• Journal of Distribution Science
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• v.20 no.12
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• pp.117-123
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• 2022

Purpose: This study investigates the relationship between earnings forecasts estimated from a cross-sectional earnings forecast model and firm characteristics such as firm size, sales volatility, and earnings volatility. Research design, data and methodology: The association between earnings forecasts and the aforementioned firm characteristics is examined using 214 firm-year observations with analyst following and 848 firm-year observations without analyst following for the period of 2011-2019. I estimate future earnings using a cross-sectional earnings forecast model, and then compare these model-based earnings forecasts with analysts' earnings forecasts in terms of forecast bias and forecast accuracy. The earnings forecast bias and accuracy are regressed on firm size, sales volatility, and earnings volatility. Results: For a sample with analyst following, I find that the model-based earnings forecasts are more accurate as the firm size is larger, whereas the analysts' earnings forecasts are less biased and more accurate as the firm size is larger. However, for a sample without analyst following, I find that the model-based earnings forecasts are more pessimistic and less accurate as firms' past earnings are more volatile. Conclusions: Although model-based earnings forecasts are useful for evaluating firms without analyst following, their accuracy depends on the firms' earnings volatility.

• Asia-Pacific Journal of Business
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• v.9 no.4
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• pp.137-155
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• 2018

It has been reported that there is a significant positive relationship between the unexpected earnings on the earnings announcement date and the cumulative abnormal returns following the earnings announcement date. This study investigates whether the results of prior studies are because the public announcement of shareholders' meeting date was selected as the event date instead of either the preliminary earnings disclosure date or the profit/loss change announcement date. The results of this study are as follows. First, post-earnings-announcement drift(PEAD) occurs when unexpected earnings were computed based on the prior period earnings and the public announcement of the shareholders' meeting date as the profit disclosure date. Second, when analyzing the PEAD with the unexpected earnings calculated using the financial analysts' forecasts, no PEAD has been found both on the date of the shareholders' meeting and the earlier date of the preliminary earnings disclosure, profit/loss change announcement, or the public announcement of the shareholders' meeting. Foster et al. (1984) analyze the PEAD using time series model and earnings forecasting model and suggest that the PEAD appears only in the time series model. In this study, too, in the case of using analysts' profit forecasts, the lack of the PEAD shows that the PEAD can be changed according to the method of measuring the unexpected earnings.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.