• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.1
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• pp.62-68
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• 2011

Usability heuristics("heuristics") are general principles for usability evaluation during user interface design. This method is commonly used by Human Computer Interaction(HCI) professionals; however, the most widely used set of heuristics which were originally created by Nielsen has not yet been found practical in the current state of Korean technologies. In our prior research, we found that some of Nielsen's heuristics are difficult for some evaluators to understand and insufficient to fully evaluate Korean applications, due to the broad-applicability of these heuristics and differences in cultural context. Therefore, in this study, professionals in computer science and related fields evaluated Nielsen's ten usability heuristics in order to gather logical bases for finding areas for improvement. The results of this study will help creating a new set of heuristics that will better valuate more recently developed applications.

• The Journal of Korean Institute for Practical Engineering Education
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• v.2 no.2
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• pp.165-171
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• 2010

Usability heuristics("heuristics") are general principles for usability evaluation during user interface design. Our ultimate goal is to extend the practice of usability evaluation methods to a wider audience(e.g. user interface designers and engineers) than Human Computer Interaction(HCI) professionals. To this end, we explored the degree to which Jakob Nielsen's ten usability heuristics are understood by professors and students in design and computer engineering. None of the subjects received formal training in HCI, though some may have had an awareness of some HCI principles. The study identified easy-to-understand heuristics, examined the reasons for the ambiguities in others, and discovered differences between the responses of professors and students to the heuristics. In the course of the study, the subjects showed an increased tendency to think in terms of user-centric design. Furthermore, the findings in this study offer suggestions for improving these heuristics to resolve ambiguities and to extend their practice for user interface designers and engineers.

• Management & Information Systems Review
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• v.35 no.4
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• pp.213-227
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• 2016

This study examines the effects of managerial ability on information asymmetry. We use analyst forecast errors as a proxy for information asymmetry, because analysts are referred to as efficient users using firm-level data. The sample consists of 2,246 non-banking firm-years listed in Korea Stock Exchange(KOSPI) during the period 2000 to 2013. We measure managerial ability using DEA(Data Envelopment Analysis) following Demerjian et al.(2012). Using those measures, we examines the effects of managerial ability on analysts' earnings forecast errors and analysts' earnings forecast bias. The results of this study are as follows. First, we find that managerial ability are positively associated with analysts' earnings forecast accuracy. Second, we show that the firms with higher managerial ability tend to have lower the optimistic errors in analysts' earnings forecasts. This study could be useful for outside stakeholders to understand the importance of managerial ability.

• Asia-Pacific Journal of Business
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• v.14 no.3
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• pp.187-204
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• 2023

Purpose - This study deals with the manager's overconfidence and stock price delay, and verified whether the stock price delay phenomenon changes as the overconfidence increases. Design/methodology/approach - Manager overconfidence means that managers have over confidence in their positions or abilities, and was measured according to Schrand and Zechman (2012). Stock price delay is a phenomenon in which information of company is not immediately reflected in the stock price, but is reflected over time, and was measured by the method suggested in a study by Hou and Moskowitz (2005). The analysis subjects used in this study are companies listed on the KOSPI market between 2011 and 2019, and the final sample is 5,509 company-years. Findings - As a result of the verification, it was shown that the stock price delay decreased as the manager's overconfidence increased, and this effect was amplified as the foreign shareholder's share ratio increased and the number of follow-up financial analysts increased. This means that as the manager's overconfidence increases, he actively provides high-quality information to the capital market. In addition, as a result of subdividing the manager's overconfidence into the investment and capital raising aspects, the capital raising aspect has a significant effect on reducing stock delays. This can be interpreted as the fact that managers with overconfident tendencies have a greater incentive to satisfy investors' information needs. Research implications or Originality - In previous studies, the characteristics of managers with strong overconfidence have both positive and negative aspects. The results of this study are significant in that they clearly demonstrated the positive aspect through the market variable of stock price delay, and it is expected to help capital market stakeholders understand the characteristics of managers with a strong propensity for overconfidence.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.1
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• pp.41-48
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• 2008

Enhanced machine reliability has dramatically reduced the rate and number of railway accidents but for further reduction human error should be considered together that accounts for about 20% of the accidents. Therefore, the objective of this study was to suggest a new taxonomy of performance shaping factors (PSFs) that could be utilized to identify the causes of a human error associated with railway accidents. Four categories of human factor, task factor, environment factor, and organization factor and 14 sub-categories of physical state, psychological state, knowledge/experience/ability, information/communication, regulation/procedure, specific character of task, infrastructure, device/MMI, working environment, external environment, education, direction/management, system/atmosphere, and welfare/opportunity along with 131 specific factors was suggested by carefully reviewing 8 representative published taxonomy of Casualty Analysis Methodology for Maritime Operations (CASMET), Cognitive Reliability and Error Analysis Method (CREAM), Human Factors Analysis and Classification System (HFACS), Integrated Safety Investigation Methodology (ISIM), Korea-Human Performance Enhancement System (K-HPES), Rail safety and Standards Board (RSSB), $TapRoot^{(R)}$, and Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). Then these were applied to the case of the railway accident occurred between Komo and Kyungsan stations in 2003 for verification. Both cause decision chart and why-because tree were developed and modified to aid the analyst to find causal factors from the suggested taxonomy. The taxonomy was well suited so that eight causes were found to explain the driver's error in the accident. The taxonomy of PSFs suggested in this study could cover from latent factors to direct causes of human errors related with railway accidents with systematic categorization.

• The Korean Journal of Financial Management
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• v.27 no.1
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• pp.31-62
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• 2010

This paper analyzes empirically how analysts' forecasts affected by ownership structure. This study examine a sample of 1,037~1,629 the analysts' forecasts of firms registered in Korean Stock Exchange in the period from 2000 to 2006. The empirical results are summarized as follows. First, from the analysis, companies which have higher major shareholder's holdings tend to increase earnings forecast errors and earnings forecast accuracy. Meanwhile, companies which have higher institution shareholder's holdings tend to decrease earnings forecast errors and earnings forecast accuracy. This result is in line with the view of previous works that companies with higher major shareholder's holdings look towards more of analysts' optimistic forecasts in order to maintain friendly relations with major shareholders. Because of analysts' private information use from major shareholders, earnings forecast accuracy is higher in high major shareholder's holdings firm than in high institution shareholder's holdings it. Second, this analysis is whether the minimal required selection condition of outside directors, audit committee adoption and audit quality affect the relation between ownership structure and analysts' forecasts. This result is that variables related corporate governance do not affect statically the relation between ownership structure and analysts' forecasts. The meanings of this paper is to suggest the positive relations between ownership structure and analysts' forecasts. After this, if analysts will notice forecasts of more many firms, capital market will be more efficient and this field works are plentiful. Also it will need monitoring systems not to distort market efficiency by analysts' dishonest forecasts.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• 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.