Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people’s life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing ‘heavy snow’ in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald’s (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

Recently the meaning of the road name address is expended as an information through the revision of the Road Name Address Act. As this revision, the address of things (AoT) become more important indicating the possibility for the expansion to the related business. However, recent study about AoT does not concern how the current priority system works from the first research. In this study, we analyze perception about addressable object between AoT experts and public using AHP analysis. We structured the importance of addressable objects as two categories; urgency and value creation. The necessity in emergency or daily, accessibility and welfare conform the urgency index. Meanwhile, public value creation in public domain or profitability in the business area and economics conform value creation index. We conducted survey for total of 89 of experts and public. The results of this study indicate the relative importance of AoT measured by experts and public. Generally, public tend to concern more about accessibility conforming the urgency index than experts. Moreover, the public WiFi and the sports complex scored the high priority among the remain addressable objects, in respect of the urgency and the value creation. This result could be implemented for the activation of the smart city industry base on the geospatial information including AoT.

Investors aim to maximize the return rate for their own investment, utilizing various information as possible as they can access. However those investors, especially individual investors, have limitations of interpretation of the domain-specific information or even the acquisition of the information itself. Thus, individual investors tend to make decision affectively and frequently, which may cause a loss in returns. This study aims to analyze analysts’ target price and to suggest the strategy that could maximize individual’s return rate. Most previous literature revealed that the optimistic bias exists in the analysts’ target price and it is also confirmed in this study. In this context, this study suggests the upper limit of target rate of returns and the optimal value named ‘alpha(α)’ which performs the adjustment of proposed target rate to maximize excess earning returns eventually. To achieve this goal, this study developed an optimization problem using linear programming. Specifically, when the analysts’ proposed target rate exceeds 30%, it could be adjusted to the extent of 59% of its own target rate. As apply this strategy, the investors could achieve 1.2% of excess earning rate on average. The result of this study has significance in that the individual investors could utilize analysts’ target price practically.

Recently, nanotechnology has grown as one of the leading science technology along with other converging technologies such as biology, information, medicine etc., bringing the continuous investment of the government in nano-related field. However, it is difficult to measure and evaluate the performance of the national research and development programs because of the multidimensional character of the expected outcomes. This study aims to measuring efficiency of the national nanotechnology research and development programs using DEA model. The decision making units are nine nano-related ministries including the Ministry of Science, ICT and Future Planning. The input variables are total expenditure, number of the programs and average expenditure per program. The output variables are science, technology and economic indicator, and the combination of these outputs are respectively measured as seven different DEA cases. The Ministry of Science, ICT and Future was the first efficient ministry in total technical efficiency. Ministry of Agriculture, Food and Rural Affairs and the Ministry of Food and Drug Safety were efficient in pure technical efficiency, when the Ministry of Commerce Industry and Energy took the first in the scale efficiency. The program efficiency was affected by organizational characteristics such as the institution’s scale, the concentration of the research paper or the patent, technology transfer or the commercialization. The result of this study could be utilized in development of the policy in the nanotechnology and the related field. Furthermore, it could be applied for the modification of expenditure management or the adjustment of the research and development programs’ input and output scale for each ministry.