Investigating the Performance of Life Cycle Based Forecasts and Determining the Components Affecting it - Behzad Kardan (Shahid Bahonar University of Kerman, 2020)
Objective: Company growth and profitability forecasts are important inputs in the valuation process. Also, mean reversion estimates can serve as inputs in estimating steady-state final value parameters. The main purpose of this study is to test the hypothesis that life cycle-based mean reversion models provide better results for forecasting profitability and growth compared to the industry-level and economy-wide models. This study also tests the hypothesis that managers realize the benefits of industry and life cycle analysis when making their predictions. Totally, this study compares the variables and factors affecting the accuracy of predictions from mean reversion life cycle-based models with industry-level and economy-wide models.  Methods: The data of 161 companies listed in the Tehran Stock Exchange, TSE, in a 10-year period of 2008-2018 were collected from the software, financial statements, and the TSE official website. To test the research hypotheses, we used statistical tests such as t- student, multivariate regression using SPSS software, econometrics estimation using Eviews. The Dickinson (2011) model was used to determine the different stages of the companies' life cycle, which is consistent with the pattern of cash flows (operating activities, investment, and financing).  Results: Test results of the first hypothesis, in most cases, provided evidence that growth and profitability forecasts derived from industry-level mean reversion models outperform the forecasts of the life cycle and the economy-wide models. By comparing the mean errors in the second hypothesis, the findings of the model are more accurate than other models, indicating that managers realize the importance of the firm's life cycle when predicting profits. The results of the study of factors affecting the accuracy of the life cycle model of prediction compared to other models indicated that the improvement of life cycle growth forecasts lacks significant relationships with systematic and non-systematic risk, beta coefficient, trading volume, the ratio of institutional owners, the market-to-book ratio, and the amount and intensity of the R &D, the volume of intangible assets, and financial leverage. However, for higher profitability scales, improved life cycle forecasts correlate with firm size, assets and equipment, and abnormal (poor) corporate returns. Also, the life cycle approach works best when the percentage of institutional shareholder ownership is high and the company's uncertainty, profitability, and assets are low.  Conclusion: In this study, we investigated the accuracy of a forecast model based on a firm life cycle for predicting future profitability and growth relative to economy-wide and industry-specific forecast models. In general, the results of the research indicate the relative superiority of the predictions of industry-level models over the predictions obtained from the life cycle and the economy-wide models. Although the research findings do not provide evidence of more explanatory power of the life cycle model compared to other models, they suggest that managers have realized the importance of the life cycle of the company when making profit forecasts. This research has important implications that help investors, analysts, managers, and others make better predictions when making financial decisions. Also, this study identifies the drivers of growth and profitability for companies through a low-cost and high employment strategy to achieve the most accurate forecasts.