• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.1-10
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• 2022

This research proposes a novel trading method based on sample entropy for the FTSE China A50 Index. The approach is used to determine the points at which the index should be bought and sold for various holding durations. The findings are then compared to three other trading strategies: buying and holding the index for the entire time period, using the Relative Strength Index (RSI), and using the Moving Average Convergence Divergence (MACD) as buying/selling signaling tools. The unique entropy trading method, which used 90-day holding periods and was called StEn(90), produced the highest cumulative return: 25.66 percent. Regular buy and hold, RSI, and MACD were all outperformed by this strategy. In fact, when applied to the same time periods, RSI and MACD had negative returns for the FTSE China A50 Index. Regular purchase and hold yielded a 6% positive return, whereas RSI yielded a 28.56 percent negative return and MACD yielded a 33.33 percent negative return.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.13 no.2
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• pp.87-100
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• 2009

Hedging strategy for European option of jump-type semimartingale asset model, which is derived from stochastic differential equation whose driving process is a jump-type semimartingle, is discussed.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.27
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• pp.69-95
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• 2005

East Asia has played an important role in the economic and social development in the Asian pacific region and in the global arena. In the region the impact of companies centralizing their logistics activities around a few distribution centers has already led some leading ports such as Singapore, Hong Kong to transform and expand their functions and business activities to provide port users with value added logistics services. Other ports in the region also have an important part to play in the total logistics Chain. In these environments, the maritime activities of Jang BoGo, who was the maritime king of the commercial maritime empire in East Asia in the 9th century, give many implications to the international logistics network strategy that Korea has to take in order to become a power of International Logistics. Though the trading and economic environments at that time may be quite different from today, the super-national maritime management pattern that Jang Bo-go, founder of the Northeast Asian maritime trading kingdom devised, gives us many implications in the global trading and economic environments, in the respects of overseas direct investment and international logistics. Accordingly, the paper aims to examine the establishment of hub port in Northeast Asia, modelled after the maritime network management strategy of Jang BoGo.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.34-49
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• 2015

Market means of spectrum trading have been utilized as a vital method of spectrum sharing and access in future cognitive radio system. In this paper, we consider the spectrum trading with multiple primary carrier providers (PCP) leasing the spectrum to multiple secondary carrier providers (SCP) for a short period of time. Several factors including the price of the resource, duration of leasing, and the spectrum quality guides the proposed model. We formulate three trading policies based on the game theory for dynamic spectrum access in a LTE based cognitive radio system (CRS). In the first, we consider utility function based resource sharing (UFRS) without any knowledge of past transaction. In the second policy, each SCP deals with PCP using a non-cooperative resource sharing (NCRS) method which employs optimal strategy based on reinforcement learning. In variation of second policy, third policy adopts a Nash bargaining while incorporating a recommendation entity in resource sharing (RERS). The simulation results suggest overall increase in throughput while maintaining higher spectrum efficiency and fairness.

• The Journal of Asian Finance, Economics and Business
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• v.1 no.3
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• pp.5-16
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• 2014

This study aims to examine the dynamics of price changes and trading volume of Kuala Lumpur Options and Financial Futures Exchange (KLOFFE) from 2000 to 2008. With augmented analysis, our results support two hypotheses. First, under information spillover, our findings support noise traders' hypothesis as the time span for variance of past trading volume to cause variance of current return is found to be asymmetric under bull and bear markets. Second, looking at the dynamic relation between volume and volatility of price changes, our findings support Liquidity-Driven Trade hypothesis as past trading volume and subsequent volatility of return exhibit positive correlation. In terms of investors' behavior in response to the news, we find that investors are more risk taking in bull market and more risk reverse in bear market. Our study suggests that investors should adjust their strategy in the futures market in a dynamic manner as the time span of new information arrival is not consistent. Also, uninformed investors with information asymmetry should expect noninformational trading from informed investors to establish their desired positions for better liquid position.

• 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 the Korean Data and Information Science Society
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• v.24 no.2
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• pp.235-244
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• 2013

In this study, we propose a new trading strategy by using a trading volume index in KOSPI200 futures market. Many studies have been conducted with respect to the relationship between volume and price, but none of them is clearly concluded. This study analyzes the economic usefulness of investment strategy, using volume index. This analysis shows that the trading volume is a preceding index. This paper contains two objectives. The first objective is to make an index using Correlated Volume Index (CVI) and second objective is to find an appropriate timing to buy or sell the Kospi200 future index. The results of this study proved the importance of the proposed model in KOSPI200 futures market, and it will help many investors to make the right investment decision.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.295-308
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• 2014

Global trading is an intrinsically complex endeavor with a number of parties involved. World-trading related international organizations have suggested that Single Window (SW) be used as a means to make the trading process simpler and smoother. However, since each firm has its own requirements and objectives with SW, yet there is no consensus as to what traits of 'good' Single Window are. This study uses IT appropriateness as a determinant to explain an impact on information systems success. Historically, IS success was understood as multi-dimensional constructs such as use and performance. In this study we propose another dimension, continuance, and investigate the relationships among these outcome constructs.

• Journal of Information Technology Applications and Management
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• v.23 no.4
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• pp.25-39
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• 2016

Asset prices decline sharply and stock markets collapse when financial crisis happens. Recently we have encountered more frequent financial crises than ever. 1998 currency crisis and 2008 global financial crisis triggered academic researches on early warning systems that aim to detect the symptom of financial crisis in advance. This study proposes a risk recovery index for detection of good opportunities from financial market instability. We use SVM classifier algorithms to separate recovery period from unstable financial market data. Input variables are KOSPI index and V-KOSPI200 index. Our SVM algorithms show highly accurate forecasting results on testing data as well as training data. Risk recovery index is derived from our SVM-trained outputs. We develop a trading system that utilizes the suggested risk recovery index. The trading result records very high profit, that is, its annual return runs to 121%.