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Journal of Applied Accounting Research The valuation implications of strategy in R&D-intensive industries Apostolos Ballas, Efthimios Demirakos,

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Article information: To cite this document: Apostolos Ballas, Efthimios Demirakos, (2018) "The valuation implications of strategy in R&Dintensive industries", Journal of Applied Accounting Research, Vol. 19 Issue: 3, pp.365-382, https:// doi.org/10.1108/JAAR-11-2015-0096 Permanent link to this document: https://doi.org/10.1108/JAAR-11-2015-0096 Downloaded on: 27 March 2019, At: 23:02 (PT) References: this document contains references to 58 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 196 times since 2018*

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The valuation implications of strategy in R&D-intensive industries Downloaded by Universitas Tarumanagara, FE Univ Tarumanagara At 23:02 27 March 2019 (PT)

Apostolos Ballas and Efthimios Demirakos Department of Accounting and Finance, Athens University of Economics and Business, Athens, Greece Abstract Purpose – The purpose of this paper is to investigate the valuation impact of firm’s strategic choices in a setting of intangibles-rich industries. Design/methodology/approach – The authors adopt the conceptual framework of Miles and Snow (1978, 2003), which identifies three viable types of firms based on their organizational strategy profiles: defenders, prospectors and analyzers. The authors use a replicable strategy score, whose scoring convention is based on rolling ratings of six ratios for the previous four years (Bentley et al., 2013), to distinguish between prospector and defender strategies. Findings – The authors offer empirical evidence that supports the hypothesis that prospector strategies are positively associated with firm value in the industries of electronics and electrical equipment, pharmaceuticals and biotechnology and technology hardware and equipment. The authors do not find evidence to support such a relationship in the software and computer services sector. Research limitations/implications – The results of this study have significant implications for researchers, who adopt a market-based accounting research framework to examine the characteristics, performance and valuation of firms with different strategic orientation. Practical implications – The findings of this paper are useful to managers, who would like to pursue a value-adding strategy in dynamic industrial environments that are characterized by high levels of innovation, risk and growth. Originality/value – Although previous studies have yielded mixed results with respect to the association between firm’s performance and prospector strategy, the authors identify a set of R&D-intensive industries, where the implementation of the prospector strategy adds significant value to the shareholders’ wealth. Keywords Intangibles, Business strategy, Research and development, Value relevance, Defenders, Prospectors Paper type Research paper

1. Introduction Both practitioners and academics consider the assessment of a firm’s competitive strategy a crucial step in the business analysis and valuation process. In an investor roundtable on the role of fundamental analysis, Michael Mauboussin argues that “there is almost no way to do intelligent valuation work without a good competitive strategy framework […] the longer I’ve been in this business, the clearer it’s become to me that your competitive strategy framework should inform and help shape your valuation approach”[1]. According to standard valuation textbooks, strategy analysis is considered the starting point in every fundamental analysis process and helps the analyst to evaluate a firm’s key performance indicators, value drivers and business risks at a qualitative level (Palepu et al., 2013; Penman, 2013). In this paper, we seek to explore whether capital markets recognize the crucial role that strategy has to play in the current business environment. In pursuit of this objective, we investigate the value relevance of strategy in a set of intangibles-rich industries within the European Union. There is an extended market-based accounting research that explores the value relevance of intangibles, such as R&D expenditures (Lev and Sougiannis, 1996), brand names (Barth et al., 1998), trademarks (Seethamraju, 2003), human capital (Zucker et al., 1998), etc.[2]. We choose to investigate the value relevance of Miles and Snow’s (1978, 2003)

Valuation implications of strategy

365 Received 21 November 2015 Revised 25 September 2016 25 January 2017 12 June 2017 1 September 2017 Accepted 23 September 2017

Journal of Applied Accounting Research Vol. 19 No. 3, 2018 pp. 365-382 © Emerald Publishing Limited 0967-5426 DOI 10.1108/JAAR-11-2015-0096

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strategy classification, which is considered a major contribution to the relevant literature (Hambrick, 2003) and is aligned with other well-established strategy frameworks (Porter, 1980, 1985; Miller and Friesen, 1982; Gupta and Govindarajan, 1984). Although, there is a large strategy literature that examines the link between Miles and Snow’s (1978, 2003) typology and firm performance (e.g. Hambrick, 1983; Zajac and Shortell, 1989; Conant et al., 1990; DeSarbo et al., 2005; Kabanoff and Brown, 2008), its value relevance has not yet been examined. We also differentiate our paper by employing, instead of interviews or surveys, a replicable strategy score, whose estimation is based on publicly available data (Bentley et al., 2013; Ittner et al., 1997). Hence, we believe that our study constitutes a significant addition to the relevant literature, since it is the first empirical paper that examines the value relevance of Miles and Snow’s (1978, 2003) strategy framework in the context of intangibles-rich and R&D-intensive industries. Miles and Snow (1978, 2003) argue that there are three viable types of firms: prospectors, analyzers and defenders. Prospectors focus on product innovation and new market development, while defenders operate in an established product market, which they perceive as stable and unaffected by broader environmental forces. As Miles and Snow (1978, 2003) point out, prospectors are market “definers” instead of market “defenders” and focus on effectiveness instead of efficiency. On the other hand, a defender is more likely to create value in an industry, where “the world of tomorrow is similar to that of today” (Miles and Snow, 2003, p. 47). Analyzers are in the middle of the continuum (defined by prospectors and defenders) and share characteristics of the two extremes. Hambrick (1983), Zajac and Shortell (1989) and DeSarbo et al. (2005) claim that these types of strategic orientation might not be equally successful across different environmental contexts. In our study, we control for industry differences and choose to investigate whether capital markets perceive positively a firm that adopts a prospector, analyzer or defender strategy within the setting of an innovative, intangibles-rich and R&D-intensive sector, such as electronics and electrical equipment, pharmaceuticals and biotechnology, technology hardware and equipment and software and computer services. Firms operating in these industries are characterized by high levels of innovation, great exposure to environmental opportunities and threats, continuous development of new product models and engagement in significant M&A activity. In such a dynamic environment, we expect that prospectors are more likely to add value for their shareholders compared to analyzers or defenders. The high levels of risks (e.g. risk to become obsolete due to technology changes or the adoption of unarticulated strategies) and opportunities (e.g. great profit and growth potential of new products and markets) are explicit if we consider that the total market capitalization of the 15 largest internet firms has increased from $17bn (in December 1995) to $2.4tn (in May 2015). However, at the same time, only one of the firms ranked among the 15 largest internet firms in 1995 is also included in the list of 2015 (Meeker, 2015). In this study, we offer empirical evidence that supports the hypothesis that prospector strategies are positively associated with firm value in the sectors of electronics and electrical equipment, pharmaceuticals and biotechnology and technology hardware and equipment. The remainder of this paper continues as follows. The next section offers an overview of Miles and Snow’s (1978, 2003) strategy framework and reviews the main empirical studies on the relationship between business strategy choice and firm performance. Section 3 presents our data selection process and describes our empirical research design. Section 4 reports and discusses our main empirical results. Section 5 presents our sensitivity analysis tests, while Section 6 offers some concluding remarks. 2. Prior research There are several frameworks that categorize firms based on their strategy profiles and organizational characteristics. Langfield-Smith (1997) identifies and evaluates the following

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strategy classifications: defenders, analyzers, prospectors and reactors (Miles and Snow, 1978); cost leadership vs product differentiation competitive strategies (Porter, 1980, 1985); conservative vs entrepreneurial firms (Miller and Friesen, 1982); and build, hold, harvest and divest strategies (Gupta and Govindarajan, 1984). In this paper, we choose to adopt Miles and Snow’s (1978, 2003) strategy typology for four reasons:

Valuation implications of strategy

(1) It is broadly regarded as a seminal work in the strategy literature. Hambrick (2003) argues that among “the several strategy classification systems introduced over the past 25 years, the Miles and Snow typology has been the most enduring, the most scrutinized, and the most used.”

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(2) Compared to other strategy frameworks, it has a broader scope (Langfield-Smith, 1997), and it explains how firms adapt to market, technological and strategic change (Lynch, 2006; Ketchen, 2003b). This issue is of critical importance for our sampled firms, which operate in rapidly changing environments. (3) Although there are more than 1,100 research studies that refer to Miles and Snow’s model (Ketchen, 2003a), we manage to find a gap in this literature stream. More particularly, the value relevance of this framework, especially in the context of R&Dintensive industries, has not yet been examined. (4) Finally, prior academic research has developed a replicable strategy score to distinguish between firms with different strategic orientation (Bentley et al., 2013; Ittner et al., 1997). While other strategy frameworks have been mostly operationalized through surveys, interviews and questionnaires, we are able to use a strategy construct, whose scoring convention is based on publicly available financial statement and operating data (Bentley et al., 2013). Miles and Snow (1978, 2003) identify four types of firms – three of which represent stable organizations with a consistent response pattern to environmental threats and opportunities. The three viable types of firms define a continuum with defenders and prospectors at the two endpoints and analyzers in the middle, sharing characteristics of the two extremes. According to Miles and Snow (1978, 2003), defenders seek to be leaders in a focused and established market segment, which they perceive as stable and unaffected by other external environmental factors. Their growth is not achieved through acquisitions or the development of new products, but is rather based on further penetration of the existing well-defined product market. Defenders also tend to be more capital intensive and focus on technological efficiency in the purchasing, production and distribution procedures. Their control systems are more centralized, while the core functions in their organizational hierarchy and structure are the production, finance and engineering departments. Defenders enjoy a low employee turnover ratio as they have a workforce that is characterized by limited bargaining power, a small and specialized portfolio of skills, and a formalized set of job responsibilities. On the other hand, based on Miles and Snow (1978, 2003), prospectors’ core competence is the identification and exploitation of new market opportunities through continuous product innovation. A substantial proportion of their products has a short (or moderate) life cycle and is expected to be soon replaced by new or improved products. Prospectors also enjoy high revenue growth rates driven by the development of new product markets or the acquisition of several small companies operating in new niche markets. Prospectors develop multiple flexible technologies to serve both their existing and prospective product mix. Their control systems are decentralized and the core functions in their organizational hierarchy and structure are the marketing, and research and development departments.

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Prospectors are more labor intensive and their employees have a shorter tenure, but a diverse set of skills and competencies that can be applied to any given situation. There is a large literature that explores generic strategic classification frameworks in order to assess the significance of the relationship between competitive strategies and firm performance. Early classic papers that investigate the association between different types of strategic orientation, based on Miles and Snow (1978), and firm performance include: Hambrick (1983), Zajac and Shortell (1989) and Conant et al. (1990). Hambrick (1983) shows that although defenders perform significantly better than prospectors in terms of return on investment and cash flow on investment, prospectors have a significantly greater market share increase compared to defenders in innovative sectors. Zajac and Shortell (1989) focus their interest on the health care sector and find that defenders perform significantly worse than analyzers or prospectors. Conant et al. (1990) find that all three viable strategic types of firms (prospectors, analyzers and defenders) perform equally well and outperform reactors. In the following years, a significant number of studies investigate this relationship of strategy and performance in various contexts. DeSarbo et al. (2005) argue that managers of the best performing firms in the USA, China and Japan adopt strategies that resemble the characteristics of Miles and Snow’s (1978, 2003) three viable types of strategic orientation: prospectors, analyzers and defenders. Garrigós-Simón et al. (2005) show that prospectors, defenders and analyzers are likely to perform well in the hospitality sector, while reactors exhibit relatively worse performance. Pleshko (2006) shows that prospectors have greater market share in the financial services sector compared to the other strategy types. Kabanoff and Brown (2008) find that analyzers perform better than prospectors in terms of return on equity (ROE) and return on assets (ROA). However, in the most innovative sectors, prospectors outperform the other strategy types in terms of the price-to-earnings ratio. Kabanoff and Brown (2008) link this latter result to Hambrick’s (1983) finding on the relative performance of prospectors in innovative sectors. More recently, Asdemir et al. (2013) use a large sample of US firms to investigate whether capital markets place a positive value on a firm’s decision to adopt a cost leadership or product differentiation strategy. They use three ratios to proxy for firm’s strategic orientation on product differentiation: R&D/SALES (research and development expenditures to sales), SG&A/SALES (selling, general and administrative expenses to sales) and SALES/COGS (sales to cost of goods sold). For cost leadership, they employ the ratios of SALES/CAPEX (sales to capital expenditures), SALES/PP&E (sales to property, plant and equipment) and EMPL/ASSETS (employees to assets). Their study is closely related to ours for two reasons: defenders (prospectors) are more likely to follow a cost leadership (product differentiation) competitive strategy, and although the empirical research method to distinguish between the two dimensions of strategic positioning differs from ours, the rationale of some of the employed ratios approximates the one of the variables used by Bentley et al. (2013) to develop their own strategy score[3]. Asdemir et al. (2013) find that capital markets place a greater value on firms pursuing a product differentiation strategy compared to cost leadership. In a follow-up study, Banker et al. (2014) apply the same research methodology with Asdemir et al. (2013) to construct the two strategy variables and find that although firms pursuing differentiation strategy are more likely to enjoy sustainable firm performance, they bear higher business risk compared to firms adopting cost leadership. In recent years, some researchers have adopted Bentley et al. (2013) research methodology to operationalize Miles and Snow’s (1978, 2003) strategy framework in various contexts. Zhang (2016) finds that although defenders outperform both prospectors and analyzers in terms of ROA and ROE, the product market competition plays a moderating role by decreasing this performance gap. Sarac et al. (2014) examine the

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relationship of strategy type and firm performance in Turkey without finding a significant association between different types of strategic orientation and the ROA. Higgins et al. (2015) explore the link between business strategy and tax planning and argue that prospectors are more likely to engage in tax avoidance strategies consistent with the attributes of their strategy type. Some researchers argue that future research on this field should take into account that the performance of each strategy type might be affected by the industry context (Hambrick, 1983; Zajac and Shortell, 1989; DeSarbo et al., 2005). In this paper, we address the above issue by offering updated evidence on the effectiveness of prospector and defender strategies in innovative, R&D-intensive and intangibles-rich sectors within the European Union. Following this rationale, we choose to study the valuation implications of Miles and Snow (1978, 2003) strategy framework in the context of high technology industries, because they constitute unique business environments characterized by fast growth and high uncertainty. Demirakos et al. (2004) and Imam et al. (2008) show that sell-side equity research analysts change their valuation model preferences, when covering firms operating in high technology sectors. McKinsey et al. (2015) claim that the task of valuing high growth firms is challenging. Bartov et al. (2002) state that the valuation of technology firms is a formidable task due to a number of reasons, including: the large investments of high technology firms in R&D; their attempt to change the business environment in which they operate; their incurrence of significant losses; and their high and volatile sales growth rates. Zingales (2000) argues that new firms, such as technology firms, are asset-light, non-vertically integrated and human capital intensive. They also face fierce competition and continuously seek to innovate. Klobucnik and Sievers (2013) summarize the valuation characteristics of high technology growth firms: frequent incurrence of losses; specific characteristics, such as volatility; and high financial distress risk. 3. Data collection and empirical research design Table I presents our sample selection process. We investigate the value relevance of firm’s strategic choices in the context of four intangibles-rich sectors within the European Union: electronics and electrical equipment, pharmaceutical and biotechnology, technology hardware and equipment and software and computer services. We first identify the listed firms that are constituents of these sectors by using the Equity Screening command of Datastream. This process leads to an initial sample of 8,484 firm/year observations for the period 2008–2014. We subsequently exclude firms with less than 100 employees or with non-available number of employees in Datastream. This procedure reduces our sample to 4,214 firm/year observations. The next steps of the data collection process delete observations with missing data and duplicate firm/year observations ( firms with more than one share class). This procedure leads to a final total sample of 1,466 firm/year observations, which comprises 340 observations for electronics and electrical equipment, 346 observations for pharmaceuticals and biotechnology, 339 observations for technology hardware and equipment and 441 observations for software and computer services[4]. Although the evaluation of the effectiveness of a defender strategy can be based on single-period financial performance measures, the benefits of the adoption of a prospector strategy require longer periods to materialize (Ittner et al., 1997). By using firm’s market capitalization as the dependent variable in our regression models we overcome this conundrum, since stock prices reflect the capital markets’ expectations of firms’ future profit and growth potential (Barker, 2001; Penman, 2013). This methodological choice differentiates our study from the prior literature, which examines the link between strategy type and single-period performance metrics.

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Electronics and Pharmaceuticals electrical and equipment biotechnology Total number of European Union listed firms

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Table I. Sample selection process

1,813

1,680

Technology hardware and equipment

Software and computer Total services sample

1,190

3,801

8,484

Less Firms with employees o100 or non-available number of employees (805) (1,085) (525) (1,855) (4,270) Firms with missing data for the estimation of STRATEGY_SCORE (661) (233) (319) (1,496) (2,709) Firms with more than one share class (7) (12) (7) (7) (33) Firms with missing data for the measurement of other variables 0 (4) 0 (2) (6) Total sample 340 346 339 441 1,466 Notes: The Table presents a summary of the sample selection process. We subtract from the initial sample of all European Union listed firms of four R&D-intensive industries over the period 2008–2014: firms with less than 100 employees or with non-available number of employees (in fiscal year 2011); firms with missing data for the estimation of the four-year rolling averages and the measurement of the STRATEGY_SCORE variable; duplicate firm observations in Datastream sector lists (firms with more than one share class); and firms with missing data for the measurement of the remaining variables in the OLS models. All data are from Datastream

Following Ohlson (1995), we can express a firm’s stock price as a function of its earnings per share and book value per share. This standard empirical model has been used extensively to examine the value relevance of accounting numbers (e.g. Collins et al., 1997; Francis and Schipper, 1999; Brown et al., 1999; Barth et al., 2001; Jiang and Stark, 2013; Ciftci et al., 2014). In this paper, we modify the model by incorporating a strategy variable to capture the business strategy typology based on Miles and Snow (1978, 2003): P it ¼ a0 þa1 EPS it þa2 BV PS it þa3 STRATEGY _SCORE it þ

6 X k¼1

a3 þ k Y EAR_kit þ

3 X

a9 þ n I NDU STRY _nit þeit ;

n¼1

where Pit is equal to the unadjusted stock price (UP) of firm i at year end date t[5]; EPSit the equal to net income before extraordinary items and preferred dividend (WC01551) of firm i scaled by its number of shares (NOSH) at year end date t; and BVPSit is equal to common equity (WC03501) of firm i scaled by its number of shares (NOSH) at year end date t. STRATEGY_SCOREit is the variable of interest in our study, which captures the business strategy typology of firm i in year t. The equation also includes year and industry dummy variables. The measurement of STRATEGY_SCORE is based on the methodology of Bentley et al. (2013). STRATEGY_SCORE takes values in the range of 6–30 depending on the quintile rankings of the firms across six financial ratios: RDS_4, EMPS_4, SGR_4, SGAS_4, PPETA_4 and SDEMP_4. RDS_4 is equal to research and development expenditures (WC01201) scaled by net sales (WC01001)[6]. EMPS_4 is equal to the number of employees (WC07011) scaled by net sales (WC01001). SGR_4 is equal to the annual growth rate of net sales (WC01001). SGAS_4 is equal to selling, general and administrative expenses

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(WC01101) scaled by net sales (WC01001). PPETA_4 is equal to net property, plant and equipment (WC02501) scaled by total assets (WC02999). All accounting amounts are converted to EUR currency and all ratios are calculated as equal to rolling averages of the previous four years[7]. SDEMP_4 is equal to the standard deviation of the number of employees (WC07011) over a rolling four-year period. Table II provides a summary of all variable definitions. A firm in the highest (lowest) quintile within the same industry/year based on RDS_4, EMPS_4, SGR_4, SDEMP_4 and SGAS_4 takes the value of 5 (1). A firm in the highest (lowest) quintile within the same industry/year based on PPETA_4 takes the value of 1 (5). The sum of the six individual scores gives the strategy score of the firm. Firms with relatively high (low) strategy scores are considered prospectors (defenders)[8]. In other words, consistent with Miles and Snow (1978, 2003), this framework assumes that prospectors are more likely to invest significantly in research and development; to commit substantial monetary amounts to product distribution, marketing, promotion, advertising and differentiation; to enjoy high sales growth rates; to be more labor intensive; and to experience a high employee turnover ratio. On the other hand, defenders are more likely to invest less in research and development; to be more cost effective by controlling more efficiently their SG&A expenses; to experience moderate sales growth rates; to be more capital intensive; and to experience low employee fluctuations.

Variable

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Definition/measurement

RDS_4

A variable that is equal to research and development expense (WC01201) scaled by net sales (WC01001). It is calculated as equal to a rolling average of the previous four years EMPS_4 A variable that is equal to the number of employees (WC07011) scaled by net sales (WC01001). It is calculated as equal to a rolling average of the previous four years SGR_4 A variable that is equal to the annual growth rate of net sales (WC01001). It is calculated as equal to a rolling average of the previous four years SDEMP_4 A variable that is equal to the standard deviation of the number of employees (WC07011) over a rolling four-year period SGAS_4 A variable that is equal to Selling, General and Administrative Expenses (WC01101) scaled by Net Sales (WC01001). It is calculated as equal to a rolling average of the previous four years PPETA_4 A variable that is equal to net property, plant and equipment (WC02501) scaled by total assets (WC02999). It is calculated as equal to a rolling average of the previous four years STRATEGY_SCORE A variable that takes values in the range of 6–30 depending on the quintile rankings of the firm across six financial ratios within each industry/year. A firm in the highest (lowest) quintile based on RDS_4, EMPS_4, SGR_4, SDEMP_4 or SGAS_4 takes the value of 5 (1). A firm in the highest (lowest) quintile based on PPETA_4 takes the value of 1 (5). The sum of the six individual scores gives the total score of the firm D_PROSPECTOR A variable that takes a value of one if a firm is a prospector, and zero otherwise P A variable that is equal to the unadjusted price (UP) of the stock at the calendar year end date EPS A variable that is equal to net income before extraordinary items and preferred dividend (WC01551) scaled by the number of shares (NOSH) at the calendar year end date BVPS A variable that is equal to common equity (WC03501) scaled by the number of shares (NOSH) at the calendar year end date WC_TA A variable that is equal to the difference between current assets total (WC02201) and current liabilities total (WC03101) scaled by total assets (WC02999) Notes: All data are from Datastream (data types in parentheses). The local currencies have been converted to EUR currency, based on calendar year end date exchange rates

Table II. Variable definitions

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We expect that in a set of intangibles-rich and R&D-intensive industries, firms that employ a prospector strategy are more likely to successfully utilize their core competencies in order to exploit environmental opportunities and cope with environmental threats. As mentioned earlier, these dynamic sectors are characterized by great opportunities and high risks, which are more likely to be exploited and dealt with effectively by a prospector compared to a defender or an analyzer. Hence, we expect that the STRATEGY_SCORE variable will be positively associated with firm value. 4. Empirical results Table III presents summary statistics for the STRATEGY_SCORE variable and the six financial ratios across the sampled sectors. The mean (median) of STRATEGY_SCORE is 17.975 (18.000) for the total sample and does not fluctuate considerably across different industries[9]. Pharmaceuticals and biotechnology is the sector with the highest R&D activity (median RDS_4 ¼ 0.131), the best growth opportunities (median SGR_4 ¼ 0.091), the greatest sales and marketing efforts (median SGAS_4 ¼ 0.490) and the top capital intensity (median PPETA_4 ¼ 0.172). When analyzing a pharmaceutical or a biotechnology firm, two of the most important areas of concern for a financial analyst include the profit and growth potential of the R&D pipeline and the ability of the firm to commercialize this potential through its sales force[10]. In line with our empirical findings, Palepu et al. (2013) claim that two of the forces that drive the robust performance of the pharmaceutical sector are the ability to create new innovative drugs through intensive R&D expenditures, and the strong sales and marketing capability. Furthermore, the high growth opportunities of pharmaceutical firms might be driven not only by organic growth, but also by substantial M&A activity. Indeed, The Economist (2014) states that “few industries have been shaped more by mergers and takeovers than pharmaceuticals.” Labor intensity does not vary substantially across the four industries. Software firms appear to employ a slightly higher number of employees (as a percentage of their revenues) with median EMPS_4 of 0.008. Finally, firms in the electronics and electrical equipment sector experience the greatest fluctuations in their personnel (Median SDEMP_4 ¼ 112 employees). Table IV offers two examples of prospector and defender firms based on the quintile rankings in pharmaceuticals and biotechnology and software and computer services sectors in year 2011. MorphoSys AG (prospector) incurs greater R&D and SG&A expenditures as a percentage of its revenues, enjoys higher sales growth rate and is less capital intensive compared to Augusta Technologie AG (defender). Although Augusta Technologie AG is more profitable, based on average return on equity (ROE_4) and return on assets (ROA_4), during the period 2008–2011, capital markets place a higher value to MorphoSys AG as it is evident by considering the valuation multiples of PE_4 (price-to-earnings ratio), PTBV_4 (price to book value ratio) and EV/EBITDA_4 (enterprise value to earnings before interest, taxes, depreciation and amortization). The remarkable differences in the pricing of the two firms can be explained by the different market expectations on their future growth and profit potential (Barker, 2001; Penman, 2013) and their different strategic orientation[11]. Table V shows a comparison of the six financial ratios across prospectors and defenders. We regard a firm as a prospector (defender) if its STRATEGY_SCORE takes values in the range of 23–30 (6–13). Our sample includes 171 prospectors and 181 defenders[12]. All median differences are statistically significant at the 1 percent level. More particularly, as expected, prospectors have significantly higher (lower) RDS_4, EMPS_4, SGR_4, SDEMP_4 and SGAS_4 (PPETA_4) than defenders. Tables VI–IX show the results of our regression models for each sampled sector with and without year dummy variables[13]. The coefficient estimates are based on pooled OLS

Technology hardware and equipment Mean Median SD

Pharmaceuticals and biotechnology Mean Median SD

Software and computer services Mean Median SD

Total sample Mean Median SD

STRATEGY_SCORE 18.000 18.000 3.487 17.976 18.000 3.784 17.928 18.000 3.794 17.993 18.000 3.820 17.975 18.000 3.727 RDS_4 0.431 0.047 2.883 0.111 0.097 0.092 1.258 0.131 13.009 0.105 0.099 0.085 0.454 0.087 6.481 EMPS_4 0.011 0.006 0.025 0.005 0.004 0.003 0.009 0.005 0.024 0.008 0.008 0.004 0.008 0.006 0.017 SGR_4 0.094 0.069 0.151 0.097 0.078 0.174 0.193 0.091 0.591 0.121 0.084 0.339 0.126 0.081 0.361 SDEMP_4 681 112 2,245 1,160 98 4,091 446 104 932 314 62 910 626 86 2,363 SGAS_4 0.744 0.253 3.703 0.312 0.306 0.138 2.230 0.490 23.962 0.424 0.404 0.190 0.899 0.352 11.789 PPETA_4 0.176 0.136 0.120 0.138 0.081 0.133 0.195 0.172 0.143 0.081 0.045 0.095 0.143 0.099 0.130 Notes: The table presents mean, median and standard deviation values of the STRATEGY_SCORE variable and the six financial ratios (RDS_4, EMPS_4, SGR_4, SDEMP_4, SGAS_4 and PPETA_4) for the four examined sectors and the total sample. For variable definitions, see Table II

Electronics and electrical equipment Mean Median SD

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Table III. Descriptive statistics

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Table IV. Examples of prospectors and defenders

MorphoSys AG: prospector – pharmaceuticals and biotechnology – Germany RDS_4 EMPS_4 SGR_4 SDEMP_4 SGAS_4 PPETA_4 STRATEGY_SCORE 0.439 0.005 0.130 57.517 0.699 0.025 24 MV_2011 ROE_4 ROA_4 PTBV_4 PE_4 EV/EBITDA_4 €404m 0.056 0.060 2.265 34.925 14.013 Augusta Technologie AG: defender – software and computer services – Germany RDS_4 EMPS_4 SGR_4 SDEMP_4 SGAS_4 PPETA_4 STRATEGY_SCORE 0.057 0.005 −0.025 59.735 0.283 0.075 11 MV_2011 ROE_4 ROA_4 PTBV_4 PE_4 EV/EBITDA_4 €118m 0.077 0.090 0.855 10.025 5.463 Notes: The table presents two examples of prospector (MorphoSys AG) and defender (Augusta Technologie AG) firms based on quintile rankings of pharmaceuticals and biotechnology and software and computer services sectors in year 2011. MV_2011 is the market capitalization (MV ) of the firm at the end of year 2011. ROE_4 is the average return on equity over the previous four years; calculated as equal to net income before extraordinary items and preferred dividends (WC01551) scaled by common equity (WC03501). ROA_4 is the average return on assets over the previous four years; calculated as equal to operating income (WC01250) scaled by total assets (WC02999). PTBV_4 is the average price to book value ratio (PTBV ) over the previous four years. PE_4 is the average price-to-earnings ratio (PE) over the previous four years. EV/EBITDA_4 is the average EV/EBITDA over the previous four years; calculated as equal to market capitalization (MV ) plus Net Debt (WC18199) scaled by earnings before interest, taxes, depreciation and amortization (WC18198). For definitions of the remaining variables, see Table II. All data are from Datastream

Median values Prospectors (n ¼ 171) Defenders (n ¼ 181)

Table V. Prospectors vs defenders

Test for median differences |z-statistic| p-value

RDS_4 0.205 0.018 14.533 0.000 EMPS_4 0.007 0.005 7.265 0.000 SGR_4 0.174 0.017 12.352 0.000 SDEMP_4 112 42 6.238 0.000 SGAS_4 0.610 0.177 15.474 0.000 PPETA_4 0.045 0.156 9.851 0.000 Notes: The table presents median values of the six financial ratios (RDS_4, EMPS_4, SGR_4, SDEMP_4, SGAS_4 and PPETA_4) for the groups of 171 prospectors and 181 defenders. We regard a firm as prospector (defender) if its STRATEGY_SCORE takes values in the range of 23–30 (6–13). The table also reports the results of tests for differences in the median (Wilcoxon rank-sum/Mann-Whitney test) of the six financial ratios across the two groups of firms. For variable definitions, see Table II

models with Huber/White robust standard errors. In all cases, the explanatory power of the models increases with the addition of the year dummy variables, but the results with respect to the significance of our independent variables remain qualitatively similar. The coefficient on STRATEGY_SCORE is positive and statistically significant in the sectors of electronics and electrical equipment (0.382, p-value ¼ 0.002), pharmaceuticals and biotechnology (0.470, p-value ¼ 0.021) and technology hardware and equipment (0.303, p-value ¼ 0.000). These results indicate that there is a strong positive association between the implementation of a prospector strategy and firm’s stock price. In other words, the decision of a firm’s management to adopt the strategic orientation of prospector has a positive impact on its value in the aforementioned three sectors. These empirical findings extend previous studies that examine the association between Miles and Snow’s (1978, 2003) strategy typology and firm performance (Hambrick, 1983; Zajac and Shortell, 1989; Conant et al., 1990; DeSarbo et al., 2005; Kabanoff and Brown, 2008).

P

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Independent variable

Coefficient

P p-value

Coefficient

p-value

EPS 2.995 0.000 3.050 0.000 BVPS 0.906 0.000 0.897 0.000 STRATEGY_SCORE 0.387 0.002 0.382 0.002 Intercept −3.002 0.235 −0.670 0.814 Year dummy variables No Yes Number of obs. 340 340 F-statistic 53.99 24.65 p-value 0.000 0.000 R2 0.6546 0.6815 Notes: The table reports the results of OLS regressions of P (firm’s stock price) on EPS (earnings per share), BVPS (book value per share) and STRATEGY_SCORE with and without year dummy variables. The sample consists of 340 observations of firms operating in the electronics and electrical equipment sector within the European Union during the period 2008–2014. Two-tailed probability values (in italics) are calculated based on robust Huber/White standard errors. For variable definitions, see Table II

P Independent variable

Coefficient

Coefficient

P Coefficient

375

Table VI. Value relevance of strategy in electronics and electrical equipment

P p-value

p-value

EPS 7.234 0.000 7.199 0.000 BVPS 0.233 0.039 0.239 0.034 STRATEGY_SCORE 0.476 0.020 0.470 0.021 Intercept 1.806 0.641 7.147 0.118 Year dummy variables No Yes Number of obs. 346 346 F-statistic 47.67 17.14 p-value 0.000 0.000 R2 0.6786 0.6897 Notes: The table reports the results of OLS regressions of P (firm’s stock price) on EPS (earnings per share), BVPS (book value per share) and STRATEGY_SCORE with and without year dummy variables. The sample consists of 346 observations of firms operating in the pharmaceuticals and biotechnology sector within the European Union during the period 2008–2014. Two-tailed probability values (in italics) are calculated based on robust Huber/White standard errors. For variable definitions, see Table II

Independent variable

Valuation implications of strategy

Table VII. Value relevance of strategy in pharmaceuticals and biotechnology

P p-value

Coefficient

p-value

EPS 2.111 0.101 1.988 0.129 BVPS 1.451 0.000 1.456 0.000 STRATEGY_SCORE 0.303 0.000 0.303 0.000 Intercept −3.880 0.015 −0.847 0.721 Year dummy variables No Yes Number of obs. 339 339 F-statistic 26.63 11.29 p-value 0.000 0.000 R2 0.5528 0.570 Notes: The table reports the results of OLS regressions of P (firm’s stock price) on EPS (earnings per share), Table VIII. BVPS (book value per share) and STRATEGY_SCORE with and without year dummy variables. The sample Value relevance of consists of 339 observations of firms operating in the technology hardware and equipment sector within the strategy in technology European Union during the period 2008–2014. Two-tailed probability values (in italics) are calculated based hardware and equipment on robust Huber/White standard errors. For variable definitions, see Table II

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P Independent variable

P

Coefficient

p-value

Coefficient

p-value

EPS 13.062 0.000 13.085 0.000 BVPS 1.283 0.002 1.266 0.001 STRATEGY_SCORE 0.018 0.878 0.017 0.886 Intercept −0.623 0.754 2.471 0.352 376 Year dummy variables No Yes Number of obs. 441 441 F-statistic 89.08 35.29 p-value 0.000 0.000 0.8189 0.8264 R2 Notes: The table reports the results of OLS regressions of P ( firm’s stock price) on EPS (earnings per share), BVPS (book value per share) and STRATEGY_SCORE with and without year dummy variables. The sample Table IX. consists of 441 observations of firms operating in the software and computer services sector within the Value relevance of European Union during the period 2008–2014. Two-tailed probability values (in italics) are calculated based strategy in software and computer services on robust Huber/White standard errors. For variable definitions, see Table II

In the software and computer services sector, although the coefficient on STRATEGY_SCORE has the expected sign, it is not statistically significant (0.017, p-value ¼ 0.886). The difficulty of software firms to protect their innovations from imitating competitors might be a possible explanation for the non-significance of the coefficient on STRATEGY_SCORE in this sector (Salazar-Elena et al., 2015). The coefficients on BVPS and EPS are statistically significant at the 1 or 5 percent level in all model specifications, except for the hardware sector where the coefficient on EPS is positive, but not significant (1.988, p-value ¼ 0.129). Table X presents the results of the OLS model for our total sample with and without industry and year dummy variables. The explanatory power of the full model is satisfactory (R2 ¼ 0.6059). The coefficients on BVPS and EPS are statistically significant at the 1 percent level, while the coefficient on STRATEGY_SCORE is positive and statistically significant at the 10 percent level (0.170, p-value ¼ 0.063) indicating that the adoption of the strategic

P Independent variable

Table X. Value relevance of strategy in R&D-intensive industries

Coefficient

P p-value

Coefficient

p-value

EPS 6.129 0.000 6.022 0.000 BVPS 0.515 0.000 0.505 0.000 STRATEGY_SCORE 0.173 0.061 0.170 0.063 Intercept 2.909 0.113 7.259 0.003 Year dummy variables No Yes Industry dummy variables No Yes Number of obs. 1,466 1,466 F-statistic 56.35 28.09 p-value 0.000 0.000 0.5915 0.6059 R2 Notes: The table reports the results of OLS regressions of P (firm’s stock price) on EPS (earnings per share), BVPS (book value per share) and STRATEGY_SCORE with and without year and industry dummy variables. The sample consists of 1,466 observations of firms operating in four R&D-intensive industries (electronics and electrical equipment, pharmaceuticals and biotechnology, technology hardware and equipment and software and computer services) within the European Union during the period 2008–2014. Two-tailed probability values (in italics) are calculated based on robust Huber/White standard errors. For variable definitions, see Table II

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orientation of prospector creates value for the firm’s shareholders in R&D-intensive industries within the European Union[14]. This empirical result complements Asdemir et al. (2013), who find that the capital markets place a higher premium on US-listed firms following a product differentiation strategy compared to cost leadership strategy. 5. Sensitivity analysis Table XI presents the results of additional sensitivity analysis tests. In our main regression model, we capture the strategic orientation of the firm by using a composite linear continuous STRATEGY_SCORE variable. As a robustness check, we explore the relationship between the strategy type and the valuation of the firm by employing a dichotomous D_PROSPECTOR variable that takes the value of 1 if a firm is classified as prospector, and 0 otherwise. The coefficient on D_RPOSPECTOR is positive and statistical significant at the 1 percent level (2.675, p-value ¼ 0.008) confirming our main empirical findings. As mentioned earlier, firms operating in high growth sectors enjoy great opportunities, but also face a substantial degree of business uncertainty. Banker et al. (2014) find that firms adopting a product differentiation strategy bear greater business risks, while Dechow et al. (2011) and Richardson et al. (2006) document that high growth firms are more likely to be subject of SEC investigations. To control for business and financial reporting risk, we include in our empirical model the WC_TA variable, which is equal to current assets (WC02201) minus current liabilities (WC03101), divided by total assets (WC02999). The WC_TA is one of the ratios that comprise the Altman’s Z-score (Altman, 1968), and is strongly associated not only with financial distress risk, but also with financial statement fraud risk (Zack, 2013). Table XI shows that the coefficient on STRATEGY_SCORE remains positive and statistical significant at the 10 percent level (0.174, p-value ¼ 0.056) after the inclusion of the WC_TA variable in our model. In order to control for the effect of outliers and/or influential observations on our OLS model estimates, we conduct a robust regression, which eliminates observations with Cook’s distance W1 and performs Huber iterations followed by biweight iterations (Stata, 2011; Li, 1985). The coefficient on STRATEGY_SCORE is positive and statistical significant at the 1 percent level (0.163, p-value ¼ 0.000). We also generate qualitatively similar results if we estimate OLS models after excluding observations with Cook’s distance W4/n, where n is the size of our total sample (Bollen and Jackman, 1990).

Independent variable

P Coefficient

p-value

P Coefficient

p-value

P Coefficient

Valuation implications of strategy

377

p-value

EPS 6.059 0.000 6.030 0.000 3.410 0.000 BVPS 0.503 0.000 0.504 0.000 1.041 0.000 STRATEGY_SCORE 0.174 0.056 0.163 0.000 D_PROSPECTOR 2.675 0.008 WC_TA −0.816 0.616 Intercept 9.998 0.000 7.324 0.004 −0.527 0.345 Year dummy variables Yes Yes Yes Industry dummy variables Yes Yes Yes Number of obs. 1,466 1,463 1,466 0.6067 0.6059 – R2 Notes: The table reports the results of two OLS regressions (with robust Huber/White standard errors) and one robust regression model. The sample consists of firms operating in four R&D-intensive industries (electronics and electrical equipment, pharmaceuticals and biotechnology, technology hardware and equipment and software and computer services) within the European Union during the period 2008–2014. For variable definitions, see Table II

Table XI. Sensitivity analysis

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6. Concluding remarks We examine the valuation implications of Miles and Snow’s (1978, 2003) strategy framework in the context of four R&D-intensive industries within the European Union: electronics and electrical equipment, pharmaceuticals and biotechnology, technology hardware and equipment and software and computer services. Following Bentley et al. (2013), we employ a replicable strategy score to capture the strategic orientation of the firm. The estimation of the strategy score is based on relative quintile firm rankings of the following six financial ratios within each industry/year: research and development expenditures to net sales; number of employees to net sales; sales growth rate; standard deviation of employees; selling, general, and administrative expenses to net sales; and net property, plant and equipment to total assets. We offer descriptive evidence on the characteristics of prospectors and defenders across the four examined industries. Consistent with our expectations, our multivariate analysis shows that in three out of four sectors capital markets place a positive value on the firm’s decision to adopt a prospector strategy. We do not find evidence to support such a relationship between firm value and analyzers or defenders. We believe that this paper constitutes a significant addition to the existing literature, since it is the first study that explores the value relevance of Miles and Snow’s (1978, 2003) strategy typology in the context of four intangibles-rich industries within the European Union. Our main finding that firms, adopting a prospector strategy in R&D-intensive sectors, appear to be valued more highly by the stock market should be useful to business executives of companies, operating in such dynamic industrial environments that are characterized by high levels of innovation, risk and growth. Based on our empirical results, the managers of these firms should pursue a prospector type of strategy in order to deal with entrepreneurial, engineering and administrative business issues. They should focus on the constant exploitation of future product and market opportunities, anticipate and create change in their industries, utilize flexible technology to accommodate a rapidly changing portfolio of products and emphasize the importance of the research and development and marketing functions of their firms (Miles and Snow, 1978, 2003). In this paper, we examine the value relevance of strategy in the high technology and pharmaceutical sectors. It would be interesting to explore the valuation implications of this framework in industries with different characteristics. It would also be interesting to investigate the profitability of investment strategies based on the type of strategic orientation of firms. Another promising area of future research is the investigation of the characteristics, performance and valuation of firms that change type of business strategy, e.g., move from the group of analyzers to prospectors or defenders. Finally, it would be worthwhile to investigate any differences in the content of sell-side analysts’ equity research reports for firms that are characterized as prospectors, analyzers or defenders. Acknowledgments The paper has benefited substantially from the comments of two anonymous reviewers and the comments and advice of the Editor, Julia Mundy. The authors acknowledge the comments of participants at the 39th European Accounting Association Annual Congress, the 11th EIASM Interdisciplinary Workshop on Intangibles, Intellectual Capital, and Extra-Financial Information, and the 14th Hellenic Finance and Accounting Association Annual Conference. The authors gratefully acknowledge financial support from the Research Center of the Athens University of Economics and Business (RE: 2252, 2257 and 2459). Notes 1. The investor roundtable with title “From Stock Selection to Portfolio Alpha Generation: The Role of Fundamental Analysis” was sponsored by Columbia University and organized by the Journal of Applied Corporate Finance (Harris, 2006). Michael Mauboussin is currently a Director of Research at

BlueMountain Capital Management. Prior to that, he used to be the Head of Global Financial Strategies at Credit Suisse and the Chief Investment Strategist at Legg Mason Capital Management.

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2. For a comprehensive literature review on intangibles, see Lev (2001); for a collection of research studies on intangibles, see Hand and Lev (2003). 3. Asdemir et al. (2013) conduct confirmatory factor analysis to develop the two strategy variables of cost leadership and product differentiation, while Bentley et al. (2013) rank all firms within an industry, based on each of the following six ratios, in order to construct their strategy metric: Research and Development Expenditures to Net Sales; Number of Employees to Net Sales; Sales Growth Rate; Standard Deviation of Employees; Net Property, Plant and Equipment to Total Assets; and Selling, General, and Administrative Expenses to Net Sales. 4. The majority of the observations in our final sample refer to UK, German and French firms. For example, in sample year 2014, 63 percent of electronic, 51 percent of pharmaceutical, 69 percent of hardware and 78 percent of software firms are based in these three countries. The similar representation of these countries in each of the sampled industries provides descriptive evidence that any differences in the firms’ country-of-origin should not have a material effect on our empirical results. 5. We use the firm’s unadjusted stock price at year end date, instead of its adjusted price, since we deflate earnings and common equity with the firm’s actual number of shares at the same date (year end date). 6. We do not take into account the change in the balance sheet item of Development Costs – Net (WC02504) due to the lack of data in Datastream for the majority of our sampled firms. For example, in the Software and Computer Services sector, the particular Worldscope data type is not available (or missing for at least one year) for more than 90 percent of the firms in the initial sample. 7. Firms in the European Union adopted the international financial reporting standards for the preparation of their financial statements in fiscal year 2005. We calculate ratio averages over a four-year (instead of five-year) rolling period in order to ensure that for the first sample year 2008, STRATEGY_SCORE’s estimation is based on accounting data prepared using uniform accounting standards across the European Union region. 8. We regard a firm as a prospector (defender) if its STRATEGY_SCORE takes values in the range of 23–30 (6–13). 9. This is consistent with our expectations, since the variable’s measurement is based on relative quintile firm rankings within each industry/year. Bentley et al. (2013) report mean, median and standard deviation equal to 18.04, 18.00 and 3.63, respectively. 10. The mean (median) value of RDS_4 for our total sample is equal to 0.454 (0.087). This compares to a mean (median) value of 0.18 (0.00) in Bentley et al. (2013), whose sample includes all US firms. This comparison provides descriptive evidence that the four sampled sectors of our study are R&D-intensive. 11. In 2011, MorphoSys AG (Augusta Technologie AG) had a STRATEGY_SCORE of 24 (11). The average annual sales growth rate over the previous four years of MorphoSys AG (Augusta Technologie AG) was 13 percent (−2.5 percent). 12. The number of firms that are characterized as prospectors or defenders represents 24.01 percent of our total sample. This compares to 13.19 percent in Bentley et al. (2013). 13. The discussion is based on the results of the full model specifications including the dummy variables. 14. Hambrick (2003) stresses the need for further research on the characteristics and performance of analyzers. In order to examine whether the strategic type of analyzers is positively associated with firm value, we replace the independent variable STRATEGY_SCORE with a dummy variable that takes the value of 1 if the firm is analyzer, and 0 otherwise. We regard a firm as analyzer if its STRATEGY_SCORE takes values in the range of 14–22. We re-estimate the

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modified OLS model, which incorporates the analyzer’s dummy variable, for the total sample of 1,466 observations, and the software and computer services sector, where we did not find a positive relationship between prospector strategy and firm value. In both cases, the coefficient on analyzer’s dummy variable is not statistically significant.

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Corresponding author Efthimios Demirakos can be contacted at: [email protected]

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