• Journal of Distribution Science
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• v.16 no.9
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• pp.65-77
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• 2018

Purpose - A trading area is very closely related to consumer life. A trading area is a cultural and social space that consumes culture and promotes human relationships as well as an economic space where consumers live their daily lives. In this context, a trading area research should be conducted objectively and empirically because it deals with the activities of consumer's life. The purpose of this study is to identify the intrinsic psychological motivation(push motivation) caused when consumers visit a trading area and to demonstrate how the push motivation for a trading area influence on consumer's flow, satisfaction, revisit intention. Research design, data, and methodology - In order to develop research hypotheses for this study, the development procedures for push motivation scale are as follows; (1) generating initial pool of items based on previous studies, (2) expert judgement to evaluate content and face validity, and (3) assessing convergent and discriminant validity using confirmatory factor analysis. In order to achieve these purposes, online surveys were conducted on frequent or familiar visitors to the trading areas around the Gangnam, Kunkuk University and Hongik University Station. Among the 1,343 questionnaires collected, 1,157 cases were analyzed by using SPSS 22.0 and SmartPLS 3.0 statistical package program, except for 186 responses in which responses were judged to be unfaithful. Results - The push motivation was classified into five sub-dimensions of excitement/stimulus, rest/relaxation, exit/refreshing, knowledge/learning and human relationship promotion as multidimensional and complex factors composed of individual and social-related dimensions. The excitement/stimulus and human relationship promotion of push motivation have positive effects on satisfaction. However, all dimensions of the push motivation have positive effects on flow. And flow has a positive effect on satisfaction and revisit intention. Meanwhile, the mediation test using boostrapping shows that flow plays a full mediating role in the relationship between rest/relaxation, exit/refreshing, knowledge/learning and satisfaction, but a partial mediating rol e between excitement/stimulus, human relationship promotion and satisfaction. Finally, satisfaction plays a partial mediating role between flow and revisit intention. Conclusions - This study shows that the push motivation is multidimensional and compositive depending on the situation of a consumer. In addition, it is found that the human relationship promotion(a social-related motivation) has a much more important effect on flow and satisfaction than other push motivations of individual dimensions. It also shows that satisfaction increases when consumers are being flowed at their visit and degree of revisit intention also grows as satisfaction increases. As implications of this study, a marketer should try to understand consumer's visit motivation at first and then develop factors that increase their flow, satisfaction, revisit intention. It also requires a marketer to approach subjects on a trading area more objectively and empirically based on the psychology and behavior of consumers, in order to establish a proper and efficient strategy on development of a trading area.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.34-46
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• 2021

In the domestic data exchanging market, unreasonable pricing of purchase data is consistently mentioned as a major obstacle in data trading. This is a problem caused by the inability to properly evaluate the value of data products due to lack of product information and experience in using them. In order to activate trading, the data exchanges need to provide information that allows consumers to comprehensively judge the value of data products in addition to prices. The cost-based, income-based, and market-based methods, which are mainly applied to data valuation, are insufficient as data valuation methods to stimulate trading and distribution because only price information, a result of valuation from a supplier's point of view, can be shared with consumers. This study aims to develop a measurable valuation method that allows data trading stakeholders (exchanges, suppliers, and consumers) to judge and share the value of data products from a common perspective. To this end, we identified the value drivers of data products, which are considered important in overseas data exchanges and related research, and derived an evaluation method that can quantitatively measure each value driver. In addition, evaluation criteria in the form of a rating table were developed using data products for transactions, and a value evaluation index was developed through stratification analysis (AHP) to enable relative value comparison. As a result of applying the evaluation criteria to actual data products, it was found that the evaluation values were differentiated according to the characteristics of individual data products, so it could be used as a relative value comparison tool.

• The Korean Journal of Financial Management
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• v.24 no.2
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• pp.41-79
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• 2007

This study examines the expiration-day effects of stock index futures and options in the Korean stock market. The so-called 'expiration-day effects', which are the abnormal stock price movements on derivatives expiration days, arise mainly from cash settlement. Index arbitragers have to bear the risk of their positions unless they liquidate their index stocks on the expiration day. If many arbitragers execute large buy or sell orders on the expiration day, abnormal trading volumes are likely to be observed. If a lot of arbitragers unwind positions in the same direction, temporary trading imbalances induce abnormal stock market volatility. By contrast, if some information arrives at market, the abnormal trading activity must be considered a normal process of price discovery. Stoll and Whaley(1987) investigated the aggregate price and volume effects of the S&P 500 index on the expiration day. In a related study, Stoll and Whaley(1990) found a similarity between the price behavior of stocks that are subject to program trading and of the stocks that are not. Thus far, there have been few studies about the expiration-day effects in the Korean stock market. While previous Korean studies use the KOSPI 200 index data, we analyze the price and trading volume behavior of individual stocks as well as the index. Analyzing individual stocks is important for two reasons. First, stock index is a market average. Consequently, it cannot reflect the behavior of many individual stocks. For example, if the expiration-day effects are mainly related to a specific group, it cannot be said that the expiration of derivatives itself destabilizes the stock market. Analyzing individual stocks enables us to investigate the scope of the expiration-day effects. Second, we can find the relationship between the firm characteristics and the expiration-day effects. For example, if the expiration-day effects exist in large stocks not belonging to the KOSPI 200 index, program trading may not be related to the expiration-day effects. The examination of individual stocks has led us to the cause of the expiration-day effects. Using the intraday data during the period May 3, 1996 through December 30, 2003, we first examine the price and volume effects of the KOSPI 200 and NON-KOSPI 200 index following the Stoll and Whaley(1987) methodology. We calculate the NON-KOSPI 200 index by using the returns and market capitalization of the KOSPI and KOSPI 200 index. In individual stocks, we divide KOSPI 200 stocks by size into three groups and match NON-KOSPI 200 stocks with KOSPI 200 stocks having the closest firm characteristics. We compare KOSPI 200 stocks with NON-KOSPI 200 stocks. To test whether the expiration-day effects are related to order imbalances or new information, we check price reversals on the next day. Finally, we perform a cross-sectional regression analysis to elaborate on the impact of the firm characteristics on price reversals. The main results seem to support the expiration-day effects, especially on stock index futures expiration days. The price behavior of stocks that are subject to program trading is shown to have price effects, abnormal return volatility, and large volumes during the last half hour of trading on the expiration day. Return reversals are also found in the KOSPI 200 index and stocks. However, there is no evidence of abnormal trading volume, or price reversals in the NON-KOSPI 200 index and stocks. The expiration-day effects are proportional to the size of stocks and the nearness to the settlement time. Since program trading is often said to be concentrated in high capitalization stocks, these results imply that the expiration-day effects seem to be associated with program trading and the settlement price determination procedure. In summary, the expiration-day effects in the Korean stock market do not exist in all stocks, but in large capitalization stocks belonging to the KOSPI 200 index. Additionally, the expiration-day effects in the Korean stock market are generally due, not to information, but to trading imbalances.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.559-575
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• 2014

Individual investors are increasingly flocking to message boards to seek, clarify, and exchange information. Businesses like Seekingalpha.com and business magazines like Fortune are evaluating, synthesizing, and reporting the comments made on message boards or blogs. In March of 2012, Yahoo! Finance Message Boards recorded 45 million unique visitors per month followed by AOL Money and Finance (19.8 million), and Google Finance (1.6 million) [McIntyre, 2012]. Previous studies in the finance literature suggest that online communities often provide more accurate information than analyst forecasts [Bagnoli et al., 1999; Clarkson et al., 2006]. Some studies empirically show that the volume of posts in online communities have a positive relationship with market activities (e.g., trading volumes) [Antweiler and Frank, 2004; Bagnoli et al., 1999; Das and Chen, 2007; Tumarkin and Whitelaw, 2001]. The findings indicate that information in online communities does impact investors' investment decisions and trading behaviors. However, research explicating the correlation between information on online communities and stock market activities (e.g., trading volume) is still evolving. Thus, it is important to ask whether a volume of posts on online communities influences trading volumes and whether trading volumes also influence these communities. Online stock message boards offer two different types of information, which can be explained using an economic and a psychological perspective. From a purely economic perspective, one would expect that stock message boards would have a beneficial effect, since they provide timely information at a much lower cost [Bagnoli et al., 1999; Clarkson et al., 2006; Birchler and Butler, 2007]. This indicates that information in stock message boards may provide valuable information investors can use to predict stock market activities and thus may use to make better investment decisions. On the other hand, psychological studies have shown that stock message boards may not necessarily make investors more informed. The related literature argues that confirmation bias causes investors to seek other investors with the same opinions on these stock message boards [Chen and Gu, 2009; Park et al., 2013]. For example, investors may want to share their painful investment experiences with others on stock message boards and are relieved to find they are not alone. In this case, the information on these stock message boards mainly reflects past experience or past information and not valuable and predictable information for market activities. This study thus investigates the two roles of stock message boards-providing valuable information to make future investment decisions or sharing past experiences that reflect mainly investors' painful or boastful stories. If stock message boards do provide valuable information for stock investment decisions, then investors will use this information and thereby influence stock market activities (e.g., trading volume). On the contrary, if investors made investment decisions and visit stock message boards later, they will mainly share their past experiences with others. In this case, past activities in the stock market will influence the stock message boards. These arguments indicate that there is a correlation between information posted on stock message boards and stock market activities. The previous literature has examined the impact of stock sentiments or the number of posts on stock market activities (e.g., trading volume, volatility, stock prices). However, the studies related to stock sentiments found it difficult to obtain significant results. It is not easy to identify useful information among the millions of posts, many of which can be just noise. As a result, the overall sentiments of stock message boards often carry little information for future stock movements [Das and Chen, 2001; Antweiler and Frank, 2004]. This study notes that as a dependent variable, trading volume is more reliable for capturing the effect of stock message board activities. The finance literature argues that trading volume is an indicator of stock price movements [Das et al., 2005; Das and Chen, 2007]. In this regard, this study investigates the correlation between a number of posts (information on stock message boards) and trading volume (stock market activity). We collected about 100,000 messages of 40 companies at KOSPI (Korea Composite Stock Price Index) from Paxnet, the most popular Korean online stock message board. The messages we collected were divided into in-trading and after-trading hours to examine the correlation between the numbers of posts and trading volumes in detail. Also we collected the volume of the stock of the 40 companies. The vector regression analysis and the granger causality test, 3SLS analysis were performed on our panel data sets. We found that the number of posts on online stock message boards is positively related to prior stock trade volume. Also, we found that the impact of the number of posts on stock trading volumes is not statistically significant. Also, we empirically showed the correlation between stock trading volumes and the number of posts on stock message boards. The results of this study contribute to the IS and finance literature in that we identified online stock message board's two roles. Also, this study suggests that stock trading managers should carefully monitor information on stock message boards to understand stock market activities in advance.

• Journal of Environmental Policy
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• v.9 no.4
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• pp.77-102
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• 2010

The purpose of this paper is to analyze the Japanese emission trading system and climate change policy thereby contributing to the instituting of similar systems that will be viable for the Korean context. In applying such analyses, it is important to include a careful consideration of cost sharing between stakeholders and firms, an enhancement of the trust worthiness of data concerning greenhouse gases, and an examination of related infrastructure such as emissions authentication agencies and their development. Moreover, it is important to minimize the outflow of domestic resources such as offset credit, green electricity certification system, and ecopoint, making compatible economic growth and carbon reduction thereby encouraging the production and dissemination of 'Environmental Value' as well as connecting 'Environmental Value' to a emission trading system.

• Korean Business Review
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• v.18 no.1
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• pp.43-57
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• 2005

This paper investigate to test the validity of the basic assumptions of strategic trading models with the broker and informed trader using daily closing data of KOSPI 200 stock index futures for the year 2001-2003. Major results are summarized as follows: (i) For these years, while foreign investors and brokerage companies traded for the directions consistent with the model, brokerage companies and individual investors traded for inconsistent directions. (ii) Cross correlation function (CCF) analysis shows no systematic dependency in the trading between all three participants(foreign investor, brokerage companies and individual investors) for these years. (iii) Chi-square validity test for the 30 days of the largest unexpected trading volume shows some systematic dependency in the trading between three participants for these years. Finally, some limitations of this paper and direction for further research were suggested.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.94-103
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• 2021

This study aims to propose technical trading rules for Bitcoin futures and empirically analyze investment performance. Investment strategies include standard trading rules such as VMA, TRB, FR, MACD, RSI, BB, using Bitcoin futures daily data from December 18, 2017 to March 31, 2021. The trend-following rules showed higher investment performance than the comparative strategy B&H. Compared to KOSPI200 index futures, Bitcoin futures investment performance was higher. In particular, the investment performance has increased significantly in Sortino Ratio, which reflects downside risk. This study can find academic significance in that it is the first attempt to systematically analyze the investment performance of standard technical trading rules of Bitcoin futures. In future research, it is necessary to improve investment performance through the use of deep learning models or machine learning models to predict the price of Bitcoin futures.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.143-161
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• 2022

This study examines the effect of used trading app's consumption value and protection motivation and the perceived usefulness and continuous use intention. The proposed research model and developed hypotheses were tested using structural equations modeling based on data collected from 293 customers with experience in used transaction app's. The results of the study confirm the positive effects of the used trading app's consumption value and protection motive theory is perceived usefulness of customer. In addition, there is a positive relationship between a customer's perceived usefulness and continuous use intention of used trading app's. The study provides On a theoretical level valuable insights into the sustainability of transaction app's after post-COVID 19 and the importance of developing used trading app's consumption value and protection motivation, but there is also a limitation that the region is limited.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.