Optimization of a firm's capital structure: A quantitative approach based on a probabilistic prognosis of risk and time of bankruptcy

This article develops a new probabilistic approach to the problem of optimization of a firm's capital structure. The main idea of the approach is straightforward. As a possible firm's bankruptcy is the principal factor restricting the amount of borrowed capital, we assess the probabilities of bankruptcy at various time horizons in the future dependent on the proportion of debt capital and other indices of a firm's current financial position and then calculate how these probabilities influence the firm's value.We identify a set of factors determining conditions of existence and the value of the optimal debt/equity ratio. These include the characteristics of a firm's debt (proportion of short-term component of the debt, cost of service, and maturity horizons of long-term component), characteristics of a firm's ability to pay the debt, and some macroeconomic factors.We represent dependencies of optimal debt/equity ratio and gains in a firm's value on the main influencing factors.The approach is based on real data of real firms and does not use superfluously formalized models. We believe it can be used in practical capital structure decisions although specific calculations must be fulfilled for each firm that needs such decision.     

A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress

Predicting financial distress has been and will remain an important and challenging issue. Many methods have been proposed to predict bankruptcies and detect financial crises, including conventional approaches and techniques involving artificial intelligence (AI). Financial distress information influences investor decisions, and investors depend on analysts’ opinions and subjective judgements in assessing such information, which sometimes results in investors making mistakes. In the light of the foregoing, this paper proposes a novel quarterly time series classifier, which reduces the sheer volume of high-dimensional data to be analysed and provides decision-makers with rules that can be used as a reference in assessing the financial situation of a company. This study employs the following six attribute selection methods to reduce the high-dimensional data: (1) the chi-square test, (2) information gain, (3) discriminant analysis, (4) logistic regression (LR) analysis, (5) support vector machine (SVM) and (6) the proposed Join method. After selecting attributes, this study utilises the rough set classifier to generate the rules of financial distress. To verify the proposed method, an empirically collected financial distress data-set is employed as the experimental sample and is compared with the decision tree, multilayer perceptron and SVM under Type I error, Type II error and accuracy criteria. Because financial distress data are quarterly time series data, this study conducts non-time series and time series (moving windows) experiments. The experimental results indicate that the LR and chi-square attribute selection combined with the rough set classifier outperform the listing methods under Type I, Type II error and accuracy criteria.     

Fuzzy Financial Pricing of Property-Liability Insurance

Abstract

This paper uses fuzzy set theory (FST) to solve a problem in actuarial science, the financial pricing of property-liability insurance contracts. The fundamental concept of FST is the alternative formalization of membership in a set to include the degree or strength of membership. FST provides consistent mathematical rules for incorporating vague, subjective, or judgmental information into complex decision processes. It is potentially important in insurance pricing because much of the information about cash flows, future economic conditions, risk premiums, and other factors affecting the pricing decision is subjective and thus difficult to quantify by using conventional methods. To illustrate the use of FST, we “fuzzify” a well-known insurance financial pricing model, provide numerical examples of fuzzy pricing, and propose rules for project decision-making using FST. The results indicate that FST can lead to significantly different decisions than the conventional approach.     

Investing in Bankrupt Firms

We examine the investment characteristics of firms electing to enter bankruptcy, between 1973 and 1982. Comparisons are made before and after the Bankruptcy Reform Act of 1978. Our results indicate that the 1978 Act had no significant impact on bankruptcy decisions or resolutions for actively traded firms. Trading in bankrupt firms' securities is becoming more common, but no abnormal returns appear to be available. Systematic risk does not change significantly with the filing of bankruptcy, but there is a significant increase in return variance. The financial markets also react to various announcements of stages in the reorganization process.     

Convertible Securities and Bankruptcy Reforms: Addressing Too Big to Fail and Reducing the Fragility of the Financial System*

Eliminating too big to fail should be the first priority of any regulatory reform. But this is easier said than done. As the crisis has taught us, when the systemic risks are perceived to be large, regulators will be very reluctant to close down insolvent firms or impose losses on creditors. So how do we reduce these risks so that regulators can credibly commit to a policy of allowing financial companies to fail and not resort to rescues or bailouts? The author proposes two complementary approaches to this problem: The first is to design capital structures with corrective mechanisms that kick in when a financial firm displays signs of trouble, but still has positive economic capital. To this end, the author endorses the Squam Lake Report's proposal that encourages financial firms to issue convertible debt with an “automatic” provision for converting to equity. In contrast to the Squam Lake proposal, however, the author argues that the conversion to equity should not depend on regulators' decisions and should take place before individual banks and the financial system are in full crisis mode. The second approach is to design a resolution mechanism that will close failing financial firms when early intervention has not led to the firm's recovery. The author argues that the best model for this mechanism is bankruptcy, because of its resolution of claims according to predetermined rules rather than regulatory discretion. However, certain forms of early intervention can also help to lower the costs of permitting firms to fail. For example, the Squam Lake idea that financial institutions be required to develop living wills should make it easier to unwind these firms in an orderly fashion and provide regulators with insight into the degree of systemic risk that these firms impose. The author notes that the challenges associated with getting the executives of healthy banks to plan for their own bankruptcy may indicate that a better use of regulatory resources might be to view the living will as one of the tools of prompt corrective action for firms that become undercapitalized but are still solvent. Once a firm has been declared undercapitalized, regulators would have greater bargaining power to insist on a serious plan for bankruptcy.     

Competitive pressure and firm investment efficiency: Evidence from corporate employment decisions

This study examines the link between product market competition and labour investment efficiency. We find that competitive pressure distorts the efficiency of corporate employment decisions by creating an underinvestment problem. This finding withstands a battery of robustness checks and remains unchanged after accounting for endogeneity concerns. Additional analysis shows that the relationship between product market competition and labour investment efficiency is stronger for firms facing higher competitive threats, greater financial constraints, higher information asymmetry and higher labour adjustment costs. Our results suggest that as competition increases bankruptcy risk, it leads managers to underinvest in labour to avoid incurring labour-related costs.     

A model for valuation of the branch offices of a savings bank based on rough sets

This work proposes a model for the valuation of branch of?ces of banks based on the rough set theory, which could be used as the basis for a decision‐making system for dimensioning strategies of a ?nancial entity. It compares the rough set approach with the competitive discriminant analysis methodology using a common set of data from 421 branches. We pay special attention to data reduction and the creation of decision rules that will allow future branches to be classi?ed. These rules could constitute the basis for the evaluation of the viability of dimensioning strategies for a ?nancial entity. In order to evaluate the predictive capabilities of the decision rules, we present the results of cross‐validation tests to evaluate the ability of the model to classify new branches. It appears that the rough sets approach provides a favourable tool for the valuation of branch of?ces. Copyright © 2004 John Wiley & Sons, Ltd.     

OPERATING LEVERAGE AND UNDERINVESTMENT

Using a contingent claims model, we examine the impacts of both operating leverage and financial leverage on a firm's investment decisions in the context of capacity expansion. Our model shows that quasi‐fixed operating costs could significantly mitigate the underinvestment problem for debt‐financed firms. The existing debt induces equity holders to delay equity‐financed expansion because the expanded earnings base will also benefit the debt holders by lowering the bankruptcy risk. The operating costs decrease this type of wealth transfer from equity holders to debt holders by magnifying the bankruptcy risk of the existing debt upon investment. By applying the Cox proportional hazard model on a large sample of publicly traded U.S. firms over 1966–2016, we offer empirical support for the theoretical predictions. The results are robust to various measures of operating leverage.     

基于非线性组合模型的P2P网贷平台危机预警研究

针对P2P网贷平台现金流较大、利润率较低和财务数据获取困难的特点,构建基于平台交易真实数据的危机预警评价指标体系和组合预测模型.将传统的财务评价指标转换成网贷平台交易数据指标,运用邻域粗糙集属性约简的方法对采集的数据指标进行降噪和约减处理,再基于机器学习理论引入神经网络、支持向量机和Logit回归等模型对数据进行训练.通过分组进行单模型和组合模拟预测,提高了新的破产指标下各模型预测的准确率.     

Strategic Bankruptcies and Price Reactions to Bankruptcy Filings

This study investigates whether the stock market differentiates between firms that file bankruptcy petitions for strategic reasons and firms that file bankruptcy petitions for financial reasons. We perform both univariate and regression tests on a sample of 245 firms that filed Chapter 11 bankruptcy petitions between 1981 and 1996. After controlling for bankruptcy outcome, probability of bankruptcy, firm financial condition, and firm size, we find that, in the period around bankruptcy filing, firms that file bankruptcy petitions for financial reasons have significantly larger stock price declines than firms that file bankruptcy petitions for strategic reasons.     

Credit Contagion Channels: Market Microstructure Evidence from Lehman Brothers’ Bankruptcy

Immediately after Lehman Brothers’ bankruptcy, many firms disclosed their financial exposure (or lack thereof) to Lehman. This offers a clean setting to test two credit contagion channels through which a financial firm's bankruptcy can affect other firms—“counterparty risk” and “information transmission” channels. Using market microstructure variables to measure the various dimensions of contagion effects, we provide robust evidence supporting the significance of counterparty risk. Firms with exposure to Lehman suffered more severe negative effects—wider bid‐ask spread, higher price impact, greater information asymmetry, and greater selling pressure—than unexposed firms. We find mixed evidence regarding the information transmission hypothesis.     

Predicting Bankruptcy Resolution

This study examines classification and prediction of the bankruptcy resolution event. Filing of bankruptcy is resolved through one of three alternative resolutions: acquisition, emergence or liquidation. Predicting the final bankruptcy resolution has not been examined in the prior accounting and finance literature. This post-bankruptcy classification and prediction of the final resolution is harder than discriminating between healthy and bankrupt firms because all filing firms are already in financial distress. Motivation for predicting the final resolution is developed and enhanced. A sample of 237 firms filing for bankruptcy is used. Classification and prediction accuracies are determined using a logit model. A ten-variable, three-group resolution logit model, which includes five accounting and five non-accounting variables is developed. The model correctly classifies 62 percent of the firms, significantly better than a random classification. We conclude that non-accounting data add relevant information to financial accounting data for predicting post bankruptcy resolution. Further, public policy implications for investors, researchers, bankruptcy judges, claimants and other stakeholders are discussed.     

DESIGNING AN IF–THEN RULES‐BASED ENSEMBLE OF HETEROGENEOUS BANKRUPTCY CLASSIFIERS: A GENETIC ALGORITHM APPROACH

This paper proposes a framework for an ensemble bankruptcy classifier that uses if–then rules to combine the outputs from a heterogeneous set of classifiers. A genetic algorithm (GA) induces the rules using an asymmetric, cost‐sensitive fitness function that includes accuracy and misclassification costs. The GA‐based ensemble classifier outperforms individual classifiers and ensemble classifiers generated by other methods. The results of the classifier are in the form of if–then rules. We apply the approach to a balanced dataset and an imbalanced dataset. Both are composed of firms subject to financial distress and cited in the US Securities and Exchange Commission's Accounting and Auditing Enforcement Releases. Copyright © 2014 John Wiley & Sons, Ltd.     

A behavioural theory of the fund management firm

The paper outlines a behavioural theory of the fund manager (FM) firm comprising investment decisions (at stock and portfolio levels) by teams and individuals, and of an organisation process and contextual resource factors affecting decisions. FM organisational processes interacted with resources to enhance investment team decision conditions, costs and processes. Enhanced conditions and reduced decision costs were expected to improve the chances of FM success via new information production and better-quality decisions. These dynamic elements to FM firms can be interpreted as tentative organisational means to deal with major problems of behaviour, uncertainty and information asymmetry at the heart of the valuation, investment and performance problems facing FMs. Field research was conducted in 15 FM firms during 2004–2011. A grounded theory approach was employed in processing the data. This led to improvements in empirical understanding of behaviour within FM firms and markets. The results were discussed relative to relevant literature and previous grounded theory. This created a new conceptual tool to investigate FM underperformance and variety in FM styles. The paper demonstrated an empirically rich model of hierarchy, information production, capital allocation and other resource usage in financial institutions and discussed how this created further opportunities for research.     

Evaluating business credit risk by means of approach‐integrating decision rules and case‐based learning

This research utilizes a new approach which uses a hybrid learning system that combines two representations of knowledge: the first in a form of decision rules referring to general knowledge, and the other of single cases corresponding to exceptions or untypical situations. The Explore algorithm was chosen as a tool for inducing general rules. It generates all simple and sufficiently strong general rules from a given data set. Examples discovered by these rules are then used to identify exceptions and untypical cases. The paper discusses problems connected with tuning parameters of this approach and introduces a new procedure for this task. This methodology is applied to solve the problem of evaluating the risk of business credit applications in a Polish commercial bank. Using information about business credit applications, as described by 35 economic parameters and using five groups of banking risk, a knowledge base consisting of 70 decision rules and 15 specific cases was induced. Testing this model in the standard ‘leaving‐one‐out’ way we achieved the best classification accuracy of 81%. A comparative study showed that results obtained by other machine‐learning algorithms resulted in significantly worse classification accuracy. Copyright © 2001 John Wiley & Sons, Ltd.     

An Empirical Investigation of Firm Longevity: A Model of the Ex Ante Predictors of Financial Distress

Empirical models of a potential failure process that incorporate distress states between the extremes of corporate health and bankruptcy are uncommon. We depict financial distress as a series of financial events that reflect varied stages of corporate adversity. Our intent is to provide information regarding the influence of certain risk dimensions and firm-specific attributes on distressed firm survival over time. Within a theorized distress framework, we utilize the techniques of survival analysis to longitudinally track firms, grouped a priori according to an initial decline in operating cash flows. We find that the event of default has a significant positive association with business failure. Further, we document that the significant accounting covariates tend to change conditional on a firm having progressed through the diverse stages of distress. These findings accentuate the heterogeneous nature of financial distress and potential business failure.     

The Impact of Corporate Governance and Audit Quality on the Cost of Private Loans*

The objective of this paper is to examine whether banks discriminate between firms on the basis of their financial condition when assessing the credit default risk, and to what extent corporate governance and auditor quality mitigate such risks in the pricing of new bank loans. The results indicate that, depending on the probability of bankruptcy, banks rely on different monitoring devices. For firms with a low probability of bankruptcy, banks do not rely on the quality of corporate governance or the auditor's industry specialization. However, auditor tenure and a change in auditor affect the spread. For firms with a high probability of bankruptcy, the spread is adjusted for the quality of corporate governance and the auditor's specialization. These results are robust to alternative specifications and measures.     

The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model

This paper investigates the extent to which the size affects the SME probabilities of bankruptcy. Using a dataset of (11,117) US non-financial firms, of which (465) filed for insolvency under chapters 7/11 between 1980 and 2013. We forecast the bankruptcy probabilities by developing four discrete-time duration-dependent hazard models for SMEs, Micro, Small, and Medium firms. A comparison of the default prediction models for medium firms and SMEs suggests that an almost identical set of explanatory variables affect the default probabilities leading us to believe that treating each of these groups separately has no material impact on the decision making process. However, comparisons between the micro and small firms with the SMEs firms strongly suggest that these categories need to be considered separately when modelling their credit risk.     

Predicting the outcome following bankruptcy filing: a three-state classification using neural networks

This paper uses artificial neural networks (ANNs), multi-state ordered logit and nonparametric multiple discriminant analysis (NPDA) for predicting the three-state outcome of bankruptcy filing. The study compares the classification accuracy of these procedures. It differs from previous studies on predicting financial distress by focusing on the firm after the filing of bankruptcy using accounting data, market data, and court-related information. Following the filing and through court approval the bankruptcy is resolved as firms are either acquired by other firms, emerging as independent operating entities, or liquidated. Distinguishing this three-state outcome is more complex than discriminating between healthy and financially distressed firms. Models suggested in previous studies for predicting the two-group financial distress perform poorly for our three-state scenario. Therefore, we develop models which focus on characteristics relevant for the bankruptcy resolution. We use a sample of 237 publicly traded firms which have complete data. For the entire sample and estimation samples, ANNs provide significantly better three-state classification than logit and NPDA. However, for some holdout samples the differences in classification accuracies are statistically insignificant. © 1997 John Wiley & Sons, Ltd.