses these issues. This method can be extended to classify firms in multiple sectors or other emerging markets.

This article explores in a qualitative manner the motivations of women entrepreneurs who start and run ethnic food businesses in London.

Our approach is qualitative and deploys phenomenographical analysis of interview narratives around categories of motivation.

We find that women ethnic food entrepreneurs are driven by a combination of desire for self-actualisation, identity-maintenance and community considerations. We demonstrate that women ethnic food entrepreneurs often go against the logic of the market, and they do so not because they lack other options, but for reasons that have to do with their (self-)identification as women and professionals, their prerogatives as mothers and daughters, their ethnic heritage, their emplacement in urban and global communities and their need to contribute. Our findings enrich understanding of female-led ethnic food entrepreneurship not as a demanding, overall unproductive undertaking for women with no other options, but as a realm of inspiration, community engagement and female-led innovation.

Our main contributions are the qualitative interrogation of perceptions and experiences of identity and difference in urban entrepreneurship from the point of view of our interviewees; providing concrete empirical evidence for it through our sample and proposing an approach to thinking women-led ethnic food entrepreneurship as a vehicle for translating urban superdiversity into social interactions across barriers of difference. We speak to the field of women entrepreneurship studies but specifically to the understudied realm of women-led food entrepreneurship, and to the cross-disciplinary field of (im)migrant entrepreneurship.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.