The contribution of this study is manifold and relevant for academics and practitioners alike. It adds to the literature in the fields of corporate finance, financial accounting and stochastic modeling. In particular, this dissertation provides answers to the following questions: given the less efficient markets, can specialists as financial analysts provide additional information, which contain investment value? How can the true value of a company be determined with publicly available data and can discrepancies between fundamental and market values be exploited? Finally, is it possible to assess the firm’s financial health and its likelihood of failure several years into the future? Adressing these questions, the study first illustrates the company valuation assessment by financial analysts as summarized in their target prices and the information processing by analysts and investors in detail. Second, this thesis offers a novel empirical implementation of a model for fundamental company valuation that employs accounting data. In this context it demonstrates severe over- and undervaluation from a fundamental perspective in the U.S. technology sector over the last 20 years. Both the analysts’ company valuation captured by their target prices and the implementation of the fundamental company valuation model translate into significant investment value before and after transaction costs, which supports the notion of non-efficient markets. Finally, one major contribution is to evaluate a new approach for bankruptcy prediction that is based on stochastic processes. It is theoretically appealing and performs better especially for longer forecast horizons than standard methods.
Contents
1 Introduction
2 To Buy or Not to Buy? The Value of Contradictory Analyst Signals
2.1 Introduction
2.2 Data and Methodology
2.2.1 Data, Variables, and Descriptive Statistics
2.2.2 Methodology
2.3 Empirical Results
2.3.1 Calendar Time Portfolios
2.3.2 Factor Loadings, Market Capitalization, and Transaction Costs
2.3.3 Potential Explanations
2.4 Conclusion
3 Valuing High Technology Growth Firms
3.1 Introduction
3.2 Related Literature: Firm Growth and Valuation
3.3 Valuation Models
3.3.1 Fundamental Pricing: The Schwartz-Moon Model
3.3.2 Introducing a Benchmark: Enterprise-Value-Sales Multiple
3.4 Data and Methodology
3.4.1 Data Collection
3.4.2 Model Implementation
3.4.2.1 Revenue Dynamics
3.4.2.2 Cost Dynamics
3.4.2.3 Balance Sheet and Remaining Firm Parameters
3.4.2.4 Environmental and Risk Parameters
3.4.2.5 Simulation Parameters
3.4.3 Summary Statistics
3.5 Main Empirical Results
3.5.1 Feasibility and Deviations from Market Values
3.5.2 Detecting Over- and Undervaluation: The Trading Strategy
3.6 Robustness Checks
3.7 Discussion and Conclusion
4 Bankruptcy Prediction Based on Stochastic Processes: A New Model Class to Predict Corporate Bankruptcies?
4.1 Introduction
4.2 Prior Research
4.3 The Model
4.3.1 Sales and Costs
4.3.2 The Accounting Volatilities
4.3.3 The Change in Net Working Capital
4.4 Data and Model Implementation
4.4.1 The Data
4.4.2 Parameter Estimation
4.5 Empirical Analyses
4.5.1 Summary Statistics and Correlations
4.5.2 Accuracy
4.5.3 Test of Information Content
4.6 Discussion and Conclusion
5 Summary and Conclusion
Objectives and Research Themes
This doctoral thesis examines methods for forecasting corporate performance in the contexts of company valuation and bankruptcy prediction, focusing on both analyst-based expertise and objective accounting data. The primary objective is to develop and evaluate stochastic frameworks that provide more accurate and forward-looking assessments than traditional models, particularly for high-growth technology firms and long-term failure risk.
- Evaluation of financial analysts’ target price signals and their interaction with recommendation levels.
- Implementation and validation of the Schwartz-Moon valuation model for high-technology growth firms using large-scale empirical datasets.
- Development of a new stochastic bankruptcy prediction model (BPSP) based on accounting-derived volatilities.
- Comparative analysis of the proposed models against established statistical benchmarks like Altman's Z-score and Ohlson’s O-score.
- Investigation into the profitability of trading strategies derived from these performance forecasts.
Excerpt from the Book
To Buy or Not to Buy? The Value of Contradictory Analyst Signals
It is well established in the academic literature that analysts’ stock recommendations can predict post-event abnormal returns. In contrast, the performance of analysts’ target prices has only recently received attention. In an influential study, Brav and Lehavy (2003) show that target price changes have considerable information value. These authors investigate the performance of target price changes conditional on the direction of the recommendation change (upgrades, reiterations, downgrades) issued by the same broker. Sorting stocks according to their target price change within each category, they show, for both the upgrade and reiteration categories, that the extreme portfolios have abnormal returns that are remarkably different from those of the collective portfolios within their respective categories.
Although these studies focus on the change in recommendations, they do not consider the level of recommendations. This has two main implications. First, it is unclear whether target price changes contain valuable information for each recommendation level. For example, large target price increases (reductions) for reiterated strong buy (sell) recommendations may not provide valuable information to the market because the recommendation already provides a trading signal. In particular, the positive performance of the portfolios with the most favorable target price revisions reported in Brav and Lehavy (2003) might be driven by buy and strong buy recommendations, and the negative performance of the portfolio with the least favorable target price revisions might be driven by hold, sell, and strong sell recommendations. In contrast, because recommendations are bounded from above (strong buy) and below (strong sell), analysts must resort to target price increases (decreases) to signal private information about an increase in the undervaluation (overvaluation) if the stock has already been given a strong buy (sell) recommendation.
Summary of Chapters
1 Introduction: Provides the foundation for the thesis, establishing the challenges of valuation and bankruptcy prediction, and outlines the two primary approaches: analyst-based forecasting and stochastic accounting-based modeling.
2 To Buy or Not to Buy? The Value of Contradictory Analyst Signals: Analyzes the information value of analyst target price changes conditioned on recommendation levels, finding that contradictory signals often neutralize each other and that investment value depends on the combination of these two signals.
3 Valuing High Technology Growth Firms: Implements the Schwartz-Moon stochastic valuation model on a large dataset of U.S. technology firms to estimate fair values based on accounting data, successfully benchmarking it against traditional multiples.
4 Bankruptcy Prediction Based on Stochastic Processes: A New Model Class to Predict Corporate Bankruptcies?: Evaluates a novel bankruptcy prediction model (BPSP) that utilizes stochastic processes to integrate accounting volatility, demonstrating superior accuracy in long-term failure prediction compared to traditional Z-score and O-score models.
5 Summary and Conclusion: Synthesizes the findings of the individual studies, highlighting the practical applicability of the developed frameworks for investors, regulators, and banks.
Keywords
Company Valuation, Bankruptcy Prediction, Financial Analysts, Target Prices, Stochastic Modeling, High Technology Firms, Schwartz-Moon Model, Accounting Volatility, Net Working Capital, Investment Strategy, Market Misvaluation, Default Probability, Financial Reporting, Equity Research.
Frequently Asked Questions
What is the core focus of this research?
This thesis focuses on improving the predictability of company performance and bankruptcy risk. It explores how analysts' information and stochastic accounting models can be leveraged to achieve more accurate corporate valuations and failure forecasts.
Which thematic areas are central to this work?
The core themes include equity valuation, financial statement analysis, analyst signaling behavior, high-technology firm growth dynamics, and the development of bankruptcy prediction models using stochastic processes.
What is the primary goal of the research?
The primary goal is to address the shortcomings of traditional, static valuation and bankruptcy models by introducing and testing theoretically grounded, dynamic approaches that are applicable to both listed and non-listed firms.
What scientific methods are utilized?
The research employs a range of methodologies, including calendar-time portfolio regressions for market event analysis, Monte Carlo simulations for valuation and bankruptcy modeling, and ordered logit regressions for information content testing.
What does the main body of the work cover?
The main body investigates the relationship between analyst target price changes and recommendations, provides a large-scale implementation of the Schwartz-Moon model for tech firm valuation, and develops a new BPSP model class for bankruptcy prediction.
Which keywords best characterize the dissertation?
Key terms include Company Valuation, Bankruptcy Prediction, Stochastic Modeling, Analyst Recommendations, Target Prices, Accounting Volatility, and Default Probability.
How does the Schwartz-Moon model perform in the U.S. technology sector?
The study finds that the model is comparable in accuracy to traditional sales multiples but offers significant advantages in valuing small or non-listed firms and identifying severe market over- or undervaluations.
Why is the stochastic bankruptcy prediction (BPSP) model advantageous?
The BPSP model is advantageous because it explicitly incorporates accounting-based volatility as a driver for failure, making it more effective for long-term prediction horizons (up to five years) compared to standard statistical models.
What do the findings imply for market efficiency?
The results demonstrate that market prices often deviate from fundamental values and that these discrepancies, identified through the proposed models, can be exploited to achieve significant investment returns, suggesting that financial markets are not always fully efficient.
- Arbeit zitieren
- Dipl. Vw. Jan Klobucnik (Autor:in), 2013, Company Valuation and Bankruptcy Prediction, München, GRIN Verlag, https://www.hausarbeiten.de/document/264986
