• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.819-829
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• 2017

The aim of this study is to analyze intraday price momentum. When price trends are formed, price momentum is the phenomenon that future prices tend to follow the trend. When the market opened and closed, a U-shaped trading volume pattern in which the trading volume was concentrated was observed. In this paper, we defined price momentum as the 10 minute trend after market opening is maintained until the end of market. The strategy is to determine buying and selling in accordance with the price change in the initial 10 minutes and liquidating at closing price. In this study, the strategy was empirically analyzed by using minute data, and it showed effectiveness, indicating the presence of an intraday price momentum. A pattern in which returns are increasing at an early stage is called a J-shaped pattern. If the J-shaped pattern occurs, we have found that the price momentum phenomenon tends to be stronger than otherwise. The DTW algorithm, which is well known in the field of pattern recognition, was used for J-shaped pattern recognition and the algorithm was effective in predicting intraday price movements. This study showed that intraday price momentum exists in the KOSPI200 futures market.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.63-70
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• 2014

This study is to analyze impact of geography and timing on the real transactions prices of apartment complexes in Seoul using data provided by the Ministry of Land, Infrastructure and Transport. The average real transactions and location data of apartment complex was combined into the GIS data. First, the pattern of apartment real transaction price change by period and by area was analyzed by kriging, the one of the spatial interpolation technique. Second, to analyze the pattern of apartment market price change by administrative district(administrative 'Dong' unit), the average of market price per unit area was calculated and converted to Moran I value, which was used to analyze the clustering level of the real transaction price. Through the analysis, spatial-temporal distribution pattern can be found and the type of change can be forecasted. Therefore, this study can be referred as of the base data research for the housing or local policies. Also, the regional unbalanced apartment price can be presented by analyzing the vertical pattern of the change in the time series and the horizontal pattern of the change based on GIS.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.93-99
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• 2012

This study aims to identify spatio-temporal land price change pattern in Gongju city including the area incorporated and surrounding area depending on the Multifunctional Administrative City Construction. For this, GIS data was built by calculating the average land price each 209 Dong and Ri by the time of the year 2000, 2005 and 2010 based on. The first, the change in the land price was to identify in the 5-year intervals through a kriging interpolation as a kind of geostatistical techniques. The second, a trend analysis was conducted to know directional change pattern of the east-west axis and the north-south axis. Finally, the weighted mean center was calculated by the land price at a weight to examine moving direction on the center point of land price, point of view. The result is that the land price change pattern appeared visible higher growth on the eastern built in the Multifunctional Administrative City, moving direction on the center point of the land price appeared that the phenomenon was concentrated in the northeastern area.

• The Journal of Information Systems
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• v.27 no.3
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• pp.123-140
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• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Korean System Dynamics Review
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• v.9 no.1
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• pp.125-141
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• 2008

It is very difficult to predict future demand/supply, price for resources with acceptable accuracy using regression analysis. We try to use system dynamics to forecast the demand/supply and price for nickel. Nickel is very expensive mineral resource used for stainless production or other industrial production like battery, alloy making. Recent nickel price trend showed non-linear pattern and we anticipated the system dynamic method will catch this non-linear pattern better than the regression analysis. Our model has been calibrated for the past 6 year quarterly data (2002-2007) and tested for next 5 year quarterly data(2008-2012). The results were acceptable and showed higher accuracy than the results obtained from the regression analysis. And we ran the simulations for scenarios made by possible future changes in demand or supply related variables. This simulations implied some meaningful price change patterns.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.257-258
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• 2016

Construction of Multi-Family Housing(MFH) was rapidly spread in the 1990s, it has been mostly passed more than 20 years and it is faced to aging time. Remodeling has emerged a major issue in the construction industry as an alternative of improvement and recovery the initial performance of the deteriorated MFH. But, Many decision-makers are struggling to determine whether to conduct a remodeling because of profitability. In this context, this research was conducted as the following steps to achieve this research goal, i) remodeled MFH cases and comparative cases were collected, ii) the price information based on three time frames (i.e., before remodeling, after remodeling, and present) was collected, and iii) the relative price variation of the remodeled cases was analyzed and finally it is revealed that there are four patterns of price variation.

• Journal of Fashion Business
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• v.2 no.4
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• pp.40-45
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• 1998

The purpose of this study is to investigate how to develop the Korean textile pattern design that can respond properly to the demand of consumers. This is very important to maintain a steady growth of the Korean textile and clothing industry. To serve this purpose, this study conduct surveys (through questionnaires) of European textile design industries centering around Italian textile design industry. The survey result shows that in most European textile pattern design studios, the "repeat" process is not necessarily considered as a part of the textile pattern design process and, in fact, the price of textile pattern design with the "repeat" is 30% higher than the price of textile pattern design without the "repeat". The survey result also exhibits that the inclusion of the "repeat" in the textile pattern design process could limit the ability of expressing creative ideas. As a proposal for the development of Korean textile pattern design, this study suggests that the "repeat" process should be separated from the textile design process and specialized as an independent area of the pattern design.

• 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.

• Journal of Korea Planning Association
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• v.54 no.2
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• pp.54-66
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• 2019

This study analyzed whether a subway accessibility impact on house price is constant since its operation over time or not. The study was approached specifically to answer two research questions. One is "Are there significant temporal variations in the relationship between subway accessibility and housing price transacted after its opening?" The other one is "How the pattern of its temporal variation in housing price is formed as a function of the distance from the nearest station?" The study area is the subway station areas in the Daejeon metropolitan city, South Korea. Its first subway line has started to be opened in 2006 with 12 stations and then opened its additional 10 stations in 2007. It can be more appropriate to observe its impacts of subway accessibility on housing price because it has only one transit line with more than 10-year reaction term to its operation. The study employed alternative models to estimate yearly variation of subway accessibility on house price for the station areas with 500-meter and 1-kilometer radius respectively. While the study originally considered both a hedonic price model with interaction terms of its access distance to yearly transacted housing and a time-variant random coefficient model, the former model was finally selected because it is better fitted. Based on our analysis results, the reaction of house price to its transit line had significant temporal variation over time after opening. In addition, the pattern in its variation from our analysis results indicates that its capitalization impact on house price is over-estimated in its first several years after the opening. In addition, its positive capitalization impact is more effective in the 1000-meter station area than in the 500-meter one.