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The recent trends toward consolidation in financial markets, both global and national, and the difficult state of the Japanese financial industry suggest the importance of an examination of consolidation in the Japanese financial industry. Recent mega-mergers have produced enormous banks such as the Bank of Tokyo-Mitsubishi, but as usual in Japan the health of the financial industry depends substantially on small and medium-size institutions. There the recent trend toward merger extends back a few decades.

This study reports a further examination of performance of mergers among credit associations (CA) in Japan, using a sample of 25 pairs of merging and non-merging institutions. Each pair is chosen for comparability in terms of variates such as size and geography. Performance is measured by 19 financial ratios sampled during the period of 1983–1998. Considering pre-versus post-merger values of various ratios, mergers have significant positive effects on ratios such as non-personnel expenses ratio, income ratio after tax, loan-deposit ratio, deposit per office and deposit per association's member. But mergers have rather negative effects on net equity ratio and ratio of current expense to current income. On balance mergers are a positive factor in the CA industry. These results contradict the conclusions of earlier studies (Hoshino, 1988).

Among relative financial ratios (comparing merging and non-merging CA), the effect of merger on yield on interest received and non-personnel expenses ratio are shown to be positive in this sample, contradicting previous results. However, in general the financial characteristics of merging CA are rather inferior to those of non-merging CA.

We use four alternative prediction models to examine the usefulness of financial ratios in predicting business failure in China. China has unique legislation regarding business failure so it is an interesting laboratory for such a study. Earnings Before Interest and Tax to Total Assets (EBITTA), Earning Per Share (EPS), Total Debt to Total Assets (TDTA), Price to Book (PB), and the Current Ratio (CR), are shown to be significant predictors. Prediction accuracy achieves a range from 78% to 93%. Logit and Neural Network models are shown to be the optimal prediction models.

This study aims to investigate the timescale effects of the corporate governance measure on predicting financial distress of corporations. A new corporate governance measure is adopted in the logistic regression model. Historical data of the companies listed on the Taiwan Stock Exchange Corporation (TSEC) were used in the empirical analysis. The analysis was based on three different prediction horizons comprising one-, two- and three-year horizons. The results confirmed that the accuracy of the logistic regression model for predicting corporate financial distress can be improved by incorporating the corporate governance measure. Moreover, the improvements of the correct rate for classification by incorporating the corporate governance measure increased as the prediction horizon was raised. The improvements of the correct rate for classification by incorporating the corporate governance measure are 2.9%, 4.4% and 5.8% for "Year 1", "Year 2" and "Year 3" models respectively.

The primary purpose of this paper is to discuss how to use the active and interdisciplinary approaches to teach corporate finance. First, I describe the content and structure of the book entitled Corporate Finance and Strategy: An Active Learning Approach [Lee, CF, AC Lee, JC Lee and M Lee (2022). World Scientific]. Second, I discuss how the interdisciplinary approach is used to integrate corporate finance and strategy with other subjects. Third, I discussed how I require students to write three projects to make this course become active instead of passive to learn corporate finance. Finally, I discuss how students can benefit from active and interdisciplinary approach to learn finance.

This study presents a decision-support method to estimate the next year performance of corporate Operating Income Margin (OIM). It is based on a unique combination of cross-section model and the rules-based evaluation mechanism. The estimate is done in terms of broad categories, and not precise numerical values.

The model is constructed as follows: its dependent variable (OIM) is one year ahead vs. the corresponding explanatory variables. This structure of the model allows us to view explanatory variables as reflecting financial potential of corporations. The evaluation component consists of a set of rules designed to identify the companies whose “potential” clearly points to an opportunity to invest. For the method presented here to succeed, it is necessary to utilize a highly reliable modeling method, even if it is “Fuzzy”. We apply Soft Regression (SR), which is a Soft Computing modeling tool based on Fuzzy Logic, and utilize all available proxy variables by creating intervals of values. Advantages of utilizing SR, and the intervals’-based modeling are extensively discussed. Modeling results for five consecutive years are consistent and stable, thus indicating high degree of reliability. Testing indicates very high success rate for the stock market related domain, the lowest being 87.9%.

The paper examines the determinants of profitability of real estate companies by using panel data of Vietnamese listed companies on the Hanoi stock exchange (HNX) and Ho Chi Minh City stock exchange (HOSE) from 2007 to 2020. Profitability ratios are measured by return on assets (ROA) and return on equity (ROE). The results indicate that the cost on revenue ratio, debt-to-equity ratio and the crisis and COVID-19 pandemic are negatively correlated with firm profitability. Meanwhile, the sales to current assets ratio, money supply growth rate and economic growth rate (GDPG) provide a positive correlation with profitability. We find that firm size and equity to total assets have positive effects on ROA, while there is a negative relationship between equity to total assets and ROE, and not enough evidence to conclude how firm size affects ROE. The study thereby provides suggestions and recommendations for the administrators of the government, real estate companies and investors in Vietnam.

In the years following the economic and financial crisis, Italy, where most firms are family-owned, has seen the demise of more companies than any other country. To avoid a similar disaster in the future, it is important to understand which determinants influence firms’ survival. Stemming from financial and family business literature, this paper investigates the role of financial ratios and family corporate governance in predicting family firms’ survival probability. To obtain empirical evidence, it performs a mediating regression analysis using a sample of 273 Italian family firms. The main findings show that family ownership concentration and the presence of a family CEO increase firms’ survival probability, while a high number of family members involved in the firm and the co-presence of more generations hinder it.

In this chapter, we will discuss four alternative security valuation models. These four models are as follows: (i) Warren and Shelton model, (ii) Francis and Rowell model, (iii) Feltham–Ohlson model, and (iv) combined forecasting model. In this chapter, we will show how accounting, stock price, and economic information can be used to determine security values in terms of finance theory. Algebraic simultaneous equation, econometrics model, and Excel program will be used for empirical studies.