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RISS 인기검색어
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- 저자 : Chia-Han Ho (검색결과 4 건)
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CEO Overconfidence or Private Information: Evidence from U.S. Property-Liability Insurance Companies
Sangyong Han,Gene C. Lai,Chia-Ling Ho 한국재무학회 2020 한국재무학회 학술대회 Vol.2020 No.08
This paper uses conventional measures of CEO overconfidence: option holdings-based and net stock purchase-based measures to examine the impact of CEOs who hold firm-specific risk on insurer’s risk-taking and firm performance in U.S. publicly traded property-liability insurance companies. We find that two CEO overconfidence measures are negatively associated with insurer’s risk-taking and positively related to firm performance. We also provide evidence that CEOs who maintain high exposure to firm-specific risk exploit their private information to time stock-option exercises in an effort to increase the cash payout from these exercises. Our overall results indicate that CEOs who have private information on their firms’ future earnings maximize their personal profits by postponing option exercises or buying additional stocks, and that they tend to take a lower risk to protect their personal wealth in property-liability insurance firms. Therefore, our findings suggest that it may not be CEO overconfidence, but rather the private information and the intention to control the company’s risk that drive our results.
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Genetic Algorithm for Shortest Driving Time in Intelligent Transportation Systems
Chu-Hsing Lin,Jung-Chun Liu,Chia-Han Ho,Jui-Ling Yu,Wei-Shen Lai 보안공학연구지원센터 2009 International Journal of Hybrid Information Techno Vol.2 No.1
The route guidance system, which provides driving advice based on traffic information about an origin and a destination, has become very popular along with the advancement of handheld devices and the global position system. Since the accuracy and efficiency of route guidance depend on the accuracy of the traffic conditions, the route guidance system needs to include more variables in calculation, such as real time traffic flows and allowable vehicle speeds. As variables considered by the route guidance system increase, the cost to compute multiplies. Since handheld devices have limited resources, it is not feasible to use them to compute the exact optimal solutions in real time systems by some well-known algorithm, such as the Dijkstra’s algorithm, which is usually used to find the shortest path with a map of reasonable numbers of vertices. To solve this problem, we propose to use the genetic algorithm to alleviate the rising computational cost. We use the genetic algorithm to find the shortest time in driving with diverse scenarios of real traffic conditions and varying vehicle speeds. The effectiveness of the genetic algorithm is clearly demonstrated when applied on a real map of modern city with very large vertex numbers.
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Anomaly Detection Using LibSVM Training Tools
Jung-Chun Liu,Chu-Hsing Lin,Jui-Ling Yu,Wei-Shen Lai,Chia-Han Ho 보안공학연구지원센터 2008 International Journal of Security and Its Applicat Vol.2 No.4
Intrusion detection is the means to identify the intrusive behaviors and provide useful information to intruded systems to respond fast and to avoid or reduce damages. In recent years, learning machine technology is often used as a detection method in anomaly detection. In this research, we use support vector machine as a learning method for anomaly detection, and use LibSVM as the support vector machine tool. By using this tool, we get rid of numerous and complex operations and do not have to use external tools for finding parameters as needed by using other algorithms such as the genetic algorithm. Experimental results show that high average detection rates and low average false positive rates in anomaly detection are achieved by our proposed approach.
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Jeon, Sang Won,Yoon, Seo Young,Ko, Young-Hoon,Joe, Sook-haeng,Kim, Yong-Ku,Han, Changsu,Yoon, Ho-Kyoung,Liu, Chia-Yih The Korean Academy of Medical Sciences 2016 JOURNAL OF KOREAN MEDICAL SCIENCE Vol.31 No.12
<P>This study aimed at exploring the psychometric characteristics of the Korean Version of the Depression and Somatic Symptoms Scale (DSSS) in a clinical sample, and investigating the impact of somatic symptoms on the severity of depression. Participants were 203 consecutive outpatients with current major depressive disorders (MDD) or lifetime diagnosis of MDD. The DSSS was compared with the Montgomery-Åsberg Depression Rating Scale (MADRS) and the 17-items Hamilton Depression Rating Scale (HAMD). The DSSS showed a two-factor structure that accounted for 56.8% of the variance, as well as excellent internal consistency (Cronbach’s alpha = 0.95), concurrent validity (<I>r</I> = 0.44–0.82), and temporal stability (intraclass correlation coefficient = 0.79). The DSSS had a high ability to identify patients in non-remission (area under receiver operating characteristic [ROC] curve = 0.887). Maximal discrimination between remission and non-full remission was obtained at a cut-off score of 22 (sensitivity = 82.1%, specificity = 81.4%). The number of somatic symptoms (the range of somatic symptoms) and the scores on the somatic subscale (SS, the severity of somatic symptoms) in non-remission patients were greater than those in remission patients. The number of somatic symptoms (slope = 0.148) and the SS score (slope = 0.472) were confirmed as excellent predictors of the depression severity as indicated by the MADRS scores. The findings indicate that the DSSS is a useful tool for simultaneously, rapidly, and accurately measuring depression and somatic symptoms in clinical practice settings and in consultation fields.</P>
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