• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• Journal of Dental Rehabilitation and Applied Science
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• v.28 no.2
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• pp.147-161
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• 2012

This study is to assess the effect of horizontal misfit of an implant supported 3-unit fixed prosthodontics on the stress development at the marginal cortical bone surrounding implant neck. Two finite element models consisting of a three unit fixed prosthodontics and an implant/bone complex were constructed on a three dimensional basis. The three unit fixed prosthodontics were designed either shorter (d=17.8mm model) or longer (d=18.0mm model) by 0.1mm than the span of two implants placed at the mandibular second premolar and second molar areas 17.9mm apart. Fitting of the fixed prosthodontics onto the implant abutments was simulated by a total of 6 steps, that is to say, 0.1mm displacement per each step, using DEFORM 3D (ver 6.1, SFTC, Columbus, OH, USA) program. Stresses in the fixed prosthodontics and implants were evaluated using von-Mises stress, maximum compressive stress, and radial stress as necessary. The d=17.8mm model assembled successfully on to the implant abutments while d=18.0mm model did not. Regardless if the fixed prosthodontics fitted onto the abutments or not, excessively higher stresses developed during the course of assembly trial and thereafter. On the marginal cortical bone around implants during the assembly, the peak tensile and compressive stresses were as high as 186.9MPa and 114.1MPa, respectively, even after the final sitting of the fixed prosthodontics (for d=17.8mm model). For this case, the area of marginal bone subject to compressive stresses above 55MPa, equivalent of the $4,000{\mu}{\varepsilon}$, i.e. the reported threshold strain to inhibit physiological remodeling of human cortical bone, extended up to 2mm away from implant during the assembly. Horizontal misfit of 0.1mm can produce excessively high stresses on the marginal cortical bone not only during the fixed prosthodontics assembly but also thereafter.