• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.249-258
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• 2021

Bitcoin prices have been soaring recently as investors flock to cryptocurrency exchanges. The purpose of this study is to predict the Bitcoin price using a deep learning model and analyze whether Bitcoin is profitable through investment strategy. LSTM is utilized as Bitcoin prediction model with nonlinearity and long-term memory and the profitability of MA cross-over strategy with predicted prices as input variables is analyzed. Investment performance of Bitcoin strategy using LSTM forecast prices from 2013 to 2021 showed return improvement of 5.5% and 46% more than market price MA cross-over strategy and benchmark Buy & Hold strategy, respectively. The results of this study, which expanded to recent data, supported the inefficiency of the cryptocurrency market, as did previous studies, and showed the feasibility of using the deep learning model for Bitcoin investors. In future research, it is necessary to develop optimal prediction models and improve the profitability of Bitcoin investment strategies through performance comparison of various deep learning models.

• Journal of Information Technology Services
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• v.23 no.4
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• pp.71-83
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• 2024

This paper explores the viability of investment opportunities through earning rewards as validators in blockchain networks, moving beyond traditional approaches to cryptocurrency investment. Recently, there has been growing interest in participating as blockchain validators to receive stable rewards, rather than merely purchasing and holding cryptocurrencies. This shift reflects a perception among investors that participating in blockchain validation is a safer investment method. Despite this, most investment decisions still focus primarily on the volatility of cryptocurrency prices, with investment strategies considering validator reward rates being relatively underexplored. This study selects five major cryptocurrencies based on the Proof of Stake (PoS) mechanism (Ethereum, Cosmos, BNB, Polkadot, Polygon) and compares the validator reward rates from the fourth quarter of 2022 to the fourth quarter of 2023. The selected cryptocurrencies were chosen based on their market capitalization, validator reward rates, and the number of wallets staked, representing popular and trustworthy options. Through this analysis, the research applies Modern Portfolio Theory (MPT) by Harry Markowitz to propose a method of portfolio composition that maintains an optimal balance between risk and return. This is expected to contribute to investors making more stable and sustainable investment decisions based on the fundamental value and long-term growth potential of blockchain technology. Additionally, this study is anticipated to provide significant insights into academic discussions related to cryptocurrency investments, deepen understanding of the cryptocurrency market, and enhance the efficiency of investment strategies.

• Journal of Information Technology Applications and Management
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• v.26 no.3
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• pp.29-41
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• 2019

As users' interests of cryptocurrency has been increased, investment volume of it also increases. In the cryptocurrency market, it cannot always be distributed homogenous information to all investors, similar to the stock market because it reflects the characteristics of a market microstructure. Cryptocurrency traders, thus, like stock investors, can experience the information asymmetry in the market and cannot but help to depend on private information. The purpose of this study is to estimate the trading intensity of informed traders and uninformed traders among cryptocurrency investors around the world based on PIN (Probability of Informed Trading). We have an aim to compare the difference of information asymmetry according to the ten types of cryptocurrency. The results of this study are expected to prevent the continuous increase of suspicious transactions related to cryptocurrency and contribute to the development of a sound cryptocurrency market.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.1
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• pp.17-28
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• 2019

In the financial investment management strategy, the distributed investment selecting and combining various financial assets is called portfolio management theory. In recent years, the blockchain based financial assets, such as cryptocurrencies, have been traded on several well-known exchanges, and an efficient portfolio management approach is required in order for investors to steadily raise their return on investment in cryptocurrencies. On the other hand, deep learning has shown remarkable results in various fields, and research on application of deep reinforcement learning algorithm to portfolio management has begun. In this paper, we propose an efficient financial portfolio investment management method based on Asynchronous Advantage Actor-Critic (A3C), which is a representative asynchronous reinforcement learning algorithm. In addition, since the conventional cross-entropy function can not be applied to portfolio management, we propose a proper method where the existing cross-entropy is modified to fit the portfolio investment method. Finally, we compare the proposed A3C model with the existing reinforcement learning based cryptography portfolio investment algorithm, and prove that the performance of the proposed A3C model is better than the existing one.