This study introduces a novel framework for building company bankruptcy models and a methodology for assessing the vulnerability of industrial economic activities. We consider the identification of bankruptcy as a classification problem and assume that bankruptcy criteria differ across industries. We build highly accurate industry bankruptcy models by constructing separate models for each industry. We also propose a method of analyzing the vulnerability of industrial economic activities in various countries and industries using new indicators we call “expected potential loss,” which we obtain using the predicted likelihood of bankruptcy and company information. (JEL G0, C0)
This study analyzes the importance of the Tokyo Stock Exchange Co-Location dataset (TSE Co-Location dataset) to forecast the realized volatility (RV) of Tokyo stock price index futures. The heterogeneous autoregressive (HAR) model is a popular linear regression model used to forecast RV. This study expands the HAR model using the TSE Co-Location dataset, stock full-board dataset and market volume dataset based on the random forest method, which is a popular machine learning algorithm and a nonlinear model. The TSE Co-Location dataset is a new dataset. This is the only information that shows the transaction status of high-frequency traders. In contrast, the stock full-board dataset shows the status of buying and selling dominance. The market volume dataset is used as a proxy for liquidity and is recognized as important information in finance. To the best of our knowledge, this study is the first to use the TSE co-location dataset. The experimental results show that our model yields a higher forecast out-of-sample accuracy of RV than the HAR model. Moreover, we find that the TSE Co-Location dataset has become more important in recent years, along with the increasing importance of high-frequency trading.
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