• Journal of Technology Innovation
• /
• v.15 no.2
• /
• pp.1-24
• /
• 2007

In this study, we estimated a CES production function for the Korean economy. We have found in the empirical results that the elasticity of the factor substitution is less than one and that the Korean economy exhibits labor-saving technological progress. In addition, we obtained the regression coefficient of R&D investment on technological change, i.e., the elasticity of R&D investment with respect to the technological change was 0.26% point. It implies that if R&D stock increases by 1%, labor efficiency increases 0.26% point through technological progress which is Hicksian non-neutral. It confirms that innovation-based growth strategy by increasing R&D investment would be effective on the one hand. Some policy consideration on the other might be needed for an increase in employment which is offset by technological progress.

• Knowledge Management Research
• /
• v.22 no.1
• /
• pp.21-37
• /
• 2021

The purpose of this study is to explore financial investment knowledge related to multi-strategy, which is not generally shared. Through case studies, we will share it with the domestic hedge fund market. Since the era of full-fledged private equity hedge funds in Korea opens, many funds are created; however, reality is that there is a lack of diversity in strategies. Initially, it started with a simple stock long/short strategy, and various strategies such as mezzanine and alternative investments are in use but funds using multi-strategy are limited. This study aims to present an empirical application plan for hedge fund management strategies using a case study. It will specifically focus on process of achieving Absolute Return using the Multi Strategy technique actively used in securities firms' Prop Trading. With the results of this study, we intend to contribute to those fund managers and desired researchers who are utilizing multiple strategies in the hedge fund management to pursue Absolute Return and to help them strengthening their financial knowledge and competitiveness.

• The Journal of Bigdata
• /
• v.1 no.2
• /
• pp.21-34
• /
• 2016

There are various investment strategy reports available online, prepared by many financial analysts. If the correlation between the title of the report and analyst forecast can be found, we can tell from the title whether analyst' forecast will be reliable or not. The objective of this study is to see the correlation between the title of analyst investment strategy report and the actual result of forecast by using the Text Mining technique. The result of actual analysis showed that "strong buy and sell call" appeared in the title lead the higher accuracy of analyst forecast and fulfillment ratio. The results that potential investors can get better information by reading the title of the analyst report. We hope that this study could be the basis for new methodologies in this area.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.38 no.4
• /
• pp.53-63
• /
• 2013

Together with soaring interest on Big Data, now there are vigorous reports that unearth various social values lying underneath those data from a number of application areas. Among those reports many are using such data as Internet search histories from Google site, social relationships from Facebook, and transactional or locational traces collected from various ubiquitous devices. Many of those researches, however, are conducted based on the data sets that are accumulated over the North American and European areas, which means that direct interpretation and application of social values exhibited by those researches to the other areas like Korea can be a disturbing task. This research has started from a validation study against Korean environment of the former paper which says an investment strategy that exploits up and down of Google search volume on a carefully selected set of terms shows high market performance. A huge difference between North American and Korean environment can be eye witnessed via the distinction in profit rates that are exhibited by the corresponding set of search terms. Two sets of search terms actually presented low correlation in their profit rates over two financial markets. Even in an experiment which compares the profit rates with two different investment periods with the same set of search terms showed no such meaningful result that outperforms the market average. With all these results, we cautiously conclude that establishing an investment strategy that exploits Internet search volume over a specified word set needs more conscious approach.

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.81-93
• /
• 2022

The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.

• Asia pacific journal of information systems
• /
• v.32 no.4
• /
• pp.707-727
• /
• 2022

Industrial revolution 4.0 makes business competition more challenging and will impact the instability of the company's financial performance. Dynamic environmental conditions make it difficult for companies to make predictions in making decisions. Investing in information technology (IT) is one way for companies to maintain financial stability and competitive advantage in dynamic competition. Resource-Based Theory (RBT) explains that information technology (IT) is a resource that can create a competitive advantage for the company. This study aims to examine the moderating role of dynamic industrial environments and IT strategic emphasis on the relationship between a lag effect of IT investment and firm's financial performance volatility. Using the data of companies listed on the Indonesia Stock Exchange (IDX) for five years starting from 2013-2017, the method used to estimate the research model's parameters is the generalized method of moments (GMM) approach. The results show that the industrial environment and the emphasis on IT strategy have a role in moderating and strengthening the relationship between the time lag in IT investment in reducing the firm's financial performance volatility.

• Korean System Dynamics Review
• /
• v.5 no.2
• /
• pp.33-66
• /
• 2004

This study is an exploratory investigation on how national R&D investments decisions are made in Korea. For developing system dynamic model, we are interested in locating three structural dilemma, occurring with the Korean NIS. In so doing, we intend to devise the ways of ameliorating problems within the NIS investment decision-making process by providing policy implications. We identify delays and side effects during transition periods between different stages of technology innovation by perceiving the switching pattern dynamically, in which form of technologies shifts from one to another stage like paradigm shift, when the R&D investment reaches a certain stock. It is also suggested that the development of strategies is necessary in order to enhance efficiency of technological development process.

• Journal of Information Technology Applications and Management
• /
• v.12 no.1
• /
• pp.191-211
• /
• 2005

This study investigates the decision criteria used in the context of IT investment decision making and empirically analyzes the impact of IT project size and types on the importance of decision criteria. 5 criteria which have been extracted from the previous studies and industry practices are budget, financial benefits. strategic value. risk, and the degree of proposer's eagerness. Data of 120 IT project proposals have been collected from 5 companies including bank, insurance. and stock trading company. As results of ANOVA test. 7 out of 10 hypothesis have been accepted statistically. That is. the bigger the project size. the higher the evaluation weight of project budget and risk criteria and the lower the weight of proposer's eagerness. And in case of the infrastructure investment type. the emphasis is placed more on strategic value and risk criteria and less on financial benefit and proposer's eagerness. These findings provide insights for both IT practitioners and researchers.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.4
• /
• pp.81-90
• /
• 2018

Modern investment theory has empirically proved that stock returns can be explained by several factors such as market risk, firm size, and book-to-market ratio. Other unknown factors affecting stock returns are also believed to still exist yet to be found. We believe that one of such factors is the operational efficiency of firms in transforming inputs to outputs, considering the fact that operations is a fundamental and primary function of any type of businesses. To support this belief, this study intends to empirically study the relationship between firm efficiency and stock price performance. Firm efficiency is measured using data envelopment analysis (DEA) with inputs and outputs obtained from financial statements. We employ cross-efficiency evaluation to enhance the discrimination power of DEA with a secondary objective function of aggressive formulation. Using the CAPM-based performance regression model, we test the performance of equally weighted portfolios of different sizes selected based upon DEA cross-efficiency scores along with a buy & hold trading strategy. For the empirical test, we collect financial data of domestic firms listed in KOSPI over the period of 2000~2016 from well-known financial databases. As a result, we find that the porfolios with highly efficient firms included outperform the benchmark market portfolio after controlling for the market risk, which indicates that firm efficiency plays a important role in explaining stock returns.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.23 no.4
• /
• pp.213-224
• /
• 1998

Since the late 1980s, an Increasing number of neural network models have been studied in the areas of financial prediction and analysis. The purpose of this study is to Investigate the possibility of building a neural network model that is able to construct a profitable trading strategy in the Korean Stock Market. This study classifies stocks into the future market winners and losers from the publicly available accounting information and builds portfolios based on this information. The performances of the winner portfolios and the loser portfolios are compared with each other and against the market index. The empirical result of this research is consistent with the traditional fundamental analysis where it is claimed that the financial statements contain firm values that may not be fully reflected In stock prices without delay. Despite the supporting empirical evidence. It is somewhat Inconclusive as to whether or not the abnormal return in excess of market return is the result of the extra knowledge obtained in the neural network models derived from the historical accounting data. This research attempts to open another avenue using neural network models for searching for evidence against market efficiency where statistics and intuition have played a major role.