• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.277-283
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• 2005

In general, stock prices do not follow normal distributions and mean trend indexes, volatility indexes, and volume indicators relating to these non-normal stock price are widely used as buy-sell strategies. These general buy-sell strategies are rather intuitive than statistical reasoning. The non-normality problem can be solved by normalizing process and statistical buy-sell strategy can be obtained by using mean trend and volatility indexes together with normalized stock prices. In this paper, buy-sell strategy based on mean trend and volatility index with normalized stock prices are proposed and applied to KOSPI200 data to see the feasibility of the proposed buy-sell strategy.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.73-88
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• 1999

There have been a series of debates to determine whether it would be possible to forecast dynamic systems such as stock markets. Recently the introduction of chaos theory has allowed many researchers to bring back this issue. Their main concern was whether the behavior of stock markets is chaotic or not. These studies, however, present divergent opinions on this question, depending upon the method applied and the data used. And the issue of predictability based on the nonlinear, chaotic nature was not dealt extensively. This paper is to test the nonlinear nature of the Korea stock market and accordingly attempts to predict its behavior. The result indicates that our stock market represents a chaotic behavior. We also found out based on our simulation that executing buy/sell transactions based upon forecasts which were derived using the local approximation method outperforms the decision of holding without a buy/sell transaction.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.103-121
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• 1995

This study proposes a artificial neural network method to predict the time to buy and sell the stocks listed on the Korea Composite Stock Price Index(KOSPI). Four types (NN1, NN2, NN3, NN4) of independent networks were developed to predict KOSPIs up/down direction after four weeks. These networks have a difference only in the length of learning period. NN5 - arithmetic average of four networks outputs - shows an higher accuracy than other network types and Multiple Linear Regression (MLR), and buying and selling simulation using systems outputs produces higher reture than buy-and-hold strategy.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.145-162
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• 1992

This paper proposes two design principles for expert systems to solve a stock market timing (SMART) problems : machine knowledge and fuzzy post-adjustment, Machine knowledge is derived from past SMART instances by using an inductive learning algorithm. A knowledge-based solution, which can be regarded as a prior SMART strategy, is then obtained on the basis of the machine knowledge. Fuzzy post-adjustment (FPA) refers to a Bayesian-like reasoning, allowing the prior SMART strategy to be revised by the fuzzy evaluation of environmental factors that might effect the SMART strategy. A prototype system, named K-SISS2 (Knowledge-based Stock Investment Support System 2), was implemented using the two design principles and tested for solving the SMART problem that is aimed at choosing the best time to buy or sell stocks. The prototype system worked very well in an actual stock investment situation, illustrating basic ideas and techniques underlying the suggested design principles.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.11
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• pp.668-674
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• 2003

The electric marketplace is in the midst of major changes designed to promote competition. No longer vertically integrated with guaranteed customers and suppliers, electric generators and distributors will have to compete to sell and buy electricity. Unit commitment (UC) in such a competitive environment is not the same as the traditional one anymore. The objective of UC is not to minimize production cost as before but to find the solution that produces a maximum profit for a generation firm. This paper presents a hi-level formulation that decomposes the UC game into a generation-decision game (first level game) and a state(on/off)-decision game (second level game). Derivation that the first-level game has a pure Cournot Nash equilibrium(NE) helps to solve the second-level game. In case of having a mixed NE in the second-level game, this paper chooses a pure strategy having maximum probability in the mixed strategy in order to obviate the probabilistic on/off state which may be infeasible. Simulation results shows that proposed method gives the adequate UC solutions corresponding to a NE.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.203-210
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• 2023

In this paper, we develope a stock trading system that automatically buy and sell stocks in Kiwoom Securities' HTS system. The system is made by using Kiwoom Open API+ with the Python programming language. A trading strategy is based on an enhanced system query method called a Condition-Search. The Condition-Search script is edited in Kiwoom Hero 4 HTS and the script is stored in the Kiwoom server. The Condition-Search script has the advantage of being easy to change the trading strategy because it can be modified and changed as needed. In the HTS system, up to ten Condition-Search scripts are supported, so it is possible to apply various trading methods. But there are some restrictions on transactions and Condition-Search in Kiwoom Open API+. To avoid one problem that has transaction number and frequency are restricted, a method of adjusting the time interval between transactions is applied and the other problem that do not support a threading technique is solved by an IPC(Inter-Process Communication) with multiple login IDs.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.202-211
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• 2014

Pairs trading is a type of arbitrage investment strategy that buys an underpriced security and simultaneously sells an overpriced security. Since the 1980s, investors have recognized pairs trading as a promising arbitrage strategy that pursues absolute returns rather than relative profits. Thus, individual and institutional traders, as well as hedge fund traders in the financial markets, have an interest in developing a pairs trading strategy. This study proposes pairs trading rules (PTRs) created from a price ratio between securities (i.e., stock index futures) using rough set analysis. The price ratio involves calculating the closing price of one security and dividing it by the closing price of another security and generating Buy or Sell signals according to whether the ratio is increasing or decreasing. In this empirical study, we generate PTRs through rough set analysis applied to various technical indicators derived from the price ratio between KOSPI 200 and S&P 500 index futures. The proposed trading rules for pairs trading indicate high profits in the futures market.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.207-212
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• 2004

This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Q-learning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents Communicate With Others Sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.211-219
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• 2010

This paper designs the dynamic symbol automatic trading system in Korean option market. This system is based on Multichart program which is convenient and efficient system trading tool. But the Multichart has an important restriction which has only one constant symbol per chart. This restriction causes very useful strategies impossible. The proposed design uses global variables, signal chart selection and position order exchange. So an automatic trading system with dynamic symbol works on Multichart program. To verify the proposed system, BS(Buythensell)-SB(Sellthenbuy) strategies are tested which uses the change of open-interest of stock index futures within a day. These strategies buy both call and put option in ATM at start candle and liquidate all at 12 o'clock and then sell both call and put option in ATM at 12 o'clock and also liquidate all at 14:40. From 23 March 2009 to 31 May 2010, 301-trading days, is adopted for experiment. As a result, the average daily profit rate of this simple strategies riches 1.09%. This profit rate is up to eight times of commision price which is 0.15 % per option trade. If the method which raises the profitable rate of wining trade or lower commission than 0.15% is found, these strategies make fascinated lossless trading system which is based on the proposed dynamic symbol automatic trading system.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.