• Journal of Digital Convergence
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• v.18 no.11
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• pp.259-266
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• 2020

Many developments have been steadily carried out by researchers with applying knowledge-based expert system or machine learning algorithms to the financial field. In particular, it is now common to perform knowledge based system trading in using stock prices. Recently, deep learning technologies have been applied to real fields of stock trading marketplace as GPU performance and large scaled data have been supported enough. Especially, LSTM has been tried to apply to stock price prediction because of its compatibility for time series data. In this paper, we implement stock price prediction using LSTM. In modeling of LSTM, we propose a fitness combination of model parameters and activation functions for best performance. Specifically, we propose suitable selection methods of initializers of weights and bias, regularizers to avoid over-fitting, activation functions and optimization methods. We also compare model performances according to the different selections of the above important modeling considering factors on the real-world stock price data of global major companies. Finally, our experimental work brings a fitness method of applying LSTM model to stock price prediction.

• Korean Journal of Construction Engineering and Management
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• v.16 no.1
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• pp.101-109
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• 2015

The expected economic effect by investment was divided by square of real discount rate annually for change to present value. Thus, the impact of real discount rate on economic analysis is larger than other factors. The existing general method for prediction of real discount rate is application of average data during past certain period. This study proposed prediction method of real discount rate for accuracy improvement. First, the economic variables which impact on interest rate of business loan and consumer price of real discount rate were determined. The variables which impact on interest rate of business loan were selected to call rate and exchange rate. The variable which impact on consumer price index was selected to producer price index. Next, the effect relation was analyzed between real discount rate and selected variables. The significant effect relation were analyzed to exit. Lastly, the real discount rate was predicted from 2008 to 2010 based on related economic variables. The accuracy of prediction result was compared with actual data and average data. The real discount rate based on actual data, predicted data, and average data were analyzed to -1.58%, -0.22%, and 6.06%, respectively. Though the proposed method in this study was not considered special condition such as financial crisis, the prediction accuracy was much higher than result based on average data.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.

• Korean Journal of Social Welfare
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• v.56 no.1
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• pp.127-153
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• 2004

In the absence of a male provider the state must decide how far and under what conditions it will provide for the mother and her children. In the case of lone mothers, there are three main possible sources of income: the labour market, the absent father, and the state. However, the relative proportions of these three sources vary significantly from country to country as well as from individual to individual within the group of lone parents. Until very recently the UK has been alone among countries of the EU in allowing lone parents to draw benefits without making themselves available for work so long as they have dependent children. However, in the 1990s, the UK government introduced major changes to his policies regarding lone parents. The UK government attempted to restrict the role of the state as a source of income for lone parent families. At the beginning of the 1990s, the emphasis in the UK was put on securing more money from 'absent fathers' to maintain. However, the policy was unsuccessful and by the mid-1990s attention to the only other possible source of income for lone parent families, the labour market, was stepped up, notwithstanding the ambivalence of politicians about the desirability of women with young children entering employment. From 1998 the Labour government introduced a series of reforms aimed at reducing both worklessness and poverty by raising welfare payments to families both in and out of work, improving financial incentives to work and introducing a more proactive welfare system. The results presented here suggest that these policies have raised the employment rates of lone parents by around 5 percentage points, or 80,000. These employment gains have come from a welfare reform package that does not require lone parents to search for jobs, or uses time limits in welfare programmes. In addition these gains have been achieved despite generous increases in welfare payments for lone parents who do not work. These earnings gains combined with the more generous welfare are making rapid progress in reducing lone parents' poverty.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.101-113
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• 2021

This paper inquires the macro-economic factors that may affect the corporate venture capital (CVC) from an industrial organization theory perspective. Unlike existing studies focusing CVC investments related to parent corporates' strategic intention, we identified CVC firm as an independent financial investor affected by macro environment and industrial structure. Specifically, we empirically investigate whether and how industry's boom, exogenous crisis, economic growth, and competition intensity affect the CVC investment for a data set of investment in the U.S. based corporate venture capital industry, 1996-2017. The empirical data analyzed in the study contained a total of 84 U.S. based CVC firms and their 2,306 investments from 1996 until 2017. After conducting a time-series negative binomial analysis, our empirical analyses suggest that the CVC investments are affected negatively by exogenous crisis and competition intensity, and positively by industrial boom and economic growth. we found the significance and direction of our independent variables strongly supported all of our four hypotheses in a highly robust manner. The results of this study are expected to contribute the literatures of corporate venture capital and venture investment by illustrating which macro-economic and industrial structure factors affect CVC investment decision to adapt to dynamic environmental change beside strategic intention of CVC firm's parent corporates.

• The Korean Journal of Archival Studies
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• no.10
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• pp.145-194
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• 2004

Church archives are the evidential instruments to remember church activity and important information aggregate which has administrative, legal, financial, historical, faithful value as the collective memory of church community. So it must be managed necessarily and the management orders are based on the Bible. The western churches which have a correct understanding about the importance of church records and management order have taken multilateral endeavor to create, manage church archives systematically. On the other hand, korean churches don't have the records management systems. Therefore, Records created in individual church are mostly managed unsystematically and exist as 'backlogs', finally, they are destructed without reasonable formalities. In those problems, the purpose of this study is to offer the way of records classification and disposition instrument with recognition that records management should be done from the time of creation or previous to it. As a concrete device for them, I tried to embody the function-based classification method and disposal schedule. I prefer the function-based classification and disposal schedule to the organization and function-based classification to present stable classification and disposal schedule, as we can say the best feature of the modern organization is multilateral and also churches have same aspect. For this study, I applied DIRKS(Designing and Implementing Recordkeeping Systems) manual which National Archives of Australia provide and guidelines in ICA/IRMT series to construct the theory of the function-based classification in individual churches. Through them, it was possible to present a model for preliminary investigation, analysis of business activity, records survey, disposal schedule. And I took an example of 'Myong Sung Presbyterian Church' which belong to 'The Presbyterian church in Korea'. I explained in detail codifying process and results of preliminary investigation in 'Myong Sung Presbyterian Church', analysis of business activity based on it, process of presenting the function-based classification and disposal schedule got from all those steps. For establishing disposal schedule, I planned 'General Disposal Schedule' and 'Agency Disposal Schedule' which categorized 'general function' and 'agency function' of an agency, according to DIRKS in Australia and ICA/IRMT. And for estimation of disposal date I had a thorough grasp of important records category presented in 'Constitution of General Assembly', interview to know the importance of tasks, and added examples of disposal schedule in western church archives. This study has significance that it was intended to embody 'the function-based classification' and 'disposal schedule' suitable for individual church, applying DIRKS in Australia and ICA/IRMT on absence of the theory or example which tried to present the function-based classification and disposal schedule for individual church. Also it is meaningful to present a model that can classify and disposal real records according to the function in individual church which has no recognition or way about records management.

• 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.

• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.75-106
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• 2007

In this study, it is analyzed whether oil price plays a major role in the pricing return on Koran stock market and examined why the covariance risk between oil and return on stock is different in each industry. Firstly, this study explores whether the expected rate of return on stock is pricing due to global oil price factors as a function of risk premium by using a two-factor APT. Also, it is examined whether spill-over effects of oil price volatility affect the beta risk to oil price. Considering the asymmetry of oil price volatility, we use the GJR model. As a result, it shows that oil price is an independent pricing factor and oil price volatility transmits to stock return in only electricity and electrical equipment. Secondly, the two step-analyzing process is introduced to find why the covariance between oil price factor and stock return is different in each industry. The first step is to study whether beta risk exists in each industry by using two proxy variables like size and liquidity as control variables. The second step is to grasp the systematic relationship between the difference of liquidity and size and beta to oil price factor by using the panel-data model which can be analyzed efficiently using the cross-sectional data formed with time series. Through the analysis, we can argue that oil price factor is an independent pricing factor in only electricity and electrical equipment having the greatest market capitalization, and know that beta risk to oil price factor is a proxy of size in the other industries. According to the result of panel-data model, it is argued that the beta to oil price factor augments when market capitalization increases and this fact supports the first assertion. In conclusion, the expected rate of return of electricity and electrical equipment works as a function of risk premium to market portfolio and oil price, and the reason to make beta risk power differentiated in each industry attributes to the size.

• Management & Information Systems Review
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• v.35 no.3
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• pp.1-22
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• 2016

We analyzed the international comovements and structural changes in the quarterly real GDP by the Markov-switching vector autoregressive model (MS-VAR) from 1971(1) to 2016(1). The main results of this study were as follows. First, the business cycle phenomenon that occurs in the models or individual time series in real GDP has been grasped through the MS-VAR models. Unlike previous studies, this study showed the significant comovements, asymmetry and structural changes in the MS-VAR model using a real GDP across countries. Second, even if there was a partial difference, there were remarkable structural changes in the economy contraction regime(recession), such as 1988(2) ending the global oil shock crisis and 2007(3) starting the global financial crisis by the MS-VAR model. Third, large-scale structural changes were generated in the economic expansion and/or contraction regime simultaneously among countries. We found that the second world oil shocks that occurred after the first global oil shocks of 1973 and 1974 were the main reasons that caused the large-scale comovements of the international real GDP among countries. In addition, the spillover between Korea and 5 countries has been weak during the Asian currency crisis from 1997 to 1999, but there was strong transmission between Korea and 5 countries at the end of 2007 including the period of the global financial crisis. Fourth, it showed characteristics that simultaneous correlation appeared to be high due to the country-specific shocks generated for each country with the regime switching using real GDP since 1973. Thus, we confirmed that conclusions were consistent with a number of theoretical and empirical evidence available, and the macro-economic changes were mainly caused by the global shocks for the past 30 years. This study found that the global business cycles were due to large-scale asymmetric shocks in addition to the general changes, and then showed the main international comovements and/or structural changes through country-specific shocks.