Luận văn manufacturing industry key metrics and stock performance

Sumnary o[ the findings of the thesis: Ly analyzing the correlation between ten key metrics and total shareholder returns, the study found that the metrics in analyzing the operational c

INFORMATION ON FINAL THESIS:

1 Full name: Nguyễn Quang Tuân 2 Sex: Male

3 Date of birt: October 23 1998 4, Place of bins Ha Nội

5 Official thesis title: Manufacturing industry: Key metrics and stock performance

6 Major: Accounting, Analyzing and Auditing

7 Code: 16071260

8 Guider Lecturer: Dr Lê Dức Thịnh

9 Sumnary o[ the findings of the thesis:

Ly analyzing the correlation between ten key metrics and total shareholder returns, the study found that the metrics in analyzing the operational conditions including Inventory tumover, RPE, ROCK, and CF/Capex of manufacturing companies

have more significant relationship with TSR than other standards metrics like GPM,

TYE ratios Tl is also noteworthy that for all 20 manufacturing companies, the D/E

ratios and GPM have the least significant nnpact on both TSR calculation

This study also finds multiple regression models to test the impact of key

metnes ou total shareholder returns The result collected using the Bayestan Model

Average (BMA) package in R shows that Revere 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 lincar 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 Tepresenlatives for the manulacluring 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: Nguyén Quang Tuan

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 suppor!

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 imporlant role in my thesis T could not imagine

how [ could deal with such difficulties without his help

1 would also like to acknowledge my friends who have given me great support

during all the tame T stucied al VNU-TS as well as the period of domg the thesis and

Lam gratefully indebted to his/her for them,

Letter of Declaration

L hereby declare that the Graduation Project Manutacturing 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 provess of this project, L 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

1 will take full responsibility for the fidelity of the number and data and other

contents of my graduation project

Hanai, May 27" 2020

Nguyễn Quang Tuấn.

Constant Exchage rates

Cash flow to Capilal Expenditure

Cost of goods sold

Debt Lo Equity ratio

Earnings per share Earnings per share growth

FEeonomic value added

Gross Profit Margin Inventory Turnover Market valuc added

Profit margin Quick ratio Revenue growth

Revenue per employee ratio

Retumn on Assets

Return on Capital Employed

Return on Equity

Relum on Tnvesiment Return on Net Assets

Tolal slock retam

2 Analyzing the correkation of metrics with TSR

2.4 Summary table of correlation between featured metrics and TSRL

2.2 Summary table of correlation between featured metrics and TSR2

2.3 Summary table of correlation between standard metrics and TSR

2.4 Summary table of carrels

3 Case study of GlaxoSmithKline:

4 Analyzing the multiple regression

Chapter 4: Conclusion, Implication and Recommeniation

1, Conclusion and discussion

References

List of figures and table Figtre 1 Average TSR1 and TSR2 of 20 mnanufacturing cermpanies 2010-2019 24

Figure 2, Average Inventory Tumnover of 20 manufueturing companies 2010-2019 25

Figure 3 Average Revenue growth of 20 manufacturing companics 2010-2019 26 Figure 4 Avorage RPE and EPSG af 20 nemufackaring companies 2010-2019 a 26 Figure 5 Average QR and D/E of 20 manufacturing comparties 2010-2019 eT Figure 6, Average CE/Capex of 20 manufacturing eompanies 2010-2019 28

Figure 7, Average Gross profit margin of 20 manufacturing companies 2010-2019 28

Figure 8, Average ROCE and RONA of 20 manufacturing companies 2010-2019 28

Table 1 Summary significant conclations with p-value < 10% with TSR1 calculation 48

Table 2 Summary significant corelations with p-value < 30% with TSR2 calculation 49

‘Table 3 Correlations between 10 metrics in the thesis - 30

Chapter 1; Introduction

1 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 retums — 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]

Manufachuring indusines are those that, engage in the lransformation 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 lypivally used

in the process of manufacturing Although, in some cases, goods can be manufactured

by hand An example of this would be baked goods, handcraled 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 specilic company and industry However, mosl manufacturers use machinery and industrial equipment to produce goods for public consumption The manufacturing process creates value, meaning companies can charge a premium for whal they orcate Today’s advaucement of computer technology allows manulaclurers

to do more with less time Now, thousands of items can be manufactured within the

space of minules Computer technology can be uscd lo assemble, test and track

production Hach year, technology continues to make manufacturing increasingly

10

efficient, faster and more cost-effective Llowever, automation also eliminates many

manufacturing, jobs, leaving skilled employees without work [2]

Manulaclunng is one of the few industries where a worker willioul an advanced

degree can earn a living wage Because it is one of the country’s largest employment sectors, a lot of families rely on manufacturing industries to pul fond on the table The industrial scctor also supports many secondary industrics Manufacturing supports roughly 1 -in-6 service jobs Even manufacturing companies need lawyers, accountants,

doctors, financial advisors and olher service professionals Manufacturing indusirics

also spur investments and encourage the building of infrastructure ‘here are few areas

of the economy that manufacturing industries don't touch Many other industries contribute directly and indircetly to manufacturing [2]

One important role of the stock market is to provide price discovery (Dow and

Gorton,1997 Dow and Rahi,2003) [3][4] Investors and managers lea from stock

price (Benjamin Bennett, René Stulz, Zexi Wang, 2019) [5], Therefore, research of

factors which can influence stock price and stock retum helps investors a lot in decision-making, [2]

2 The goal of topic:

This research provides an empirical study to identify the relationship between

key metrics including financial rating or [catured indicators and total stock retum of

manufacturing companies to see whether those metries have any significant influences

on The fluctualion of manufacturing compamiss’ stock price

Annual data are collected from the financial statement among the biggest manufacturing companies around the world then used to caloulate the necessary

metrics and the correlations between these metrics and TSR Furthermore, these data

are used to create a multiple regression model to evaluate the effects of each metric to

the TSR

11

3 Research outcomes:

This study analyzes data of twenty manufacturing companies in the US, UK,

Europe, and Asia: folal shareholder retums (TSR), five featured metrics [or

manufacturing and for standard metrics used in corporate finance analysis The correlations between TSR and these metrics are calculated to see whether there are positive/negative linear relationships between stock-returns and these metrics This study also finds a multiple linear repression model between TSR and the key metrics

4 Practical contributions:

‘This research provides some useful knowledge about manufacturing industries, especially the featured metrics used to analyze them It may also provide an indication

for Vietnamese manufacturmg companics in the future te disclose their financial and

operational ratios in an effective way to attract more investors

Chapter 2: Literature review and Research methodology

1 Literature re

1.1 Theoretical background

‘The State Board of Administration (SI3A) sponsored an executive

compensation research study by Farient Advisors LLC, covering 1,800

companies, 24 Indusiry groups, and fourteen years of data (from 1998-2011}

‘The research project identifies the primary metrics used in executive

compensation plans, overall and by industry, company size and valuation

premiums, and then tests these metrics to determine whether the metrics used

have the highest impact on total stock retums (or total shareholder returns TSR) The study found thal, in aggregate, performance motries arc generally well-aligned with shareowner value Karnings growth, followed by returns and revenne prowth, has the greatest impact on stock prices This review also

found that many industries have a number of metrics lo choose from; wille

half of the 24 industrial groups studied having at least three metric categories

with strong correlations to TSR However, the optimal use of measures

differs considerably by industry [1

In “Manufacturing the future: The next era of global growth and innovation”,

a major report from the McKinsey Global Tustitute, presenis a clear view of how manufacturing contributes to the global economy today and how it will probably evolve over the coming decade It also claims that manufacturing's role is changing The way i contributes to the

conomy shills as nations mature: in today's advanced economies, manufacturing, promotes innovation, productivity, and trade more than growth and employment In these countries, manufacturing also has begun to consume more services and to rely more heavily on them to operate [6]

‘Ihe economic strength of one country can be measure by the manufacturing development The importance of manufacturing can be defined by these senlcncos “The growth of manufacturing machinery output, and technological improvements in that machinery, are the main drivers of ecouomic growth No machinery industries, no sustained, long-term economic growth”, said in the article “Six reasons manufacturing is contral to the economy” by Rossevelt institute [7]

Tr the research “Predictability of Stock Returns using Financial Staicment

Information: Evidence on Semi-strong Kfficiency of Emerging Greek

Stock Market” by Christos Alexakis, Theophano Patra and Sunil Poshakwale,

the paper examines the predictability of stock xeturms in the Athons stock

exchange during 1993-2006 by using accounting information Using panel

data analysis, the paper concludes that the selected set of financial ratios

contain significant information for predicting the cross-section of stock

retums Results indicate that portfolios selected on the basis of financial

rahos produce higher than average returns, suggesting that the emerging Greek market does not fully incorporate accounting information into stock prices and hence it is not semi-strong efficient [8]

Tr the research “International Stock Return Predictability: Evidence from New Statistical Tests” by Amelie Charles, Olivier Dame and Jae H KIM,

they investigate whether stock telus of intornalional markets arc predictable from a range of fundamentals including key financial ratios (dividend-price ratio, dividend yield, eamings-price ratio, dividend-payout ratio), technical indicators (price pressiwe, change in volume), and short-term interest rates

‘They adopt two new alternative testing and estimation methods: the improved augmented regression method and wild bootstrapping of predictive madel

based on a restricled VAR fonn Roth methods take explicit, account of

endogeneity of predictors, providing bias-reduced estimation and improved

14

statistical inference in small samples l'rom monthly data of 16 Asia-Pacific

{including U.S.) and 21 European stock markets from 2000 to 2014, they find

that the financial ratios show weak predicuve ability with small effect sixes

and poor out-of-sample forecasting performances In contrast, the price pressure and interest rale are found to be strong predictors for stock relumm with large cffcct sizes and satisfactory out-of-sample forecasting performance [9]

Tn 2016, the research “Price to Karrongs Multiple and Stock Selection:

Evidence from Malaysian Listed ¥ums” by 1 Shitty, A Masud and Y.M

Alkali investigated the influence of price to eamings PE multiple in predicting the value of growth and value securities The study utilized data of

233 randomly selected listed firms in Malaysia covering the period of 2008-

2013 Pooled Ordinary Least Square CLS was used to estimate the repression however failed to satisfy the post estimation tests ‘Then random effect and

fixed effect models are used to estimate the regression and selection test between the models favoured the random effect model The results reveal a

significant positive relationship between price to earnings nrultiple and the stock retums The implication is that, growth stocks provide higher stack relums compared to the value stock The low R2 suggests that prediction of stock returns is not only determined by price to earnings multiple, but, by combinalion of various factors.[10]

‘The research with the topic “Risk-return through financial ratios as determinants of stock price: a study from ASDAN region” by Kittisak

Jermsittiparsert, Dedy E Ambarita, Leanardus W W Mihardjo, Erlane K

Ghani, helps to analyze the risk-retwmn through financial ratios as determinants of stock price in ASEAN region To address this purpose, business firms from Malaysia, Tndonosia, Thailand and Singapore are

selected with a sample of 10 firms in each state over 2012 to 2016, Multiple

15

regression technique is applied to analyze the relationship between financial ratios and stock prices It is observed that current ratio, quick ratio, assets growth, reium on assels, Tetum on equily, return on capital employed, and price to earning ratio are significant determinants of stock price Although

us study is 4 reasonable addition in existing literature of financial ratios as determinants of stock price However, contribution of the study can be viewed through covering a gap from the context of ASEAN region, which is

under reserachers alientions for stock price delerminants Core lnmitalions ol”

the study covers limited number of sample size and five years of time

duration Besides, some ratios are missing which can be reconsidered in

upcoming studies Those ratios inchide debt ratios, interest payment ratios,

and fixed cost cavered ratios as well.[11]

In the research “Determinants of Stock Return of Nigerian-Listed Firms”

(Olowoniyi A O and Ojenike J.0),based on the result from Hausman test, results of fixed effect model is better Expected growth would significantly

improve stock reusm level of firmus in developing, countries if appropriate

attention is focused on it Efforts at mereasing assets of firms are also

expected to raise the level of listed firms Lower net profit after tax is also

expected to reduce level of stock retum of listed fina Similarly leverage of

firms is also expected to lower the level of stock retum ‘'he finding suggests thal allention needs to be paid Lo the improving growlh and size of the firms

in order to benefit the advantages that could arise from substantial stock

retum The finding of this study may also imply a need to further assess how

tangibility can be improved upon to raise the level of stock retum This will

ensure the correctness of several policies formulated to stabilise financial

base of firms based on either capital structure or stock return [12]

Several studies, explaining the factors affecling slock returns, have been

published both in developed and developing countries In many of these

16

papers, either cross-sectional or time series methods have been applied In the study ‘actors Affecting Stack Retums of Firms Quoted in ISE Market: A

Dynamic Panel” by Sebnem Fr and Bengt Vuran, Dynatnic Panel Data Analysis Methods have been conducted to explain the factors affecting stack Telus of 64 tanufacturing firms that, are continuously quoted im TSE during

the period of 2003-2007 The results indicate that stock performance,

financial structure, activity and profitability ratios can he used to explain the

slogk returns as well as the oil prices, ceonomie growlh, exchange rate,

interest rate, and money supply.[13]

In 2016, a research on the impact of firm’s performance on stook retum was conducted by Maryyam Anvwaar, covering listed companies of FTSE-100

Index London, UK and ten years of data (from 2005-2014) The research

used earings per share, quick ratio, retum on assets, retum an equity, and net

profit margin as independent variables while total stock return was used as dependent variable These independent metrics then were tested to determine whelher they fad great impacls on tolal stock returns The result shows that net profit margin, return on assets has got significant positive impact on stock retums while earnings per share has got significant negative impact on stock relums Moreover, return on cquily and quick ratio show insignificant impact

ơn stock returns [14]

To the same yeur of Maryyam Anwaar’s research, a study by Nurah Musa

Alloa and Ghassan S Obeidat was conducted to examine the relationship

between several financial indicators (profitability & leverage measures) and

stock return in manufacturing companies listed in Amman Stock Exchange

‘They found that four out of five financial ratios (which are ROA, ROE, GPM

and EPS) used have a significant relationship with stock return while all loverage measures used do nol have a signilicant relationship wilh slack

retum As a result, they suggest that financial ratios should be more

17

concentrated on as they have significant relationship with stock retum

Furthermore, they recommend future research should examine this relation in

otber financial ralios, which ean show us #4 significant mllucnce of Cinancial

ratios on stock retum [15]

Tr 2018, Saradhadevi Anandasayanan performed a study aboul slock retim in Sti Lanka This study specifically examines the financial ratios (dividend yield earings per share, and earnings yield), which are considered as the prediclors of slock rolurns in share market The study used the data during the period of 2012-2017 and reveals that only earnings yield has positive impact

on share price of listed companies in Sti Lanka The limitation of the study is that the data conducted in only 5 years, which perhaps is not long period of

time to find out the impacts on stock return Moreover, the research should

use more financial ratios ta examine instead of using only three [16]

‘There was already a research by Nguyen Ngoc Lam student majoring in

and Dr Le Duc Thinh — lecturer of Vietnam National University —

Tnlemational School aboul the key metrics and stock performance of airline companies which focusing on calculating the correlation between the special

metrics used in airlines analysis and their total stock return with data of filicon leading airlines in the world [17]

For investigating the impacts of companies’ performance on stock return, in

2020, Jumiarla T Wayan also has a research in manufacturing companies of

Indonesia The study shows that ROI, EPS and MVA have insignificant

impacts on stock performance while EVA has positive impact and Operational cash flow has negative impact The study also has a limitation, which is that it is only conducted in one area or one country (in Indonesia) 30 perhaps it may not be used as globally representative models to forecast stock

performance Cor the world manufacturing industrics.[18]

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Finally, there is a document refer to other key ratios of manufacturing industries analysis mentioning about three different important key metrics which are Inventory turnover, Revenue per employee ratio, and Retum on

background lor this thesis [19]

1.2 Key metrics

L2L Total Stock Return (TSR)

TSR is the lotal retum of a slock 10 an investor, or the capital gain plus dividends TSR

can be calculated with the following fonnula:

(Ending Price — Beginning Price) + Dividends

Beginning Price

= (Ending adjusted closing price — Beginning adjusted closing price

Beginning adjusted closing price

The stock price used in this study is adjusted close price that has already been included

the dividend The stock price is obtained [rom Yahoo Finance In this thesis, TSR1 1s

calculated according to the fiscal year stated in the financial statement I'SR2 is calculated according 1o the financial stalement release date

1.2.2 Featured metrics

1 Inventory turnover helps to measure the effectiveness of a firm’s manufacturing

process This ratio indicates how many times a company’s inventory are sold and

replaced over a specific period of time {t is useful for a company to make devisions about their pricing, manufacturing and also marketing,

Cost of goods sold Average balance in inventory

2 Revenue per employee (RPE) helps to measure the revenue generated by each

Inventory turnover =

cinployce of the company on average, Revenues per employee rnlio is a usclul tool for

19

manufacturing industries where human capital is more important than physical capital for generating revenue In this research, RPE unit is millions USD per employee

Total revenues

RPE = ——— _

Number of employees generate the revenue

3 Return on capital employed (ROCE) is a financial ratio which helps to measure a

company’s profitability and the cificiency with which its capital is used Since the ratio gauges how well a company generates profits from its capital, it is commonly used by

investors while considering for suitable investment candidates

EBIT

ROCE = ca Tompbyed

where

EBIT — Eamings betiore interest and tax

Capital employed = Total assets — Current liabilities

4 Return on net assets (RONA) is used to show how well a organization utilizes those assets to generate eamings The high retum on net assets means the use of the firm’s assets is maximized by the management Return on net assets is also used to assess how well a company is performing compared to others in its industry

Net income

RONA = eee

Fixed assets + (Current Assets — Current Liabilities)

5 Cash flow do Capital Expenditure (CP/Capex) is a ratio thal measures a company’s ability to acquire long-term using free cash flow he ratio will often fluctuate as businesses go through cycles of large and small capital expenditures The higher the ratio is, the more suflivient capital the firm has to fund operations

Cash flow from operations

There are many financial ratios can be used for making decisions by investors in

companics These ratios are typically divided into four categories: Profitability ratios,

liquidity ratios, solvency ratios and valuation ratios Hence, for four types of common

financial ratios, I chose the representatives as follow:

6 Gross profit margin is one of the commonly used profitability ratios to measure the degrec to which a firm activity makes moncy It shows what percentage of sales has tumed into profits Gross profit margin is considered to be an indicator of an organization’ financial health, and growth potential

Profit Margin = ————_—_

Net sales

7 Revenue growth shows sales imcreases/decreases aver time This a tool to moasure

how fast a business is expanding It helps to illustrate trends in order to gauge revenue

growlh overtime

Revenue of current year — Revenue of previous year

Revenue of previous year

8 Earnings per share growth (EPS growth) is the peroenlage change in normalized earnings per share over the previous 12 months period to the last year end It helps to illustrate the rate at which a company has grown its profitability

EPS of current year — EPS of previous year

EPS growth = EPS of previous year where:

Prefered Starks End —of—Periad Common Shares Outstanding

Net Incat

Earnings per share (EPS) =

9 Quick ratio is 4 financial ralio winch helps to measure a company’s ability to cover its short-term obligation with its most liquid assets Since it indicates a company’s

capacily Lo pay ils current habihties without selling ils invenlory or get additional

financing, the higher the ratio results, the better a company’s liquidity and finance

10, The debt-to-cquity (D/E) ratio helps to measure the degree to which a company is

financing its operations through liabilities versus wholly-owned funds Moreover, it

21

reflects the shareholder ability of equity to cover all outstanding debt in the event of a business downturn

Total liabilities

D/E ratio = Total shareholder equity

2 Research questions, methodology and scope

2.1, Research questions

— What metrics have a positive/negative linear relationship with TSR for each manufacturing companies?

— Find out a linear multiple regression model between TSR and key metrics when

considering cach analyzed company as a sample

‘The data had been collected from the financial statement of 20 among the 100

largest manufacturing companies in the world in which 9 companies are from the US, 1 from UK, 6 from Europe, and 4 from Asia Some metrics are already been calculated

by the companies in their financial report, however, I only use the metrics which are

calculated by these formulas mentioned above for consensus purpose TSR1 is

calculated according to the fiscal ycar stated im the financial statement TSR2 is calculated according to the financial statement release date [11]

The correlations between both TSR and key molrics for cach company are calculated using the metrics in the original currency used in the financial statement so that the correlations are nol affected by the exchange rale When crealing the multiple

regression model, all thc metrics including currency are converted into USD using the

exchange rate stated in the financial statement or the exchange rate at fiscal year ended [12]

2.3 Scope of research

To cover the leading manufacturing companies all aver the world, we chose ta analyze ihe data of 20 among 100 largesl manulacturmg in the world according to Fortune Global 500 listing of the top manufacturing companics Examining the data of manufacturing: companies from various regions will provide an averview of how investors all over the world reaol to the key metrics [13] Twenty companies include

— Nine (09) companies are from the US

— One (01) company from the UK

— Six (06) companies from Europe

Four (04) companies from Asia

Some companies do not have enough data for the period of 2010-2019 to collect,

others do nol present the information needed to calculate the featured metrics

tờ 3

Chapter 3: Main results

Figure 1 Average TSR1 and TSR2 of 20 manufacturing companies 2010-2019

Overall, the average total stock return of one company with TSR1 calculation is

approximately equal to the average total stock return of that company with TSR2 However, there are big discrepancies between TSR1 and TSR2 in Magna, Volkswagen,

Sony and Hitachi The reasons for these big gaps may be because investors only care much about the public date of financial statement to invest instead of the year-end or

fiscal year of these companies.

of 2010-2019 is approximately 6.8% IBM is the company having the highest average

inventory turnover at 23.53, Following the IBM, Ford and Magna ranked the second

and third largest average inventory turnover at 15.49 and 11.19 respectively Toyota,

Pepsico, Bunge, Nissan, and Sony also have the average inventory turnover being at more than 7% and exceeding the industry average The average inventory turnover of

these companies is quite high, which indicates that IBM, Ford and Magna have quick

sales and have no stagnant inventory in the business Other remaining companies have

lower average inventory tumover than the industry average, especially

GlaxoSmithKline, Boeing, and Sanofi are these companies having lowest average

inventory tumover at 1.94, 1.73 and 1.7 respectively.

Figure 3 Average Revenue growth of 20 manufacturing companies 2010-2019

Magna and Volkaswagen have the highest average revenue growth during the

period of 2010-2019 both at nearly 10% Intel and Caterpillar also ranked the third and

fourth place with 7.81% and 6.97% In contrast, General Electric, IBM and Hitachi

experience negative average revenue growth as their revenues decrease significantly in

some years between 2010 and 2019

The average RPE of Bunge is outstanding from other companies at 1.54,

following the Bungae are Ford, Nissan and Toyota that share the second, third and

fourth largest average RPE at 0.79, 0.75 and 0.71 respectively The higher the RPE is,

the more efficient technologies with greater capabilities is Meanwhile, Magna, IBM,

Hitachi and Pepsico are in the lowest companies average RPE ratios, ArcelorMittal has

the highest average EPSG at approximately 157% while Sony is the second largest

average EPSG at approximately 148% Hitachi and General Electric, whose average

revenue growth are among the smallest, are also two out of three having the smallest average earings per share growth Caterpillar is the last companies which has the

smallest average earnings per share growth as the company experience a great shift in

2017 when the ESP change significantly

=

mek ——D/E

Figure 5 Average QR and D/E of 20 manufacturing companies 2010-2019

Ford is the company having the largest average quick ratio at 2.81 during 10

years periods General Electric and Intel share the second and third place with the

average quick ratio value at 2.05 and 1.76 respectively With high quick ratio, Ford,

General Electric and Intel have high ability to fulfil their liabilities and have good financial health Meanwhile, Boeing and ArcelorMittal are these companies which

have the smallest average quick ratio at 0.39 and 0.53 respectively The low average

quick ratio means that these companies are likely to struggle with paying debts

Although Ford owns the highest average quick ratio, it is the company which has the

lowest and negative average debt-to-equity ratio due to its negative shareholder's equity in 2010

100

Figure 6 Average CF/Capex of 20 manufaeturing companies 2010-2019

There are three companies which are ArcelorMittal, Bunge and Volkaswagen

have the lowest average CF/Capex at 0.32, 0.48 and 0.78 respectively The CF/Capex

measures ability to acquire long-term assets using free cash-flow, therefore,

ArcelorMittal, Bunge and Volkaswagen are companies having low capability of

sufficient capital to fund operations Nestle seems to be successful in managing their

long-term assets as the company has the highest average CF/Capex at nearly 8.6.

Figure 7 Average Gross profit margin of 20 manufacturing companies 2010-2019

The average Gross profit margin of Sanofi, Johnson & Johnson, Novartis and

GlaxoSmithKline are nearly equal at 0.69, these companies all rank in top by average

Gross profit margin Meanwhile, ArcelorMittal and Bunge are these companies which

have the lowest average Gross profit margin at less than 0.1

Figure 8 Average ROCE and RONA of 20 manufacturing companies 2010-2019

The largest average ROCE belongs to Intel and IBL and more than 0.2, which may result from high return of both companies General Electric has the largest average

29

RONA during 10 years period from 2010 to 2019, ArcelorMittal has the lowest average RONA and ROCE, The reason leads to this may be because ArcelorMittal has low return, as 4 resull, both ROCE and RONA are quite low

2 Analyzing the correlation of metrics with TSR

2.1, Summary table of correlation between featured metrics and TSR1

jon | 0.06513 -00081 0036/23 0.196 = 03251

1BM Corrdation

P-value Corrdation | 0.02984 0.34901 0.0426 -435 0.07536 PepdCa

P valne Tata Correlation | 0.03254 0.15071 0.0925 -0.1221 -0.2208

P-value

Hs nsnieicpa- | CôrrdaHon |069812 0.4737 9.32557 0.36514 0.65903

GlaxaSmlthKltine P-value 00134 00833 0.0191

Correlation | 0.04827 015528 0.1457 -02148 0.18811 Magna

P-valne Corrdation | 0.07015 004236 044122 0430395 -0.0746 Nestle P value

correlation of -0.457 and p-value of 0.0921 As a result, there is cnough evidence to

support linear relationship between these two metrics

For Bunge, RPE js the only featured metric that has positive linear corelation with TSRI al 0.513 with the p-value of 0.06, so there is enough evidenee to conclude that there is a linear relationship between RPE and TSR1 Other featured metrics do not have any effect on TSR1

In the case of Johnson & Johnson, there is no featured metrics has relationship

wilh TSR1

Tn Bocing, there is only one (oalured metric which is CF/Capex has positive relationship with TSR1 The correlation is 0.548 and the p-value is 0.05, which shows

enough evidence to support the linear relationship

Tn Caterpillar case, there arc RPE and RONA which have negative relationship with I'SR1 as its correlations are -0,52 and -0.512 respectively The p-value being, 0.061 and 0.065 shows that there is enough evidence to support the linear relationship

For General Electric company, there are four cut of five featured metrics have significant impacts on TSR including Inventery turnover, Reverme per employ ralio,

Return on capital employed and Cl'/Capex ‘They positively correlate to ‘'SR1 with the