This study develops a firm life cycle proxy using cash flow patterns. The patterns provide a parsimonious indicator of life cycle stage that is free from distributional assumptions (i.e., uniformity). The proxy identifies differential behavior in the persistence and convergence patterns of profitability. For example, return on net operating assets (RNOA) does not mean-revert (spread of 7 percent after five years between mature and decline firms) when examined by life cycle stage, which has implications for growth rates and forecast horizons. Further, determinants of future profitability such as asset turnover and profit margin are differentially successful in generating increases in profitability conditional on life cycle stage. Finally, investors do not fully incorporate the information contained in cash flow patterns and, as a result, undervalue mature firms. The cash flow proxy is a robust tool that has applications in analysis, forecasting, valuation, and as a control variable for future research.
Data Availability: All data are available from public sources identified in the paper.
The economics and management literature provides theoretical support for both leader and laggard firms to earn higher future operating returns. However, prior empirical research lacks a generalizable proxy to capture leader versus laggard behavior, thus limiting prior findings to specific contexts. This study utilizes a combination of firm-specific and industry life cycle identification to categorize leaders and laggards and validates the designation against constructs established in prior literature. Additionally, we examine each strategy’s effect on future performance, finding that, in general, laggards earn greater operating returns. Laggards gain their advantage through product differentiation—specifically, through marketing/advertising expenditures. Leaders are, on average, unable to convert their first movers’ advantage into sustainable future profitability once we control for other determinants of profitability. The leader/laggard classification using financial statement information has useful applications in analysis, forecasting, and valuation. This paper was accepted by Suraj Srinivasan, accounting.
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