• Title/Summary/Keyword: Knowledge stock

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Interaction Effect of Network Structure and Knowledge Search on Knowledge Diffusion (지식 전파에 있어 네트워크 구조와 지식 탐색의 상호작용)

  • Park, Chulsoon
    • Korean Management Science Review
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    • v.32 no.4
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    • pp.81-96
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    • 2015
  • This paper models knowledge diffusion on an inter-organizational network. Based on literatures related to knowledge diffusion, the model considers critical factors that affect diffusion behavior including nodal property, relational property, and environmental property. We examine the relationships among network structure, knowledge search, and diffusion performance. Through a massive simulation runs based on the agent-based model, we find that the average path length of a network decreases a firm's cumulative knowledge stock, whereas the clustering coefficient of a firm has no significant relationship with the firm's knowledge. We also find that there is an interaction effect of network structure and the range of knowledge search on knowledge diffusion. Specifically, in a network of a larger average path length (APL) the marginal effect of search conduct is significantly greater than in that of a smaller APL.

Examining Incentives to License Technology in U.S. High-Tech Industries

  • Kim, Young-Jun
    • Management Science and Financial Engineering
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    • v.10 no.1
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    • pp.43-52
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    • 2004
  • This paper empirically investigates potential factors that might affect firms' incentives to license out technology. The analysis is done with the help of a panel data set of observed licensing transactions involving U.S. public companies in high-technology industries. The important explanatory factors relate to the firm characteristics such as the company's stock of technological knowledge (patent stock). prior involvement in technology licensing. the company size, R&D intensity and capital expenditure. The results suggest that there seems to be significant inter-sectoral differences as well as similarities in determinants of the propensity to transfer technology through licensing agreements.

A Performance Analysis by Adjusting Learning Methods in Stock Price Prediction Model Using LSTM (LSTM을 이용한 주가예측 모델의 학습방법에 따른 성능분석)

  • Jung, Jongjin;Kim, Jiyeon
    • 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.

The Effect of Research and Development Expenditure on Firm Value: The Case of Earning Persistence and Patent (특허권과 이익지속계수에 따른 연구개발비 지출이 기업가치에 미치는 영향)

  • Xu, Jingwen;Lee, Ki Se;Jeon, SUNG Il
    • Knowledge Management Research
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    • v.12 no.3
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    • pp.59-71
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    • 2011
  • This study intends to examine the effect of research and development (R&D) expenditure effects on firm value through patent and earing persistence. The patent is the representative intangible asset which objectively indicates a typical product of research and development activities to external parties. If a firm has acquired the patent, it receives amicable evaluation from the market compared to the firm which has not acquired patent. Empirical analysis is performed for non-banking firms (1,860 firm-years) listed on Korean Stock Exchange with December fiscal year-end over 2004-2009. Research results are as follows. First, the multiple pricing of patent acquiring firm and earing persistence increased group showed that they have higher prices than the other groups. Second, the multiple pricing of R&D expenditures of earing persistence increased group showed that they have higher prices than the other group. Third, the R&D expenditures of earing persistence increased group is receiving more friendly evaluation from the stock market than the other group.

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Fair Performance Evaluation Method for Stock Trend Prediction Models (주가 경향 예측 모델의 공정한 성능 평가 방법)

  • Lim, Chungsoo
    • The Journal of the Korea Contents Association
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    • v.20 no.10
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    • pp.702-714
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    • 2020
  • Stock investment is a personal investment technique that has gathered tremendous interest since the reduction in interest rates and tax exemption. However, it is risky especially for those who do not have expert knowledge on stock volatility. Therefore, it is well understood that accurate stock trend prediction can greatly help stock investment, giving birth to a volume of research work in the field. In order to compare different research works and to optimize hyper-parameters for prediction models, it is required to have an evaluation standard that can accurately assess performances of prediction models. However, little research has been done in the area, and conventionally used methods have been employed repeatedly without being rigorously validated. For this reason, we first analyze performance evaluation of stock trend prediction with respect to performance metrics and data composition, and propose a fair evaluation method based on prediction disparity ratio.

The Trickle-Down Effect of Intellectual Capital on Banks' Macro Performance in Indonesia

  • WAHAB, Abdul;ABBAS, Nurhasnah;SYARIATI, Alim;SYARIATI, Namla Elfa
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.12
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    • pp.703-710
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    • 2020
  • The stock market serves as a representation of economic well-being in a country. Along with the myriad of economic predictors, specific knowledge possession may lead to different macro consequences of stock performance and market value. This study empirically investigates the capacity of possessing excellent intellectual capital to increase the performance and values of listed banks in Indonesia. The selection of banks as the primary data represents such sectors' capability to attract, employ, or exploit the excellent internal capacity under the discussion of resource-based view theory. At best to the authors' knowledge, this topic's findings are still elusive and debatable upon considering the direct and indirect relationships between the proposed exogenous and endogenous variables. Eighteen listed banks form the panel data throughout 2011-2016. This study employs a path analysis and Sobel test to obtain the results of the proposed hypothesis. The results report some positive relationships of the intellectual capital to firms' performances and values, directly and indirectly, with a substantial effect on the second model compared to the first model. This study highlighted knowledge's capacity as a vital basis to gauge the banks' performance and valuation. However, a better formulation of intellectual capital is required to capture a better measurement.

A Study on the Development of Multiple Experts' Knowledge Combining Algorithm by Using Fuzzy Cognitived Map (퍼지인식도를 이용한 다수 전문가지식 결합 알고리즘 개발에 관한 연구)

  • 이건창;주석진;김현수
    • Journal of the Korean Operations Research and Management Science Society
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    • v.19 no.1
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    • pp.17-40
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    • 1994
  • The objectives of this paper are to apply fuzzy cognitive map (FCM)- related techniques to (1) extract causal knowledge from a specific problem-domain and (2) perform a series of causal analysis in complicated decision making area. We propose a set operation-based augmentation (SOBA) algorithm to combine multiple FCMs developed by multiple experts. Based on the SOBA knowledge acquisition algorithm, we can obtain a causal knowledge base fairly representing multiple experts' knowledge about a problem domain. The causal knowledge base built by SOBA algorithm can be described as a matrix form, guaranteeing mathematically compact operation compared with a production (if-then) knowledge base. We applied out method to stock market analysis problem whichis a typical of highly unstructured problems in OR/MS fields.

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The Impact of Information on Stock Message Boards on Stock Trading Behaviors of Individual Investors based on Order Imbalance Analysis (온라인 주식게시판 정보가 주식투자자의 거래행태에 미치는 영향)

  • Kim, Hyun Mo;Park, Jae Hong
    • Information Systems Review
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    • v.18 no.2
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    • pp.23-38
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    • 2016
  • Previous studies on information systems (IS) and finance suggest that information on stock message boards influence the investment decisions of individual investors. However, how information on online stock message boards influences an individual investor's buy or sell decisions is unclear. To address this research question, we investigate the relationship between a number of posts on stock message boards and order imbalance in stock markets. Order imbalance is defined as the difference between the daily sum of buy-side shares traded and the daily sum of sell-side shares traded. Therefore, order imbalance can suggest the direction of trades and the strength of the direction with trading volumes. In this regard, this study examines how the number of posts (information on stock message boards) influences order imbalance (stock trading behavior). We collected about 46,077 messages of 40 companies on the Korea Composite Stock Price Index from Paxnet, the most popular Korean online stock message board. The messages we collected were divided based on in-trading and after-trading hours to examine the relationship between the numbers of posts and trading volumes. We also collected order imbalance data on individual investors. We then integrated the balanced panel data sets and analyzed them through vector regression. We found that the number of posts on online stock message boards is positively related to prior order imbalance. We believe that our findings contribute to knowledge in IS and finance. Furthermore, this study suggests that investors should carefully monitor information on stock message boards to understand stock market sentiments.

An Optimization Model for Resolving Circular Shareholdings of Korean Large Business Groups (대규모 기업집단의 순환출자 해소를 위한 최적화 모형)

  • Park, Chan-Kyoo;Kim, Dae-Lyong
    • Journal of the Korean Operations Research and Management Science Society
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    • v.34 no.4
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    • pp.73-89
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    • 2009
  • Circular shareholdings among three companies are formed when company A owns stock in company B, company B owns stock in company C, and company C owns stock in company A. Since circular shareholdings among large family-controlled firms are used to give the controlling shareholder greater control or more opportunities to expropriate minority investors, the government has encouraged large business groups to gradually remove their circular shareholdings. In this paper, we propose a combinatorial optimization model that can answer the question, which equity investments among complicated investment relationships of one large business group should be removed to resolve its circular shareholdings. To the best knowledge of the authors, our research is the first one that has approached the circular shareholding problem in respect of management science. The proposed combinatorial optimization model are formulated into integer programming problem and applied to some Korean major business groups.

Fuzzy System and Knowledge Information for Stock-Index Prediction

  • Kim, Hae-Gyun;Bae, Hyeon;Kim, Sung-Shin
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.172.6-172
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    • 2001
  • In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock, or other economic markets. Most previous experiments used multilayer perceptrons(MLP) for stock market forecasting, The Kospi 200 Index is modeled using different neural networks and fuzzy system predictions. In this paper, a multilayer perceptron architecture, a dynamic polynomial neural network(DPNN) and a fuzzy system are used to predict the Kospi 200 index. The results of prediction is compared with the root mean squared error(RMSE) and the scatter plot. The results show that the fuzzy system is performing slightly better than DPNN and MLP. We can develop the desired fuzzy system by learning methods ...

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