Double Ensemble Approaches to Predicting Firms’ Credit Rating

Several rating agencies such as Standard & Poor\u27s (S&P), Moody\u27s and Fitch Ratings have evaluated firms’ credit rating. Since lots of fees are required by the agencies and sometimes the timely default risk of the firms is not reflected, it can be helpful for stakeholders if the credit ratings can be predicted before the agencies publish them. However, it is not easy to make an accurate prediction of credit rating since it covers a variety of range. Therefore, this study proposes two double ensemble approaches, 1) bagging-boosting and 2) boosting-bagging, to improve the prediction accuracy. To that end, we first conducted feature selection, using Chi-Square and Gain-Ratio attribute evaluators, with 3 classification algorithms (i.e., decision tree (DT), artificial neural network (ANN), and Naïve Bayesian (NB)) to select relevant features and a base classifier of ensemble models. And then, we integrated bagging and boosting methods by applying boosting method to bagging method (bagging-boosting), and bagging method to boosting method (boosting-bagging). Finally, we compared the prediction accuracy of our proposed model to benchmark models. The experimental results showed that our proposed models outperformed the benchmark models

Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem

Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. The new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional CF technique computes it only if they rated common items. Results from several experiments using an online shopping mall dataset in Korea demonstrate that our approach significantly outperforms the traditional CF technique

The More the Worse? Mining Valuable Ideas with Sentiment Analysis for Idea Recommendation

Many firms have an interest in an open innovation community, recognizing its business value. They can collect and analyze the ideas of their customers from the community to get valuable ideas which can lead to innovation such as a new product or service. However, such a community overloaded with too many ideas from customers cannot make use of them at the right time because of the limited time and human resources to deal with them. Therefore, it would be a great help to those firms if they have a recommendation system which recommends top n ideas for innovation. MyStarbucksIdea (MSI) is such an open community, created by Starbucks. To build such an innovative idea recommendation system for Starbucks, we analyzed a dataset collected from MSI, utilizing data mining and sentiment analysis techniques. Experimental results show that our recommendation system can help firms identify prospective ideas which can be valuable enough for their innovation among a large amount of ideas, efficiently

Quick-and-Wide Propagation of Disaster Tweets: Its Measurement and Implications

In the situation of various types of disasters where dynamic, non-routine events appear and disappear in a short time span, access to timely information like emergency warnings and alerts determines whether people™s lives will be saved or lost. According