Manufacturing industry key metrics and stock performance

VIETNAM NATIONAL UNIVERSITY, HANOI INTERNATIONAL SCHOOL GRADUATION PROJECT MANUFACTURING INDUSTRY: KEY METRICS AND STOCK PERFORMANCE Nguyễn Quang Tuấn Hanoi - 2020 VIETNAM NATIONAL UNIVERSITY, HANOI INTERNATIONAL SCHOOL GRADUATION PROJECT MANUFACTURING INDUSTRY: KEY METRICS AND STOCK PERFORMACE SUPERVISOR: Dr Lê Đức Thịnh STUDENT: Nguyễn Quang Tuấn CODE: 16071260 COHORT: AC2016B MAJOR: Accounting, Analyzing and Auditing Hanoi - 2020 INFORMATION ON FINAL THESIS Full name: Nguyễn Quang Tuấn Sex: Male Date of birth: October 23 1998 Place of birth: Hà Nội Official thesis title: Manufacturing industry: Key metrics and stock performance Major: Accounting, Analyzing and Auditing Code: 16071260 Guider Lecturer: Dr Lê Đức Thịnh Summary of the findings of the thesis: By analyzing the correlation between ten key metrics and total shareholder returns, the study found that the metrics in analyzing the operational conditions including Inventory turnover, RPE, ROCE, and CF/Capex of manufacturing companies have more significant relationship with TSR than other standards metrics like GPM, D/E ratios It is also noteworthy that for all 20 manufacturing companies, the D/E ratios and GPM have the least significant impact on both TSR calculation This study also finds multiple regression models to test the impact of key metrics on total shareholder returns The result collected using the Bayesian Model Average (BMA) package in R shows that Revenue Growth and ROCE have a very high probability of appearing in any regression model and have a positive linear relationship with TSR while EPS Growth has a negative linear relationship with TSR 10 Practical applicability, if any: This study is an academic research which provides basic understanding of financial analysis of manufacturing industries from different regions around the world These manufacturing companies are among leading companies, which can be used as representatives for the manufacturing industries The study ananlyzes and provides overview of understanding about the impacts of featured and standard metrics on the effectiveness and efficiency of manufacturing companies outcomes which reflects on stock price of these companies in different regions The study also contributes to the research part of financial analysis and stock evaluation By using findings of this study, the financial analyst or investor can have information about what metrics can be important in evaluating each manufacturing companies and its stock performance Date: 27/05/2020 Signature: Full name: Nguyễn Quang Tuấn Acknowledgement With this topic, I would first like to thank my thesis instructor Dr Le Duc Thinh of the International School, Vietnam National University for the continuous support of my study and research, for his patience, enthusiasm, and profound knowledge His advice gave me the right direction in writing this thesis Moreover, his comments my thesis play a very important role in my thesis I could not imagine how I could deal with such difficulties without his help I would also like to acknowledge my friends who have given me great support during all the time I studied at VNU-IS as well as the period of doing the thesis and I am gratefully indebted to his/her for them Letter of Declaration I hereby declare that the Graduation Project Manufacturing Industry: Key metrics and stock performance is the result of my own research and has never been published in any work of others During the implementation process of this project, I have seriously taken research ethics; all findings of this project are results of my own research and surveys; all references in this project are clearly cited according to regulations I will take full responsibility for the fidelity of the number and data and other contents of my graduation project Hanoi, May 27th 2020 Nguyễn Quang Tuấn List of abbreviation CER Constant Exchage rates CF/Capex Cash flow to Capital Expenditure COSG Cost of goods sold D/E Debt to Equity ratio EPS Earnings per share EPSG Earnings per share growth EVA Economic value added GPM Gross Profit Margin IT Inventory Turnover MVA Market value added PM Profit margin QR Quick ratio RG Revenue growth RPE Revenue per employee ratio ROA Return on Assets ROCE Return on Capital Employed ROE Return on Equity ROI Return on Investment RONA Return on Net Assets TSR Total stock return Table of contents Chapter 1: Introduction 10 The necessity of topic: 10 The goal of topic: 11 Research outcomes: 12 Practical contributions: 12 Chapter 2: Literature review and Research methodology 13 Literature review 13 1.1 Theoretical background 13 1.2 Key metrics 19 Research questions, methodology and scope 22 2.1 Research questions 22 2.2 Methodology 22 2.3 Scope of research 23 Facilities and the difficulty of the researching process 23 3.1 Facilities: 23 3.2 Difficulty: 23 Chapter 3: Main results 24 Descriptive statistics: 24 Analyzing the correlation of metrics with TSR 30 2.1 Summary table of correlation between featured metrics and TSR1 30 2.2 Summary table of correlation between featured metrics and TSR2 34 2.3 Summary table of correlation between standard metrics and TSR1 39 2.4 Summary table of correlation between standard metrics and TSR2 43 Case study of GlaxoSmithKline: 51 Analyzing the multiple regression 54 Chapter 4: Conclusion, Implication and Recommendation 56 Conclusion and discussion 56 Implications 57 2.1 Literature implications 57 2.2 Practical implications 57 Limitations 57 Recommendation 58 References 59 List of figures and table Figure Average TSR1 and TSR2 of 20 manufacturing companies 2010-2019 24 Figure Average Inventory Turnover of 20 manufacturing companies 2010-2019 25 Figure Average Revenue growth of 20 manufacturing companies 2010-2019 26 Figure Average RPE and EPSG of 20 manufacturing companies 2010-2019 26 Figure Average QR and D/E of 20 manufacturing companies 2010-2019 27 Figure Average CF/Capex of 20 manufacturing companies 2010-2019 28 Figure Average Gross profit margin of 20 manufacturing companies 2010-2019 29 Figure Average ROCE and RONA of 20 manufacturing companies 2010-2019 29 Table Summary significant correlations with p-value < 10% with TSR1 calculation 48 Table Summary significant correlations with p-value < 10% with TSR2 calculation 49 Table Correlations between 10 metrics in the thesis 50 Chapter 1: Introduction The necessity of topic: In 2013, the State Board of Administration (SBA) sponsored an executive compensation research study by Farient Advisors LLC identifying the primary metrics used in executive compensation plans, company size and valuation premiums, and testing whether the metrics used have any impact on total stock returns (or total shareholder returns – TSR) The study found that, in aggregate, performance metrics are generally well-aligned with shareowner value However, the optimal use of measures differs considerably by industry [1] Manufacturing industries are those that engage in the transformation of goods, materials or substances into new products The transformational process can be physical, chemical or mechanical Manufacturers often have plants, mills or factories that produce goods for public consumption Machines and equipment are typically used in the process of manufacturing Although, in some cases, goods can be manufactured by hand An example of this would be baked goods, handcrafted jewelry, other handicrafts and art There are several massive manufacturing industries in the world including food, beverage, tobacco, textiles, apparel, leather, paper, oil and coal, plastics and rubbers, metal, machinery, computers and electronics, transportation, furniture and others [2] Manufacturers create physical goods How these goods are created varies depending on the specific company and industry However, most manufacturers use machinery and industrial equipment to produce goods for public consumption The manufacturing process creates value, meaning companies can charge a premium for what they create Today’s advancement of computer technology allows manufacturers to more with less time Now, thousands of items can be manufactured within the space of minutes Computer technology can be used to assemble, test and track production Each year, technology continues to make manufacturing increasingly 10 There is no standard financial ratio showing significant correlation with TSR2 then these metrics are not useful in evaluating Hitachi’s stock performance In Toyota company, except from EPSG, which is the only metric that has strongly positive relation with TSR2 at the correlation of 0.792 and the p-value of 0.0054 With the p-value < 10%, there is enough evidence to claim that there is a linear relationship between EPSG and TSR2 47 Companies IT RPE ROCE Ford RONA CF/Capex QR GPM RG EPSG D/E -0.45 Bunge -0.68 0.51 Johnson & -0.513 Johnson Boeing 0.548 Caterpillar General Electric -0.52 0.453 0.52 -0.512 0.714 -0.614 0.608 IBM -0.539 0.458 Pepsico -0.681 Intel GlaxoSmithKline -0.482 0.698 0.473 0.659 0.503 Magna -0.556 Volkswagen 0.467 ArcelorMittal -0.521 0.593 Sanofi 0.472 0.648 -0.47 -0.578 Nestle Novartis -0.576 Sony 0.607 0.565 0.478 0.528 0.601 0.766 0.513 0.533 5 Nissan 0.597 Hitachi 0.742 0.577 Toyota Amount Table Summary significant correlations with p-value < 10% with TSR1 calculation 48 Companies IT RPE ROCE Ford RONA CF/Capex -0.498 -0.453 QR GPM RG 0.515 EPSG D/E -0.491 -0.49 Bunge Johnson & -0.501 -0.46 Johnson Boeing 0.492 Caterpillar 0.464 -0.692 General Electric 0.641 0.653 0.487 0.666 0.471 -0.511 0.551 -0.474 0.487 0.47 0.501 IBM 0.599 -0.51 0.562 -0.64 Pepsico Intel GlaxoSmithKline 0.055 0.528 0.63 0.489 Magna Volkswagen 0.68 -0.719 0.568 ArcelorMittal -0.477 -0.467 0.5 0.578 0.458 -0.591 Sanofi 0.563 0.531 -0.454 Nestle 0.606 -0.511 Novartis 0.453 0.51 Sony Nissan 0.547 Hitachi 0.472 0.566 -0.704 0.675 0.557 Toyota Amount 0.66 0.792 6 5 Table Summary significant correlations with p-value < 10% with TSR2 calculation 49 As we can see from both TSR calculation, IT, RPE and CF/Capex are the indicators that have the largest number of significant correlations which overwhelms that of standard metrics ROCE is the metric whose number of significant correlations in TSR2 calculation is large but very small in TSR1 calculation Notably, most of the significant correlations between featured metrics and TSR are from the Europe companies Moreover, D/E and GPM are still the least corelated with both TSR calculation Meanwhile, standard metrics as QR and EPSG also have high number of significant correlations IT RPE QR GPM RG IT RPE 0.033 QR 0.244 0.128 GPM -0.236 -0.35 0.090 RG 0.0188 0.011 -0.02 -0.05 EPSG D/E ROCE RONA CF/Capex 1 EPSG 0.014 0.062 -0.03 -0.03 0.186 D/E -0.156 -0.06 -0.04 0.039 ROCE 0.056 -0.22 0.111 0.416 0.144 -0.008 0.032 RONA 0.049 -0.05 0.080 0.141 0.091 0.052 0.023 0.362 CF/Capex 0.029 -0.15 -0.01 0.286 -0.084 0.018 0.267 0.102 -0.05 -0.12 -0.022 Table Correlations between 10 metrics in the thesis From Table 3, it can be seen that all the metrics are independent with each other, no metric is correlated The correlation between ROCE and GPM have the largest correlation at 0.416, however, since it is still smaller than 0.5 so there is not a significant relationship between them 50 Case study of GlaxoSmithKline: GlaxoSmithKline has significant correlation between IT and TSR with the both calculations Inventory turnover is equal to Cost of sales divide the Average ending inventory, so the change in Cost of sales annually is quite important We analyze the change in Cost of sales to see why this metrics is considered to be indicators when evaluating stock performance The change in cost of sales annually also reflects the events that happened in that year, which can affect the stock performance In 2019, Total cost of sales as a percentage of turnover was 35.1%, 1.9 percentage points higher at AER and 2.4 percentage points higher in CER terms compared with 2018 This reflected an increase in the costs of Major restructuring programmes, primarily as a result of write-downs in a number of manufacturing sites, the unwind of the fair market value uplift on inventory arising on completion of the Consumer Healthcare Joint Venture with Pfizer and increased amortization of intangible assets In 2018, Cost of sales as a percentage of turnover was 33.2%, down 1.0 percentage points AER and 1.4 percentage points CER This primarily reflected a favourable comparison with the writedowns of assets in 2017 related to the decision to withdraw Tanzeum, together with a more favourable product mix in Vaccines and Consumer Healthcare In 2017, Cost of sales as a percentage of turnover was 34.3%, up 1.0 percentage points in Sterling terms and up 1.4 percentage points in CER terms compared with 2016 This primarily reflected the phasing of costs of manufacturing restructuring programmes including non-cash write downs as a result of plant closures and the write down of assets related to the progressive withdrawal of Tanzeum, as well as continued adverse pricing pressure in Pharmaceuticals, primarily Respiratory, and additional supply chain investments This was partly offset by a more favourable product mix across all three businesses, particularly in Pharmaceuticals, reflecting the impact of higher HIV sales, and in Vaccines, reflecting the benefit of a settlement for lost third 51 party supply volume and a favourable year-on-year comparison to inventory adjustments in 2016 There was also a continued contribution from integration and restructuring savings in all three businesses In 2016, Cost of sales as a percentage of turnover was 33.3%, down 3.7 percentage points in Sterling terms and 2.4 percentage points in CER terms compared with 2015 This reflected improved product mix, particularly the impact of higher HIV sales in Pharmaceuticals, but also in Vaccines and Consumer Healthcare and lower restructuring costs as well as an increased contribution from integration and restructuring savings in all three businesses These benefits were partly offset by continued adverse pricing pressure in Pharmaceuticals, primarily Respiratory, as well as continued investments in the supply chain In 2015, Cost of sales as a percentage of turnover was 37.0%, 5.2 percentage points higher than in 2014 and 5.4 percentage points higher on a CER basis The increase reflected the disposal of our higher margin Oncology business and the acquisition of the lower margin Vaccines and Consumer Healthcare businesses from Novartis In addition, there were adverse price movements, particularly in US Pharmaceuticals, and increased investments in Vaccines to improve the reliability and capacity of the supply chain, together with increased intangible asset amortisation and impairment charges and higher integration and restructuring costs This was partly offset by an improved product mix, particularly as a result of the growth in HIV sales, and the benefits of the Group’s ongoing cost reduction programmes In 2014, Cost of sales as a percentage of turnover was 31.8% compared with 32.4% in 2013 Net of adverse currency translation effects, the cost of sales percentage decreased 1.3 percentage points This reflected adverse price and mix movements, particularly the decline in Pharmaceuticals sales in the US, the costs of supply remediation activities and continuing investments in new launch capacity and future manufacturing technology, more than offset by lower intangible write-offs and the benefit of our ongoing cost reduction programmes and lower intangible impairments 52 In 2013, Total cost of sales was 32.4% of turnover compared with 30.0% in 2012 The increase primarily reflected the expected impact of the unwinding of costs of manufacturing volume shortfalls, adverse mix effects, the impact of preparing for the launches of new pipeline products and higher amortisation and impairments of intangible assets, partially offset by ongoing cost management, better price realisation and restructuring benefits In 2012, Core cost of sales increased to 26.8% of turnover (2011 – 26.5%) This primarily reflected the impact of lower sales, lower volumes and adverse regional and product mix partially offset by ongoing cost management and one-off royalty and pension adjustments In 2011, Cost of sales increased to 26.8% of turnover (2010 – 26.7%) This reflected the impact of the reduction of higher margin sales of pandemic related products, Avandia and Valtrex, together with the effect of regional mix and the impact of US healthcare reform and European austerity price cuts These adverse impacts were partially offset by lower restructuring costs, lower inventory write-offs and greater savings from the Operational Excellence programme In 2010, Cost of sales increased to 26.7% of turnover (2009 – 26.0%) reflecting the impact of generic competition to higher margin products in the USA (principally Valtrex), lower Avandia sales, US healthcare reforms and European austerity price cuts, and inventory and other asset write-downs, partially offset by savings from the Operational Excellence programme and lower restructuring costs of £187 million In summary, Cost of goods sold (COGS) is the cost of acquiring or manufacturing the products that a company sells during a period, so the only costs included in the measure are those that are directly tied to the production of the products, including the cost of labor, materials, and manufacturing overhead According to the analysis of case study for GlaxoSmithKiline, COGS can be changed because of financing policies from the companies and company’s investment COGS also is affected by the demand of consumer especially in epidemic outburst and 53 capacity of supply chain In addition, COGS also reflects the disposal of high margin business and the acquisition of the low margin business Combined with the analysis of correlation of Inventory Turnover with stock performance, it can be concluded that Inventory turnover can be influenced by the companies’ investment strategies, policies from the government, the demand from customer, and acquisition or disposal of some related business The change in Inventory turnover can affect the investor’s expectation, which is very important with stock return Therefore, the change in COGS/Average Ending Balance Inventories (depends mainly on COGS) has positive relationship with stock performance Analyzing the multiple regression Regression analysis helps to understand how the value of the dependent variable changes when independent variables are varied We start with: - Dependent variable: Ln(1 + TSR1) or Ln(1 + TSR2) That is, we consider logarithm of returns rather than simple returns - Independent variables: Ten (10) key metrics: Inventory turnover, RPE, ROCE, RONA, CF/Capex, Quick ratio, Gross profit margin, Revenue growth, EPSG and D/E ratio A sample of 196 observations from 20 companies listed in Section 2.3 are used to create the models Notice that for companies that not use US dollars on their financial reports, data are converted to USD by annual exchange rates stated in the financial report or the exchange rate at their fiscal year end By using the Bayesian Model Average (BMA) package in R, we find out that: For TSR1 (fiscal year calculation), the BMA package gives us the best models with cumulative posterior probability = 0.68 Here are the five models: Model 1: Ln(1+TSR1) = -0.035506 + 0.415486(Revenue Growth) 0.020414(EPS Growth) + 0.850248(ROCE); R^2 = 0.157 Model 2: Ln(1+TSR1) = -0.034854 - 0.016786(EPS Growth) + 0.926593(ROCE); R^2 = 0.126 54 Model 3: Ln(1+TSR1) = -0.035098 + 0.399073(Revenue Growth) 0.020574(EPS Growth) -0.002535(D/E Ratio) + 0.864860(ROCE); R^2 = 0.172 Model 4: Ln(1+TSR1) = -0.040820 + 0.930414(ROCE); R^2 = 0.102 Model 5: Ln(1+TSR1) = -0.042369 + 0.331656(Revenue Growth) + 0.870131(ROCE); R^2 = 0.122 Similarly, for TSR2 (public date calculation), the BMA package also gives us the best models with cumulative posterior probability = 0.716 Here are the first models: Model 1: Ln(1+TSR2) = -0.041252 + 0.525267(Revenue Growth) + 0.779314(ROCE); R^2 = 0.134 Model 2: Ln(1+TSR2) = -0.036022 + 0.589143(Revenue Growth) 0.015554(EPS Growth) + 0.764163(ROCE); R^2 = 0.153 We can conclude that there is not much difference between two ways of calculating stock returns For both ways, the BMA package shows that Revenue Growth and ROCE have a very high probability of appearing in any regression model and have a positive linear relationship with TSR while EPS Growth has a negative linear relationship with TSR 55 Chapter 4: Conclusion, Implication and Recommendation Conclusion and discussion This study analyzes annual data of twenty among top 100 manufacturing companies in the US, Europe, Asia and Australia in a ten-year period (2010-2019) to see whether there are positive/negative linear relationships between total shareholder returns and some key metrics including standard metrics used in corporate finance and featured metrics for manufacturing companies By analyzing the correlation between ten key metrics and total shareholder returns, the study found that the metrics in analyzing the operational conditions including Inventory turnover, RPE, ROCE, and CF/Capex of manufacturing companies have more significant relationship with TSR than other standards metrics like GPM, D/E ratios As a result, featured metrics should be more focus on when analyzing stock performance It is also noteworthy that for all 20 manufacturing companies, the D/E ratios and GPM have the least significant impact on both TSR calculation This may be resulted from the special characteristic of manufacturing companies as they usually have large amount of debt from lease Meanwhile, standard metrics as QR and EPSG also have high number of significant correlations This study also finds multiple regression models to test the impact of key metrics on total shareholder returns The result collected using the Bayesian Model Average (BMA) package shows that there is not much difference between two ways of calculating stock returns For both ways, the BMA package shows that Revenue Growth (RG) and ROCE have a very high probability of appearing in any regression model and have a positive linear relationship with TSR This result is reasonable since revenue plays a very important role in profitability of the companies and it directly influence the return of the shareholder Meanwhile, EPS Growth (EPSG) has a negative linear relationship with TSR 56 Implications 2.1 Literature implications Regarding to theoretical contribution, this thesis is an academic research which helps to provides a better knowledge about the special metrics used in analyzing and evaluating the effectiveness of manufacturing’s metrics and other common metrics used in fundamental analysis This research shows evidences to support the linear relationship between the metrics, especially the featured one, with the stock performance of manufacturing companies 2.2 Practical implications In terms of the implications of study findings, this study is an academic work to contribute to the research field of financial analysis and stock evaluation As a role of the financial analyst or investor, for manufacturing fundamental analysis, it is vital to have profound thought and comprehension towards the key metrics and their impacts on the fluctuation of stock price By using findings of this study, the marketer financial analyst or investor can have more knowledge about what metrics are important in evaluating each manufacturing company and its stock performance Furthermore, this research can be a guideline or at least a suggestion for the Vietnamese manufacturing companies of how leading manufacturing companies in world disclose their operational and financial situation for the stakeholders using the special metrics It also somehow reflects the attitude of investors toward the operational and financial ratios that they tended to look for the sustainable development and long-term growth of the manufacturing industry instead of focusing on the net income in the income statement As manufacturing is an indispensable industry owing to the people’s increasing demand, this research recommends that Vietnamese manufacturing companies, at the early stage of being listed on the stock exchange, should ponder to disclose the featured metrics showing the potential of their companies to attract more investment from investors, especially from the foreign ones Limitations 57 Since the necessary data are not fully disclose on the financial statement and the limited access to those, together with time limitation, this research only covers the data of 20 manufacturing companies among top manufacturing companies in the world, which may not be representative enough to give an overview for manufacturing industries Moreover, as the Vietnamese manufacturing companies are on their early stage of listing on stock exchange, they not disclose data needed to calculate the featured metrics Therefore, there is no Vietnamese manufacturing companies being included in this research Recommendation The future research can continue to examine the relationship between 10 mentioned metrics and the TSR with the quarter data However, quarter data is seasonal so appropriate deseasonalizing method should be consider to smooth the data Moreover, the future research may consider to examine other metrics or financial ratios which can both be featured and standard to see whether there is a better outcome 58 References Corpgov.law.harvard.edu (2018) Performance Metrics and Their Link to Value [online] Available at: https://corpgov.law.harvard.edu/2013/02/20/performance-metrics-and-theirlink-to-value/ Bizfluent.com (2020) [online] Available at: https://bizfluent.com/facts-6853113-definition-manufacturing-industry.html Dow, J., Gorton, G., 1997 Stock market efficiency and economic efficiency: is there a connection?Journal of Finance 52, 1087-1129 Dow, J., 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Listed Manufacturing Companies in 60 Sri Lanka International Journal of Accounting and Financial Reporting 471 10.5296/ijafr.v8i4.14137 17 Ngoc Lam, N and Duc Le, T (2018) Airlines industry: key metrics and stock performance 18 Juniarta, I & Purbawangsa, I.B.A (2020) THE EFFECT OF FINANCIAL PERFORMANCE ON STOCK RETURN AT MANUFACTURING COMPANY OF INDONESIA STOCK EXCHANGE Russian Journal of Agricultural and Socio-Economic Sciences 97 11-19 10.18551/rjoas.202001.02 19 David Gorton Key Financial Ratios for Manufacturing Companies [online] Available at: https://www.investopedia.com/articles/financial-analysis/091016/key-financialratios-manufacturing-companies.asp 20 Twenty manufacturing companies’ official websites for investors and information publications 21 Macrotrend.net (2020) [online] Available at: https://www.macrotrends.net/ 22 Fortune.com (2020) [online] Available at: https://fortune.com/global500/ 23 Finance.yahoo.com (2020) Yahoo Finance [online] Available at: https://finance.yahoo.com/ 24 Reference in person: Nguyen Ngoc Lam – Graduated Student covering airline industry in U.S 61 ... 23 1998 Place of birth: Hà Nội Official thesis title: Manufacturing industry: Key metrics and stock performance Major: Accounting, Analyzing and Auditing Code: 16071260 Guider Lecturer: Dr Lê Đức... for the manufacturing industries The study ananlyzes and provides overview of understanding about the impacts of featured and standard metrics on the effectiveness and efficiency of manufacturing. .. doing the thesis and I am gratefully indebted to his/her for them Letter of Declaration I hereby declare that the Graduation Project Manufacturing Industry: Key metrics and stock performance is