Prediction of company’s life cycle stage change; creation of an ordered 2D map allowing to explore company’sfinancial soundness from a rating agency perspective; and prediction of trends of main valuation attributes usually used by investors are the main objectives of this article. The developed algorithms are based on a random forest (RF) and a nonlinear dat mapping technique ‘‘t-distributed stochastic neighbor embedding’’. Information from five different perspectives, namely balance, income, cash flow, stock price, and risk indicators was aggregated via proximity matrices of RF to enable exploration of company’s financial soundness from a rating agency perspective. The proposed use of information not only from companie  financial statements but also from the stock price and risk indicators perspectives has proved useful in creating ordered 2D maps of rated companies. The companies were well ordered according to the credit risk rating assigned by the Moody’s rating agency. Results of experimental investigations substantiate that the developed models are capable of predicting short term trends of the main valuation attributes, providing valuable information for investors, with low error. The models reflect financial soundness of actions taken by company’s management team. I was also found that company’s life cycle stage change can be determined with the average accuracy of Bearing in mind fuzziness of the transition moment, the obtained prediction accuracy is rather encouraging