• Title/Summary/Keyword: 비제조/서비스 산업

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A Study on Industries's Leading at the Stock Market in Korea - Gradual Diffusion of Information and Cross-Asset Return Predictability- (산업의 주식시장 선행성에 관한 실증분석 - 자산간 수익률 예측 가능성 -)

  • Kim Jong-Kwon
    • Proceedings of the Safety Management and Science Conference
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    • 2004.11a
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    • pp.355-380
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    • 2004
  • I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

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An Audit Method on Information System Audit using Delphi Method - Based on Hyperledger Fabric (델파이기법을 이용한 감리점검항목 도출 방안 - 하이퍼레저 패브릭 기반으로)

  • Lee, Youngjoo;Park, Sooyong
    • Journal of Software Engineering Society
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    • v.29 no.2
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    • pp.7-12
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    • 2020
  • A project to establish an information system with a project cost of more than 500 million won at a local public firm is subject to the Information System Audit (hereinafter referred to as IS Audit), and the IS Auditor shall conduct audit according to the audit criteria. Recently, as blockchain technology has been applied to various fields such as manufacturing, healthcare, distribution, and public sectors beyond the financial industry, the development of systems that apply blockchain technology is increasing. The use of Hyperledger Fabric, a private blockchain, is on the rise to utilize blockchain technology in public firms and private firms. However, the newly emerging blockchain-based system is not sufficient to carry out auditing with existing audit check items, so it has no choice but to make and use audit items individually. Therefore, a need for verified audit items for systems that base on blockchain technology has emerged. In this study, we customized the Delphi process to derive audit items suitable for the system development project using the blockchain technology, and verified the completeness and accuracy of the audit items derived through a survey by the IS Auditor. This research will be of direct help to the IS Auditor, and it is expected that operators and organizers who provide services through the blockchain-based system will also contribute to improving the quality of the system.

Textile material classification in clothing images using deep learning (딥러닝을 이용한 의류 이미지의 텍스타일 소재 분류)

  • So Young Lee;Hye Seon Jeong;Yoon Sung Choi;Choong Kwon Lee
    • Smart Media Journal
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    • v.12 no.7
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    • pp.43-51
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    • 2023
  • As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.