Search results

Management and especially strategy research rely heavily on the use of ratios to measure a variety of firm, industry, and societal characteristics. Most often, these ratios are created simply to control for size effects (i.e., scaling) emanating from differences in the size of firms, industries, populations, or national economies on the variables of interest. In addition, ratios may also hold theoretical meaning apart from that of their components. Despite the popularity of ratios and regardless of their purpose, the use of ratios is not without controversy. In particular, several studies have demonstrated that the use of ratio measures in correlations and multiple regressions can exaggerate relations of interest leading to biased and unstable results. In this chapter, I review the debate surrounding the use of ratio measures, discuss the problems for estimation and inference that are likely to arise when ratios are used in statistical estimation, and provide alternatives to the use of ratio variables that still satisfy the purpose for which ratio measures are often created.

This paper primarily uses statistical methods to establish financial early‐warning models that make it possible to predict, in advance, the probability of a company experiencing financial distress.

In its empirical analysis, this is the first study that attempts to use financial ratios and non‐financial ratios as variables to analyze business groups, and the present study uses the (K‐S tests), and (M‐U tests) and logit regressions model.

Financial ratio variables remain the primary variables for predicting corporate financial distress. Upon examining the predictor variables for corporate financial distress at one, two, and three years prior to distress, it was found that financial ratio variables were the main ones at one and two years prior to distress, while at three years prior to distress there was one financial ratio variable and two ownership structure variables that showed significant differences. Financial structure, solvency, profitability, and cash flow indicators are the principal financial ratio variables. Ratios of director and supervisor ownership stakes after pledging of shares differed significantly between financially distressed and non‐distressed companies. Establishing independent directors and supervisors can lower the likelihood of financial distress.

As the time remaining before occurrence of financial distress grows shorter, test results show that the number of financial ratios with significant differences goes up. But the longer the time that remains before occurrence of financial distress, the more the financial ratios show non‐significant differences. That is why a number of scholars hold that the longer the period under study, the less explanatory power it has.

The mean contribution of this paper is that establishing independent directors and supervisors can lower the likelihood of financial distress. The paper is useful to researchers or practitioners who are focused on financial risk management and corporate governance implementation.

This paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.

The income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.

The gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.

Investors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.

Ratio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.

The graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.

A consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

This paper examines the profitability and performance measurement of U.S. regional banks during the period 1994–2011, using the GMM estimator technique. Our study extends prior research by including several factors not previously considered using U.S. data.

We use bank-specific, industry-specific, and macroeconomic determinants of profitability contemporaneous with our performance indicators. We follow the accounting fundamental analysis path in explaining the bank performance.

Among the performance measures, the efficiency ratio and provisions for credit losses are negatively and equity scaled by assets is positively related to profitability. However, these relationships either reverse (efficiency ratio and provisions for credit losses) or become insignificant (equity scaled by assets) when the target becomes change in profitability. The level of nonperforming assets is negatively related to profitability across all measures of profitability used. Macroeconomic variables are largely unrelated to profitability during the year they are measured. However, they have a significant relationship with earnings change measures, suggesting they have a lagged effect on profitability. The slope of the yield curve is especially strong in this regard.

We use our determinants to model changes in bank profitability one year ahead, in addition to including several factors not previously considered, using the predictive focus of the fundamental analysis research.

The purpose of the paper is to investigate the extent to which a model based on financial and non-financial variables predicts auditors' decisions to issue qualified audit reports in the case of companies listed on the Tehran Stock Exchange (TSE).

The authors utilized data from the financial statements of 96 Iranian firms as the sample over a period of five years (2012–2016). A total of 480 observations were analysed using a probit model through 11 primary financial ratios accompanying non-financial variables, including the type of audit firm, auditor turnover and corporate performance, which affect the issuance of audit reports.

The results demonstrated high explanatory power of financial ratios and type of audit firm (the national audit organization vs other local audit firms) in explaining qualifications through audit reports. The predictive accuracy of the estimated model is evaluated using a regression model for the probabilities of qualified and clean opinions. The model is reliable, with 72.9% accuracy in classifying the total sample correctly to explain changes in the auditor's opinion.

This study contains some limitations. First, it is likely that similar researches in developed countries set a large sample (e.g. over 1,000 firms) including more years, but the authors cannot follow such a trend due to data access restrictions. Second, banks and financial institutions, investment and holding firms are removed from the sample, because their financial structure is diverse. The third limitation of the study represents the different economic and cultural conditions of Iran compared to other countries. Future studies could focus on internal control material weaknesses or earnings management to predict audit opinion in emerging economies including Iran.

The paper has practical implications and can assist auditors in identifying factors motivating audit report qualifications, mainly in emerging economies.

The paper contributes to auditing research, since very little is known about the determinants of audit opinion in emerging markets including Iran; it also constitutes an addition to previous knowledge about audit opinion in the context of TSE. The paper is one of the rare studies predicting auditor opinions using both financial variables and non-financial metrics.

Using the next-day and next-week returns of stocks in the Korean market, we examine the association of option volume ratios – i.e. the option-to-stock (O/S) ratio, which is the total volume of put options and call options scaled by total underlying equity volume, and the put-call (P/C) ratio, which is the put volume scaled by total put and call volume – with future returns. We find that O/S ratios are positively related to future returns, but P/C ratios have no significant association with returns. We calculate individual, institutional, and foreign investors’ option ratios to determine which ratios are significantly related to future returns and find that, for all investors, higher O/S ratios predict higher future returns. The predictability of P/C depends on the investors: institutional and individual investors’ P/C ratios are not related to returns, but foreign P/C predicts negative next-day returns. For net-buying O/S ratios, institutional net-buying put-to-stock ratios consistently predict negative future returns. Institutions’ buying and selling put ratios also predict returns. In short, institutional put-to-share ratios predict future returns when we use various option ratios, but individual option ratios do not.

This study aims to analyze the relationship between the financial crisis of Korean construction firms and macroeconomic fluctuations.

In this study, current ratio has been used an acting variable for liquidity ratio, and debt ratio for leverage ratio. GNI (Gross National Income), L (index of Liquidity), exchange rate, interest, and CPI (Consumer Price Index) were used for the macroeconomic variables. VECM consisted of Cr_t model and Dr_t model to analyze the relationship between current ratio and macroeconomic variables, and between debt ratio and macroeconomic variables, in order to analyze each model through variance decomposition and impulse response function.

In Cr_t model, L is revealed as highly influencing current ratio. In other words, most fundraising is focused on highly capable financial institutes, investment corporations and public funds, since the scale of construction project funds is huge. Such financial sources actually belong to index L (index of Liquidity), but are calculated as current liability in the financial statements of construction firms, knotting an inverse relationship with current ratio. In Dr_t model, interest is revealed as significant against debt ratio. This seems to be because each construction project needs to raise substantial funds, and the amount to repay is directly influenced by interest fluctuation.

The collected data are limited, as the time series data of current ratio and debt ratio were secured based on the financial statements of the most capable 30 construction firms in Korea. If the sample companies were divided in future research according to scale, in order to analyze the relation between financial crisis and macroeconomic fluctuation by company scale, a more developed result could be obtained.

This study is a useful research to analyze the dynamic relationship between the financial crisis of construction firms and macroeconomic fluctuations. This study can be used to establish a set of countermeasures to apply in the event of macroeconomic fluctuation.

The financial ratios of construction firms are directly used for analysis, making this a more practical analysis than studies of the relationship between macroeconomic fluctuations and the comprehensive indices of construction business.

A large number of empirical studies investigate the determinants of price-earnings (P/E) ratio by focusing on fundamental factors. However, there has been an increasing concern that stock valuation is also driven by investor sentiment. This paper aims to extend the existing literature by exploring whether investor sentiment impacts the P/E ratio.

The paper examines the determinants of P/E ratio by applying latent variable models with investor sentiment as a latent variable and several fundamental factors as control variables. Investor sentiment is proxied by trading volume, advance-decline ratio and price volatility.

Using annual data of the US industries over the period of 1998-2014, the current paper produces new empirical evidence that investor sentiment significantly affects the P/E ratio. This result is robust to the inclusion of several control variables that have been documented to explain the P/E ratio.

The findings have important implications for investors, as downplaying sentiment can lead to significant errors in making equity investment choices based on the P/E ratio.

The analytical framework of the current paper is differentiated from the conventional analysis in which the P/E ratio is regressed against control variables and proxies for sentiment, thus falling into the trap of implicitly presupposing that proxies are perfect measures of investor sentiment. As all proxies may have measurement errors to the true but unobservable investor sentiment, the current paper uses latent variable models to shed new light on the influence of investor sentiment on the P/E ratio.

The purposes of this paper are 1) to develop model of long term financial health for logistics companies in Thailand 2) to identify factors that determine long term financial stability. Many researchers currently provide factors affecting financial health. Most factors refer to financial ratios, not many non-financial ratios such as age and size have been mentioned. This paper considers both financial and non-financial ratios that affect financial performance of Logistics companies in Thailand. The study has covered some interesting non-financial ratios such as Nationality of Shareholders, type of network in Logistics Company, growth rate (consisted of sales growth rate/profit growth rate/asset growth rate / Liability growth rate) and variable of growth rates. The target group is 110 logistics companies in Thailand enlisted from Department of International Trade Promotion Ministry of Commerce, Royal Thai Government. The group is divided into three categories according to financial health of company; Healthy financial, Unhealthy (Distress) and normal situation. The Multidiscriminant Analysis (MDA) is applied to analyze the differentiations among the three categories. Significant variables from MDA will be used as the independent variables for Multimonial Logistic Regression Analysis (MLRA) to identify factors that determine long terms financial stability. This paper find CF/D, RE/TA, BE/TL, Size, Age, Type of network, Nationality of Shareholders and Number of Shareholders are significant factors determine long term financial stability of Logistics company in Thailand.

This study aims to analyze the factors related to the failure of 535 Federal Deposit Insurance Corporation (FDIC)-Insured United States banks in conjunction with the 2008 financial crisis.

The research consists of an analysis of the following three five-year partitions: pre-crisis (2002–2006), crisis (2007–2011) and post-crisis (2012–2016). The main hypothesis is that the factors explaining bank failures vary by period. Using logistic regression analysis, the authors identify the desirable models by period based on three model selection strategies.

Liquidity and non-risk-based capital ratios are important explanatory factors in all three periods. As the authors can see from the results, when comparing the full period (2002–2016) and the three five-year period partitions (2002–2006, 2007–2011 and 2012–2016), the ratios change from period to period, but they measure the same financial areas of concern in different contexts as follows: liquidity, leverage/risk exposure and capital adequacy. Risk-based capital ratios are not effective predictors of bank failures.

Recent academic studies have analyzed bank failures during periods that cover the years before, during and after the crisis, but most of these studies discuss bank failures in the forecasting context only. This study includes an analysis of failure determinants during pre-crisis, crisis and post-crisis subperiods based on the FDIC monitoring system of bank failures and identifies what ratios are more relevant during each period and how they change from period to period.