• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.71-103
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• 2009

Considering the expenditure size of public institutions centering on public enterprises, about 28% of Korea's GDP in 2007, public institutions have significant influence on the Korean economy. However, still in the new government, there are voices of criticism about the need of constant reform on public enterprises due to their irresponsible management impeding national competitiveness. Especially, political controversy over appointment of executives such as CEOs of public enterprises has caused the distrust of the people. As one of various reform measures for public enterprises, this study analyzes the effect of internal governance structure of public enterprises on their managerial performance, since, regardless of privatization of public enterprises, improving the governance structure of public enterprises is a matter of great importance. There are only a few prior researches focusing on the governance structure and managerial performance of public enterprises compared to those of private enterprises. Most of prior researches studied the relationship between parachuting employment of CEO and managerial performance, and concluded that parachuting produces negative effect on managerial performance. However, different from the results of such researches, recent studies suggest that there is no relationship between employment type of CEOs and managerial performance in public enterprises. This study is distinguished from prior researches in view of following. First, prior researches focused on the relationship between employment type of public enterprises' CEOs and managerial performance. However, in addition to this, this study analyzes the relationship of internal auditors and managerial performance. Second, unlike prior researches studying the relationship between employment type of public corporations' CEOs and managerial performance with an emphasis on parachuting employment, this study researches impact of employment type as well as expertise of CEOs and internal auditors on managerial performance. Third, prior researchers mainly used non-financial indicators from various samples. However, this study eliminated subjectivity of researchers by analyzing public enterprises designated by the government and their financial statements, which were externally audited and inspected. In this study, regression analysis is applied in analyzing the relationship of independence and expertise of public enterprises' CEOs and internal auditors and managerial performance in the same year. Financial information from 2003 to 2007 of 24 public enterprises, which are designated by the government, and their personnel information from the board of directors are used as samples. Independence of CEOs is identified by dividing CEOs into persons from the same public enterprise and persons from other organization, and independence of internal auditors is determined by classifying them into two groups, people from academic field, economic world, and civic groups, and people from political community, government ministries, and military. Also, expertise of CEOs and internal auditors is divided into business expertise and financial expertise. As control variables, this study applied foundation year, asset size, government subsidies as a proportion to corporate earnings, and dummy variables by year. Analysis showed that there is significantly positive relationship between independence and financial expertise of internal auditors and managerial performance. In addition, although business expertise and financial expertise of CEOs were not statistically significant, they have positive relationship with managerial performance. However, unlike a general idea, independence of CEOs is not statistically significant, but it is negatively related to managerial performance. Contrary to general concerns, it seems that the impact of independence of public enterprises' CEOs on managerial performance has slightly decreased. Instead, it explains that expertise of public enterprises' CEOs and internal auditors plays more important role in managerial performance rather than their independence. Meanwhile, there are limitations in this study as follows. First, in contrast to private enterprises, public enterprises simultaneously pursue publicness and entrepreneurship. However, this study focuses on entrepreneurship, excluding considerations on publicness of public enterprises. Second, public enterprises in this study are limited to those in the central government. Accordingly, it should be carefully considered when the result of this study is applied to public enterprises in local governments. Finally, this study excludes factors related to transparency and democracy issues which are raised in appointment process of executives of public enterprises, as it may cause the issue of subjectivity of researchers.