Finance in Crises

Valuation seems to be impossible since the future is uncertain. Nevertheless, company values are necessary for many reasons, so the question arises as to how valuations can take these uncertainties into account.

Traditionally, the past is used as a basis for planning, i.e., it is more or less simply extrapolated. Given current experiences (Corona pandemic, Ukraine war, inflation, and energy crisis, to name but a few), the hope remains that these will not be perpetuated. On the other hand, however, we must also expect new and as yet unknown developments, or developments that are not considered likely or unlikely.

To produce reliable valuations as a basis for economic decisions even in times of increased uncertainty, selecting suitable procedures and the appropriate handling of risks is important.

Basically—and recurring to the mathematical model of discounting future cash flow—uncertainties or risks can be taken into account above the line (in the numerator or the cash flows) or below the line (in the denominator or the cost of capital). Double counting must be avoided, meaning that the denominator matches the numerator (equivalence principle) and that the cost of capital adequately reflects the fluctuations in cash flows.

Up to now, most valuations reflect only one path, the future developments may take. In this paper, we want to demonstrate how the traditional valuation approach can be extended by sensitivity analyses, scenario calculations, and simulations to move from pure point estimates to reliable value ranges that may reflect reality more accurately.

Even in normal, non-crisis periods, a company’s financial reporting is affected by uncertainty. Financial reporting is based on uncertain assumptions and valuations. However, there is no doubt that the going concern principle applies. This means that the continuation of the company as a whole is possible and planned for the foreseeable future. Times of crisis are characterized by increased uncertainty. Uncertainty can be so great that the going concern status of the entity is called into question, with implications for the preparation of the financial statements and the reporting. This chapter discusses some of the implications of preparing and reporting on the going concern basis and some of the challenge for those involved, particularly corporate management and the auditors.

Given the recent crises and ongoing structural changes in the automotive industry, future success potentials from formerly acquired investments within the automotive industry should be questioned. In this context, the estimation of future cash inflows and outflows as well as the corresponding discount rate, which is necessary within the IFRS framework of the impairment test of assets, becomes much more challenging. To shed new light on the impact of these effects on the litmus test, i.e. on the value of goodwill, the chapter is using the example of the automotive sector in Germany. The possibility of a goodwill bubble in the automotive industry cannot be ruled out, as our chapter suggests.

Predictive Analytics is a tool in corporate financial management to gain a better understanding of the future based on forecasting models helping companies to prepare for future uncertainties and crises based on past data. This chapter describes a case study on an earnings before interest and taxes (EBIT) forecasting model based on a bachelor thesis for a global logistics company specializing in the transportation of liquid bulk commodities. The company was faced with the challenge of predicting monthly profitability due to the lack of correlation between order volume and financial performance. The objective of the case study was to improve business processes and profitability by developing a forecasting model. The study analyzed 29 months of profit and loss accounts and used a step-wise regression analysis to develop a formula to estimate monthly EBIT. The results show the importance of understanding the mechanics of the income statement and the inefficiencies of the processes to generate accurate forecasts. The chapter recommends further research to optimize the EBIT formula and explore innovative machine learning techniques for forecasting. It emphasizes the importance of forecasting models for both prediction and explanation and highlights the need for regular model adjustments to account for changing dynamics. The case study’s findings demonstrate the value of financial forecasting for strategic planning and business management.

Crises can occur at any time with serious consequences for organizations. A well-implemented Internal Control System (ICS) helps managing crises and serves to provide adequate assurance regarding the achievement of an organization’s goals in relation to its operations, reporting and compliance. It is mandatory for listed Swiss organizations as well as for Swiss organizations exceeding certain economic key figures. In that case, the existence of the ICS is checked by the external auditor during the so-called ordinary audit. Although it is not mandatory for all other Swiss organizations, they are able to implement an ICS to their own advantage to strengthen their processes regarding the following five components of internal control: control environment, risk assessment, control activities, information/communication and monitoring activities. The authors of this chapter give a brief summary about the theoretical foundations of the ICS with focus on its implementation in Switzerland and how it helps managing crises. Why Swiss organizations are able to benefit from a mandatory or voluntary implementation of an ICS is shown and explained as well as how a well-implemented ICS serves as an important instrument regarding crises—in a proactive manner before and in a reactive manner during crises.

After the bursting of the stock market bubble at the end of the 1980s Japan began to comprehensively reform its corporate governance system. Modernising Japan’s corporate governance system was seen as one of the key levers to making Japanese companies more profitable, faster growing and less risk-prone, with the overarching goal of revitalising the Japanese economy in the long term.

The first phase of the reform lost some of its momentum in the wake of the great financial crisis that broke out in 2008. Prime Minister Abe then pushed the reform process forward again with vehemence from 2012 onwards. Abe’s reform efforts encompassed several elements, such as the reform of company law and the introduction of a corporate governance code and a stewardship code. At the same time, the Tokyo Stock Exchange has influenced the corporate governance of Japanese companies through its listing rules.

From a regulatory perspective, the Japanese corporate governance system can be described as modern and efficient. However, corporate practice often lags behind. The Fujitec case study at the end of this chapter shows two things: first, how lacking the actual practice of corporate governance can be despite good rules. Second, it shows how determined shareholders today have the power to protect their interests and ensure that Japan’s modern corporate governance rules are brought to life, to the benefit of employees, shareholders and the Japanese economy as a whole.

This chapter analyzes the role of the controlling function in the transformation process from a traditional to a circular business model. Although the task of controlling lies precisely in measuring and managing corporate strategy, controlling has not yet established itself as a strong player when it comes to implementing a sustainable business model. Controlling still plays too passive a role and lacks the necessary expertise. Furthermore, controlling views sustainability more as a marketing or compliance issue than as a fundamental basic understanding of future entrepreneurial action. The chapter describes the linear business model and the need for a shift to a circular model. Based on this, the requirements for sustainability oriented corporate management are outlined and the form in which controlling can support and accompany such sustainability management in a meaningful way and with suitable key performance indicators (KPIs).

We analyze the price discovery process between credit spreads and credit default swaps (CDS) of sovereign credit risk in GIIPS countries (Greece, Ireland, Italy, Portugal, and Spain) as well as Germany and France after the implementation of the regulatory ban on outright short selling of sovereign CDS, implemented after the euro area sovereign debt crisis. Our findings show evidence, that the CDS market continues to be relevant for price discovery in euro area sovereign credit markets. However, unlike earlier studies with shorter sample periods, the bond market has been observed to incorporate information more rapidly than the CDS market for most countries in the sample. In the case of Ireland and Greece, both markets significantly contribute to the price discovery process, but the bond market in most countries exhibits faster adjustment dynamics. Our findings deviate from previous research with shorter sample periods after the introduction of the ban, which indicated CDS market leadership in price discovery for most markets. However, in our analysis, CDS leadership was observed only in Portugal and Spain, suggesting that further investigation is warranted to comprehend the evolving dynamics of the sovereign credit market.

One intriguing finding concerns the Italian credit market, where the implementation of the unconventional European Central Bank (ECB) monetary policy, specifically the Quantitative Easing (QE) program in January 2015, disrupted market functioning due to excessive liquidity. Consequently, the CDS and bond yield spreads in the Italian market were no longer cointegrated during that period.

This chapter shows some evidence that sovereign risk remains a significant concern for European ‘Global Systemically Important Banks’ (G-SIBs) despite the lessons learned from the euro area sovereign debt crisis. Analysis of data from the European Banking Authority (EBA) reveals that many of these banks continue to hold substantial levels of sovereign debt exposure to GIIPS (Greece, Ireland, Italy, Portugal, and Spain) countries, with exposure ranging from 10% to 20%. Notably, UniCredit and Santander stand out with exposure levels exceeding 25% towards GIIPS nations, exposing them to potential sovereign risk from market disturbances within those countries. We also show that partial correlations between Credit Default Swaps (CDS) spreads of certain G-SIBs and GIIPS countries remain relatively high, likely influenced by the significant sovereign debt exposure these banks maintain. Furthermore, the high Debt-to-GDP ratios of GIIPS countries, along with the potential cascading effects of a government’s failure on financial institutions, highlight the interconnectedness and vulnerability of the European banking system.

While regulatory frameworks like the Basel Accords have played a crucial role in maintaining financial stability, the approach to risk-weighted assets associated with sovereign debts remains contentious. The allowance for a 0% risk weight on sovereign bonds issued by EU member states, as part of the Capital Requirements Regulation (CRR), may incentivize increased holdings of such debts, amplifying sovereign risk. Moreover, High Quality Liquid Assets (HQLA) regulations encouraging banks to retain sovereign debt result in an increased risk for a new sovereign-bank nexus. Our findings reveal potential weaknesses within the European banking system, emphasizing the need for thorough scrutiny and systemic management of this persisting issue.

The physical nature of electricity makes it difficult to store, which means that current demand must always match current production. This requires both flexible and internationally interconnected generation capacity and appropriate hedging strategies. Electricity is typically traded ‘forward’, i.e., future energy volumes and prices are hedged in advance with derivative instruments to minimize price risks. In times of crisis, when energy prices can be highly volatile, such instruments can also be used for speculative purposes. However, hedging and speculative positions can trigger margin calls on derivatives exchanges or increased collateral requirements in the over-the-counter (OTC) market. The causes, interrelationships and possible consequences of such margin calls on the financial situation of buyers and sellers of electricity (e.g., on balance sheet liquidity) are discussed.

Paradoxically, the use of hedging instruments to protect against price volatility, together with prudential accounting standards, has led to financial problems for many electricity producers during the market turmoil of 2022, and to governmental bail outs. Whether the problems of large energy companies in Switzerland are due solely to hedging motives or to speculative proprietary trading is difficult for outsiders to judge.

In risk management, the probability distribution must adequately capture the risks that matter most, namely the outliers. I estimate the parameters as if the sample were upper censored to put the focus to the most negative observations (non-censored outliers) and then only estimate the probability that I obtain an “ordinary” observation (censored). The left tail is assumed to follow a location-scale Student’s t-distribution, yet no distributional assumptions are made outside the tail. Cauchy’s and Gauss’ distributions are two special cases of Student’s t-distribution. For empirical stock index returns, the left tail is neither Gauss nor Cauchy, a Student’s t-distribution with around four degrees of freedom offers the best fit. As an empirical application, I compare the pricing of a short-term far out-of-the-money put option on the Dow Jones with a Gauss, a Cauchy, and a Student left tail. The Gaussian Black–Scholes model underprices such insurance contracts, Cauchy overprices them, and the Student model, however, is a good match.

In times of economic uncertainty, understanding household saving behavior is of particular importance to researchers and policymakers alike. We review the recent empirical literature on household saving over the life cycle, cross-sectional determinants of saving rates, and retirement saving, focusing on studies based on high-quality administrative data. We also discuss recent evidence on similarities in economic behavior and outcomes across generations. There are some common themes. While less financially savvy and low-income households are particularly at risk of under-saving, the literature also shows surprising differences in savings rates among financially well-off households. Individuals tend to exhibit present bias and inertia in their saving and consumption behavior and are strongly influenced by their upbringing and genetic predisposition.