Evaluating a Company’s Past Financial Performance

One of the key reasons an analyst looks at the financial statements of a business is to evaluate the past financial performance of a company. In the previous readings we learned a number of financial ratios including profitability, liquidity, solvency, and leverage ratios. An analyst will be interested in understanding how these ratios have changed over the past years and what are the reasons behind the change. An analyst can compare the key ratios and trends for the company’s own past performance, compare it with the competitor  or benchmark it to the industry average.

An analyst will also be interested to know what aspects of the performance are critical for maintaining competitive advantage in the industry. An analyst will look at the core activities that give the company its competitive advantage and then look at the performance measures relating to those core activities. For example, in healthcare, one company may be spending huge amount of money on developing innovative solutions, so there R&D budget will be high and they will also have high margins in their products. Another firm in the same industry may instead focus on already established products and compete on a price basis. For such a firm, the profit margins will be less. If the competitive advantage in an industry is about being the lowest cost provider then the analyst will look for activity ratios and profitability ratios.

An analyst also needs to understand the business model and the strategy of the firm and assess whether the strategy is reflected in the performance metrics. For example, if a company’s strategy is to be the cost leader, then it should reflect in its ratios. An analysis of operating ratios and profitability will reveal the sources of improvement in earnings per shareand whether it is because of the tight control on costs.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.