This paper introduces univariate regression models with the aim of understanding how key Revenues and Earnings variables explain the valuation of American-listed Software companies, building on findings from previous research. Companies are divided into three samples – Small, Medium and Large – according to their Market Capitalization. Cross-sectional data are regressed using 5-year time windows from 2000 to 2019, as well as 2-year periods from 1998to 2021. Additionally, three other variables – (1) dummy variable distinguishing positive from negative EBITDA companies, (2) dummy differenciating firms with a Net Cash from a NetDebt position, and (3) a market-based explanatory variable – are included in three further multivariate models in order to understand how they interact with the two main variables. We find that while Revenues is always value relevant in all time windows and throughout time, there are four occasions where Earnings is not value relevant in explaining the value of Software companies. Also, apart from the occasion of two time periods, Revenues is more effective than Earnings in explaining the value of Software firms. Both the explanatory power of these variables and the relevancy of the inclusion of new ones in multivariate models are highly dependent on the time horizon and period.
Date of Award | 18 Oct 2022 |
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Original language | English |
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Awarding Institution | - Universidade Católica Portuguesa
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Supervisor | Jyoti Gupta (Supervisor) |
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- Software companies valuation
- Time-variability
- Market capitalization
- Financial indicators
- Revenues
- Earnings
- EBITDA
- Net Debt
- Negative EBITDA
- Univariate regression analysis
- Multivariate regression analysis
- Log-linear model
Value relevancy of revenues and earnings in explaining the market capitalization of American software companies
Ferreira, R. P. M. S. (Student). 18 Oct 2022
Student thesis: Master's Thesis