• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.81-93
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• 2022

The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.

• Journal of Fashion Business
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• v.21 no.1
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• pp.148-165
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• 2017

An assortment of daily supplies have been documented in and accounting book that Hwa-sun Song, a wholesale dealer in Hongcheon, Gangwon-do, sent to Young-hui Sin, a customer. This study analyzed a total of 163 documentations in the accounting book between 1910 to 1916, which includes types of daly supplies, trading volume, and prices, maintained accounting between. Consequently, we are able to indentify companies that produced the applicable goods, names of products, units by which goods were counted, and the lowest and highest prices prevailing, along with kinds of goods patronized in everyday life in Hongcheon in the early 20th century. Paper had the maximum trading volume. The second, most traded were cigarettes, a symbol of the new culture. These were traded under various brand names, such as Kkotpyo, Guksyu, Sanhopyo, Syonghak, and Joil. Foodstuffs, were the third most traded items, including fish, fruits, sugar, Waeddeok, Chilwaeddeok, Color candies and Okchyun candies. Our results indicate that the snack food business had developed since the 19th century. Lighting equipment, oil, candles, matches as well as traditional oil lamps and flints cornered the fourth largest stock being traded. Medications were fifth, with prescriptions written for Insohwan, Hoechyungsan and Siungo, including quinine, a medicine for malaria. Other trades included kitchen appliances such as soup bowls, porcelain bowls, kettles, and drinking cups, and a variety of daily supplies such as mirrors, mats, umbrellas, Geumjiwaemil, hair oil imported from Japan, and soap.

• Knowledge Management Research
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• v.5 no.1
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• pp.39-56
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• 2004

Mergers and Acquisitions(M&A) have long played an important role in the growth of firm. M&A has been considered a effective strategy for Korean government to restructure industry. Previous studies provided mixed results on the synergy effect of M&A This study provides investigation on 39 mergers occurred over the sample period from 2000 to 2001. In this study, event study methodology arc used to calculate abnormal return(AR) and cumulative abnormal return(CAR) based on mean-adjusted model. The testing period of this study from date -30 through date +30, where date zero is the date of the first public announcement of the merger. The empirical results in this study can be summarized as follows. First, the return rates of KOSDAQ registered firms with M&A appears higher than that of KSE listed firms. This means that public announcement of M&A is more influential on stock price for KOSDAQ registered firms than KSE listed firms. Second, The difference between actual merging price and fair value is significant in KSE listed firms and KOSDAQ registered firms. This means that the investors take M&A of KOSDAQ registered firms as a good news. Third, the impact on the market prices of merging firms take place after the first public announcement of the merger in KSE registered firms. But the impact on the market prices take place not only merging firms but also merged firms in KOSDAQ registered firms. This result shows that the investors recognize a M&A is a strategy of technology transfer in small & venture business.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.359-364
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• 2021

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.23-30
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• 2022

Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM's performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.

• Economic Analysis
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• v.27 no.4
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• pp.43-69
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• 2021

As the trade linkages and the financial relationship between China and Korea grow stronger, China's influence on Korea is also growing larger. Therefore, it is meaningful to examine key features of Chinese monetary policy operations and the current situation, and to analyze the transmission mechanism of China's monetary policy shocks onto the Korea economy. China's monetary policy shocks can have an impact on the Korea economy through the trade, financial and oil-price channels. In the trade channel, an expansionary Chinese monetary policy can increase Korea's exports of intermediate goods to China under the vertical trade structure, via the vertical trade integration effect. Meanwhile, the expenditure switching effect and the income demand effect show no statistical significance. In the financial and oil-price channels, expansionary Chinese monetary policy shocks can decrease the interest rate and increase both stock prices and the consumer price index in Korea through changes in global portfolio capital flows, interest rates, and raw material prices.

• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.51-61
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• 2023

Purpose - Since COVID-19, the government's expansion of liquidity to stimulate the economy has resulted in an increase in private debt and an increase in asset prices of such as real estate and stocks. The recent sharp rise of the US Federal fund rate and tapering by the Fed have led to a fast rise in domestic interest rates, putting a heavy burden on the Korean economy, where the level of household debt is very high. Excessive household debt might have negative effects on the economy, such as shrinking consumption, economic recession, and deepening economic inequality. Therefore, now more than ever, it is necessary to identify the causes of the increase in household debt. Design/methodology/approach - Main methodology is regression analysis. Dependent variable is household loans from depository institutions. Independent variables are consumer price index, unemployment rate, household loan interest rate, housing sales price index, and composite stock price index. The sample periods are from 2017 to May 2022, comprising 72 months of data. The comparative analysis period before and after COVID-19 is from January 2017 to December 2019 for the pre-COVID-19 period, and from Jan 2020 to December 2022 for the post-COVID-19 period. Findings - Looking at the results of the regression analysis for the entire period, it was found that increases in the consumer price index, unemployment rate, and household loan interest rates decrease household loans, while increases in the housing sales price index increase household loans. Research implications or Originality - Household loans of depository institutions are mainly made up of high-credit and high-income borrowers with good repayment ability, so the risk of the financial system is low. As household loans are closely linked to the real estate market, the risk of household loan defaults may increase if real estate prices fall sharply.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.127-150
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• 2003

This study analyzes how capital market comovement can affect investors' decision making. We first analyze time-varying correlation coefficient between stock indices of U.S.A. and Korea. and then, using our empirical results, attempt to draw implications on investors' behavior. We find that the tendency of comovement between Korea and U.S.A. equity returns has considerably increased after the financial crisis of late 1997. Through the analysis of investors' behavior, we find that foreign investors, contrary to ITC's (Investment Trust Company) and individual investors, buy more shares in Korean markets as American stock prices go up. Foreign investors employ dynamic hedging strategy and give more weight on global economic factors than domestic ones. Our empirical results as a whole imply that investment behavior of foreign investors is most closely related to comovement of U.S.A. and Korea capital markets.

• Korean Business Review
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• v.19 no.1
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• pp.81-92
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• 2006

This study investigates the relation between institutional investor's net purchase and the volatility of KOSPI. Some portion of volatility in stock prices comes from noise trading of irrational traders. Observed volatility may be defined as the sum of the portion caused by information arrival, fundamental volatility, and the portion caused by noise trading, transitory volatility. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. Most studies investigates the effect on the observed volatility. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Estimation results show that institutional investor's net purchase was not significantly related to all kinds of volatility(observed volatility, fundamental volatility and transitory volatility). This means that institutional investor's net purchase did not increase noise trading.