• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1477-1489
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• 2008

This article deals with the problem of testing whether the effect size in paired study exists. We propose Bayesian hypothesis testing procedures for the effect size in paired study under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.47-52
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• 2022

The purpose of this study is to identify the risk level of Chinese commercial banks' loan assets and to analyze what factors affect the stability of Chinese commercial banks. In addition, Chinese commercial banks are classified based on the asset size of 200 billion yuan, and the difference in stability according to size is investigated. The analysis results are as follows. First, it was estimated that as the proportion of household and corporate loans of commercial banks in China increased, the stability of banks decreased. Although the Chinese financial authorities are currently restricting the conservative management of loan assets, it will be necessary to preemptively manage risk on loan assets by setting an appropriate standard for loan-to-deposit ratio in the future. Second, as a result of analyzing the stability of large banks based on 200 billion yuan of bank assets, it was estimated that the stability of large banks was lower. As large banks are likely to conduct aggressive loan asset management, continuous management of non-performing assets is required in the future. This study will serve as a measure for improving the stability of commercial banks in China by estimating the effect of loan asset management of Chinese commercial banks on financial stability. In particular, by examining the stability of large banks, a strategy for sustainable development of the financial industry is required by diagnosing the weaknesses of large banks.

• Journal of Applied Tourism Food and Beverage Management and Research
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• v.11
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• pp.135-135
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• 2000

The financial performance over the twenty four-year period (1968-1991) was analyzed with respect to six performance measures : current ratio, net sales to working capital for liquidity, total liabilities to net worth for solvency, asset turnover for activity, return on assets for profitability, and cost of operations for operating. Interesting enough, small size hotel companies have enjoyed great profitability while relatively big hotel companies have fallen under the average. Further, after a certain level of firm size, the costs of operations increase, not decrease, as plant size increase. This results lead to a conclusion that getting bigger is not always good financial decision.

• Journal of Navigation and Port Research
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• v.43 no.2
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• pp.134-141
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• 2019

The objective of this study was to establish whether global macroeconomic indicators affect the profitability of Korean shipping companies by using panel regression analysis. OROA (operating return on assets) and ROA (ratio of net profit to assets) were selected as proxy variables for profitability. OROA and ROA were used as dependent variables. The world GDP growth rate, interest rate, exchange rate, stock index, bunker price, freight, demand and supply of the world shipping market were set as independent variables. The size of the firm was added to the control variable. For small-sized firms, OROA was not affect by macroeconomic indicators. However, ROA was affected by variables such as interest rates, bunker prices, and size of firms. For medium-sized firms, OROA was affected by demand, supply, GDP, freight, and asset variables. However, macroeconomic indicators did not affect ROA. For large-sized firms, freight, GDP, and stock index (SCI; Shanghai Composite Index) have an effect on OROA. ROA was analyzed to be influenced by bunker price and SCI.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.84-90
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• 2023

ASL estimation of public building is based on how appropriate the maximum age of the asset is derived based on the age record of the asset in the statistical data owned by public institutions. This is because we get a 'constrained' ASL by that number. And it is especially true because other studies have assumed that the building is an Iowa curve R3. Also, in this study, the survival rate is 1% as the threshold value at which the survival curve and the predictable life curve almost coincide. Rather than a theoretical basis, in the national statistical survey, the value of residual assets was recognized from the net value of 10% of the acquisition value when the average service life has elapsed, and 1% when doubling the average service life has elapsed. It is based on the setting mentioned above. The biggest constraint in fitting statistical data to the Iowa curve is that the maximum ASL is selected at R3 150%, and the 'constrained' ASL is calculated by the proportional expression on the assumption that the Iowa curve is followed. In like manner constraints were considered. First, the R3 disposal curve for the RCC(reinforced cement concrete) building was prepared according to the discarding method in the 2000 work, and it was jointly worked on with the National Statistical Office to secure the maximum amount of vintage data, but the lacking of sample size must be acknowledged. Even after that, the National Statistical Office and the Bank of Korea have been working on estimating the Iowa curve for each asset class in the I-O table. Another limitation is that the asset classification uses the broad classification of buildings as a subcategory. Second, if there were such assets with a lifespan of 115 years that were acquired in 1905 and disposed of in 2020, these discarded data would be omitted from this ASL calculation. Third, it is difficult to estimate the correct Iowa curve based on the stub-curve even if there is disposal data because Korea has a relatively shorter construction history, accumulated economic wealth since the 1980's. In other words, "constrained" ASL is an under-estimation of its ASL. Considering the fact that Korea was an economically developing country in the past and during rapid economic development, environmental factors such as asset accumulation and economic ability should be considered. Korea has a short period of accumulation of economic wealth, and the history of 'proper' architectures faithful to building regulations and principles is short and as a result, buildings 'not built properly' and 'proper' architectures are mixed. In this study, ASL of RCC public building was estimated at 70 years.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.4
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• pp.81-95
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• 2020

The main objective of this research is to investigate the impact of board size and board composition on financial performance of banks. The sample of this study consists on two countries listed bank sector Pakistan and China. The annul data is used from 2009-2018 to find the objective of this study. The Panel regression model is used to check the relationship between dependent and independent variables. Return on Asset and Return on Equity is used as performance checker dependent variables. The results of this study confirm board size coefficient value positive for ROA and negative for ROE but shows insignificant behavior for Pakistani banking sector while in Chinese banking sector the coefficient value of board size positively for ROA and ROE at 10% level. The board composition coefficient shows the negatively significant with ROA but insignificantly related to ROE for Pakistani banking sector. However, in Chinese banking sector the coefficient value of board composition is insignificant for both ROA and ROE. This study is helpful for banks, management of banks, policy makers, researcher as well as Government.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2759-2769
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• 2018

The behavioral finance view on the existence of asset pricing anomalies is based on two factors: investors' sentiment and limits to arbitrage. This paper tries to examine the effect of investors' sentiment on the stock price in the Korean stock market. In order to measure investors' sentiment, we constructed the sentiment index using principal component of five sentiment variables. By using sentiment index as an additional independent variable to three risk factors, impacts of the sentiment index on individual stocks and 25 portfolios sorted by BM-size are examined. Main results found are as follows: 1) not only all three risk factors show positive impacts on the return of individual stock, but also the sentiment index has a positive impact. SI alone explains 15% of individual return variation. 2) among four independent variables, the most important factor turned out to be the market risk factor and investors' sentiment has better explanatory power on stock price than the size effect. 3) after controlling the market risk factor, the coefficient of the sentiment index for the smallest size and highest book/market value portfolios is significantly positive. 4) all the coefficients of the sentiment index for 25 portfolios sorted by BM-size have significant positive value after controlling size or (and) value.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.35-53
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• 2018

Recently, there is a growing interest in housing policy to prepare for the aging society. The purpose of this study is exploring the factors that explain housing consumption and adjustment of pre-retirees after retirement. 1,351 samples were collected from A bank and analyzed. There categories of housing consumption adjustment were considered including downsizing, maintain, and upsizing. Gender, educational level, housing size, housing type, asset size, and willingness to work after retirement were examined to see if they can explain the housing consumption adjustment of pre-retirees using orderd- probit model. The finding of this study is that housing size, asset size, and willingness to work after retirement statistically significantly explain the housing consumption adjustment. At specific, firstly, if the current size of the housing is relatively large, it is highly likely to downsize housing after retirement. Second, pre-retiree whose assets exceeded 1 billion won were more likely to scale up housing than assets of over 300 million to less than 500 million won. Lastly, unless there is absolutely no willingness to work after retirement, it is indicated that it intends to up-sizing consumption rather than down-sizing adjustment. The results of this study can provide useful information for the housing policy in order to prepare for the (post) aged society.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.605-613
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• 2020

In unfrictionless markets, one measure of asset pricing is its height of friction. This study develops a three-factor model by loosening the assumptions about stocks without friction, without risk, and perfectly liquid. Friction is used as an indicator of transaction costs to be included in the model as a variable that will reduce individual profits. This approach is used to estimate return, beta and other variable for firms listed on the Indonesian Stock Exchange (IDX). To test the efficacy of friction-adjusted three-factor model, we use intraday data from July 2016 to October 2018. The sample includes all listed firms; intraday data chosen purposively from regular market are sorted by capitalization, which represents each tick size from the biggest to smallest. We run 3,065,835 intraday data of asking price, bid price, and trading price to get proportional quoted half-spread and proportional effective half-spread. We find evidence of adjusted friction on the three-factor model. High/low trading friction will cause a significant/insignificant return difference before and after adjustment. The difference in average beta that reflects market risk is able to explain the existence of trading friction, while the difference between SMB and HML in all observation periods cannot explain returns and the existence of trading friction.