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Factors that Impact Firms Profitability

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Factors that Impact Firms Profitability

  1. 1. 1 FACTORS THAT IMPACT FIRM’S PROFITABILITY: EVIDENCE FROM EUROPEAN BIOTECH STUDENT: MARIJA NIKOLIKJ MSC. IN BUSINESS ADMINISTRATION, MAJOR IN BUSINESS DEVELOPMENT AND PROMOTION MASTER THESIS DEFENCE June 21st, 2016
  2. 2. 2 OUTLINE BACKGROUND BIOTECH INDUSTRY RESEARCH QUESTION AND HYPOTHESIS METHODOLOGY RESULTS DISCUSSION CONCLUSION QUESTIONS LITERATURE REVIEW LIMITATIONS 1 2 3 4 5 6 7 8 9 10
  3. 3. 3BACKGROUND STATEMENT OF THE PROBLEM • To explore the existing findings and conduct an econometric research in order to draw a conclusion on the factors that influence profitability in the European biotech companies; • To bridge the gap between the academic and the industry research on this topic. There is a very limited amount of research, on factors that influence profitability, in particular in the European Biotech sector; PURPOSE OF THIS STUDY
  4. 4. 4 MAIN CHARACTERISTICS OF BIOTECH INDUSTRY BIOTECH INDUSTRY Highly regulated; Knowledge intensive; Long time frame to develop a new product; Highly capital-intensive; Ethical clearing is essential for human /animal testing; Intelectual property essential for success; Collaboration and alliences with external partners (Universities, other biotech companies etc.); Capital raising is essential.
  5. 5. 5 Literature Review
  6. 6. 6 No. Authors Positive Relationship Europe Biotech Industry 1 Branch (1974) 2 Chiou and Lee (2011) 3 Coad and Rao (2006) 4 Cozza, Malerba, Mancusi, Perani and Vezzuli (2012) 5 Del Monte and Papagni (2002) 6 Geroski, Machin and Van Reenen (1993) 7 Grabowski, Vernon and DiMasi (2002) 8 Hall and Bagchi-Sen (2002) 9 Jefferson, Huanmao, Xiaojing and Xiaoyun (2006) 10 Mank and Nystrom (2001) 11 Morbey and Reitner (1990) 12 Yang, Chiao and Kuo (2010) 13 Nunes and Serrasquiero (2014) LITERATURE REVIEW
  7. 7. 7 Hypothesis and Research Question
  8. 8. 8 HYPOTHESIS AND RESEARCH QUESITON Hypothesis 1a: R&D expenses have a positive effect on firm`s profitability in the biotech industry. ? Does firm`s profitability in the biotech sector increase with more investment in the R&D?
  9. 9. 9 METHODOLOGY
  10. 10. 10 Defining variables, based on academic literature Data collection from ORBIS (Bureau Van Dijk) Data analysis in SPSS Defining the econometric model Data re- adjustment in Excel (variables, years, winsorising) Results interpretation STEPS OF CONDUCTING THE RESEARCH 1 2 3 4 5 6
  11. 11. 11 DEFINING VARIABLES Profitability PROF i,t= EBIT i,t / Total Assets i,t (Pattitoni, Petracci and Spisni, 2014, p. 6); R&D Expenses R&D EXP i,t= R&D expenses i,t / Total Assets i,t (Nunes, Serrasqueiro, 2014, p.53); Outsourcing dummy variable: „1” if the company outsources and “0” if it does not outsource (Ohnemus, 2007, p.9) Firm’s Size SIZE i,t=log Sales i,t (Nunes, Serrasqueiro, 2014, p.53);
  12. 12. 12 DEFINING VARIABLES (cont.) Firm’s Age AGE i,t= log of number of years of firm`s existence (Nunes, Serrasqueiro, 2014, p.53); Firm’s Liquidity LIQ i,t = Total Current Assets i,t / Total Current Liabilities i,t (Nunes, Serrasqueiro, 2014, p.53); Long Term Debt LLEV i,t=Long-term debt i,t / Total Assets i,t (Nunes, Serrasqueiro, 2014, p.53).
  13. 13. 13 DEFINING VARIABLES (cont.) Country of firm’s origin UK is the reference country; Countries are coded as follows: COUNTRY= coded as “1” if country is France, if other country “0”; coded as “1” if country is Germany, if other country “0”; coded as “1” if country is Sweden, if other country “0”; coded as “1” if country is Switzerland, if other country “0”; coded as “0” if country is UK, if other country “0”; (Field, 2013, p.420)
  14. 14. 14 METHODOLOGY THE ECONOMETRIC MODEL 𝑷𝑹𝑶𝑭 𝜾, 𝒕 = 𝛃𝟎 𝑹&𝑫 𝑬𝑿𝑷 𝟎 𝑶𝑼𝑻𝑺𝑶𝑼𝑹𝑪𝑬 𝛃𝟏 𝑺𝑰𝒁𝑬𝜾, 𝝉 𝛃𝟐 𝑨𝑮𝑬,   𝛃𝟑 𝑳𝑰𝑸,   𝛃𝟒 𝑳𝑳𝑬𝑽,   𝟏𝑪𝑶𝑼𝑵𝑻𝑹𝒀
  15. 15. 15ORBIS DATA COLLECTION 12 COUNTRIES BY NO. OF PIPELINES 1. UK 2. SWITZERLAND 3. GERMANY 4. FRANCE 5. SWEDEN 11 NO. OF COMPANIES BY COUNTRY REPRES. IN THE SAMPLE 12 1 2 4 11
  16. 16. 16 8229 ORBIS DATA COLLECTION DATA SET OF COMPANIES OBTAINED AFTER INSERTING ALL CRITERIAS PRELIMINARY DATA SET Total number of companies obtained, based on geographic region and industry classification (NACE Rev.2) 8229 30 INITIAL NO. OF OBSERVATIONS 300 FINAL NO. OF OBSERVATIONS AFTER DEDUCTING OBERVATIONS WITH MISSING VALUES 176 Period for data collection : Year 2006-2015
  17. 17. 17 DATA ANALYSIS
  18. 18. 18 ASSUMPTIONS OF MULTIPLE LINEAR REGRESSION LINEARITY Weak positive and non-linear relationship between Profitability and R&D Expenses; MULTICOLLINEARITY Pearson Correlations test shows values below 0.9, in addition VIF values are below 10; INDEPENDENCE OF ERRORS Durbin-Watson is 2.191 and falls in the range of 1 and 3;
  19. 19. 19 ASSUMPTIONS OF MULTIPLE LINEAR REGRESSION (cont.) ASSUMPTION OF HOMOSCEDASTICITY AND LINEARITY OF ERRORS Scatterplot shows the data funnel out which is a sign of heteroscedasticity, showing an increasing variance across the residuals; ASSUMPTION OF NORMALLY DISTRIBUTED ERRORS Histogram shows a normal distribution of residuals, with a slight level of leptokurtosis; Central Limit Theorem; UNUSUAL CASES Cook’s distance is .177, below 1.
  20. 20. 20 RESULTS
  21. 21. 21 REGRESSION RESULTS MODEL SUMMARY • R 2 =.471, adj. R2=.446, F=18.590, p=.000. • 47.1% of the variability in PROFi,t, is explained by the independent variables in the model; • Model is a moderate predictor of the variability in the dependent variable; • Adjusted R-square of .446, shows that if the model was derived from a population instead of a sample it would account for approximately 2.5 % less variation in the outcome.
  22. 22. 22 REGRESSION RESULTS (cont.) ANOVA • Model predicted PROFi,t, F (8,167) =18.590, p = 0.000; • The model is a significant fit of the data overall.
  23. 23. 23 REGRESSION RESULTS (cont.) B Beta t Sig. (Constant) -.587 -6.995 .000 RDEXP i,t .115 .067 1.124 .263 SIZE i,t .140 .644 9.677 .000 AGE i,t -.098 -.148 -2.500 .013 LIQ i,t .004 .063 1.031 .304 LLEV i,t -.425 -.256 -4.361 .000 FranceDummy -.056 -.069 -1.087 .279 GermanyDummy .170 .187 3.050 .003 SwedenDummy .098 .143 2.203 .029 PROFi,t=-0.587 + (0.115 RDEXPi,t) + (0.140SIZEi,t) + (- 0.098 AGEi,t)+ (0.04 LIQi,t) + (-0.425 LLEVi,t) + (- 0.56 FranceDummy) + (0.170 GermanyDummy) + (.098 SwedenDummy).
  24. 24. 24 REGRESSION RESULTS (cont.) • Not enough evidence that R&D expences have positive impact on firm’s profitability; • Based on regression results,I I fail to reject null hypothesis; • The answer to the research question is as follows: “Higher level of R&D investment does not provide a statistically significant relationship with the higher level of profitability”.
  25. 25. 25 LIMITATIONS
  26. 26. 26 LIMITATIONS • NACE Rev.2 industry classification; • ORBIS database does not provide information for the outsourcing activities; • Missing values account for 40% of the total number of observations: • Assumption of linearity is breached; • Assumption of homoscedasticity is breached , could be an indication of possible systematic relationship between the errors in the results; • Time horizon of 10 years, does not capture the time frame to develop a product in the biotech industry;
  27. 27. 27 LIMITATIONS (cont.) • innovators position, market awareness, niche operations, internationalization (Qian and Li, 2003); • market orientation (Appiah-adu and Ranchold, 1998); • firm`s growth, opportunity cost of capital, shareholders commitment level (Pattitoni, Petracci and Spisni, 2015); • financial risk (Golec and Vernon, 2007); • firm`s market share, gearing ratio (Goddard, Tavakoli and Wilson, 2005); • union density, import penetration, industry concentration, real wage inflation (McDonald, 1999) ; • organizational factors (Hansen and Wernerfelt, 1989).
  28. 28. 28 DISCUSSION
  29. 29. 29 DISCUSSION AND RECCOMENDATIONS Recommendation: Future research should incorporate productivity level as part of the model; Relationship between Profitability and R&D Expenses depends on the productivity level (Morbey and Reitner (1990, p.14); R&D expenses are not always good proxy for engagement in R&D and innovation (Cozza et al., 2012, p.1968); Recommendation: Future research should use at least one proxy for R&D expenses (possible combination of qualitative and quatitative research;
  30. 30. 30 DISCUSSION AND RECCOMENDATIONS (cont.) Recommendation: Future research should incorporate a use of dynamic estimators; OLS regressions do no tell the whole story in the relationship between two or more variables (Nunes and Serrasqueiro (2014, p.52); Using R&D expenses of the current year, provides little association with the current year profitability performance Del Monte and Papagni (2003, p.1011); Recommendation: Future research should use a lagged R&D variables in order to better associate the relationship between R&D expenses and profitability;
  31. 31. 31 DISCUSSION AND RECCOMENDATIONS (cont.) The model modestly predicted the dependent variable PROF i,t, meaning that a large part of the variation in the model remains unexplained. Recommendation: Future research should use additional variables to better explain the dependent variable PROFi,t;
  32. 32. 32 CONCLUSION
  33. 33. 33 CONCLUSION The purpose of this research is to provide an evidence of the impact of R&D expenses on firm`s profitability in the European biotech industry; Hypothesis: R&D expenses have positive impact on firm`s profitability, based on the majority of previous academic findings; This research uses multiple linear regression to measure the impact of R&D expenses on firm`s profitability, at the same time controlling for outsourcing, firm`s size, age, liquidity, long-term leverage and country of origin;
  34. 34. 34 CONCLUSION (cont.) The results of this research show that there is not enough evidence that R&D expenses contribute to higher level of profitability. This finding is contrary previously stated findings in a number of studies; In order to better explain the relationship between the R&D and firms profitability, future studies should incorporate both qualitative and quantitative techniques and a use of dynamic estimators, as well as additional measures for R&D in companies.
  35. 35. 35 QUESTIONS?
  36. 36. 36 THANK YOU

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