Soenke Sievers

Valuing High Technology Growth Firms

For the valuation of fast growing innovative firms Schwartz and Moon (Financ Anal J 56:62–75, 2000), (Financ Rev 36:7–26, 2001) develop a fundamental valuation model where key parameters follow stochastic processes. While prior research shows promising potential for this model, it has never been tested on a large scale dataset. Thus, guided by economic theory, this paper is the first to design a large-scale applicable implementation on around 30,000 technology firm quarter observations from 1992 to 2009 for the US to assess this model. Evaluating the feasibility and performance of the Schwartz-Moon model reveals that it is comparably accurate to the traditional sales multiple with key advantages in valuing small and non-listed firms. Most importantly, however, the model is able to indicate severe market over- or undervaluation from a fundamental perspective. We demonstrate that a trading strategy based on our implementation has significant investment value. Consequently, the model seems suitable for detecting misvaluations as the dot-com bubble.

Determinants of market beta: the impacts of firm-specific accounting figures and market conditions

This article examines and extends research on the relation between the capital asset pricing model market beta, accounting risk measures and macroeconomic risk factors. We employ a beta decomposition approach that nests competing models with different business risk proxies and allows to frame cross-model comparison. Because model tests require estimated independent variables resulting in measurement error, we empirically estimate three comparable model specifications with instrumental variable estimators and for the first time provide thorough instrument diagnostics in this setting. Correcting for the heretofore neglected weak instruments problem we find that growth risk (i.e., the risk of firm sales variations that are inconsistent with the market wide trends), is the business risk that explains cross-sectional variations in market beta best. Copyright Springer Science+Business Media New York 2014

Loan Pricing: Do Borrowers Benefit from Cost-Efficient Banking?

This study examines the loan pricing behavior of German banks for a large variety of retail and corporate loan products. We find that a bank’s operational efficiency is priced in bank loan rates and alters interest-setting behavior. Specifically, we establish that a higher degree of operational efficiency leads to lower loan markups, which involve more competitive prices, and smoothed interest rate-setting. By employing stochastic frontier analysis to comprehensively capture cost efficiency, we take the bank customers’ perspective and demonstrate the extent to which borrowers benefit from cost efficient banking. To our knowledge this is the first study that analyzes this relationship by using advanced efficiency measures such as stochastic frontier analysis. For the German market this relationship is unexplored.

The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments

This study shows how venture capital investors can identify potential biases in multi-year management forecasts before an investment decision and derive significantly more accurate failure predictions. By advancing a cross-sectional projection method developed by prior research and using firm-specific information in financial statements and business plans, we derive benchmarks for management revenue forecasts. With these benchmarks, we estimate forecast errors as an a priori measure of biased expectations. Using this measure for our proprietary dataset on venture-backed start-ups in Germany, we find evidence of substantial upward forecast biases. We uncover that firms with large forecast errors fail significantly more often than do less biased entrepreneurs in years following the investment. Overall, our results highlight the implications of excessive optimism and overconfidence in entrepreneurial environments and emphasize the relevance of accounting information and business plans for venture capital investment decisions.

To Buy or Not to Buy? The Value of Contradictory Analyst Signals

We study the predictive ability of individual analyst target price changes for post-event abnormal stock returns within each recommendation category. Although prior studies generally demonstrate the investment value of target prices, we find that target price changes do not cause abnormal returns within each recommendation level. Instead, contradictory analyst signals (e.g., strong buy reiterations with large target price decreases) neutralize each other, whereas confirmatory signals reinforce each other. Further, our analysis reveals that large target price downgrades can be explained by preceding stock price decreases. However, upgrades are not preceded by stock price increases, thereby demonstrating asymmetric analyst behavior when adjusting target prices to stock prices. Our results suggest that investors should treat recommendations with caution when they are issued with large contradictory target price changes. Thus, instead of blindly following a recommendation, investors might put more weight on the change in the corresponding target price and consider transaction costs. Copyright Swiss Society for Financial Market Research 2012

Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence

This study offers an implementation of a novel and theoretically grounded accounting based approach for the estimation of default probabilities which is derived from recent research on company valuation. It simulates the firm’s operating income and net working capital into the future by employing stochastic processes. Additionally, as volatilities have been found relevant for bankruptcy prediction in previous studies, this paper includes three sources of volatility in the comprehensive framework. Benchmarking this new approach against the classical accounting based models by Altman (1968) and Ohlson (1980) along different dimensions on more than 200,000 firm quarters demonstrates several advantages. The bankruptcy prediction based on stochastic processes approach is more accurate in distinguishing between non-delisting and delisting firms than the prominent Z- or O-score models. Most importantly, it significantly outperforms the two statistical models for longer horizons in predicting delistings related to bankruptcy. Consequently, it provides important early warning signals for regulators and investors.

Predicting Early Warning Signals of Financial Distress: A Stochastic Approach

This study offers an implementation of a novel and theoretically grounded accounting based approach for the estimation of default probabilities which is derived from recent research on company valuation. It simulates the firm’s operating income and net working capital into the future by employing stochastic processes. Additionally, as volatilities have been found relevant for bankruptcy prediction in previous studies, this paper includes three sources of volatility in the comprehensive framework. Benchmarking this new approach against the classical accounting based models by Altman (1968) and Ohlson (1980) along different dimensions on more than 200,000 firm quarters demonstrates several advantages. The bankruptcy prediction based on stochastic processes approach is more accurate in distinguishing between non-delisting and delisting firms than the prominent Z- or O-score models. Most importantly, it significantly outperforms the two statistical models for longer horizons in predicting delistings related to bankruptcy. Consequently, it provides important early warning signals for regulators and investors.

Predicting Early Warning Signals of Financial Distress: The Role of Accounting Volatility Measures

This study offers an implementation of a novel and theoretically grounded accounting based approach for the estimation of default probabilities which is derived from recent research on company valuation. It simulates the firm’s operating income and net working capital into the future by employing stochastic processes. Additionally, as volatilities have been found relevant for bankruptcy prediction in previous studies, this paper includes three sources of volatility in the comprehensive framework. Benchmarking this new approach against the classical accounting based models by Altman (1968) and Ohlson (1980) along different dimensions on more than 200,000 firm quarters demonstrates several advantages. The bankruptcy prediction based on stochastic processes approach is more accurate in distinguishing between non-delisting and delisting firms than the prominent Z- or O-score models. Most importantly, it significantly outperforms the two statistical models for longer horizons in predicting delistings related to bankruptcy. Consequently, it provides important early warning signals for regulators and investors.

Investment Distortions and the Value of the Government's Tax Claim

This article integrates the government in the context of company valuation. Our framework allows to analyse and to quantify the risk-sharing effects and conflicts of interest between the government and the shareholders when firms follow different financial policies. We provide novel evidence that firms with fixed future levels of debt might invest more than socially desirable. Economically, this happens if the gain in tax shields is big enough to outweigh the loss in the unlevered firm value. Our findings have implications for the practice of investment subsidy programmes provided by the government to avoid fostering investments beyond the socially optimal level.

Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors

This study provides evidence of significant biases in multi-year management forecasts by analyzing a proprietary dataset on venture-backed start-ups in Germany. We find that revenues and expenses are highly overestimated in each of the investigated one- to five-year-ahead planning periods. Furthermore, entrepreneurs underestimate one-year-ahead profit forecasts but clearly overestimate their profit forecasts for all longer-term forecast horizons. Additional analyses reveal that teams with prior management experience issue even more overestimated forecasts and misrepresent their forward-looking information. In contrast, greater asset verifiability and corporate lead investors are associated with lower levels of forecast errors. All key results hold if bias is either measured by traditionally comparing forecasts to ex-post realizations or by using a cross-sectional projection approach based on historical accounting data developed by prior research.