Takashi Namatame

Contrarian Investment Strategy With Data Envelopment Analysis Concept

One of the typical issues in financial literature is that the market tends to be overly pessimistic about value stocks, many of which are past losers. Therefore, over-reactions might capture by measuring earnings surprise vary with past return levels. In this paper, we propose a new index for an effective investment strategy to capture the return-reversal effect using both Data Envelopment Analysis (DEA) and Inverted DEA in order to consider the above characteristics of the market. Our investment strategy using the new index exhibits better performance than the naive return-reversal strategy that only uses past returns or earnings surprise. In addition, the correlations between our new index and commonly used value indices are insignificant, and the value indices cannot represent the over-valued (under-valued) situations perfectly. Hence, considering both proposed and value indices like book-to-price one, we could select value stocks more effectively than by using only one of these indices.

Evaluation of Network Structure Using Similarity of Posts on Twitter

Social networking service (SNS) is very popular in our lives, with expanding internet environments and mobile device. Through the SNS, user can submit their opinion or reputation freely, anytime and anywhere. These activities are getting great attention on a various business scenes in recently. Twitter is one of the most popular SNS, and used by numerous people in the world. In addition, since various information is posted on Twitter, it is expected to be utilized as a business strategy, and there have been many studies on the marketing using Twitter data. Moreover, we can get some information about user’s network in Twitter. In this research, we attempt to evaluate the network structure using similarity of post on Twitter. We created the user network using similarity of posts mentioned about four titles of Japanese TV drama, and we grasped the post categories that is easy to get user’s interest. From the result, we discussed the difference between TV drama and suggestions for promotion strategies of TV drama production company.

Evaluation of Store Layout Using Eye Tracking Data in Fashion Brand Store

In this study, we conducted purchasing simulation experiment using eye tracking device in fashion brand store. Using the gaze measurement data obtained through experiments, we conducted several analyses to evaluate the store layout. Firstly, we divided the inside of the store into several areas. We tried to identify the areas that can become areas that are easily visible (Golden Zone) by performing multiple comparison on visual time for each area. Through the result, we identify the area that could be Golden Zone. In addition, it became clarifying that the characteristics of the areas which can become Golden Zone. Secondly, we tried to clarify that relationship between good impression item and visual time. It is clarified that there had a positive correlation between “Purchasing time” and “The number of item held in hand.” Moreover, “Purchasing time” and “The number of good impression item” also had a positive correlation. From the results, we proposed improvement plans for better store layout.

Proposal of Learning Support SNS Utilizing Gamification.

Recently, a learning support system using the internet is often used in the field of education. However, many of them focus only on improving academic ability and communication among students. Also, as education problem in Japan, there is a lack of voluntary and learning motivation. In this research, we aim to propose a system to promote student’s self-learning motivation to learn and establishment of learning habits. The proposed system enables well communication not only among students but also between students and teachers. We also utilize gamification for the proposed system. We believe that propose system can effectively promote student’s self-learning motivation and establishment of learning habits. Finally, we describe an experiment plan using the propose system.

Analysis of the Characteristics of Repeat Customer in a Golf EC Site

In recent years, acquisition of repeat customers is emphasized for EC sites. On the other hand, the defection rate from the first purchase to the second purchase is the highest. There are much attention to acquire the repeat customers in the EC sites in this situation. The purpose of this study is to clarify factors necessary for acquiring repeat customers. Especially, we construct models that predict whether or not to repurchase within a certain period using membership information variables, purchase behavior variables and web browsing behavior variables. Using these models, we extract characteristics relate to presence or absence of repurchase and propose marketing measures to promote to repurchase.

Analysis of Trade Area for Retail Industry Store Using Consumer Purchase Record

For retail industry such as supermarket and convenience store, it is important to understand customers. In marketing perspective, to match marketing activity to customer needs is one of the most important strategy for retail industry. In this study, we focus on trade area of retail store. If we can grasp the trade size of a store, manager can plan optimal strategy, e.g. how to spend for advertise activity and where we should open a new store. In this study, we use ID-POS data which is the purchase record with customer identification data of a super market chain and calculate the trade area radius, then we show the cause and effect model to estimate trade area size using store causal data. Moreover, we evaluate our model and discuss how to be decided the trade area size.

Analysis of Cancellation Factors Based on the Characteristics of Golf Courses in Reservation Sites

In this study, we analyze cancellation factors based on the characteristics of golf courses in reservation sites. Firstly, we classify each golf course using causal data such as price range, course type and capacity and review data using k-means clustering. As a result, golf courses were classified into four clusters and we analyzed characteristics of golf courses each cluster. Secondly, in order to identify factor of cancellation each characteristic of golf courses, we performed logistic regression analysis targeting on each cluster using golf course reservation data and user attribute data. From the result of these analysis, we identified same common cancellation factors and different cancellation factors by characteristics of each golf course.

Valuation of Customer and Purchase Behavior of a Supermarket Chain Using ID-POS and Store Causal Data.

Along with the growth of internet market a shopping, retailers such as supermarket chains need a strategy corresponding to customers in each store. The purpose of this research using ID-POS data of supermarket chain is to clarify customer characteristics and purchase behavior for each store. First of all, we categorize stores based on causal data concerning each store such as sales floor area and peripheral population. Second, we analyze customer’s purchasing behavior using ID-POS data for each class that we tried to classify above, and extract characteristic purchasing behavior. Finely we evaluate these results together and clarify customer characteristics and purchasing behavior for each store causal.

Proposal for a Visualization System of Purchase Relationship Using ID-POS Data

A R T I C L E I N F O A B S T R A C T Article history: Received: 31 May, 2017 Accepted: 06 July, 2017 Online: 01 August, 2017 In recent years, big data analysis is gaining immense credence in the fields of academics and business. Businesses such as management and marketing have demonstrated a strong inclination and interest in data analytics. However, many businesses are unable to utilize data even if they have access to it. The main reason for that is the lack of familiarity with data analytics procedures. Hence, a system needs to be developed that can perform data analytics and demonstrate its benefits. In this study, we use point of sales data obtained from a supermarket chain to analyze and show the relationship between purchase goods at the same time. A supermarket is one of the ideal places to demonstrate data analysis because retail stores have many purchase records and are always conducting various marketing activities. We propose an easy-to-handle visualization system to show the goods that are inter-related. By using our system, a store manager can obtain information about the item sales easily and interactively.

Proposal of the review recommendation system using the concurrent network

Recently, reviews of goods are one of the important factors which have an influence on the consumers buying behavior. Under the situation, information aggregation of reviews has attracted much attention in electronic commerce (EC) site. In this study we propose a review recommendation system target on golf EC site. In our proposal system, reviews are scored by the evaluation of characteristic words which were obtained by TF-IDF and reputation analysis. Moreover, our proposal system visualize concurrent network using concurrent relations between the characteristic words. In order to verify the effectiveness of our proposal system, we conducted an experimental evaluation. In the experimental evaluation, we compared our proposal reviews with other review (selected randomly, recently date and reference counts). As the result, it was found that the result score of our proposed reviews were almost the same as the result score of reference count. Moreover, we received positive evaluation about concurrent network. Through these experimental evaluate, we consider that the effectiveness of this system was successfully verified.