Periods Of Financial Distress Research Articles

Cognitive load, social values and financial distress: drivers of restructuring decisions

PurposeThis study aims to analyze the effect of cognitive load and social value orientation on managers’ preferences when they face with two types of restructuring choices in financially distressed firms: the first belonging to the family of organizational restructuring (massive layoffs) and the second to the family of financial restructuring (debt increases).Design/methodology/approachThe authors investigate experimentally the impact of managers’ cognitive load and social value orientation on the decision to restructure leveraged buyout (LBO) firms in financial distress by using either massive layoffs or debt increases.FindingsBy investigating the impact of managers’ cognitive load and social value orientation on the restructuring decision of an LBO firm in financial distress, the research reveals that, on average, cognitively loaded managers prefer massive layoffs over increased debt levels. The massive layoffs seemingly provide a relatively easier way to avoid conflict with influential, residual claimants. In contrast, social value–oriented managers actively avoid massive layoffs and prefer to increase debt.Research limitations/implicationsThese results imply that the performance mechanisms emphasized to improve agency relations, for example, in LBOs, have their own limitations during periods of financial distress. This study shows that one of these limits is related to cognitive distortions and personality traits.Originality/valueIn this research, the originality lies in understanding how managers’ internal factors affect their restructuring decision-making, in the case of LBO firms in financial distress.

A wavelet analysis of investing in cryptocurrencies in the Indian stock market

PurposeThis study is designed to examine the dynamic interrelationships between four cryptocurrencies (Bitcoin, Ethereum, Dogecoin and Cardano) and the Indian equity market. Additionally, the study seeks to investigate the potential safe haven, hedge and diversification uses of these digital currencies within the Indian equity market.Design/methodology/approachThis study employs the wavelet approach to examine the time-varying volatility of the studied assets and the lead-lag relationship between stocks and cryptocurrencies. The authors execute the entire analysis using daily data from 1st October 2017 to 30th September 2023.FindingsThe result of the study shows that financial distress due to the pandemic and the Russian invasion of Ukraine have a negative effect on the Indian equities and cryptocurrency markets, escalating their price volatility. Also, the connectedness between the returns of stock and digital currency exhibits a strong positive relationship during periods of financial distress. Additionally, cryptocurrencies serve as a tool of diversification or hedging in the Indian equities markets during normal financial circumstances, but they do not serve as a diversifier or safe haven during periods of financial turmoil.Originality/valueThis study contributes to understanding the relationship between the Indian equity market and four cryptocurrencies using wavelet techniques in the time and frequency domains, considering both normal and crisis times. This can offer valuable insights into the potential of cryptocurrencies inside the Indian equities markets, mainly with respect to varying financial conditions and investment horizons.

Approximating Botswana's financial cycle: Expanding the macroprudential toolkit

AbstractThis study estimates the financial cycle for Botswana using the Christiano–Fitzgerald band‐pass filter, unobserved components approach and a Markov switching dynamic factor model. Using real credit and equity prices, together with several macroeconomic variables from 2001Q1 to 2021Q4, we find that the domestic financial cycle generally captures movements along the business cycle, with peaks reflecting the cumulative build‐up of risks during the boom that ultimately burst, coinciding with periods of financial distress. These findings shed light on the implications of financial fluctuations on domestic financial stability, hence, inform effective calibration of macroprudential tools, while also providing traction for implementation of the recently approved macroprudential policy framework for Botswana.

A Dynamic Latent-Space Model for Asset Clustering

Abstract Periods of financial turmoil are not only characterized by higher correlation across assets but also by modifications in their overall clustering structure. In this work, we develop a dynamic Latent-Space mixture model for capturing changes in the clustering structure of financial assets at a fine scale. Through this model, we are able to project stocks onto a lower dimensional manifold and detect the presence of clusters. The infinite-mixture assumption ensures tractability in inference and accommodates cases in which the number of clusters is large. The Bayesian framework we rely on accounts for uncertainty in the parameters’ space and allows for the inclusion of prior knowledge. After having tested our model’s effectiveness and inference on a suitable synthetic dataset, we apply the model to the cross-correlation series of two reference stock indices. Our model correctly captures the presence of time-varying asset clustering. Moreover, we notice how assets’ latent coordinates may be related to relevant financial factors such as market capitalization and volatility. Finally, we find further evidence that the number of clusters seems to soar in periods of financial distress.

Borrowing Stigma and Lender of Last Resort Policies

Abstract How should the lender of last resort provide liquidity to banks during periods of financial distress? During the 2008–2010 crisis, banks avoided borrowing from the Fed’s long-standing discount window but actively participated in its special monetary program, the Term Auction Facility, although both programs had the same borrowing requirements. Using an adverse selection model with endogenous borrowing decisions, we explain why the two programs suffer from different stigma costs and how the introduction of TAF incentivized banks’ borrowing. We discuss the empirical relevance of the model’s predictions. [Banks] deliberately did not ask for the liquidity they needed for fear of damaging their reputation—the ‘stigma’ problem… I do not think we were conscious of this before the crisis started and I do not think central banks have a convincing answer to it… This is, I think, still a challenge in how to manage the process of central bank provision of liquidity support. This is one of the big intellectual issues that has not been fully resolved. (Governor Mervyn King, Bank of England (2016)) For various reasons, including the competitive format of the auctions, [Term Auction Facility] has not suffered the stigma of conventional discount window lending and has proved effective for injecting liquidity into the financial system… Another possible reason that [Term Auction Facility] has not suffered from stigma is that auctions are not settled for several days, which signals to the market that auction participants do not face an immediate shortage of funds. (Ben Bernanke, testimony to U.S. House of Representatives (2010))

The effects of financial frictions on Slovenian companies: A panel VAR approach

In this paper, we investigate the impact of imperfect financial markets on the business investment of Slovenian non-financial companies. We focus on exploring the borrower's balance sheet channel, through which companies’ worsening balance sheets, reduce the availability of external finance. The effects of exogenous shocks are, therefore, amplified through the financial accelerator mechanism and affect the overall economy. Using data from annual reports of Slovenian non-financial companies from 2005 to 2020, we estimate a panel VAR model on various sample definitions. The results confirm the existence of the balance sheet channel, as the impulse response of investment to cash flow shocks is statistically significant. This effect is especially noticeable during the period from 2006 to 2012. Our findings suggest that financial frictions affect the availability of external financing and, consequently, the investment activity of Slovenian non-financial companies. Policymakers should prioritize measures to ensure adequate external financing, particularly for financially constrained firms, to mitigate the negative impact of reduced economic activity during periods of financial distress and economic downturns.

Exploring illiquidity risk pre and during the COVID-19 pandemic era: Evidence from international financial markets

Research aims: Illiquidity risk is one of the complex issues that institutional investors and market participants continually face over time. It is because the constructs of illiquidity risk are sometimes complicated, robust, and not so evident in secondary markets. Hence, this study aims to empirically explore illiquidity risk before and during the COVID-19 pandemic to understand how much investors were expected to lose if they invested in stock markets during these periods.Design/Methodology/Approach: This study used a GARCH model and the Amihud illiquidity ratio to achieve its objective. Trading volumes and price returns for the JSE, CAC 40, DAX, Nasdaq, BIST 100, and SSE were from June 30, 2017, to June 30, 2019, and January 1, 2020, to December 31, 2021.Research findings: As expected, the findings revealed higher illiquidity risk during periods of financial distress, such as the COVID-19 pandemic. During the financial crisis, investors could lose up to $22268.44 a day in less developed markets, such as the JSE, while the average loss in developed markets ranged between $0.22 to $11.53 in the Nasdaq and DAX, respectively. On average, a much lower figure was observed before the financial crisis. The BIST100, CAC 40, DAX, and Nasdaq are excellent options for those seeking lower-risk premiums.Theoretical and Practitioner/Policy implication: Policies such as adequate market microstructure and greater transparency in trading are strongly recommended for less developed markets, especially during periods of financial distress. Also, the findings of this study provide valuable insight into short-term traders and market participants attracted to liquid markets, where they can easily enter and exit their positions with minimal transaction costs. To the author's knowledge, this paper is the first to model illiquidity risk in stock markets.Research limitation/Implication: It is possible that the current study did not accurately capture the cost of illiquidity in the sampled financial markets and cannot be applied to other financial markets.

Investigating Joint Market Hypothesis during Periods of Financial Distress and its Implications

Empirical finance is about building an understanding of security prices and financial markets so as to foster better decision making while avoiding anecdote biases. Successful investors are often cognisant of their risk exposures coupled with extreme discipline and leverage. Therefore, the aim of this study was to explore the concept of joint market hypothesis in five international stock indexes. This study used the Taleb’s ratio which is an extension of the Parkinson model. The financial distress periods were the financial crisis (December 1, 2007 to June 30, 2009) and the Covid-19 pandemic (January 1, 2020 to December 31, 2021). Joint hypothesis was evident in the JSE, CAC 40, the DAX and the Nikkei 225 while the Nasdaq displayed high levels of market efficiencies. Allocating more capital to the JSE, CAC 40, the DAX and the Nikkei 225 during periods of financial distress in conjunction with the Fama French five factor model will generate higher returns. As per the author’s knowledge, this study is the first to explore the concept of joint market hypothesis during periods of distress

A Macro-Finance Model with Sentiment

Abstract This paper incorporates diagnostic expectations into a general equilibrium macroeconomic model with a financial intermediary sector. Diagnostic expectations are a forward-looking model of extrapolative expectations that overreact to recent news. Frictions in financial intermediation produce non-linear spikes in risk premia and slumps in investment during periods of financial distress. The interaction of sentiment with financial frictions generates a short-run amplification effect followed by a long-run reversal effect, termed the feedback from behavioural frictions to financial frictions. The model features sentiment-driven financial crises characterized by low pre-crisis risk premia and neglected risk. The conflicting short-run and long-run effect of sentiment produces boom–bust investment cycles. The model also identifies a stabilizing role for diagnostic expectations. Under the baseline calibration, financial crises are less likely to occur when expectations are diagnostic than when they are rational.

Measuring non-exchangeable tail dependence using tail copulas

AbstractQuantifying tail dependence is an important issue in insurance and risk management. The prevalent tail dependence coefficient (TDC), however, is known to underestimate the degree of tail dependence and it does not capture non-exchangeable tail dependence since it evaluates the limiting tail probability only along the main diagonal. To overcome these issues, two novel tail dependence measures called the maximal tail concordance measure (MTCM) and the average tail concordance measure (ATCM) are proposed. Both measures are constructed based on tail copulas and possess clear probabilistic interpretations in that the MTCM evaluates the largest limiting probability among all comparable rectangles in the tail, and the ATCM is a normalized average of these limiting probabilities. In contrast to the TDC, the proposed measures can capture non-exchangeable tail dependence. Analytical forms of the proposed measures are also derived for various copulas. A real data analysis reveals striking tail dependence and tail non-exchangeability of the return series of stock indices, particularly in periods of financial distress.

Investigating Stochastic Volatility during Periods of Financial Distress: Evidence from International Financial Markets

Capturing the dynamic properties of financial market volatility has always been very auspicious in asset pricing. The mean reverting properties of financial markets are of paramount importance for investment decision makers with far reaching implications. The purpose of this study was to investigate stochastic volatility in international financial markets during periods of financial distress. Accordingly, a Heston model was used to stochastic volatility in the CAC 40, DAX, JSE, Nasdaq Index and the Nikkei-225 during the 2007-2008 financial crisis and the Covid-19 pandemic. This study used January 1, 2020 to December 31, 2021 and December 1, 2007 to June 30, 2009 as the sample period representing the Covid-19 pandemic and financial crisis respectively. The findings of this study revealed the extortionate stochastic volatility during the Covid-19 pandemic compared to the financial crisis. There will be slightly higher than normal arbitrage opportunities and mispricing in most financial markets as a result of the pandemic.

Stock Market Liquidity during Periods of Distress and its Implications: Evidence from International Financial Markets

Traditional market pricing models assume frictionless markets with abundant liquidity. This traditional models also incorporate stock market liquidity as an exogenous cost. However, this paradigm has many shortcomings due to its inability to explain some of the problems associated with security market illiquidity. The aim of this study was to explore the concept of stock market liquidity during periods of financial distress. A Markov switching GARCH model was used to investigate market liquidity in the CAC 40, DAX, JSE, Nasdaq Index and the Nikkei-225 during the 2007-2008 financial crisis and the Covid-19 pandemic. The sample period was January 1, 2020 to December 31, 2021 and December 1, 2007 to June 30, 2009. From the findings, some financial markets where still liquid despite the financial crisis with the exception of the Nasdaq index. Conversely, all the financial markets under consideration displayed strong illiquidity during the covid-19 pandemic. In essence, the level of market depth has significantly decreased from the financial crisis to the covid-19 pandemic which may be attributed to increasing margin requirements and information asymmetry as well as price restrictions. There is an urgent need for regulatory authorities to review some of the trading regulations during financial distress.

Investigating Causality And Market Contagion During Periods Of Financial Distress And Its Implications

Purpose: A notable observation in the literature of financial markets is the debate on market contagion and causality. During periods of financial distress, global financial markets experience record low market prices partly due to the spread of fear. It was therefore necessary to investigate market contagions using causality relationships during periods of financial distress. Methodology: A unit root test, Granger causality and Test for equality of means was used as the blueprint. The sample periods where December 1, 2007 to June 30, 2009 and January 1, 2020 to December 31, 2021. Findings: Contrary to the perceptions that prevails in most stock markets during distress, there was little empirical evidence to support market contagions. Although very few markets are indeed related. Originality/Value: The implications of this study extends the efficient market hypothesis concept to market efficiency during periods of financial distress. It is evident that financial markets display greater efficiencies during periods of financial distress. This study is the first to investigate market contagion during periods of distress as per author’s knowledge.

Risk of declined company performance during COVID-19–Spatial quantile autoregression based on network analysis

There is a great deal of literature regarding the performance of listed companies during periods of financial distress and/or financial crisis. However, very few of them considered the interaction of companies and its impact on performance. This paper introduces network structure and a spatial quantile autoregressive model to study the heterogeneous effect of exogenous variables on the different quantiles of company performance during COVID-19 pandemic. We find that the interconnectedness between listed companies in China changed significantly after the outbreak of COVID-19. The pandemic outbreak, as well as company size, robustness of the supply chain, and other exogenous variables, significantly affect company performance. In addition, we identify four major drivers of company performance from the perspective of spatial analysis. We also find that the impact of explanatory variables shows distributional heterogeneity.

Banking diversity, financial complexity and resilience to financial shocks: evidence from Italian provinces

ABSTRACT In this paper, we investigate the influence of banking and financial diversity on stability. We compute an index of banking diversity for Italian provinces and, drawing on network theory, we propose a measure of the diversity and development of the overall provincial financial sector. Our results show that diversity in the banking and financial markets promotes greater stability. Such beneficial effects are particularly evident during periods of financial distress. We ascribe our findings to the better diversification achieved by more diverse financial systems, as documented by lower loan concentration and higher loan diversification in terms of economic destination and borrower category.

How do firms hedge in financial distress?

AbstractWe examine how firms hedge in financial distress. Using hand‐collected data from oil and gas producers, we find that these firms hedge oil prices during periods of financial distress. Derivative portfolios in these firms are characterized by short put options. These positions are part of a composite three‐way (3W) collar strategy that combines buying put options and selling put and call options with differing strike prices. Because liquidity demand varies with the degree of financial distress, the 3W collar strategy preserves incentives for future growth.

Financial Distress Prediction for Digital Economy Firms: Based on PCA-Logistic

Financial distress prediction is important for risk prevention and control of digital economy firms, as well as going concern guarantee. This paper takes 100 Chinese A-share listed digital economy firms from 2017 and 2021 as samples, obtains financial indicators by combining the characteristics of digital economy firms, the first three periods of financial distress are systematically modeled employs Logistic regression, while we use the Principal Component Analysis method to deal with the problem of multicollinearity. The results show that the profitability factor has the greatest contribution to the predictive role; the closer to the year in which the financial distress occurred, the higher the prediction accuracy rate. Finally this model achieves 86% prediction accuracy. The successful modelling provides a basis for information users to determine the financial distress of firms accurately and prospectively in the digital economy.

Balancing Before and After: Treasury Market Reform Proposals and the Connections Between Ex-Ante and Ex-Post Liquidity Tools

This paper develops a simple framework that helps to draw out some of the potential connections between ex-ante liquidity risk management tools such as liquidity requirements or mandatory fees and ex-post liquidity tools such as a lender of last resort. A central message of this analysis is that policy actions that expand the lender of last resort function so as to better address periods of financial distress are likely to be most effective when accompanied by regulations or other mechanisms that encourage socially-responsible ex-ante liquidity risk management on the part of financial firms. Regulation in the form of a liquidity coverage requirement can be helpful in moving private sector outcomes toward a social optimum. A mandatory fee schedule also emerges as a potentially very useful tool. The structure of the optimal fee schedule depends on both the scale of volatile liabilities and the extent of “liquidity coverage” maintained to cover potential funding shortfalls. Both liquidity requirements and mandatory fees can help to address a form of time consistency problem in connection with the provision of ex-post liquidity support through a lender of last resort. The framework also provides some potentially useful benchmarks in evaluating the distribution of liquidity risks across different classes of financial firms.

The recurrence of financial distress: A survival analysis

Companies often suffer periods of financial distress before filing for bankruptcy. Unlike one-off bankruptcies, financial distress can occur repeatedly within the same individual firm. This paper is focused on the recurrence of financial distress and studies the Chinese stock market, where Special Treatment – an official indicator of financial distress – can be repeatedly applied to a listed company. We employ a stratified hazard model to predict the probability of subsequent distress with variables, including duration dependency, event-based factors, institutional variables, financial ratios, market-based variables and macroeconomic conditions. Our empirical results show that accounting and market-based variables have limited power in predicting the recurrence of distress, whereas the duration of recovery, restructuring events and their interaction terms with the accounting and macroeconomic factors affect the recurrent risk significantly. Tested on out-of-time samples, our proposed hazard models show a robust performance in the prediction of recurrent risk over time.

Bank systemic risk exposure and office market interconnectedness

We empirically examine how systemic risk in the banking sector leads to correlated risk in office markets of global financial centers. In so doing, we compute an aggregated measure of systemic risk in financial centers as the cumulated expected capital shortfall of local financial institutions. Our identification strategy is based on a double counterfactual approach by comparing normal with financial distress periods as well as office with retail markets. We find that office market interconnectedness arises from systemic risk during financial turmoil periods. Office market performance in a financial center is affected by returns of systemically linked financial center office markets only during a systemic banking crisis. In contrast, there is no evidence of correlated risk during normal times and among the within-city counterfactual retail sector. The decline in office market returns during a banking crisis is larger in financial centers compared to non-financial centers.