• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.225-227
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• 2006

The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) first developed ADVISOR in 1994. Between 1998 and 2003 it was downloaded by more than 7,000 individuals, corporations, and universities world-wide. In early 2003 NREL initiated the commercialisation of ADVISOR through a public solicitation. AVL responded and was awarded the exclusive rights to license and distribute ADVISOR world-wide. AVL is committed to continuously enhance ADVISOR's capabilities. Provides rapid analysis of the performance and fuel economy of conventional and advanced, light and heavy-duty vehicle models as well as hybrid electric and fuel cell vehicle models. ADVISOR Simulates the Following Vehicle Characteristics. - Optimal drivetrain component sizes that provide the best fuel economy Vehicle's ablility to follow the speed trace - Amount of fuel and/or electric energy required by various vehicle concepts - Peak power and efficiency achieved by the drivetrain components - Torque and speed distribution of the engine - Average efficiency of the transmission - Gradeability of vehicles at various velocities

• Journal of Oriental Neuropsychiatry
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• v.21 no.2
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• pp.141-155
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• 2010

Objectives : This study was performed to develop a standard instrument of oriental medical evaluation for hwa-byung. Methods : The advisor committee on this study was organized by 17 neuropsychiatry professors of oriental medical colleges. The items and structure of the instrument were quoted from the instrument of pattern identification for hwa-byung. We took consultation twice from the advisor committee and we also took additional advices by e-mail. Results : We discriminated between bian-zheng and su-zheng from the answers of the advisor committee. We got the mean weight of each symptom and sign from the answers of the advisor committee. We got the final weight from the combination of the ratio of bian-zheng to the number of all answers of the advisor committee and mean weight. Conclusions : The instrument of oriental medical evaluation for hwa-byung was developed through experts' discussion. If the validity and reliability of this instrument is confirmed through additional clinical trial, the instrument of oriental medical evaluation for hwa-byung is expected to be applied to the subsequent research.

• Knowledge Management Research
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• v.19 no.1
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• pp.79-95
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• 2018

The purpose of this case study is to analyze the B2C security information service model using the robo-advisor, to develop various service models and to urge new companies to enter. Overseas robo-advisor service market is growing rapidly with the launch of various B2C service models beyond B2B. On the other hand, as the domestic market is dominated by B2B services and serviced just index portfolio which is nascent, it lacks products which are used for active asset management. Recently as the government announced the approval of online investment advisory service, the B2C market of domestic asset management has entered a growth phase, centered on generations familiar with IT. We propose to extend the concept of Robo-Advisor service in accordance with the financial market change. By that model, we will study the case of the algorithm of the investment masters' philosophy and contribute to the expansion of the B2C service market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4270-4284
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• 2019

This study presents a reference model (RM) and the architecture of a cognitive health advisor (CHA) that integrates information with ambient intelligence. By controlling the information using the CHA platform, the reference model can provide various ambient intelligent solutions to a user. Herein, a novel approach to a CHA RM based on evolutional cyber-physical systems is proposed. The objective of the CHA RM is to improve personal health by managing data integration from many devices as well as conduct a new feedback cycle, which includes training and consulting to improve quality of life. The RM can provide an overview of the basis for implementing concrete software architectures. The proposed RM provides a standardized clarification for developers and service designers in the design and implementation process. The CHA RM provides a new approach to developing a digital healthcare model that includes integrated systems, subsystems, and components. New features for chatbots and feedback functions set the position of the conversational interface system to improve human health by integrating information, analytics, and decisions and feedback as an advisor on the CHA platform.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.287-295
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• 2017

Since Google's AlphaGo defeated a world champion of Go players in 2016, there have been many interests in the deep learning. In the financial sector, a Robo-Advisor using deep learning gains a significant attention, which builds and manages portfolios of financial instruments for investors.In this paper, we have proposed the a deep learning algorithm geared toward identification and forecast of the KOSPI index direction,and we also have compared the accuracy of the prediction.In an application of forecasting the financial market index direction, we have shown that the Robo-Advisor using deep learning has a significant effect on finance industry. The Robo-Advisor collects a massive data such as earnings statements, news reports and regulatory filings, analyzes those and recommends investors how to view market trends and identify the best time to purchase financial assets. On the other hand, the Robo-Advisor allows businesses to learn more about their customers, develop better marketing strategies, increase sales and decrease costs.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.199-207
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• 2019

This study aims to analyze the profitability of Robo-Advisors portfolio combined with the analysts' forecasts on the Korean stock prices. Sample stocks are 8 blue-chips and sample period is from 2003 to 2019. Robo-Advisor portfolio was suggested using the Black-Litterman model combined with the analysts' forecasts and its profitability was analyzed. Empirical result showed the suggested Robo-Advisor algorithm produced 1% annual excess return more than that of the benchmark. The study documented that the analysts' forecasts had an economic value when applied in the Robo-Advisor portfolio despite the prevalent blames from investors. The profitability on small or medium-sized stocks will need to be analyzed in the Robo-Advisor context because their information is relatively less known to investors and as such is expected to be strongly influenced by the analysts' forecasts.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.123-129
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• 2020

The objective of this study is to examine the differentiated influence of sell-side advisors and buy-side advisors on mergers and acquisitions (M&A). Unlike prior studies on M&A advisors, the study addresses different roles of target and acquirer advisors, and explores their influences on the cumulative abnormal returns (CAR) and acquisition premiums with an empirical analysis of longitudinal data of M&As conducted by Japanese listed firms except financial companies from 1995 to 2012. M&A data were obtained from the Securities Data Corporation's (SDC) database, and the individual firm data were collected from the Nikkei Economic Electronic Databank System (NEEDS), which provides a wide range of corporate information including financial status, operational performance, and strategy. Using a sample of 452 cases for the CAR and 498 cases for the analysis of acquisition premiums, the empirical results support the hypotheses of the target advisor's positive association with CAR and acquirer advisor's positive association with acquisition premiums. The findings of this study indicate the target advisor's positive contribution to the success of acquisition process and performance, and acquirer advisor's negative influence on the deal progress. The study provides theoretical implications on M&A research and practical insights into the investment banking industry.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.1-24
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• 2016

With the innovation of information technology, non-face-to-face robo advisor with high accessibility and convenience is spreading. The current robot advisor recommends appropriate investment products after understanding the investment propensity based on the structured data entered directly or indirectly by individuals. However, it is an inconvenient and obtrusive way for financial consumers to inquire or input their own subjective propensity to invest. Hence, this study proposes a way to deduce the propensity to invest in unstructured data that customers voluntarily exposed during consultation or online. Since prediction performance based on unstructured document differs according to the characteristics of text, in this study, classification algorithm optimized for the characteristic of text left by financial consumers is selected by performing prediction performance evaluation of various learning discrimination algorithms and proposed an intelligent method that automatically recommends investment products. User tests were given to MBA students. After showing the recommended investment and list of investment products, satisfaction was asked. Financial consumers' satisfaction was measured by dividing them into investment propensity and recommendation goods. The results suggest that the users high satisfaction with investment products recommended by the method proposed in this paper. The results showed that it can be applies to non-face-to-face robo advisor.

• Communications of Mathematical Education
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• v.30 no.2
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• pp.225-249
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• 2016

This study focuses on enhancing expertise as a study advisor for mathematic teacher in field based on self-study method. By advising math study with students in school, the research was carried out 'process & content of mathematic study method advisement', 'process & content of the self-questioning by the math study adviser', and 'enhancing expertise as a math study counsellor by self-study method'. Overall process has been proceeded through preparation, experiment, result & analysis. Experiment has been done based on consultation modeling for academic high school which ran five times. During consultation, based on analysis & result, researcher has recorded 'self-questioning' report. This report is utilized for 'self-examination' for the researcher along the discussion with counselor for enhancing expertise as a study advisor. By above process, practitioner identifies each own's pros & cons as a mathematic study advisor and strengthens the skill while understanding the subject: student. by 'self-studying' method, advisor enhances its own expertise as a teacher with the achieving student and learns practical knowledge for a math study advisor.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.