• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.387-396
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• 2018

Stock price prediction has been a difficult problem to solve. There have been many studies to predict stock price scientifically, but it is still impossible to predict the exact price. Recently, a variety of types of cryptocurrency has been developed, beginning with Bitcoin, which is technically implemented as the concept of distributed ledger. Various approaches have been attempted to predict the price of cryptocurrency. Especially, it is various from attempts to stock prediction techniques in traditional stock market, to attempts to apply deep learning and reinforcement learning. Since the market for cryptocurrency has many new features that are not present in the existing traditional stock market, there is a growing demand for new analytical techniques suitable for the cryptocurrency market. In this study, we first collect and process seven cryptocurrency price data through Bithumb's API. Then, we use the gradient boosting model, which is a data-driven learning based machine learning model, and let the model learn the price data change of cryptocurrency. We also find the most optimal model parameters in the verification step, and finally evaluate the prediction performance of the cryptocurrency price trends.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.39-57
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• 2021

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

• The Korean Journal of Financial Management
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• v.15 no.2
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• pp.369-399
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• 1998

Most of the studies on stock price predictability using the linear model conclude that there are little possibility to predict the future price movement. But some anomalous patterns may be generated by remaining market inefficiency or regulation, market system that is facilitated to prevent the market failure. And these anomalous pattern, if exist, make them difficult to predict the stock price movement with linear model. In this study, I try to find the anomalous pattern using the ANN model. And by comparing the predictability of ANN model with the predictability of correspondent linear model, I want to show the importance of recognitions of anomalous pattern in stock price prediction. I find that ANN model could have the superior performance measured with the accuracy of prediction and investment return to correspondent linear model. This result means that there may exist the anomalous pattern that can't be recognized with linear model, and it is necessary to consider the anomalous pattern to make superior prediction performance.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.249-258
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• 2020

This study examines and analyzes the effect of innovation on the price to book value mediated by managerial ownership in Indonesian companies. In order to achieve the goals and objectives, the company increases its value by increasing shareholders. Improving the welfare of shareholders can be done through investment and financial policies, and is reflected in share prices in the capital market. The higher the share price, the better the owner's welfare, and the company's value will also increase. The population of this study is the manufacturing companies - as many as 162 - listed on the Indonesia Stock Exchange in 2012-2017. By using a purposive sampling method, 25 companies met the criteria for the sample. The data comes from the companies' annual report taken from the Indonesia Stock Exchange website. The data is further analyzed using partial least square (PLS). The results of the study showed that innovation has a significant effect on price to book value. The companies with high marketing innovation produce high company performance as well. The companies get a commensurate reward from marketing innovation activities to carry out continuous marketing innovations. In addition, managerial ownership does not mediate the relationship between innovation and price to book value.

• KDI Journal of Economic Policy
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• v.33 no.4
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• pp.27-48
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• 2011

This paper investigates the extent of global and regional integration in East Asia using stock price index as a measure of economic performance. We employ a structural VAR model to separate the underlying shocks into "global", "regional" and "country-specific" shocks. The estimation results show that country-specific shocks still play a dominant role in East Asia although their role appears to have declined over time, especially after the 1997 financial crisis. Global and regional shocks are responsible for small but increasing shares of stock price fluctuations in all countries. The results indicate that the stock markets in East Asia remain dissimilar and are subject to asymmetric shocks in comparison to European countries.

• The Korean Journal of Financial Management
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• v.27 no.2
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• pp.85-114
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• 2010

Using the financial institutions that have adopted performance-based stock option plan, this paper examines whether performance-based executive stock options improves effectively firm value. Over the period 2002~2005, we investigate short-term and long-term effects of the performance-based stock options on stock price. The empirical results are summarized as follows. First, the announcement of plain vanilla stock options generates no significant effects on firm value. Meanwhile, the announcement of performance-based stock options results in negative and significant abnormal returns, which is contrary to the expectation. In addition, we find that there are strong, significant and negative announcement effects when banks grant performance-based stock options. Secondly, there is no significant difference between the long-term performance of the sample granting stock options and that of the benchmarks, which is similar to the findings of the previous research. Also, we fail to get any evidence that performance-based stock option awards have improved the long-term firm value.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.23-30
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• 2021

The purpose of this study is to compare the level of risk and return of Islamic stocks in the Jakarta Islamic Index (JII) with conventional stocks on the IDX30 in the period from January 2017 to July 2019. The Sharpe ratio method is used to calculate risk and stock returns. The performance of the stock portfolio is measured by comparing the risk premium portfolio with the portfolio risk that is expressed as a standard deviation of the total risk. This study uses secondary data collected by the Indonesia Stock Exchange (IDX), which provides the names of stock issuers included in the JII and IDX30 indices along with their montly closing price. The results of the descriptive analysis show that the JII Sharpe ratio index from January 2017 to July 2019 is from the minimum range of -0.28820 to a maximum range of 0.05622, while the IDX30 Sharpe ratio index from January 2017 to July 2019 is from the minimum range of -0.09290 to the maximum range of 0.17436. The results of inferential analysis using a different test show that there is a significant difference between the Sharpe ratio JII and IDX30 in measuring the performance of the stock portfolio.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.702-714
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• 2020

Stock investment is a personal investment technique that has gathered tremendous interest since the reduction in interest rates and tax exemption. However, it is risky especially for those who do not have expert knowledge on stock volatility. Therefore, it is well understood that accurate stock trend prediction can greatly help stock investment, giving birth to a volume of research work in the field. In order to compare different research works and to optimize hyper-parameters for prediction models, it is required to have an evaluation standard that can accurately assess performances of prediction models. However, little research has been done in the area, and conventionally used methods have been employed repeatedly without being rigorously validated. For this reason, we first analyze performance evaluation of stock trend prediction with respect to performance metrics and data composition, and propose a fair evaluation method based on prediction disparity ratio.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.