• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.1
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• pp.35-46
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• 2016

Purpose : BoS(Base of Skull) Frame, the fixation tool which is used for the proton of brain cancer increases the lateral penumbra by increasing the airgap (the distance between patient and beam jet), due to the collision of the beam of the posterior oblique direction. Thus, we manufactured the fixation tool per se for improving the limits of BoS frame, and we'd like to evaluate the utility of the manufactured fixation tool throughout this study. Materials and Methods : We've selected the 3 patients of brain cancer who have received the proton therapy from our hospital, and also selected the 6 beam angles; for this, we've selected the beam angle of the posterior oblique direction. We' ve measured the planned BoS frame and the distance of Snout for each beam which are planned for the treatment of the patient using the BoS frame. After this, we've proceeded with the set-up that is above the location which was recommended by the manufacturer of the BoS frame, at the same beam angle of the same patient, by using our in-house Bos frame fixation tool. The set-up was above 21 cm toward the superior direction, compared to the situation when the BoS frame was only used with the basic couch. After that, we've stacked the snout to the BoS frame as much as possible, and measured the distance of snout. We've also measured the airgap, based on the gap of that snout distance; and we've proceeded the normalization based on each dose (100% of each dose), after that, we've conducted the comparative analysis of lateral penumbra. Moreover, we've established the treatment plan according to the changed airgap which has been transformed to the Raystation 5.0 proton therapy planning system, and we've conducted the comparative analysis of DVH(Dose Volume Histogram). Results : When comparing the result before using the in-house Bos frame fixation tool which was manufactured for each beam angle with the result after using the fixation tool, we could figure out that airgap than when not used in accordance with the use of the in-house Bos frame fixation tool was reduced by 5.4 cm ~ 15.4 cm, respectively angle. The reduced snout distance means the airgap. Lateral Penumbra could reduce left, right, 0.1 cm ~ 0.4 cm by an angle in accordance with decreasing the airgap while using each beam angle in-house Bos frame fixation tool. Due to the reduced lateral penumbra, Lt.eyeball, Lt.lens, Lt. hippocampus, Lt. cochlea, Rt. eyeball, Rt. lens, Rt. cochlea, Rt. hippocampus, stem that can be seen that the dose is decreased by 0 CGE ~ 4.4 CGE. Conclusion : It was possible to reduced the airgap by using our in-house Bos frame fixation tool for the proton therapy; as a result, it was possible to figure out that the lateral penumbra reduced. Moreover, it was also possible to check through the comparative analysis of the treatment plan that when we reduce the lateral penumbra, the reduction of the unnecessary irradiation for the normal tissues. Therefore, Using the posterior oblique the Brain cancer proton therapy should be preceded by decreasing the airgap, by using our in-house Bos frame fixation tool; also, the continuous efforts for reducing the airgap as much as possible for the proton therapy of other area will be necessary as well.