Company Valuation and Bankruptcy Prediction

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

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.