• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.95-99
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• 2015

The IRR(internal rate of return) is often used by investors for the evaluation of engineering projects. Unfortunately, it has serial flaws: (1) multiple real-valued IRRs may arise; (2) complex-valued IRRs may arise; (3) the IRR is, in special cases, incompatible with the net present value (NPV) in accept/reject decisions. The efforts of management scientists and economists in providing a reliable project rate of return have generated over the decades an immense amount of contributions aiming to solve these shortcomings. Especially, multiple internal rate of returns (IRRs) have a fatal flaw when we decide to accep it or not. To solve it, some researchers came up with external rate of returns (ERRs) such as ARR (Average Rate of Return) or MIRR (MIRR, Modified Internal Rate of Return). ARR or MIRR. will also always yield the same decision for a engineering project consistent with the NPV criterion. The ERRs are to modify the procedure for computing the rate of return by making explicit and consistent assumptions about the interest rate at which intermediate receipts from projects may be invested. This reinvestment could be either in other projects or in the outside market. However, when we use traditional ERRs, a volume of capital investment is still unclear. Alternatively, the productive rate of return (PRR) can settle these problems. Generally, a rate of return is a profit on an investment over a period of time, expressed as a proportion of the original investment. The time period is typically the life of a project. The PRR is based on the full life of the engineering project. but has been annualised to project one year. And the PRR uses the effective investment instead of the original investment. This method requires that the cash flow of an engineering project must be separated into 'investment' and 'loss' to calculate the PRR value. In this paper, we proposed a tabulated form for easy calculation of the PRR by modifing the profit and loss statement, and the cash flow statement.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.83-95
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• 2014

In this paper, we develop a portfolio selection model that can be used to invest in markets with margin requirements such as the foreign exchange market. An investment algorithm to implement the proposed portfolio selection model based on objective historical data is also presented. We further conduct empirical analysis on the performance of a hypothetical investment in the foreign exchange market, using the proposed portfolio selection model and investment algorithm. Using 7 currency pairs that recorded the highest trading volume in the foreign exchange market during the most recent 10 years, we compare the performance of 1) the Dollar Index, 2) a 1/N Portfolio which equally allocates capital to all N assets considered for investment, and 3) a hypothetical investment portfolio selected and managed according to the portfolio selection model and investment algorithm proposed in this paper. Performance is compared in terms of accumulated returns and Sharpe ratios for the 10-year period from January 2003 to December 2012. The results show that the hypothetical investment portfolio outperforms both benchmarks, with superior performance especially during the period following financial crisis. Overall, this paper suggests that a mathematical approach for selecting and managing an optimal investment portfolio based on objective data can achieve outstanding performance in the foreign exchange market.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.573-588
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• 2023

Purpose: This study presents a research approach that utilizes deep reinforcement learning to construct optimal portfolios based on the business cycle for stocks and other assets. The objective is to develop effective investment strategies that adapt to the varying returns of assets in accordance with the business cycle. Methods: In this study, a diverse set of time series data, including stocks, is collected and utilized to train a deep reinforcement learning model. The proposed approach optimizes asset allocation based on the business cycle, particularly by gathering data for different states such as prosperity, recession, depression, and recovery and constructing portfolios optimized for each phase. Results: Experimental results confirm the effectiveness of the proposed deep reinforcement learning-based approach in constructing optimal portfolios tailored to the business cycle. The utility of optimizing portfolio investment strategies for each phase of the business cycle is demonstrated. Conclusion: This paper contributes to the construction of optimal portfolios based on the business cycle using a deep reinforcement learning approach, providing investors with effective investment strategies that simultaneously seek stability and profitability. As a result, investors can adopt stable and profitable investment strategies that adapt to business cycle volatility.

• Management & Information Systems Review
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• v.35 no.3
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• pp.195-212
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• 2016

Utilizing intra-day volume weighted average price (VWAP) based on 1 minute return data of stocks traded on the Korean Stock Exchange, this paper examines and analyzes abnormal returns in reaction to patent listing disclosures as well as the cumulative abnormal returns, traded volumes, the interaction of VWAP spreads, the reaction of volumes, the reaction of VWAP spreads and the realized returns obtained from trading using an event driven arbitrage strategy. The results of the aforementioned research topics are follows. First, our analysis suggests that on average, 0.92% positive cumulative returns arise 1 minute after the patent listing disclosure announcement with high statistical significance, thereby reconfirming that the Korean stock market is a semi-strong form of the efficient market. Employing 3 separate panel tests differentiated by the size factor, we find that the abnormal returns of small sized stocks were less than the returns of medium sized stocks, which goes to support recent research findings suggesting that the size premium is no longer existent in the Korean stock market. Secondly, we show that among the event driven type strategies, the most outstanding realized returns are from the market making strategies. Furthermore, placing market order trades only at the bid or ask price resulted in negative returns. This implies that strategies utilizing a combination of market orders and limit orders, order cancelations ratios and order flows can enhance realized returns.

• East Asian Journal of Business Economics (EAJBE)
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• v.3 no.1
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• pp.8-16
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• 2015

In the later quarter of the twentieth century, the need for foreign capital is realized among the various countries of the world. Developing countries especially developed multi-pronged strategies to attract foreign capital into the country. One such strategy is the adoption of liberalization policy. Almost all the developing countries started opening their economy, out of the compulsion, to achieve faster rate of economic growth and development. Even a communist country like China adopted liberalization policy as a strategy for accelerated economic growth during 1979. India also joined the race by 1991, when the government announced the policy of liberalization. The importance of FDI extends beyond the financial capital that flows into the country. The huge size of the market in this sector and high returns on investment are two important factors in boosting FDI inflows to power sector. 100 percent FDI is allowed under automatic route in almost all the sub sectors of power sector except the atomic energy. Major foreign investment is made in this sector during 2000 to 2009 is Mauritius with an investment of US$ 4490.96 i.e., 4.24 percent of the total FDI inflows into the country during the period. The estimation of future FDI flow shows a marginal decline in the year 2010. Then from 2011 to 2015 onwards upward trend of FDI was observed.

• Knowledge Management Research
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• v.19 no.1
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.63-73
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• 2023

This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.135-144
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• 2017

This study is to identify the growth rate and volatility of logistics related firms in the stock market. To do this, we used monthly data for 197 years from June 2000 to October 2016 by selecting KOSPI and Transport & Storage(T&S), KOSDAQ, Transportation(TRANS) index. The purpose of this study is to compare the T&S and TRANS stock index returns with the KOSPI and KOSDAQ index. And we are to judge whether the development potential of the logistics industry and the value of the investment of related companies in the future is high. For this purpose, we will analyze the basic statistics, correlation and growth rate of each index, and compare T&S and TRANS with market returns. Analysis result, for the past 197 months logistics related T&S and TRANS have been higher than market returns. The correlation was highly related to TRANS and T & S in KOSPI, but it was not related to KOSDAQ. TRANS represents high risk and high return, while KOSDAQ represents high risk and low return market. TRANS is considered to be an efficient investment. We expect the future development of logistics related industries and T & S and TRANS to show a high rate of increase compared to the market returns.

• Knowledge Management Research
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• v.22 no.4
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• pp.87-101
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• 2021

ESG Investment is emerging as a trend and common sense in the financial market. ESG Investment is an investment method that simultaneously pursue social sustainability and investment returns from a long-term perspective by reflecting non-financial factors such as environment, society and governance in addition to corporate financial performance in investment decisions. This study checked how the characteristics of ESG investment have been changed after Covid-19. Afterwards, it was confirmed that Covid-19 actually acted as a negative factor in the securities market by applying VAR model. At the same time, it was demonstrated that ESG indices of the US and Korea outperformed their benchmark in terms of return and risk during the pandemic regime. The result of this study hints that the importance of ESG investment will be unchanged after Covid-19. At the same time, it suggests that managers should avoid passive ESG management and engage in strategic ESG management based on knowledge management.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.