The paper is systematic scrutiny of studies on financial distress, prediction, and strategies firms adapt to deal with the difficulty. To this end, the paper offers a dissection and assortment of 72 articles published between 2005 and 2017 in Scopus, Web of Science, and Science Direct. The authors chose the three databases as articles that are published only in indexed journals. The studies were selected based on the key terms "financial distress", "financial strategies", "financial distress prediction", and "financial distress strategies". The selected articles were evaluated based on seven categories: content, methodology, scope, and data analysis techniques, study period, study focus, and data analyzed. The evaluation and assortment of studies identified existing disparities in the literature on financial distress, offering opportunities for future researchers. Exceptional articles on financial challenges, prediction, and strategies adopted by firms were identified. The study finds that most of the studies centered on mature economies, whereas those on emerging markets-focused only on Asian markets. Equally, there are very few qualitative studies on the subject matter. Through the study, the authors paint a picture of existing literature on the subject matter; further, the authors expect the review to stimulate debate and further research among scholars.

Over the past decade, a number of modern and sophisticated methods have been developed to optimize the composition of equity portfolios. Most of these methods are based on complex mathematical or financial modelling. Less emphasis has been placed on companies’ internal data, while in recent years external data have become increasingly important. However, for long-term investments, the dominance of external data is not necessarily an efficient way to construct an appropriate portfolio. In this paper, we highlight the phenomenon that complex mathematical models, the based on simpler fundamental indicators can also be an efficient investment tool for in making investment decisions. Our results show that our hypothesis has been confirmed that some basic-based indicators can achieve alpha returns. Our analysis is based on financial reporting data in the form of various financial indicators. We used the S&P500 index as benchmark. A comparative analysis of the stock portfolio created illustrates that basic analysis can be more effective than a chosen market-based stock index. By the end of the period under review, the portfolio based on the selected five core financial indicators had a market capitalization 1.68% higher than the benchmark. The alpha return achieved also demonstrates that even simpler models can be efficient and effective in creating an equity portfolio.