This study aims to identify the different financial behaviors of stock-listed companies in Japan and the United States, focusing on the perspectives of the Return on Equity (ROE) ratio and company default. Previous studies have mainly examined areas in profitability and returns to shareholders in Japan and the USA, so there have been criticisms that Japanese companies have not considered shareholders’ interests, compared to the USA. This study adds a credit risk perspective to previous studies while acknowledging the issue that Japanese companies have lower profitability and shareholder returns. Furthermore, this research reveals that the highest principle of the ROE ratio in the USA is also a problem. Fifty listed companies were randomly selected from both Japan and the USA before a comparative analysis of the 100 companies was performed. The results show that companies in the US have higher profitability and ROE ratios, which, however, they externally flow out to increase shareholder returns, resulting in a larger number of defaulted stock-listed companies. On the contrary, Japanese companies have lower profitability, but there is a correlation between the growth of retained earnings and equity, causing lower shareholder returns but maintaining a very low number of defaulted stock-listed companies.

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In recent years, despite the increase in the share of intangible assets in company assets, most are not recorded on their balance sheets. Therefore, intangible asset valuation methods have been studied widely. This study proposes a method for valuing intangible assets using machine learning, based on which a method to extract companies with a high probability of having intangible assets not recorded on their balance sheets has been verified. First, a model to predict intangible assets using machine learning was built. To construct the machine learning model, the data of the financial statements of 28 stocks in the electric machinery industry, forming the Nikkei 225, was used. Data on intangible assets were prepared as the target variable, and data on items listed in financial statements as features were prepared to build the model. A machine learning model was constructed by a regression analysis using the holdout method, and the model was evaluated using the coefficient of determination. Then the valuation of intangible assets was estimated using this machine learning model. After creating a scatter plot in which the valuation of intangible assets estimated by the machine learning model was plotted on the horizontal axis, the valuation of intangible assets recorded on the balance sheet was plotted on the vertical axis. The results revealed that companies with plots below the 45-degree line in the scatter plot may be judged as companies with a high probability of having intangible assets not recorded on their balance sheets.

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The purpose of this study is to determine the extent to which computers have replaced the work of accounting clerks. Deep learning was the focus of attention in the 2000s before the third artificial intelligence boom arrived. Advances in technology have brought with them new challenges. There is a general concern that computers will take away human jobs. The development of technology has made it possible for machines to perform many tasks, but intellectual work has always been done by humans. However, even intellectual work may be done by computers. Many studies have pointed out that it is likely that computers will replace the occupation of accounting clerks. This study uses long-term time-series data from 2005 to 2020 to clarify the balance of receipts of accounting clerical occupations in Japan compared with other occupations. The results show that, compared to other clerical occupations, the number of new job openings, effective job offers, and effective job seekers for clerical accounting occupations appears to be on a downward trend, but this is inferred to be largely influenced by economic trends than technological progress.

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