This paper seeks to examine different models to forecast revenue of companies. This is being achieved by examining costs of capital, which are a good representative therefor. The models examined in this paper can be divided into two sections. First, there are mechanical models, second there is one characteristic-based model. The models stand in contrast to analysts’ forecasts. This paper sums up different authors who illustrate, that mechanical models outperform analysts’ forecasts in terms of revenue forecasting. First, the HVZ mode is introduced which is due to outperform analysts’ forecasts. Second, the EP and RI model are introduced, next to a random walk model (RW model) as a benchmark. Objective of this paper is to find out which advantages go along with mechanical models, and whether the quality of forecast could be influenced positively.
The topic of revenue forecast is highly relevant for different stakeholders in the financial industry. Based on revenue forecasts investment decisions are met by investors. One advantage of mechanical models therefore, is the greater feasibility due to the greater coverage. Mechanical models rely on firm fundamentals and are hence available for much more companies. Analysts’ forecasts are only available for firms of a certain size upwards. Costs of capital are a topic of focus not only for investment decisions but also for internal application. Apart from the use as a financial ratio it is negatively associated with customer satisfaction.
The paper finds out, that the HVZ model outperforms analysts’ forecasts in terms of forecast bias and earnings response coefficient. However, the HVZ model does not outperform analysts’ forecasts in terms of accuracy. The EP and RI model both outperform the HVZ model in terms of all three criteria: forecast bias, earnings response coefficient and accuracy. The characteristic-based model sets up a linear function solely by firm fundamentals, that avoids including unobservable future covariances. Besides, it concludes certain key findings about abnormal earnings volatility and economy-wide risk.
Table of Contents
1. Introduction
2. Mechanical Revenue Forecast
2.1. Preliminaries
2.2. HVZ model
2.3. RW model
2.4. EP model
2.5. RI model
2.6. Conclusion
3. Characteristic-based rate of return forecast
3.1. Preliminaries
3.2. Three propositions
3.2.1. First proposition
3.2.2. Second Proposition
3.2.3. Third Proposition
3.3. Conclusion
4. Conclusion and Outlook
Research Objectives and Themes
This paper evaluates the performance of mechanical earnings forecast models compared to traditional analysts' forecasts and investigates characteristic-based valuation models to determine if they offer superior accuracy and reliability in financial forecasting.
- Comparison of mechanical models (HVZ, RW, EP, RI) against analysts' forecasts.
- Evaluation of criteria such as forecast bias, accuracy, and earnings response coefficient.
- Application of the implied cost of capital (ICC) in valuation frameworks.
- Analysis of characteristic-based return models and their relation to firm fundamentals.
- Examination of economy-wide risk factors and their impact on equity pricing.
Excerpt from the Book
2.2. HVZ model
The HVZ model derives its name as an acronym of their authors Hou, van Dijk and Zhang. Part of the motivation for the HVZ model as compared to analyst’s forecasts is coverage, as approximately half of the firms do not have analysts forecast available, which thus makes it a reasonable alternative for firm valuation. Its equation is:
The model operates with data that is easily accessible for every firm. “Et+τ is earnings in year t + τ (τ = 1 to 5), At is total assets, Dt is dividends, DDt is an indicator variable for dividend paying firms Et is earnings, NegEt is an indicator variable for loss firms and ACt is working capital accruals”. Hou et al. (2011) use data gained from all NYSE, Amex and Nasdaq listed companies in the time of 1963 to 2009. They claim that their model outperforms analysts’ forecasts. The look-ahead bias is avoided by using data of past years. For their respective set of data Hou et al. (2011) receive coefficients of 0.8304; 0.7924; and 0.7871 for lagged earnings one, two and three years ahead, respectively. This is being illustrated in Table 1, Panel B (see Appendix). Paying dividends is positively correlated with higher earnings, whereas higher accruals are negatively correlated, which leads to lower earnings, presented in Table 1, Panel A (see Appendix).
The HVZ generated estimates are less accurate than analysts’ forecasts, therefor they have lower levels of forecast bias, and higher levels of earnings response coefficient.
Summary of Chapters
1. Introduction: This chapter outlines the paper's focus on comparing mechanical and characteristic-based models against analyst forecasts for corporate revenue prediction.
2. Mechanical Revenue Forecast: This section provides a detailed analysis of four models, explaining their core reliance on the implied cost of capital and their respective methodologies for forecasting.
3. Characteristic-based rate of return forecast: This chapter introduces models that utilize firm fundamentals to predict returns and incorporate dynamic risk factors into the valuation process.
4. Conclusion and Outlook: This section summarizes the performance findings, highlighting the superiority of mechanical models in coverage and specific accuracy criteria, and provides final remarks on the study's implications.
Keywords
Mechanical Revenue Forecast, Analysts' Forecasts, Implied Cost of Capital, HVZ Model, RW Model, EP Model, RI Model, Firm Fundamentals, Forecast Bias, Earnings Response Coefficient, Characteristic-based Valuation, Economy-wide Risk, Accounting Conservatism, Equity Return, Valuation Models.
Frequently Asked Questions
What is the fundamental purpose of this paper?
The paper aims to evaluate and compare different mechanical and characteristic-based models for forecasting corporate revenue and cost of capital against traditional analysts' forecasts.
What are the primary thematic areas covered?
The study covers financial modeling, implied cost of capital, analyst forecast limitations, firm fundamental analysis, and dynamic risk assessment in stock valuation.
What is the main objective of the research?
The primary objective is to determine if mechanical models offer advantages over analysts' forecasts, specifically regarding feasibility, coverage, and the quality of the forecast.
Which scientific methodology is employed?
The paper conducts a comparative analysis of established mechanical models (HVZ, RW, EP, RI) and characteristic-based models, utilizing empirical data from stock exchanges and previous academic findings to assess performance metrics like forecast bias and accuracy.
What is discussed in the main body?
The main body breaks down the mathematical equations and logic behind the HVZ, RW, EP, and RI models, followed by an in-depth exploration of the three propositions related to characteristic-based rate of return forecasting.
How would you characterize this work with keywords?
Key terms include Mechanical Revenue Forecast, Implied Cost of Capital, Firm Fundamentals, Forecast Bias, and Characteristic-based Valuation.
Why is the HVZ model specifically considered a reasonable alternative?
It is considered a reasonable alternative primarily due to its greater coverage; approximately half of all companies do not have analyst forecasts available, whereas the HVZ model can be applied using easily accessible firm-level data.
How does accounting conservatism impact residual income in the models mentioned?
Accounting conservatism results in lower reported book values, which, according to the valuation models, mechanically increases the future residual income estimates.
What does the characteristic-based model suggest about abnormal earnings volatility?
The model suggests that abnormal earnings volatility is only relevant for equity pricing if that volatility is systematic, rather than idiosyncratic.
- Arbeit zitieren
- Anonym (Autor:in), 2018, Evaluation of Mechanical Earnings Forecast Models, München, GRIN Verlag, https://www.hausarbeiten.de/document/491784
