• 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 the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.49-66
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• 2014

This paper studies optimal consumption and investment behaviors of an individual when risky asset returns and her income are affected by the business cycle. The investor considers the incomplete information risk of unobservable macroeconomic conditions and updates her belief of expected risky asset returns through Bayesian learning. We find that the optimal investment strategy, certainty equivalent wealth, and portfolio hedging demand significantly depend on the belief about the macroeconomic conditions.

• Journal of the Korean Mathematical Society
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• v.59 no.2
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• pp.311-335
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• 2022

This paper treats Merton's classical portfolio optimization problem for a market participant who invests in safe assets and risky assets to maximize the expected utility. When the state process is a d-dimensional Markov diffusion, this problem is transformed into a problem of solving a Hamilton-Jacobi-Bellman (HJB) equation. The main purpose of this paper is to solve this HJB equation by a deep learning algorithm: the deep Galerkin method, first suggested by J. Sirignano and K. Spiliopoulos. We then apply the algorithm to get the solution to the HJB equation and compare with the result from the finite difference method.

• 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.

• Asia Marketing Journal
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• v.15 no.3
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• pp.79-95
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• 2013

New product innovation is a process of embodying new knowledge in a product and technology licensing is getting popular as a means to innovations and introduction of new product to the market in today's competitive global market environment. Incumbents often rely on technology licensing to access new product opportunities created by other firms. Prior research has examined various aspects of technology licensing agreements such as specific contract terms of licensing agreements, e.g., distribution of control rights, exclusivity of licensing agreements, cross-licensing, and the scope of licensing agreements. This study aims to provide answers to an important, but under-researched question: why do some incumbents initiate more licensing agreement for exploratory learning while others do it for exploitative learning along the innovation process? We attempt to extend our knowledge of licensing agreements from an organizational learning perspective. Technology licensing as a specific form of interfirm linkages can be initiated with different learning objectives along the process of new product innovation. The exploratory stages of the innovation process such as discovery or research stages involve extensive searches to create new knowledge or capabilities, whereas the exploitative stages of the innovation process such as application or test stages near the commercialization are more focused on developing specific applications or improving their efficiency or reliability. Thus, different stages of the innovation process generate different types of learning and the resulting technological resources. We examine when incumbents as licensees initiate more licensing agreements for exploratory learning objectives and when more for exploitative learning objectives, focusing on two factors that may influence a firm's formation of exploratory and exploitative licensing agreements: 1) its past radical and incremental innovation experience and 2) its internal investments in R&D and marketing. We develop and test our hypotheses regarding the relationship between a firm's radical and incremental new product experience, R&D investment intensity and marketing investment intensity, and the likelihood of engaging in exploratory and exploitive licensing agreements. Using data collected from various secondary sources (Recap database, Compustat database, and FDA website), we analyzed technology licensing agreements initiated in the biotechnology and pharmaceutical industries from 1988 to 2011. The results of this study show that incumbents initiate exploratory rather than exploitative licensing agreements when they have more radical innovation experience and when they invest in R&D activities more intensively; in contrast, they initiate exploitative rather than exploratory licensing agreements when they have more incremental innovation experience and when they invest in marketing activities more intensively. The findings of this study contribute to the licensing and interfirm cooperation studies. First, this study lays a foundation to understand the organizational learning aspect of technology licensing agreements. Second, this study sheds lights on how a firm's internal investments in R&D and marketing are linked to its tendency to initiate licensing agreements along the innovation process. Finally, the findings of this study provide important insight to managers regarding which technologies to gain via licensing agreements. This study suggests that firms need to consider their internal investments in R&D and marketing as well as their past innovation experiences when they initiate licensing agreements along the process of new product innovation.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• 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.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.129-140
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• 2019

This study investigated cyber university students' motivation and demotivation in learning English. Data was collected from a survey of 498 students in a general English course at a cyber university located in Seoul. The survey inquired into participants' English learning motivation and demotivation. To analyze the data, frequency analysis, descriptive statistics, t-test, and ANOVA were used. The findings reveal that among the motivation factors ideal L2 self was the main cause of motivation followed by promotion-based instrumentality. Among the demotivation factors the anxiety factor was found to have the highest mean followed by negative investment value for learning English. The statistical analysis of English learning motivation and demotivation according to the participants' characteristics indicates that, in terms of English learning motivation factors, the male participants' ought-to L2 self was significantly higher than that of the females' and the promotion-based instrumentality of the students who are unemployed was higher than those who are employed. Moreover, the younger the students' age, the higher their competitive motivation and promotion-based instrumentality. In terms of English learning demotivation, the female respondents achieved higher scores in the factors of anxiety, passive learning style, and negative investment value for learning English than their male counterparts. In addition, employed students showed higher demotivation in negative investment value for learning English than those without employment. The findings of this research can be used in developing online English programs for cyber university students who possess diverse learning goals.

• 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 Journal of the Korea Contents Association
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• v.19 no.7
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• pp.526-537
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• 2019

This study researched the effect of application systems for media industry by using machine learning method focusing on industrial organization theory. First, for applying the system successfully, formation of sympathy about needs is required. The introduction of machine learning can bring change in each stage of value chain especially, decision making process of investment and production process. In investment side, objective performance prediction data can enhance efficiency, and content diversity can decrease with concentrated investment phenomenon to secured content by the system. In production side, if the system support to make creators decrease simple repeat works, production efficiency will increase.