1. Data Analysis The Tsukuba Univ. MBA IB Team “M” Nishantha Fernando Tomoe Nagase Maiko Osawa Bipul Singha

2. Objectives Developing a regression model to predict the trend of sales income against specific items from BS and P&L Identifying the degree of significance of each item to the sales income Examine residuals to see who are the outliers

3. Hypothesis We assume that the following eight factors from BS and P&L may affect sales income B/S P/L Personnel CostAsset Fixed Asset Capital Stock Debt Equity Current Asset R&D Advertisement Short- term debt Long-term debt Sales

4. Outline of Data Analysis (1) lm(formula = log10(SalesIncome) ~ log10(CurrentAsset + 0.5) + log10(TangibleFixedAsset) + log10(ShortTermDebt + 0.5) + log10(ExperimentalAndResearchExpense + 0.5) + log10(LongTermDebt + 0.5) + log10(CapitalStock + 0.5) + log10(PersonnelExpense) + log10(AdvertisementExpenses + 0.5))

5. Outline of Data Analysis (2) lm(formula = log10(SalesIncome) ~ log10(CurrentAsset + 0.5) + log10(TangibleFixedAsset) + log10(ShortTermDebt + 0.5) + log10(ExperimentalAndResearchExpense + 0.5) + log10(PersonnelExpense) + log10(AdvertisementExpenses + 0.5))  Remove the statistically insignificant items such as long term debt and capital stock from the model for improvement

6. Outline of Data Analysis (3)  Compare “before” and “after” in predicted sales income vs. actual sales income to see the degree of improvement Before modification After modification

7. Outline of Data Analysis (4) Residual analysis Outliers2 Outliers1 Residuals <-0.5 MIYUKI KEORI ORIX INTERIOR Green Cross INTERNATIONAL REAGENTS ISHII PRECISION TOOL ISEKI & CO. SANYO ELECTRIC HANKYU REALTY MITSUI REAL ESTATE SALES IZUKYU Nankai Electric Railway Kobe Electric Railway AWAJI FERRY BOAT WESCO Koshien Tochi Kigyo Residuals > +0.6 ITOCHU Marubeni TOMEN Nichimen KANEMATSU CHUO GYORUI TOHTO SUISAN TSUKIJI UOICHIBA OSAKA UOICHIBA DAITO GYORUI SUMITOMO TOKYO SANGYO PARCO SHINKO GYORUI

8. Findings The improved model is effective –Current assets have the most influential factors to predict sales (coefficient: 0.60), the second best is personnel expense (coefficient: 0.37) and others have relatively small impact on sales (less than 0.1). –R&D expense has a negative coefficient, meaning, investing into R&D expense has a negative contribution on sales income –Residuals analysis showed that outliers cannot be well- characterized by specific industries

9. Issues and To be investigated  Find out why companies still continue investment on R&D even though it has a negative impact on sales income.  Examine the implication of outlined companies

10. Role and responsibility of members Nishantha Fernando: Data analysis / Speaker Tomoe Nagase: Analysis direction Maiko Osawa: Hypothesis development Bipul Singha: Objective setting / Speaker