• The Korean Journal of Financial Studies
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• v.2 no.2
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• pp.171-211
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• 1995

Relatively little is known about the relationship between taxes and asset prices. Differential tax treatment of assets in the same risk class implies differential pricing. Conversely, the ability of tax-exempt investors to engage in tax arbitrage should drive any pricing differences away. The differential tax treatment of classes of US Treasury securities provides a straightforward setting for the examination of possible tax-effects in asset prices. Using the Cox-Ingersoll-Ross Term Structure Model as our framework, we examine the pricing of US Treasury securities over two distinct tax regimes. Evidence that tax effects are not arbitraged away is presented.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.783-797
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• 2010

Based on the importance of asset allocation in the return of an investment portfolio, this article attempts to verify the appropriateness of mutual funds as means of investment to obtain optimal asset allocation. The return-based style analysis is applied to determine a mutual fund's allocation(or a style) among a set of specified asset classes. Assuming a particular investor who defines a range allowed a fund's style to differ from its original one, it is examined whether or not the fund style is continued over an investment time horizon. After verifying the fact that the original style of the investment fails to remain unchanged from the empirical analysis limited to domestic equity mutual funds, we further investigated the reasons for the style drift. Despite several limitations of the analysis, it yields the conclusion that domestic equity mutual funds do not seem to be an appropriate investment tool to achieve a target asset allocation.

• 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 Asian Finance, Economics and Business
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• v.9 no.4
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• pp.299-306
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• 2022

Entrepreneurs with behavioral finance biases are more likely to make irrational or financially detrimental decisions. Understanding financial behavior biases can assist in making sound financial decisions. Behavioral finance is a new topic that can assist researchers in better understanding investor behavior and preferences while purchasing and selling stocks. Using measures such as independent t-tests and average Likert five-point scale scores, this study seeks to determine how entrepreneurs make investment decisions and whether gender makes a difference. The study is empirical, and data from 1000 entrepreneurs were collected through convenience sampling. The study's main findings show that there are numerous factors to consider while investing in stocks, including family planning, children's education, investment security, and recurring income. Both men and women attempt to invest in many asset classes, but certain investments are extremely risky, while others are low risk. As a result, investors should assess risk based on their age and experience rather than their gender; this indicates that an investment in venture capital has nothing to do with gender but everything to do with the investor's age.

• Fourth Industrial Review
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• v.2 no.1
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• pp.19-32
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• 2022

Purpose - As new technologies that have led the 4th industrial revolution spread after the COVID-19 pandemic, the business crisis of existing financial institutions and the threat of employee jobs are growing, especially in the financial sector. The purpose of this study is to propose a human-technology convergence curriculum for creating high value-added in financial institutions and upskilling financial manpower. Research design, data, and methodology - In this study, a curriculum was designed to strengthen job competency for Private Bankers, high-quality employees of a bank dealing with high-net-worth owners. The focus of the design is that learners acquire skills to use robo-advisors as a tool and supplement artificial intelligence ethics. Result - The curriculum is organized into a total of 16 classes, and the main contents are changes in the financial environment and financial consumers, the core technology of robo-advisors and AI ethics, and establishment and evaluation of hyper-personalized asset management strategies using robo-advisors. To achieve the educational goal, two evaluations are performed to derive individual tasks and team project results. Conclusion - Human-centered upskilling convergence education will contribute to improving employee value and expanding corporate high value-added business areas by utilizing new technologies as tools. It is expected that the development and application of convergence curriculum in various fields will continue to be advanced in the future.

• The KIPS Transactions:PartD
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• v.12D no.7 s.103
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• pp.1071-1078
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• 2005

Use case modeling is a widely used technique for functional requirements analysis of business system but it is difficult to identify a use cases at the right level and use case specifications are too long and confusing. It is also hard to determine a functional decomposition Phases·s of use cases. Therefore customer doesn't understand the use cases. This paper is defining concept of the Information Structure Modeling(ISM) and analyzing business system for the customer's perspective. ISM is an efficient mechanism for analyzing user requirements and for Identifying objects in a business system using Attribute Structure Diagram which is a major tool of the ISM that describes user event. This paper is also to show how the classes are classified and derived as event-asset-transaction type in ISM. It provides a user-friendly approach to visually representing business model.

• CRM연구
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• v.4 no.1
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• pp.1-18
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• 2011

The consumer's expectation of companies' social responsibilities has been continuously getting higher as the consumerism has been matured. So, the company has faced the shift to move forward to the positive social activity such as charity, donation, and sponsorship. In addition, the company which does make a success needs to reach goals not only to maximize profits but also to make justices of social and cultural boundaries. Thus, success of an enterprise aims at the maximization of profits as the economic objective and the creation of competitive, powerful brands. Accordingly, as enterprises consider social responsibility as the concept of effective investment to enhance the asset value of corporation, they seek to extend their brands in order to pursue cause-related marketing, which accomplishes and complements two objectives each other the performance of social responsibility and the pursuit of powerful brand assets. In Korea, there are traditional ritual ceremonies such as ceremonies of coming-of-age, marriage, funeral, and ancestor worship and they consider those ceremony occasions as very important. Moreover, social positional grade of rank like the two upper classes of old Korea made people pretend to be noble and sensitive to other people around themselves. This old custom could influence Korean people's way of life, especially, consumer-action. This deep rooted custom also could influence consumption life considerably. Through this study, we can understand the consumer behaviors of Korean who consider ritual ceremonies and saving face as essential and are influenced by this culture. on another hand, we intend to check the effects on buying luxury brands.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.