22 research outputs found

    Complicated Firms Moderate Analyst Forecast Errors When Sentiment is Higher

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜ํ•™๊ณผ, 2014. 2. ํ™ฉ์ด์„.์ด ๋…ผ๋ฌธ์€ ์‹œ์žฅ ๋ถ„์œ„๊ธฐ(market sentiment)๊ฐ€ ์ข‹์„ ๋•Œ ์• ๋„๋ฆฌ์ŠคํŠธ๋“ค์˜ ์ด์ต ์˜ˆ์ธก(earnings forecast) ์ •ํ™•์„ฑ์ด ๊ฐ์†Œํ•œ๋‹ค๋Š” ๊ธฐ์กด ๋…ผ๋ฌธ์˜ ๋…ผ์ง€๋ฅผ ์žฌํ™•์ธํ•œ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์—…์˜ ๊ตฌ์กฐ์  ๋ณต์žก์„ฑ(complexity or diversification)์ด ์• ๋„๋ฆฌ์ŠคํŠธ์˜ ์ด์ต ์˜ˆ์ธก ์ •ํ™•์„ฑ์„ ๊ฐ์†Œ์‹œํ‚จ๋‹ค๋Š” ๊ธฐ์กด ๋…ผ๋ฌธ๊ณผ๋Š” ๋‹ฌ๋ฆฌ, ์ด ๋…ผ๋ฌธ์€ ๊ธฐ์—…์˜ ๊ตฌ์กฐ์  ๋ณต์žก์„ฑ์ด ์˜คํžˆ๋ ค ์• ๋„๋ฆฌ์ŠคํŠธ์˜ ์ด์ต ์˜ˆ์ธก ์˜ค์ฐจ๋ฅผ ์ค„์—ฌ ์ •ํ™•์„ฑ์„ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ์• ๋„๋ฆฌ์ŠคํŠธ๋“ค์€ ๊ตฌ์กฐ์ ์œผ๋กœ ๋ณต์žกํ•œ ๊ธฐ์—…(complicated firms)์— ๋Œ€ํ•ด ๋” ์ •ํ™•ํ•˜๊ณ  ๋œ ๋‚™๊ด€์ ์ธ ์ด์ต ์˜ˆ์ธก์น˜๋ฅผ ์ œ์‹œํ•˜๋ฉฐ, ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ ์‹œ์žฅ ๋ถ„์œ„๊ธฐ๊ฐ€ ์ข‹์„ ๋•Œ ๋” ๋‘๋“œ๋Ÿฌ์ง„๋‹ค. ์ฆ‰, ์‹œ์žฅ ๋ถ„์œ„๊ธฐ๊ฐ€ ์ข‹์„ ๋•Œ ์• ๋„๋ฆฌ์ŠคํŠธ๋“ค์˜ ์ด์ต ์˜ˆ์ธก ์ •ํ™•์„ฑ์ด ๊ฐ์†Œํ•˜๋‚˜, ๊ธฐ์—…์˜ ๊ตฌ์กฐ์  ๋ณต์žก์„ฑ์€ ๊ทธ๊ฒƒ์„ ์™„ํ™”ํ•ด ์ด์ต ์˜ˆ์ธก ์ •ํ™•์„ฑ์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์ž‘์šฉํ•œ๋‹ค. ๊ธฐ์—…์˜ ์ด ์ด์ต์€ ๊ทธ ๊ธฐ์—…์ด ๊ฒฝ์˜ํ•˜๋Š” ๊ฐ ์‚ฐ์—…๋ถ€๋ฌธ์˜ ์ด์ต์˜ ์ดํ•ฉ์ด๋ฉฐ, ์ด๋Ÿฌํ•œ ์ƒํ˜ธ๋ณด์™„์„ฑ์€ ์ด์ต์˜ ๋ณ€๋™์„ฑ์„ ๊ฐ์†Œ์‹œ์ผœ ์• ๋„๋ฆฌ์ŠคํŠธ๋“ค์€ ๊ณผ๊ฑฐ ์ด์ต ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋” ์ •ํ™•ํ•˜๊ณ  ๋œ ๋‚™๊ด€์ ์œผ๋กœ ์ด์ต์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์—…์˜ ๊ทœ๋ชจ๊ฐ€ ์• ๋„๋ฆฌ์ŠคํŠธ๋“ค์˜ ์ด์ต ์˜ˆ์ธก ์˜ค์ฐจ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ œ๊ฑฐํ•œ ํ›„์—๋„ ์ด ๋…ผ๋ฌธ์˜ ๊ฒฐ๊ณผ๋Š” ์—ฌ์ „ํžˆ ์œ ํšจํ•˜๋‹ค.This paper confirms that analyst forecasts are influenced by market sentiment and supports the prior findings that analyst earnings forecast errors increase when sentiment is higher. However, contrary to the prior literature that firms complexity or diversification make analysts difficult to forecast earnings and produce bigger forecast errors, this study finds that firms structural complexity moderates analyst forecast errors. Analyst earnings forecasts are more accurate and less optimistically biased for complicated firms and it is pronounced when sentiment is higher. That is, analyst earnings forecast inaccuracy and optimism when sentiment is higher are much reduced for complicated firms. This study also finds that earnings of complicated firms are more persistent. The earnings characteristics of complicated firms (i.e., lower earnings volatility, lower changes in earnings and higher earnings predictability) make analysts issue more accurate and less biased estimates. Because earnings of each segment are complementary to a given firms total earnings, these offset effects reduce earnings fluctuation in complicated firms. Even after controlling for the size effect, the findings are still supported.1. Introduction 2. Literature Review and Hypotheses Development 3. Data and Variables Definition 3.1. Data 3.2. Analyst Forecast errors 3.3. Complexity 3.4. Control Variables 4. Research Design 5. Empirical Results 6. Robust Check 7. ConclusionMaste

    ๊ตญ๋ฐฉ๋ถ€๋ฌธ์˜ ์•„์›ƒ์†Œ์‹ฑ์€ ๊ฐ€๊ฒฉํšจ์œจ์ ์ธ๊ฐ€? : ์ด๋ผํฌ์ „์Ÿ์—์„œ์˜ ๋ฏผ๊ฐ„๊ตฐ์‚ฌ๊ธฐ์—…์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    Thesis(master`s)--์„œ์šธ๋Œ€ํ•™๊ต ๊ตญ์ œ๋Œ€ํ•™์› :๊ตญ์ œํ•™๊ณผ ๊ตญ์ œํ˜‘๋ ฅ์ „๊ณต,2007.Maste

    ์œ„ํ—˜-์ˆ˜์ต ๊ฐ„ ์ด์ƒํ˜„์ƒ๊ณผ ์‚ฐ์—…๋ณ„ ๊ฒฝ๊ธฐ๋ณ€๋™

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ, 2019. 2. ํ™ฉ์ด์„.This article examines whether industry plays a role in explaining the negative beta-return relation. Using the two-beta model, I isolate the risk exposure to an affiliated industry (i.e., industry beta) from the risk exposure to the rest of the market (i.e., market beta). I find that the industry beta is statistically and economically significant, highlighting the importance of industry in explaining the risk-return relation. The cross-sectional variation in industry beta is closely related to the beta anomaly, but the beta anomaly weakens and disappears in the market-beta-sorted portfolios. In addition, while the risk-return relation is negative in overvalued industry, it is positive in undervalued industry. In sum, I suggest that the industry-level overpricing is a potential mechanism which is responsible for the failure of the CAPM. This article examines the risk-return relation conditional on macroeconomic uncertainty in an effort to relax the constant beta assumption. Macro uncertainty is closely related to time-varying betas and alphas. The risk-return relation has a distinctive pattern given macro uncertainty. While the beta anomaly exists in the low uncertainty periods, it weakens as uncertainty increases. Specifically, the long-short beta-based strategy earns positive abnormal returns in the high uncertainty periods. In sum, this paper suggests that macro uncertainty plays a crucial role in determining the risk-return relation. Based on the theoretical framework of Lambert, Leuz, and Verrecchia (2007) and Gao (2010), the cost of equity increases with information quality if the two conditions are met: (i) firms increase their investment, and (ii) risk grows faster than profitability as a result of investment. Capturing the increasing relative speed of risk over profitability from additional investment with investment risk, I provide empirical evidence that information quality has an asymmetry effect on the cost of equity depending on investment risk. That is, the cost of equity increases (decreases) with information quality for growing firms with high (low) investment risk. This study contributes to the literature by providing empirical evidence that information quality can increase the cost of capital in the presence of the investment effect.์ฒซ ๋ฒˆ์งธ ๋…ผ๋ฌธ์€ ์‚ฐ์—…์ด ์œ„ํ—˜-์ˆ˜์ต ๊ฐ„ ์ด์ƒํ˜„์ƒ์„ ์„ค๋ช…ํ•˜๋Š”์ง€ ์—ฐ๊ตฌํ•œ๋‹ค. Two-beta ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ธฐ์—…์ด ์†ํ•œ ์‚ฐ์—… ๊ด€๋ จ ์œ„ํ—˜ (industry beta)๊ณผ ๊ธฐ์—…์ด ์†ํ•œ ์‚ฐ์—…์„ ์ œ์™ธํ•œ ๊ฑฐ์‹œ๊ฒฝ์ œ ๊ด€๋ จ ์œ„ํ—˜ (market beta)์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. ์ฒซ์งธ, Industry beta๊ฐ€ ํ†ต๊ณ„์ , ๊ฒฝ์ œ์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ๊ฐ’์„ ๊ฐ€์ง€๋ฏ€๋กœ, ์‚ฐ์—…์ด ์œ„ํ—˜-์ˆ˜์ต ๊ด€๊ณ„์— ํฐ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, industry beta์™€ market beta๋กœ ๊ฐ๊ฐ ํฌํŠธํด๋ฆฌ์˜ค๋ฅผ ๊ตฌ์„ฑํ•˜์—ฌ, ์œ„ํ—˜์ด ๋†’์€ ์ฃผ์‹์„ ์‚ฌ๊ณ  ์œ„ํ—˜์ด ๋‚ฎ์€ ์ฃผ์‹์„ ํŒŒ๋Š” ์ „๋žต์ด ์ดˆ๊ณผ ์ˆ˜์ต์„ ์–ป๋Š”์ง€ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋‘˜์งธ, Industry beta๊ฐ€ ์œ„ํ—˜-์ˆ˜์ต ๊ฐ„ ์ด์ƒํ˜„์ƒ๊ณผ ๊นŠ์€ ๊ด€๋ จ์ด ์žˆ๋‹ค๋Š” ํฅ๋ฏธ๋กœ์šด ํ˜„์ƒ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ์ฆ‰, ์‚ฐ์—… ๊ด€๋ จ ์œ„ํ—˜์— ํฌ๊ฒŒ ๋…ธ์ถœ๋˜๋Š” ๊ธฐ์—…์˜ ์ฃผ์‹์„ ์‚ฌ๊ณ  ์ž‘๊ฒŒ ๋…ธ์ถœ๋˜๋Š” ์ฃผ์‹์„ ํŒŒ๋Š” ๊ฒฝ์šฐ ์Œ์˜ ์ฃผ์‹ ์ˆ˜์ต์œจ์„ ์–ป๋Š”๋‹ค. ๋ฐ˜๋ฉด, market beta๋กœ ํฌํŠธํด๋ฆฌ์˜ค๋ฅผ ๊ตฌ์„ฑํ•˜๋ฉด ์œ„ํ—˜-์ˆ˜์ต ๊ฐ„ ์ด์ƒํ˜„์ƒ์ด ์•ฝํ•ด์ง€๊ฑฐ๋‚˜ ์‚ฌ๋ผ์ง„๋‹ค. ์…‹์งธ, ์œ„ํ—˜-์ˆ˜์ต ๊ฐ„ ์ด์ƒํ˜„์ƒ์€ ๊ณผ๋Œ€ํ‰๊ฐ€๋œ ์‚ฐ์—…์—์„œ ๋ฐœ๊ฒฌ๋œ๋‹ค. ๋ฐ˜๋ฉด, ๊ณผ์†Œํ‰๊ฐ€๋œ ์‚ฐ์—…์—์„œ๋Š” ์œ„ํ—˜๊ณผ ์ˆ˜์ต์ด ์–‘์˜ ๊ด€๊ณ„๋กœ, ์œ„ํ—˜์ด ์ ์ ˆํžˆ ๋ณด์ƒ๋˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ์ด ๋…ผ๋ฌธ์€ ์‚ฐ์—…์˜ ๊ณผ๋Œ€ํ‰๊ฐ€ ์—ฌ๋ถ€๊ฐ€ ์œ„ํ—˜-์ˆ˜์ต ๊ฐ„ ์ด์ƒํ˜„์ƒ์„ ์„ค๋ช…ํ•˜๋Š” ์ค‘์š”ํ•œ ์š”์ธ์ž„์„ ์„ค๋ช…ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋…ผ๋ฌธ์€ ์œ„ํ—˜-์ˆ˜์ต ๊ฐ„ ์ด์ƒํ˜„์ƒ์ด ๊ฑฐ์‹œ๊ฒฝ์ œ์˜ ๋ถˆํ™•์‹ค์„ฑ์— ๋”ฐ๋ผ ๋ณ€ํ•˜๋Š”์ง€ ๊ฒ€์ฆํ•œ๋‹ค. beta๊ฐ€ ๋ถˆ๋ณ€ํ•œ๋‹ค๋Š” ์ž๋ณธ์ž์‚ฐ๊ฐ€๊ฒฉ๊ฒฐ์ •์ด๋ก ์˜ ๊ฐ€์„ค๊ณผ ๋‹ฌ๋ฆฌ, ์ด ๋…ผ๋ฌธ์€ beta์™€ alpha๊ฐ€ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ•จ์„ ๋ณด์ธ๋‹ค. ๋˜ํ•œ ์ด ํ˜„์ƒ์ด ๊ฑฐ์‹œ๊ฒฝ์ œ์˜ ๋ถˆํ™•์‹ค์„ฑ๊ณผ ํฐ ์—ฐ๊ด€์ด ์žˆ์Œ์„ ์„ค๋ช…ํ•œ๋‹ค. ๋ถˆํ™•์‹ค์„ฑ์ด ๋‚ฎ์„ ๋•Œ์—๋Š” ์œ„ํ—˜-์ˆ˜์ต ๊ฐ„ ์ด์ƒํ˜„์ƒ์ด ๋ฐœ๊ฒฌ๋˜์ง€๋งŒ, ๋ถˆํ™•์‹ค์„ฑ์ด ์ปค์ง€๋ฉด์„œ ์ด์ƒํ˜„์ƒ์ด ์ค„์–ด๋“ ๋‹ค. ํŠนํžˆ, ๋ถˆํ™•์‹ค์„ฑ์ด ํฐ ์‹œ๊ธฐ์—๋Š” ์œ„ํ—˜๊ณผ ์ˆ˜์ต์ด ์–‘์˜ ๊ด€๊ณ„๋กœ, ์œ„ํ—˜์— ๋Œ€ํ•œ ๋ณด์ƒ์ด ์ ์ ˆํžˆ ์ด๋ฃจ์–ด์ง„๋‹ค. ๋”ฐ๋ผ์„œ ์ด ๋…ผ๋ฌธ์€ ๊ฑฐ์‹œ๊ฒฝ์ œ์˜ ๋ถˆํ™•์‹ค์„ฑ์ด ์œ„ํ—˜-์ˆ˜์ต ๊ฐ„ ๊ด€๊ณ„๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ์ค‘์š” ์š”์ธ์ž„์„ ์ฃผ์žฅํ•œ๋‹ค. Lambert, Leuz, and Verrecchia (2007) and Gao (2010)๋Š” (1) ํšŒ์‚ฌ๊ฐ€ ํˆฌ์ž๋ฅผ ์ฆ๊ฐ€์‹œํ‚ค๊ณ , (2) ํˆฌ์ž๋กœ ์ธํ•œ ์œ„ํ—˜์ด ํˆฌ์ž๋กœ ์ธํ•œ ์ˆ˜์ต๋ณด๋‹ค ํฐ ๊ฒฝ์šฐ ํšŒ์‚ฌ๊ฐ€ ์ œ๊ณตํ•˜๋Š” ์ •๋ณด์˜ ์งˆ์ด ๋†’์„์ˆ˜๋ก ์ž๋ณธ์กฐ๋‹ฌ๋น„์šฉ์ด ์ปค์งˆ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ด๋ก ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ฆ‰, ์–‘์งˆ์˜ ์ •๋ณด๊ฐ€ ํˆฌ์ž๋ฅผ ์ด‰์ง„ํ•˜๊ณ , ํˆฌ์ž ์ฆ๊ฐ€๋กœ ์ธํ•ด ํšŒ์‚ฌ๊ฐ€ ์ง๋ฉดํ•˜๋Š” ์œ„ํ—˜๊ณผ ์ˆ˜์ต์ด ๋™์‹œ์— ์ปค์ง„๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ํˆฌ์ž๋กœ ์ธํ•œ ์œ„ํ—˜๊ณผ ์ˆ˜์ต์˜ ์ƒ๋Œ€์  ์ฆ๊ฐ€ ์†๋„์— ๋”ฐ๋ผ ์ž๋ณธ์กฐ๋‹ฌ๋น„์šฉ์ด ๊ฒฐ์ •๋˜๊ฒŒ ๋œ๋‹ค. ์ด ์ƒ๋Œ€์  ์ฆ๊ฐ€ ์†๋„๋ฅผ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด ์ž์‚ฐ๊ฐ€๊ฒฉ๊ฒฐ์ •๋ชจํ˜•์„ ์ด์šฉํ•˜์—ฌ investment beta๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ์ด ๋…ผ๋ฌธ์€ investment beta, ์ฆ‰ ํˆฌ์ž๋กœ ์ธํ•œ ์œ„ํ—˜์˜ ์ฆ๊ฐ€ ์ •๋„์— ๋”ฐ๋ผ ์ •๋ณด์˜ ์งˆ์ด ์ž๋ณธ์กฐ๋‹ฌ๋น„์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ๋น„๋Œ€์นญ์ ์ž„์„ ์‹ค์ฆ์ ์œผ๋กœ ๋ณด์—ฌ์ค€๋‹ค. ์ฆ‰, ํˆฌ์ž๋กœ ์ธํ•œ ์œ„ํ—˜์ด ํˆฌ์ž๋กœ ์ธํ•œ ์ˆ˜์ต๋ณด๋‹ค ํฐ (์ž‘์€) ๊ฒฝ์šฐ, ์ •๋ณด์˜ ์งˆ๊ณผ ์ž๋ณธ์กฐ๋‹ฌ๋น„์šฉ์ด ์–‘์˜ (์Œ์˜) ๊ด€๊ณ„์ž„์„ ๋ฐํžŒ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ์ด ๋…ผ๋ฌธ์€ ํˆฌ์ž๋ผ๋Š” ์ฑ„๋„์„ ํ†ตํ•ด ์–‘์งˆ์˜ ์ด์ต ์ •๋ณด๊ฐ€ ์ž๋ณธ์กฐ๋‹ฌ๋น„์šฉ์„ ์ฆ๊ฐ€์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ์ฃผ์žฅํ•œ๋‹ค.Essay 1. Beta Anomaly and Industry Booms and Busts 1 1. Introduction 2 2. Hypothesis Development.. 11 3. Research Design. 13 3.1. Sample. 13 3.2. Model 14 4. The Beta Anomaly. 16 4.1. Descriptive Statistics. 16 4.2. The Beta Anomaly. 17 5. The Beta Anomaly Conditional on Industry Booms and Busts. 21 5.1. The Validity of the Industry-level Misvaluation Measure 22 5.2. The Beta Anomaly Conditional on Industry Booms and Busts 26 6. Changes in Compensation for Risk 29 7. Robustness checks 31 7.1. The Alternative Measure for the Industry-level Misvaluation. 31 7.2. The Beta Anomaly Conditional on Market Sentiment. 33 7.3. Standalone Firms vs. Conglomerate Firms 34 7.4. Controlling for the Standard Deviation of Industry Returns... 35 7.5. The Beta Anomaly Conditional on the Industry-level Growth Opportunity. 36 7.6. Controlling for the Cross-industry Trading Behavior. 37 8. Conclusion.. 38 Appendix 40 Reference... 41 Essay 2. Beta Anomaly Conditional on Macro Uncertainty 59 1. Introduction 60 2. Research Design .... 63 2.1. Data. 63 2.2. Model.. 65 3. The Distribution of Betas Conditional on Macro Uncertainty. 66 4. The Risk-Return Relation Conditional on Macro Uncertainty. 68 5. Analyses with Industry Beta and Market Beta.. 69 6. Firm Characteristics Conditional on Macro Uncertainty 71 7. The Risk-Return Relation Conditional on the Combination of Macro Uncertainty and Investment Growth. 72 8. Conclusion 74 Reference... 75 Essay 3. Information Quality, Investment Risk, and the Cost of Equity 97 1. Introduction. 98 2. Prior Literature and Hypothesis. 104 3. Research Design. 109 3.1. Sample. 109 3.2. Information Quality. 110 3.3. Investment risk. 111 3.4. Model 113 4. The Conditional CAPM.. 114 5. Ordinary Least Squares Regression (OLS) 117 5.1. The Fama and French Three-factor Model 119 5.2. The Implied Cost of Equity 120 6. The Cost of Capital Model Based on Earnings Realization. 122 7. Robustness Checks. 125 7.1. Controlling for the Direct Effect 125 7.2. Growing and Non-growing Firms. 126 7.3. Aggregate investment growth 128 8. Conclusion 129 Appendix 131 Reference. 133Docto

    Surface change and mechanical properties of the orthodontic mini-screw by repeated steam autoclaving

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    ์น˜์˜ํ•™๊ณผ/์„์‚ฌ๊ต์ •์šฉ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜๋Š” ์ œ์กฐ์‚ฌ์— ๋”ฐ๋ผ ๋ฏธ๋ฆฌ ๋ฉธ๊ท ์ฒ˜๋ฆฌ ๋˜์–ด ๋‚˜์˜ค๋Š” ์ œํ’ˆ๋„ ์žˆ์œผ๋‚˜ ๋Œ€๋ถ€๋ถ„์˜ ๊ฒฝ์šฐ ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ๋ฅผ ํ•˜์—ฌ ์‚ฌ์šฉํ•  ๊ฒƒ์„ ๊ถŒ์žฅํ•˜๊ณ  ์žˆ๋‹ค. ์น˜๊ณผ์šฉ ๊ธฐ๊ตฌ์˜ ๋ฉธ๊ท  ๋ฐฉ๋ฒ•๋“ค ์ค‘ ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์€ ๊ฐ€์—ดํ•œ ํฌํ™”์ˆ˜์ฆ๊ธฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์—ด์„ ์ „๋‹ฌํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ๊ณ ์˜จ ๊ณ ์••์˜ ์ˆ˜์ฆ๊ธฐ์— ์˜ํ•ด ๋‚ ์ด ์žˆ๋Š” ๊ธฐ๊ตฌ๋‚˜ ๊ธˆ์† ์žฌ๋ฃŒ๋ฅผ ์†์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ๊ฐ€ ๊ต์ •์šฉ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜์˜ ์—ญํ•™์  ํŠน์„ฑ๊ณผ ํ‘œ๋ฉด ํŠน์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•˜์—ฌ ํ‘œ๋ฉด ์ฒ˜๋ฆฌ ๋˜์ง€ ์•Š์€ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜์™€ ํ‘œ๋ฉด ์ฒ˜๋ฆฌ๋œ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜๋ฅผ ์„ ํƒํ•˜์—ฌ ๋ฐ˜๋ณต์  ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ๋ฅผ ์‹œํ–‰ํ•œ ํ›„ ์‹๋ฆฝ ํ† ์˜คํฌ๋ฅผ ์ธก์ •ํ•˜๊ณ , ํ‘œ๋ฉด์˜ ํ˜•ํƒœํ•™์  ๋ณ€ํ™”์™€ ํ™”ํ•™์  ์กฐ์„ฑ ๋ฐ ๊ฒฐ์ •๊ตฌ์กฐ์˜ ๋ณ€ํ™”๋ฅผ ์กฐ์‚ฌํ•˜๊ณ ์ž ํ•จ์ด๋‹ค. ์ด 40๊ฐœ์˜ ์™ธ๊ฒฝ 1.6 mm, ๊ธธ์ด 8 mm์˜ ํ‰ํ™œ๋ฉด์„ ๊ฐ€์ง„ ์›ํ†ตํ˜• ๊ต์ •์šฉ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜ (Dual-Topโ“‡ Anchor, 16-JA-006H, Jeil medical, Seoul, Korea)๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ 0, 10, 20, 30ํšŒ ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๊ณ , ์ด 20๊ฐœ์˜ ์™ธ๊ฒฝ 1.6 mm, ๊ธธ์ด 8 mm์˜ SLA (Sand-blasted, Large-grit, Acid-etched) ํ‘œ๋ฉด์ฒ˜๋ฆฌ ๋œ ์›์ถ”ํ˜• ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜ (OrLus IO16108, Ortholution, Seoul, Korea)๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ 0, 20ํšŒ ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ฐ๊ฐ์˜ ์Šคํฌ๋ฅ˜์— ๋Œ€ํ•œ ์›์žฌ๋ฃŒ์ธ ํ‹ฐํƒ€๋Š„ ํ•ฉ๊ธˆ์œผ๋กœ ์ง๊ฒฝ 3 mm, ๊ธธ์ด 5 mm์˜ ์‹œํŽธ์„ ์ œ์ž‘ํ•˜์—ฌ 0, 10, 30ํšŒ ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋ฉธ๊ท ์ฒ˜๋ฆฌ ํ›„ ์‹๋ฆฝ ํ† ์˜คํฌ ์‹œํ—˜์„ ์‹œํ–‰ํ•˜์—ฌ ๋ฉธ๊ท  ํšŸ์ˆ˜๊ฐ€ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜์˜ ์—ญํ•™์  ํŠน์„ฑ์— ์˜ํ–ฅ์„ ์ฃผ๋Š”์ง€ ์•Œ์•„๋ณด์•˜๊ณ , ์ฃผ์‚ฌ์ „์žํ˜„๋ฏธ๊ฒฝ (HITACHI, S-3000N, Tokyo, Japan)์„ ์ด์šฉํ•˜์—ฌ ํ‘œ๋ฉด์˜ ํ˜•์ƒ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜๊ณ , ์—๋„ˆ์ง€ ๋ถ„์‚ฐ X-์„  ๋ถ„๊ด‘๋ถ„์„๊ธฐ (EDS, EX-250, HORIBA, Japan)๋ฅผ ์ด์šฉํ•˜์—ฌ ํ‘œ๋ฉด ์›์†Œ ์„ฑ๋ถ„์˜ ์ฐจ์ด๋ฅผ ๋น„๊ตํ•˜์˜€์œผ๋ฉฐ X-์„  ํšŒ์ ˆ๊ธฐ (XRD, Rigaku, DMAX 2500, Texas, USA)์™€ X-์„  ๊ด‘์ „์ž ๋ถ„๊ด‘๋ฒ• (XPS, Ulvac-PHI, PHI 5000, MN, USA)์„ ์ด์šฉํ•˜์—ฌ ์žฌ๋ฃŒ์˜ ์ •๋Ÿ‰์ ์ธ ๊ตฌ์กฐ ๋ถ„์„๊ณผ ์ƒ์˜ ๋ณ€ํ™” ๊ทธ๋ฆฌ๊ณ  ํ™”ํ•™์  ์ƒํƒœ๋ฅผ ๋น„๊ต ๋ถ„์„ํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ๋‹ค.1. ๋ฐ˜๋ณต์  ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ๋Š” ๋ฉธ๊ท  ํšŸ์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ๊ต์ •์šฉ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜์˜ ์ตœ๋Œ€ ์‹๋ฆฝ ํ† ์˜คํฌ๋ฅผ ๊ฐ์†Œ์‹œ์ผฐ์ง€๋งŒ ํ†ต๊ณ„ํ•™์  ์œ ์˜์ฐจ๋Š” ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š์•˜๋‹ค (p > 0.05).2. ๋ฐ˜๋ณต์  ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ์— ์˜ํ•ด ํ‰ํ™œ๋ฉด ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜๋Š” ํ‘œ๋ฉด์˜ ๊ฒฐ์ด ๊ท ์ผํ•˜์ง€ ๋ชปํ•˜๊ณ  ๋” ๋งŽ์€ ๋ฒฝ๊ฐœ๋ฉด์ด ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์œผ๋กœ ๊ด€์ฐฐ๋˜์—ˆ์œผ๋ฉฐ, SLA ํ‘œ๋ฉด์ฒ˜๋ฆฌ๋œ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜๋Š” ํ‘œ๋ฉด์˜ ๊ท ์ผ์„ฑ์ด ๊ฐ์†Œํ•˜๊ณ  ํ‘œ๋ฉด ์š”์ฒ ์ด ๋ฌด๋ŽŒ์ง€๋Š” ๊ฒƒ์œผ๋กœ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค.3. ๋ฐ˜๋ณต์  ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ๋Š” Ti-6Al-4V๋กœ ์ œ์ž‘๋œ ๊ต์ •์šฉ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜์˜ ํ‘œ๋ฉด ์›์†Œ ์„ฑ๋ถ„์„ ๋ณ€ํ™”์‹œํ‚ค์ง€ ์•Š์•˜๋‹ค.4. ๋ฐ˜๋ณต์  ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ์— ์˜ํ•ด ๊ต์ •์šฉ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜์˜ ํ‘œ๋ฉด์„ฑ๋ถ„์€ ๊ฒฐ์ •๊ตฌ์กฐ์˜ ๋ณ€ํ™”๋ฅผ ์ผ์œผํ‚ค์ง€ ์•Š์•˜์œผ๋ฉฐ ํ‘œ๋ฉด ํ™”ํ•ฉ๋ฌผ์˜ chemical shift๋ฅผ ๋ณด์˜€๋‹ค. ๊ต์ •์šฉ ๋ฏธ๋‹ˆ ์Šคํฌ๋ฅ˜์˜ ๊ต์ฐจ ๊ฐ์—ผ์˜ ์˜ˆ๋ฐฉ์„ ์œ„ํ•ด ๋ฐ˜๋“œ์‹œ ํ•„์š”ํ•œ ๊ณ ์•• ์ฆ๊ธฐ ๋ฉธ๊ท ์ฒ˜๋ฆฌ๋Š” ์ˆ˜์ฐจ๋ก€ ๋ฐ˜๋ณต๋ ์ˆ˜๋ก ํ‘œ๋ฉด ํ˜•ํƒœํ•™์  ๋ณ€ํ™”์™€ ํ‘œ๋ฉด ์„ฑ๋ถ„์˜ ๋ณ€ํ™”๋ฅผ ์ผ์œผํ‚ฌ ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ์ด๋ฅผ ์ž„์ƒ์  ์ง€์นจ์œผ๋กœ ์‚ผ์•„ ๋ฐ˜๋ณต๋ฉธ๊ท ์„ ํ”ผํ•˜๋Š” ๊ฒƒ์ด ์ถ”์ฒœ๋œ๋‹ค.restrictio
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