The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.

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BACKGROUND: Today, sedentary lifestyles are very common for children. Therefore, maintaining a good posture while sitting is very important to prevent musculoskeletal disorders. To maintain a good posture, the formation of good postural habit must be encouraged through posture correction. However, long-term observation is required for effective posture correction. Additionally, posture correction is more effective when it is performed in real time. OBJECTIVE: The goal of this study is to classify nine representative sitting postures of children by applying a machine learning technique using pressure distribution data according to the sitting postures. METHODS: In this study, a customized film-type pressure sensor was developed and pressure distribution data from nine sitting postures was collected from seven to twelve year-old children. A convolutional neural network (CNN) was applied to classify the sitting postures and three experiments were conducted to evaluate the performance of the model in three applicable usage scenarios: usage by familiar identifiable users, usage by familiar, but unidentifiable users, and usage by unfamiliar users. RESULTS: The results of our experiments revealed model accuracies of 99.66%, 99.40%, and 77.35%, respectively. When comparing the recall values for each posture, leaning left and leaning right postures had high recall values, but good posture, leaning forward, and crossed-legs postures had low recall values. CONCLUSION: The results of experiments indicated that CNN is an excellent classification method to classify the posture when the pressure distribution data is used as input data. This study is expected to contribute a development of system to aid in observing the natural sitting behavior of children and correcting poor posture in real time.

Contingent convertible (Coco) bonds have been issued in 2009 after financial crisis for improvement of capital structure in international banks. With more focuses on coco bonds in financial market, academic fields have paid attention to the instrument for optimal structure for issuers and rational pricing methodologies. However, there is a crucial discrepancy in prevailing pricing model and their target subjects. Though most of the coco bonds have been issued based on accounting triggers, many of existing models are based on market prices and therefore exhibit limitations in practical use. In this paper, a more practical pricing method for accounting triggered coco bonds is proposed using stochastic equity ratio process. Empirical results tested on coco bond issued by JB financial group supported the proposed approach with favorable performance in tracking actual market prices. †