• Environmental and Resource Economics Review
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• v.9 no.4
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• pp.747-771
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• 2000

This paper reports the results of a study to elicit willingness to pay (WTP) for changes in health risks from exposure to As, Pb, THM in tap water using experimental market method. The experimental market method, compared with other non-market valuation methods, allows us to use incentive compatible demand revealing scheme, to acquire market-like experience through repetitive auctions, and to incorporate learning process by providing new information during the session. Participants seemed to utilize the objective risk information in a 'rational' manner, and to change their WTP bids accordingly. Moreover they were able to reduce the 'ambiguity' in risk perception processes when objective risk probabilities provided are quite different from their subjective perceptions. Nonetheless, anchoring effects appeared to be still persistent in spite of market-like experience and learning opportunity. And implicit values entailed by WTP bid/risk tradeoffs indicate a wide variation in values across alternative risk reductions and overrated responses to very small risk reductions.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.21-36
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• 2020

Purpose - This study analyzes the effect of sellers' dishonesty on various market outcomes such as seller profit, buyer profit, and market welfare, through precisely measuring the level of sellers' information disclosure and its economic impacts. As an explicit observation of sellers' dishonesty is not easy in most other settings, this study is expected to suggest unique and meaningful implications on the effect of sellers' incomplete information disclosure to researchers, managers, and policy makers. Design/methodology/approach - In order to precisely measure the level of sellers' dishonesty under information asymmetry, this study analyzes the data from an incentive-based economic experiment using z-Tree software. This experimental method enables us to focus on the strategic interactions among participants, observe the integrity of seller's information disclosure, and reproduce real market situations. Findings - The analysis of sellers' dishonesty has provided the following important and counterintuitive findings about the reality of buyer-seller interactions under information asymmetry. First, sellers' lies do not affect seller profit even when they are very intensive. Second, sellers' dishonesty negatively affects buyer profit and the entire market welfare. Third, a seller's quality claim has a positive effect on the seller profit only when a seller is being honest. Research implications or Originality - This study analyzes sellers' dishonesty using incentive-based economic experiment using z-Tree software which provides a straightforward examination on dishonest behavior of sellers, that is not readily available with other types of observational or experimental data.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.53-59
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• 2021

This research aims to examine the model of investor herding behavior in making investment decisions in the Indonesian capital market, which is influenced by social and information impacting on the value of the Book Value Per Share (BVPS). The latest stock market conditions show that most investors make the same error pattern in making investment decisions that result in losses. The experiment involves two independent variables, namely, information about BVPS and social influence. This study used a 2×2 factorial design laboratory experimental method. Data collection was carried out through treatment of a sample of 100 individual investors listed on the Indonesia Stock Exchange. Univariate Two-Way Analysis of Variance (ANOVA) statistical tool was used to test the independent variable on the dependent variable. Research results showed that the social influence originating from expert investors is more influential than the Book Value Per Share (BVPS) information on the behavior of herding investors in making investment decisions. These findings suggest that investors know their psychological factors, thereby increasing self-control and investment analysis skills. Further research can use psychological bias and other indicators of accounting relevant information such as Earning Per Share (EPS) to test herding behavior in investment decision making in the capital market.

• The Journal of Industrial Distribution & Business
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• v.9 no.12
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• pp.23-29
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• 2018

Purpose - The purpose of this study is to apply a cost effective ultrasonic odor reduction method that generated micro-bubbles using ejector to the Southeast Asian wastewater market. Research design, data, and methodology - A leather maker located in Ansan-city, Gyunggi-do, South Korea was sampled from the collection tank to select experimental materials. Experimental setup consisted of circulating water tank-air ejector-ultrasonic device, and circulating wastewater. Sample analysis was performed by CODcr, T-N, T-P, and turbidity by the National Environmental Science Institute. Results - Experimental results show that it is most effective in removing odors when the frequency range of ultrasonic wave is 60~80 Khz and the output is 200 W. It showed that the concentration of complex odor dropped from a maximum of 14,422 times to a minimum of 120 times. Also, analysis of ammonia and hydrogen sulfide in specific odor substances has shown that they were reduced from 1.5 ppm to 0.4 ppm and from 0.6 ppm to 0.1 ppm, respectively. Conclusions - It is possible to shorten more than 12 hours in the treatment of micro-organisms. It can be seen that the processing time of odor after ultrasonic treatment in the pre-treatment facility is reduced by 25% when compared to the resultant micro-organisms after the chemical treatment, that is, the time of the bio-treatment of micro-organisms. Based on the results, it was confirmed that the pre-treatment method using the ultrasonic and the air ejector device of the experiment shows the effect of reducing the water pollutants and odor more effectively in a relatively short time than the conventional advanced oxidation method.

• Environmental and Resource Economics Review
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• v.17 no.3
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• pp.135-166
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• 2008

This paper reports the empirical results of comparing the willingness to pay(WTP) for health risk reductions and the willingness to accept(WTA) for risk increases using experimental market methods in the first time in Korea. Health risks were defined as probabilities of premature death from exposure to one of As, Pb, and THM in tap water. A total of six experimental markets with 15 participants in each experiments were held using 20 repetitive Vickrey second-price sealed-bid auctions. To compare the effects of market experiences, trading a marketed good, candy bar, was introduced before the trading the non-marketed good, drinking water risks. Moreover, an objective risk information was provided after the first 10 trials to incorporate learning processes. Regardless of marketed or non-marketed goods, the mean of WTA exceeded the mean of WTP at the first auction trial. As experimental trials proceeded, the disparity between WTA and WTP for marketed goods disappeared. However results for non-marketed goods were rather mixed to the extent that WTA for health risks from As (relatively high risk leves) were significantly larger than WTP, while there were no significant difference between WTA and WTP for health risks fro Pb and THM (relatively low risk levels). On the other hand, participants seemed to respond in a 'rational' manner to the objective risk information provided, with positive learning effects of market-like experience(especially in the WTA experiments).

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.21-26
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• 2014

A lot of researches have been conducted to estimate the volatility smile effect shown in the option market. This paper proposes a method to approximate an implied volatility function, given noisy real market option data. To construct an implied volatility function, we use Gaussian Processes (GPs). Their output values are implied volatilities while moneyness values (the ratios of strike price to underlying asset price) and time to maturities are as their input values. To show the performances of our proposed method, we conduct experimental simulations with Korean Equity-Linked Warrant (ELW) market data as well as toy data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2809-2821
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• 2023

Effective recommendation of similar business groups is a critical factor in obtaining market information for companies. In this study, we propose a novel method for enhancing similar business group recommendation by incorporating derivative criteria and web crawling. We use employment announcements, employment incentives, and corporate vocational training information to derive additional criteria for similar business group selection. Web crawling is employed to collect data related to the derived criteria from 'credit jobs' and 'worknet' sites. We compare the efficiency of different datasets and machine learning methods, including XGBoost, LGBM, Adaboost, Linear Regression, K-NN, and SVM. The proposed model extracts derivatives that reflect the financial and scale characteristics of the company, which are then incorporated into a new set of recommendation criteria. Similar business groups are selected using a Euclidean distance-based model. Our experimental results show that the proposed method improves the accuracy of similar business group recommendation. Overall, this study demonstrates the potential of incorporating derivative criteria and web crawling to enhance similar business group recommendation and obtain market information more efficiently.

• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.17-27
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• 2012

Open Market is one of the key factors to accelerate the profit. Usually retailers sell items in several Open Market. One of the challenges for retailers is to assign categories of items with different classification systems. In this research, we propose an item category recommendation method to support appropriate products category registration. Our recommendations are based on semantic relation between existing and any other Open Market categorization. In order to analyze correlations of categories, we use Morpheme analysis, Korean Wiki Dictionary, WordNet and Google Translation API. Our proposed method recommends a category, which is most similar to a guide word by measuring semantic similarity. The experimental results show that, our system improves the system accuracy in term of search category, and retailers can easily select the appropriate categories from our proposed method.

• The Journal of Society for e-Business Studies
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• v.4 no.3
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• pp.63-76
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• 1999

Internet-based electronic trade has been growing fast. But most users are not yet familiar with the system and find it very difficult to purchase and sell the products in the cyber market place. To handle these problems, agent-based virtual market place system has been proposed where agents instead of individuals participate in trading of goods. Most of the proposed models have been in the two general categories. The first is the direct transaction among sellers and buyers, and the second is the agent-based transaction. However, the transaction is not fair and the best deal can't be guaranteed for both models. In this paper, we propose a new broker based synchronous transaction algorithm which is fair to both parties and guarantees the best deal. Our algorithm is implemented using Visual C++ and the experimental results show that our method is better than the two traditional transaction models in every performance metrics, Number of transactions are increased up to 21% and price adjustment is up to 280% better for some transactions.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.