Narrow Results

Uncertainty accompanies almost every situation in our lives and it influences our decisions. On many occasions uncertainty is so severe that we can only predict some upper and lower bounds for the outcome of our (collaborative) actions, i.e., payoffs lie in some intervals. Cooperative interval games have been proved useful for solving reward/cost sharing problems in situations with interval data in a cooperative environment. In this paper we propose two procedures for cooperative interval games. Both transform an interval allocation, i.e., a payoff vector whose components are compact intervals of real numbers, into a payoff vector (whose components are real numbers) when the value of the grand coalition becomes known (at once or in multiple stages). The research question addressed here is: How to determine for each player his/her/its payoff generated by cooperation within the grand coalition – in the promised range of payoffs to establish such cooperation – after the uncertainty on the payoff for the grand coalition is resolved? This question is an important one that deserves attention both in the literature and in game practice.

This paper extends the theory of fuzzy diseases predictions in order to detect the causes of business failure. This extension is justified through the advantages of the reference model and its originality. Moreover, the fuzzy model is completed by this proposal and some parts of it have been published in isolated articles. For this purpose, the fuzzy theory is combined with the OWA operators to identify the factors that generate problems in firms. Also, a goodness index to validate its functionality and prediction capacity is introduced. The model estimates a matrix of economic- financial knowledge based on matrices of causes and symptoms. Knowing the symptoms makes it possible to estimate the causes, and managing them properly, allows monitoring and improving the company’s financial situation and forecasting its future. Also with this extension, the model can be useful to develop suitable computer systems for monitoring companies’ problems, warning of failures and facilitating decision-making.

Modern financial institutions require sophisticated risk assessment tools to integrate human expertise and historical data in a market that is changing and broadening qualitatively, quantitatively, and geographically. The need is especially acute in newly developed countries where expertise and data are scarce, and knowledge bases and assumptions imported from the West may be of limited applicability.

Second order logical models can be a valuable tool in such situations. They integrate the robustness of neural or statistical modeling of data, the perspicuity of logical rule induction, and the experience and understanding of skilled human experts. The approach is illustrated in the context of risk assessment in the Korean surety insurance industry.

This paper demonstrates analytically how short sellers can put non-transitory downward pressure on the stock market prices and intrinsic values of companies that need to raise external capital because of insufficient internal liquidity. The model helps explain anomalous empirical findings in the extant literature on negative returns to stocks subjected to heavy shorting activity. The implications of the model also supply normative justification for the sizable cash reserves held by corporations and their reluctance to raise external capital. The equity pricing effects implied by the model are illustrated for a large empirical sample of companies negatively impacted by heavy short sales. Empirical tests are also conducted in this research that provide evidence consistent with the theory.

This paper analyzes the reorganization procedure introduced into the Chinese bankruptcy system in 2007. It shows that managers devote more effort during the reorganization than before the bankruptcy when the emergence value of the bankrupt firm is substantial. In addition, in the pre-bankruptcy period, managers were shown to input less effort under the new law than under the old law. Finally, the paper demonstrates that the market interest rate under the new bankruptcy law is not necessarily lower than that under the old law. These results call attention to the potential costs of the reorganization procedure.

The global financial crisis in 2008 increased the number of business failures in the U.S. as well as in China. The Chinese economy has also been affected by the recent global financial crisis given the fact that the Chinese economy depends heavily on international trade. Our study tries to find the determinants of bankruptcy in Chinese firms. Both logit and survival model analyses provide consistent results on the determinants in predicting distressed firms in China. Our results suggest that firms with liquidity problems and firms experiencing a decline in profits are more likely to file for bankruptcy. In addition, we find that, compared to state-owned enterprises (SOEs), collectively-owned enterprises, private-owned enterprises, and foreign-owned businesses are more likely to file for bankruptcy. This conclusion is robust after controlling for regional differences. The findings of this study show that the financial variables developed by Altman [Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(3), 589–609] and Ohlson [Financial ratios and probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131] perform reasonably well in determining business failures of Chinese firms even though SOEs and shadow financing exist in China.

This study examines whether negative book equity (BE) firms are in financial distress by analyzing their operating performance, financial characteristics, distress risk, and survivability when they first report negative BE. Firms with small magnitude of negative BE (SNBE firms) suffer from persistent negative earnings and financial distress, while firms with large magnitude of negative BE (LNBE firms) experience a temporary non-distress related earnings shock. LNBE firms report consecutive years of negative BE, but have lower distress risk and failure rate than both SNBE and control firms. However, all negative BE stocks have abysmal returns subsequent to their first report of negative BE.

We contrast bankruptcy Section 363 Sales with the traditional path of Chapter 11 reorganization and find that among financial institutions, higher measures of creditors' coordination problems favor the Chapter 11 path, while greater profitability, available cash, asymmetric information between shareholders and creditors, and potential growth rate support the choice of 363 Sales. Among the non-financial firms, higher measures of creditors' coordination problem and available cash favor the course of Chapter 11, while greater profitability, liquidity, and asymmetric information support the path of 363 Sales. We further detect that bankruptcy Section 363 Sales exhibits lower direct fees, and it lasts significantly less time than formal Chapter 11 before the final emergence.

We focus on firms that chronically underperform and evaluate ways that institutional investors can facilitate asset redeployment. Increases in institutional holdings are associated with subsequent acquisition and decreases are associated with subsequent failure. For surviving underperformers, holdings and changes are associated with improved performance, but long-run abnormal returns remain negative and Q remains low. This association between holdings and performance is not causal. Rather, it is explained by “flight to quality” combined with persistence of financial performance, including persistence of abnormal returns for underperformers. The evidence casts doubt on interpretations in previous findings of positive relationships between holdings and performance.

Banks are the most important financial institutions in Turkey because other financial institutions are not developed efficiently yet. Turkish banks experienced financial difficulties and a substantial amount of banks failed in the past. This event urged the government to initiate measures to prevent banks from getting into financial difficulties. As a result of these measures, Turkish banking system currently seems to be very attractive for the foreign investors willing to invest in this sector. One of the main concerns of the foreign investors is a possibility of a new banking crisis although it is very remote at this time. The purpose of this study is to develop early warning systems predicting the financial failure at least three years ahead of financial failure date.

A number of multivariate statistical models such as multiple regression, discriminant analysis, logit, probit are used. We found that the most appropriate model is logit. The significant variables obtained from the models explain very well the causes of the bank failures. Our models can be used to assist interested parties to predict the probability of financial failure of Turkish banks.

Comments by the Federal Reserve Chairman often evoked concerns about whether the government would protect bondholders in the event of default by Fannie Mae and Freddie Mac (F&F). Using a model of capital structure, we analyze the impact of this uncertainty on the value of the implicit subsidy for F&F (and similar institutions). We show that, counter to intuition, an increase in the likelihood that the government will not subsidize these entities via a guarantee may increase the expected cost of the subsidy to the federal government. A cap on the value of the investment portfolio is a more effective mechanism to reduce the risk exposure of the federal government. We also assess the design and impact of proposed receivership rules and highlight the problems in regulating GSE portfolios. Even though F&F are now in conservatorship, the framework is applicable to other government sponsored entities where there is ambiguity about the extent of government backing.

After struggled for nearly 50 years, the city of Detroit filed for Chapter 9 bankruptcy on 18 July, 2013 and emerged from court protection on 10 December, 2014 brining to a close the largest municipal bankruptcy in American history after 17 months. In this research, we argue that the failure of Detroit is collectively attributed to the lack of a well-thought industry strategy. We structure a conceptual framework relating a city's industry mix to the population growth and its human capital stock. The stock of human capital is not only an outcome of the industry strategy but it feeds back to the industry portfolio. By using a time series data, we show a significant impact of the market share of the Detroit Three on the employment of the traditional industries in the Detroit area. The education attainment of the adult population in Detroit suggests a stagnant trend since 2005. Our empirical evidence jointly implies that an un-diversified industry portfolio of a city not only intensifies the correlation between its economic fate with the fluctuation of the focused industry but it also generates barriers to upgrade its work force and as a result, rebalancing its industry mix becomes difficult.

This paper investigates the method for calculating funding banks must provide to Deposit Guarantee Schemes (DGSs) according to the “ex-ante” and “risk-based” criteria introduced by Directive 2014/49/EU (DGSD). We aim to further support the existing documents providing an approach to identify risk indicators and assign weights thereof to determine fundings to be paid to DGSs. It is worth noting that such an approach could enhance current practices as it takes into account the risk and performance of banks as opposed to the overall banking market. By doing so, a more targeted funding can be encouraged. Our results consider the Texas ratio as one of the indicators to look out for. Likewise, such methods could serve as self-assessment frameworks for Institutional Protection Schemes (IPSs) and banks, fostering sound and prudent management, in turn warding off “moral hazard” issues.

We aim at quantitatively measuring the liquidation risk of a firm subject to both Chapters 7 and 11 of the US bankruptcy code. The firm value is modeled by a general time-homogeneous diffusion process in which the drift and volatility are level dependent and can be easily adjusted to reflect the state changes of the firm. An explicit formula for the probability of liquidation is established, based on which we gain a quantitative understanding of how the capital structures before and during bankruptcy affect the probability of liquidation.

In this chapter, we propose the structural model in terms of the Stair Tree model and barrier option to evaluate the fair deposit insurance premium in accordance with the constraints of the deposit insurance contracts and the consideration of bankruptcy costs. First, we show that the deposit insurance model in Brockman and Turle (2003) is a special case of our model. Second, the simulation results suggest that insurers should adopt a forbearance policy instead of a strict policy for closure regulation to avoid losses from bankruptcy costs. An appropriate deposit insurance premium can alleviate potential moral hazard problems caused by a forbearance policy. Our simulation results can be used as reference in risk management for individual banks and for the Federal Deposit Insurance Corporation (FDIC).

This chapter shows examples of applying several current data mining approaches and alternative models in an accounting and finance context such as predicting bankruptcy using US, Korean, and Chinese capital market data. Big data in accounting and finance context is a good fit for data analytic tool applications like data mining. Our previous study also empirically tested Japanese capital market data and found similar prediction rates. However, overall prediction rates depend on different countries and time periods (Mihalovic, 2016). These results are an improvement on previous bankruptcy prediction studies using traditional probit or logit analysis or multiple discriminant analysis. The recent survival model shows similar prediction rates in bankruptcy studies. However, we need longitudinal data to use the survival model. Because of computer technology advances, it is easier to apply data mining approaches. In addition, current data mining methods can be applied to other accounting and finance contexts such as auditor changes, audit opinion prediction studies, and internal control weakness studies. Our first paper shows 13 data mining approaches to predict bankruptcy after the Sarbanes–Oxley Act (SOX) (2002) implementation using 2008–2009 US data with 13 financial ratios and internal control weaknesses, dividend payout, and market return variables. Our second paper shows application of a Multiple Criteria Linear Programming Data Mining Approach using Korean data. Our last paper shows bankruptcy prediction models using Chinese firm data via several data mining tools and compared with those of traditional logit analysis. Analytic Hierarchy Process and Fuzzy Set also can be applied as an alternative method of data mining tools in accounting and finance studies. Natural language processing can be used as a part of the artificial intelligence domain in accounting and finance in the future (Fisher et al., 2016).

This chapter examines the seminal heuristic of anchoring and adjustment and its effects on personal bankruptcy proceedings. Using a unique and detailed database of bankruptcy files we analyze the effect of the official receiver’s recommendation on court decisions. The official receiver in bankruptcy proceedings is appointed by a judicial authority and is required to bring before the court any relevant information needed in order to reach a judicial decision. As part of her responsibilities, the official receiver is required to submit a financial report, which serves as the basis for the court’s proposal for the debtor’s payment plan. This chapter sets out the main factual infrastructure for determining the payment order under bankruptcy proceedings and should include information relevant to the court’s discretion. The richness of the data allows us to investigate the impact of the receiver’s recommendation on court final decisions. We find that overall, the receiver’s recommendation serves as an anchor to the judges, and, moreover, that deviations from this recommendation by the court are extremely rare. Notably, this outcome differs dramatically from that of corporate proceedings. Since personal bankruptcy proceedings do not allow for substantive oversight, which examines the plausibility of the actions or recommendations that the receiver seeks, there is no pure rational explanation for this finding.

The Contingent Claims Analysis (CCA) is a general approach to analyze the stakeholders of a corporation who have contingent claims on the future, uncertain cash-flows generated by the operations of the firms. The CCA allows valuing each stakeholder’s claim and also to assess the risk incurred by the stakeholders. The CCA highlights the potential conflicts of interest among the various claimholders. In this paper, we review applications of CCA including valuation of various forms of debt, rating, credit spread, probability of default and corporate events like dividends, employee stock options and M&A. The CCA framework is shown to be useful to address all these financial questions. In this approach the starting point is that the value and the risk of the firm’s assets are given. The future distribution of the assets’ rates of return is also known and given. The focus is on the liability side of the balance sheet, i.e., the funding sources of the activity of the firm, and more generally on the financial claims of the various claimholders of the firm.

We study the optimal behavior of an investor who is forced to withdraw funds continuously at a fixed rate per unit time (e.g., to pay for a liability, to consume, or to pay dividends). The investor is allowed to invest in any or all of a given number of risky stocks, whose prices follow geometric Brownian motion, as well as in a riskless asset which has a constant rate of return. The fact that the withdrawal is continuously enforced, regardless of the wealth level, ensures that there is a region where there is a positive probability of ruin. In the complementary region ruin can be avoided with certainty. Call the former region the danger-zone and the latter region the safe-region. We first consider the problem of maximizing the probability that the safe-region is reached before bankruptcy, which we call the survival problem. While we show, among other results, that an optimal policy does not exist for this problem. we are able to construct explicit ∊-optimal policies, for any ∊ > 0. In the safe-region, where ultimate survival is assured, we turn our attention to growth. Among other results, we find the optimal growth policy for the investor, i.e., the policy which reaches another (higher valued) goal as quickly as possible. Other variants of both the survival problem as well as the growth problem are also discussed. Our results for the latter are intimately related to the theory of Constant Proportions Portfolio Insurance.