• International Journal of Aerospace System Engineering
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• v.5 no.2
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• pp.1-7
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• 2018

This study examines the history of Korean aviation industry and presents the investment strategy based on the evidence and the 4th industrial revolution. Looking at the evolution of the Korean aviation industry and its technological development will be a great help to support industrial and technological innovation in the future. The modern aviation industry is divided into stages of development, focusing on maintenance of equipment introduced in advanced countries, localization through license assembly, production of products based on technology, and international joint development. The development of aeronautics technology has been progressing towards a general improvement of economic efficiency, aircraft safety efficiency through environmental-friendliness, unmanned operation, and downsizing. The Korea Aerospace Research Institute has secured key technologies through development of several aircrafts such as Experimental Aircraft Kachi, EXPO Unmanned Airship, Twin-engine Composite Aircraft, Canard Aircraft, Multi-Purpose Stratosphere unmanned-airship, Medium Aerostats, Smart UAV, Surion, EAV-2H, KC-100, and OPV. The development strategy is discussed at the level of the evidence-based investment strategy that is currently being discussed, and so the investment priorities in aircraft is high. Current drone usage and development direction are not only producing parts using 3D printer, but also autonomous flight, communication (IoT, 5G), information processing (big data, machine learning). Therefore, the aviation industry is expected to lead the fourth industrial revolution.

• 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-Pacific Journal of Business
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• v.13 no.4
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• pp.45-59
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• 2022

Purpose - This study investigates the performance of investment strategies incorporating estimated stock market cycle based on a lead-lag relationship between business cycle and stock market cycle, thereby deriving empirical implications on risk management. Design/methodology/approach - The data period ranges from June 1953 to September 2022 and de-trended short rate, term spread, credit spread, stock market volatility are considered as major input variables to estimate business cycle and stock market cycle by applying probit model. Based on the estimated stock market cycle, two types of strategies are constructed and their performance relative to the benchmark is empirically examined. Findings Two types of strategies based on stock market cycle are considered: The first strategy is to long(short) on stocks when stock market stage is expected to be an expansion(a recession), and the second one is to long on stocks(bonds) when expecting an expansion(a recession). The empirical results show that the strategies based on stock market cycle outperforms a simple buy and hold strategy in both in-sample and out-of-sample investigation. Also the out-of-sample evidence suggests that the second strategy which is in line with asset allocation is more profitable than the first one. Research implications or Originality The strategies considered in this study are based on the estimated stock market cycle which only depends on a few easily available financial variables, thereby making easier to establish such a strategy. It implies that investors enhance investment performance by constructing a relatively simple trading strategies if they set their position on stocks or choose which asset class to buy conditioning on stock market cycle.

• Korea Trade Review
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• v.47 no.1
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• pp.145-161
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• 2022

For multinational manufacturers, China is an attractive consumer market, but the unique attributes and tastes of Chinese customers present challenges in achieving desired investment performance. In this paper, the influence (mediating function) of consumer-centered market orientation adopted as a strategic means by multinational companies entering China on investment performance was examined utilizing samples collected through questionnaires and statical analysis through structural equation models. This paper, based on value chain and product attributes, divided market orientation into two types: production impacted market orientation and service impacted market orientation. The empirical analysis results of 233 samples showed that, service impacted market orientation with downstream activities and support service (as a variable) has a greater impact on investment performance than production impacted market orientation with upstream activities and product attributes. This indicates to managers that focusing on service impacted market orientation when implementing consumer-centered marketing strategies in the Chinese local market is an efficient/effective localization strategy to increase expected investment performance.

• Journal of Arbitration Studies
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• v.27 no.4
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• pp.133-156
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• 2017

The purpose of this paper is to take a countermeasure of the investment treaty arbitration that Lone Star claimed to the Korean government. In particular, this study suggests procedural measures to be prepared by the Korean government after the arbitration award. The actual remedy in ICSID arbitration is the annulment procedure of arbitration award. Therefore, this study analyzed the measures that the Korean government can prepare based on the annulment grounds: the inadequacy of the constitution of the arbitral tribunal, the excessive power of the arbitrator, the corruption of the arbitrator, and the serious violation of the rules. First, the Korean government should decide whether to proceed with the annulment procedure after the arbitration award. Second, if they decide to do it, they should review the grounds of annulment. For example, it is possible to analyze whether the relationship between the arbitrator and Lone Star can be properly in the constitution of the arbitral tribunal, whether Lone Star is eligible to apply for ICSID arbitration, or whether arbitration tribunal ignores the crucial evidence that can affect the arbitration award. Independently, the Korean government needs to discuss the investment arbitration appeal system in a long-term perspective.

• Journal of Distribution Science
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• v.20 no.6
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• pp.43-56
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• 2022

Purpose: This study aims to investigate the impact of investing into technology development on banking performance in an emerging country. Research design, data and methodology: Based on the data of 12 commercial banks listed in Vietnam from 2011 to 2019 and performing multivariable regression analyses as well as conducting a variety of robustness tests, we carry out the empirical investigation to discover this impact. Results: Our empirical evidence shows that these spending help to improve significantly performance of banks. Particularly, the technology expenditures have positive effect on the net interest margin and the non-interest income in which the level of influence on the latter is relatively remarkable in comparison with the former. At the same time, the result does not support the view that increasingly spending on technology may lead banks to face the risk of instability. Conclusions: Overall, our empirical analysis indicates that increasing investment into distribution information and communication technologies will help to enhance business strategies of banks and thus we advocate the bright side of technology development and digitalization in banking sector. We believe that the research is useful for both managers, regulators and policy makers in Vietnam as well as in countries having similar financial structure.

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

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.203-215
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• 2022

Bayesian Networks are multivariate probabilistic factor graphs that are used to assess underlying factor relationships. From January 2005 to December 2018, the study examines how Dynamic Bayesian Networks can be utilized to estimate portfolio risk and return as well as determine inter-factor relationships among reversal profit-generating components in Pakistan's emerging market (PSX). The goal of this article is to uncover the factors that cause reversal profits in the Pakistani stock market. In visual form, Bayesian networks can generate causal and inferential probabilistic relationships. Investors might update their stock return values in the network simultaneously with fresh market information, resulting in a dynamic shift in portfolio risk distribution across the networks. The findings show that investments in low net profit margin, low investment, and high volatility-based designed portfolios yield the biggest dynamical reversal profits. The main triggering aspects related to generation reversal profits in the Pakistan market, in the long run, are net profit margin, market risk premium, investment, size, and volatility factor. Investors should invest in and build portfolios with small companies that have a low price-to-earnings ratio, small earnings per share, and minimal volatility, according to the most likely explanation.

• Journal of Information Technology Applications and Management
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• v.15 no.3
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• pp.1-25
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• 2008

IT platform migration to open systems (IPMO) bears a great deal of risk over all the associated processes, in terms of a major IT investment. Hence it requires empirical data and references for decision making. Although there have been a number of published papers encouraging or discouraging IPMO, the studies that deliver useful empirical evidence for IPMO decisions are rare. The obvious first step to resolve this problem would be to gain lessons from the organizations who experienced IPMO. Based on the Delphi study, we examine both the pre and post evaluations on IPMO benefits and risks and analyze the underlying reasons of different evaluations from different stages. Our results identify the most important factor the organizations should seriously consider, and which factor is easy to neglect at the ex-ante appraisal stage.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.1-16
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• 2022

Although start-up is a key national strategy to increase national competitiveness and create employment, the survival rate of start-ups has not improved significantly. This is an important reason for the inability to provide timely and appropriate support to startups, which are in the early stages of start-up, due to the unique limitations of existing start-up support institutions and investors. The relatively recent accelerator is attracting attention as a subject of solving the above problems through professional incubation and investment. However, there are only a few empirical studies on investment determinants that affect the survival and success of accelerators, and there is a lack of theoretical evidence. Accordingly, in previous studies, 12 investment determinants were derived from a static, strategic, and dynamic perspective as accelerator investment determinants based on a business model innovation framework. This study subdivided the accelerator investment determinants derived through previous studies into 21 and analyzed the importance and priority of each factor using AHP (Analytic Hierarchy Process) analysis technique for domestic accelerator investment experts. As a result of the analysis, the top factors of importance of accelerator investment determinants were in the order of 'human resources', 'customer and market', 'intellectual resources', and 'entrepreneur's ability to realize opportunities'. It can be seen that the accelerator considers the core competencies of startups to implement solutions as the most important factor when making startup investment decisions. It was also confirmed that accelerators are strategic to create a clear value proposition and differentiated market position based on the core competitiveness of startups, and that the core value delivery method prefers a market-oriented business model and recognizes entrepreneurs's innovation capability is an important factor to realize a business model with limited resources in a rapidly changing market. This study is of academic significance in that it analyzes the importance and priority of accelerator investment determinants through demonstration as a follow-up study on accelerator investment determinants derived based on business model innovation theory that reflects the nature, goals, and major activities of accelerator investment. In addition, it is of practical value as it contributes to revitalizing the domestic startup investment ecosystem by providing accelerators with theoretical grounds for investment decisions and specific information on detailed investment determinants.