Because in many SMEs (Small and Medium-sized Enterprises), the amount of work largely fluctuates, it isdifficult to secure human resources in keeping with the changing environment. While in busy periods, workers may notfinish all the work, in idle periods, they may not have enough work to do. In order to adjust to the fluctuating workload,there are various employment methods such as temporary staff services and outsourcing workforce and services. However,it is very important to make effective use of regular employees. The aim of this paper is to level out the working hours ofworkers and introduce a new inter-company P2P (Peer to Peer) human-resource sharing platform where companies duringtheir idle periods can offer their employees to companies in their busy periods using an automated negotiation technology.The effectiveness of this new platform will be evaluated and verified using multi-agent simulation.

Pedestrian traffic is reported to be a component of rental price. However, rental price analysiswith an agent-based simulation has not been conducted. In this article, shop rental around Shimokitazawastation is analyzed by conducting an agent-based simulation. The result shows that there is a positivecorrelation between shop rental and the number of footprint, which is the output of the agent-basedsimulation.

This paper proposes a method of building a polarity dictionary using news articles and stockprices in the Chinese market by textual analysis in finance. In order to measure the degree of polarity, weassociated the news articles' sparse composite document vectors to a score. The score is calculated by themethod of event study with the abnormal change rate of stock prices on the publication date. Weconducted support vector regression (SVR) and built a polarity dictionary with polarity data from learners.Furthermore, we made a comparison on accuracy to traditional ways of calculating word polarity inwhich news articles are represented by a one-hot wordlist. The comparison of the existed polarity is made.

There are many types of goods in retail stores such as supermarkets. While the goods positionmay be changed by store staffs, management of goods position may be insufficient. In this study, weproposed a model for estimating goods position in retail stores through association analysis and analyzingthe staying time of customers in divided areas in the store, using customer's purchase history and movementhistory of customers. In this paper, we show some results from our experiments and point out several futureworks.

The purpose of this research is to realize service that leads to improvement of management ability byusing POS data of restaurant service industry. The labor productivity of the food-service industry in Japan isvery low, has long suffered from lack of personnel and continues to struggle maintaining talent. Therefore, as ameasure to solve this problem, we analyzed the POS data of a High-end barbeque shop with a high customerunit price, making a hypothesis that the improvement per customer price per customer is effective.And Weclarify the characteristics of customer's purchasing behavior and examine the sales policy including FinTechapplication using machine learning.

To keep up with rapid changes in the business environment, Japanese companies have requiredto conduct business transformation in recent years. In this paper, we classified companies by using FuzzyC Means, and estimated the synergy effect of multi-business companies based on that classification. Inaddition to this, we investigated the corporate behavior, especially when making a decision to sell a business,focusing on divestment through M&A.

Merger and Acquisition (M&A) has become more and more popular these days. While M&A is aneffective strategy, it also has a big risk for companies. According to the study in U.S., over 60% of M&Aended in decreasing the shareholder value. What is worse,even though M&A is one of the biggest bet for thecompanies, we still don't have the clear answer for the reason of those failures. The previous studies aremostly based on comparing success cases and failure ones. However, since M&A is a dynamic activity andinvolves many stakeholders, surveying the past cases may not be enough and we need the way to simulatethis dynamics. Thus, in this paper, we would like to emulate M&A activity as gaming and aim to find theinsights on success / failure factor of M&A.

Company executives play an important role in corporate activities. In this research, we focus onthe characteristics of the top executive, who make the important decisions. We attempt to measure the facialemotional score of these executives from the photographs published on their firm's annual report. Inaddition, we create a narcissism index from the number of times these executives appear on their reports.Finally, we analyze how these emotional scores or narcissism indexes relate with their company'sperformance and behavior.

Research on the association between stock price fluctuations and news data is popular andnumerous studies have been conducted. This paper aims to improve the accuracy of text classification intothree categories (negative, neutral, or positive) by employing high frequency data and LSTM model. Oneof the novelties of our paper is to use several types of news articles in the analysis.

News reports can be one of the main factors that influence commodity futures prices. In thispaper, we conducted analysis on the relationship between news reports and commodity futures. Firstly, wedeveloped a fluctuation index of the futures prices on a daily basis, through event study. Next wevectorized the news report data and analyzed their relationships with the futures prices. In this analysis,we used the multivariate autoregressive model to examine the possibilities of forecasting commodityfutures prices by using the news report index. We also analyzed the relationship between the news indexdeveloped in this paper and the stock prices of enterprises related with commodity futures.