Оценка эффективности сделок слияний и поглощений для публичных компаний с целью приобретения технологий и знаний тема диссертации и автореферата по ВАК РФ 08.00.10, кандидат наук Очирова Елена Сергеевна

• Очирова Елена Сергеевна
• кандидат наук
• 2022

Оглавление диссертации кандидат наук Очирова Елена Сергеевна

Table of contents

Introduction

Motivation and related research

The objectives of the research

Contribution to research

Brief literature review

Methodology

a.The DEA approach on technological efficiency of M&As

b.Technological efficiency of M&As in Fintech Industry

Main findings

Conclusion

Approbation of results

Appendix 1. Literature review of research on M&As and their efficiency with respect to technology and knowledge acquisitions

1.1. Extended summary

1.1.1. Motives of technological M&As

1.1.2. The assessment of TM&As efficiency

1.1.3. Determinants of TM&As efficiency

1.1.4. Conclusion

1.2. Paper 1. Literature review of mergers and acquisitions with the aim to obtain technology and knowledge

Appendix 2. The impact of R&D expenditure on the efficiency of M&A deals with Hi-Tech companies

2.1. Extended summary

2.1.1. Hypotheses development

2.1.2. Methodology

2.1.3. Data

2.1.4. Empirical Results

2.1.5. Robustness check

2.1.6. Conclusion

2.2. Paper 2. The impact of R&D expenditure on the efficiency of M&A deals with High-Tech companies

Appendix 3. The impact of Fintech M&A on stock returns

3.1. Extended summary

3.1.1. Hypotheses development

3.1.2. Methodology

3.1.3. Data

3.1.4. Empirical Results

3.1.5. Conclusion

3.2. Paper 3. The impact of Fintech M&A on stock returns

Appendix 4. The technological efficiency of M&As

4.1. Hypotheses development

4.2. Methodology

4.3. Data

4.4. Empirical results

4.5. Robustness check

4.6. Conclusion

References

Введение диссертации (часть автореферата) на тему «Оценка эффективности сделок слияний и поглощений для публичных компаний с целью приобретения технологий и знаний»

Introduction Motivation and related research

The development and successful application of technologies may improve and maintain competitive advantages of companies, which in turn, leads to revenue growth (e.g. Cruz-Cazares et al., 2013; Grant, 1996). One of the ways to accelerate R&D processes and innovation activities of companies are mergers and acquisitions (M&A), that provide, through obtaining the necessary knowledge and skills, the development and implementation of technologies or innovations (Hitt et al., 1991). Such transactions may be effective strategic decisions for the long-term growth. Several studies argue that strengthened knowledge base and enhanced technologies that compatible with existing development is one of the main motives for companies to engage in mergers and acquisitions deals (M&As) (e.g. Sirmon et al. 2011; Haleblian et al., 2009; Capron, Hulland, 1999).

Technologically complex industries rely on development and integration of technological capacities, which associated with a high level of uncertainty (Wagner, 2011). For this reason, the M&As in those industries may neutralize risks of internal ineffective development of technologies through external knowledge sources or channels. Such strategy is a primary motive of M&As in technology-intensive industries (Ortega-Argiles et al., 2010; Desyllas, Hughes, 2008). In addition, mergers with technologically advanced target companies may provide vital development opportunities for acquiring companies through the implementation of new knowledge (Hitt et al., 1996).

Studies consider under technological mergers and acquisitions (TM&As or M&As with the aim to obtain technology and knowledge) either deals where technologies and knowledge acquired from the target company are characterized by the presence of non-zero R&D expenses and/or patents (e.g. Ahuja, Katila, 2001; Makri et al., 2010) or deal with companies form hightech sectors (e.g. Cloodt et al., 2006). The observed trend of an increase in the number of mergers and acquisitions in order to acquire technology and knowledge in the global market is associated with the desire of acquiring companies to expand their technological capabilities, using acquisitions in order to accelerate their own modernization and digitalization (PwC, 20211; S&P Global, 20202). In the era of digital transformation, it can be assumed that the acquisition of firms in Information and Communications Technologies (ICT) or Financial

1 PwC - Technology deals insights: 2021 Outlook

2 S&P Global - Fintech M&A 2020 Deal Tracker

Technologies (Fintech) may be more efficient than the acquisition of companies from other industries.

The literature on M&As with the aim to obtain technologies and knowledge investigates various aspects of its effectiveness. Several studies (e.g. Ahuja, Katila, 2001) examine the impact of such transactions on innovation activities of acquiring companies, others (e.g. Bena, Li, 2014) investigate market reaction of investors. Despite theoretical assumptions, empirical findings demonstrate neutral (Prabhu et al., 2005) or negative (Hitt et al., 1991, 1996; Ravenscraft, Scherer, 1987) effect of mergers and acquisitions on the innovation development of companies, as well as mixed reaction of investors on the announcements of M&A deals (e.g. Skvortsova, Krasovitsky, 2018). These ambiguous results may be due to negative impact of merged companies on research and development (R&D) processes (Ranft, Lord, 2002; Haspeslagh, Jemison, 1991), loss of key personnel (Ernst, Vitt, 2000; Ranft, Lord, 2000), low level of technological compatibility, as well as organizational and technological differences between merging companies (Cloodt et al., 2006; Hagedoorn, Duysters, 2002a; Chakrabarti et al., 1994). Majority of studies demonstrate one-dimensional mergers and acquisitions efficiency measurement (King et al, 2020; King et al. 2004). However, these studies do not address technology acquisition. In other words, the impact of technical characteristics of companies on technological efficiency of M&A.

In this study technological efficiency of M&As deals is determined by technological parameters and assessed with data envelopment analysis (DEA). Compared to conventional methods of efficiency assessment, the DEA allows to analyze simultaneously multiple inputs and outputs characteristics without specification of its functional form. The DEA approach was developed by Charnes, Cooper and Rhodes (1978) and used in several studies on relative M&As efficiency (Wanke et al., 2017; Peyrache, 2013; Lozano, Villa, 2010; Liu et al., 2007; Bogetoft, Wang, 2005; Worthington, 2001). This method allows, before transactions, to assess the impact of a number of characteristics of potential target companies on the characteristics of buying companies if such transactions occur. The efficiency of M&As is also assessed by event study, which measures the reaction of investors on acquisition of technology (e.g. Cartwright, Cooper, 2012; Haleblian et al., 2009). Therefore, these two approaches allow to conduct comprehensive analysis of technology efficiency of M&A deals.

The identification of determinants of M&As success is usually based on the evaluation of acquirer's financial results in post-merger period (e.g. DeYoung et al., 2009). In general, studies investigate the change in the financial performance of the company employing

regression analysis. In order to assess the influence of technological characteristics of merging companies on the M&As efficiency, in this study a nonparametric method of data envelopment analysis is considered.

The objectives of the research

The aim of this research is to analyze the technological efficiency of mergers and acquisitions of public companies taking into account the parameters of their technological development.

The object of the research is mergers and acquisitions and their efficiency with respect to technology and knowledge acquisitions in developed and emerging markets.

The subject of the research is the technological efficiency of mergers and acquisitions with the aim to obtain technology and knowledge.

In order to achieve the primary goal of the research, we propose following objectives:

1. Analyze and compare existing approaches to assessment of the efficiency of mergers and acquisitions, including deals with the aim to obtain technology and knowledge;

2. Develop and test the model of efficiency of mergers and acquisitions with respect to obtain technology and knowledge based on research and development expenses, number of patents;

3. Identify determinants of efficiency of mergers and acquisitions for different types of deals: horizontal, conglomerate and vertical.

4. Develop and test the model of short-term and long-term efficiency of mergers and acquisitions of financial technology companies in developed and emerging markets.

5. Identify determinants of short-term and long-term efficiency of M&A of financial technology companies in developed and emerging markets.

6. Develop and test the model of efficiency of mergers and acquisitions with respect to obtain technology and knowledge for ICT companies.

7. Identify determinants of efficiency of mergers and acquisitions of ICT companies.

8. Make key implications about determinants of efficiency of technology-driven mergers and acquisitions with respect to obtain technology and knowledge

Given the goal of the research, the assessment of efficiency of technology-driven mergers and acquisitions of public companies follows two-stage approach. Picture 1 summarizes the research outline. Firstly, the measures of TM&As efficiency are identified. Specifically, separately and combined we investigate the efficiency throughout market and accounting

metrics. Secondly, the determinants of TM&As efficiency are analyzed. The overall scheme of the research is illustrated on Picture 1.

TM&As Efficiency

I

Market measures

Efficiency measures

Accounting mesures

CAR and BHAR (based on Event Study); Market to Book

ROE and ROA

Market and accounting measures (comnbined)

DEA Score

Picture 1. Scheme of the research

Contribution to research

In this research the new assessment methods of technological efficiency of M&As with technological parameters of companies are proposed and analyzed. Specifically, the study contributes by providing DEA approach in assessment of technological efficiency of M&As; investigating technological efficiency of M&As in fintech industry context.

The DEA approach in assessment of technological efficiency of M&As. The

acquisition of external technologies and knowledge are a tool for companies to increase their technological and innovation development. M&As motivated by the potential synergy effect which can lead to strengthen technological and financial performance. However, numerous studies examined technological efficiency of M&As and the results demonstrate either substitution or complementary effects. To study which effect prevail we employ Data Envelopment Analysis (DEA), which may help to address several research gaps:

Firstly, studies on technological efficiency of M&As are based on ratio or regression analyses, both of which have their shortcomings (Harris et al., 2000). Regression analyses only allow to measure the impact of implicitly independent inputs on a single output variable (Thanassoulis, 1993; Donthu, Yoo, 1998). In case of M&A performance the impact may be multi-dimensional and dependent on contextual factors (Homberg et al., 2009; King et al., 2021; Strobl et al., 2022). Additionally, the error term in regression analyses demonstrates inefficiencies and hides its sources (Thanassoulis, 1993). Financial ratio-based analyses are a subject to certain methodological criticisms. In particular, these measures do not consider or reflect the current market value of a company, economic value-maximizing behavior, the input price and the output mix (Thanos, Papadakis; 2012). Moreover, selection of the weights of financial ratios is subjective (Berger and Humphrey, 1992).

By addressing these shortcomings, the DEA approach appear to be superior to financial ratio or regression analyses on technological efficiency of M&As (Broadstock et al., 2020). Firstly, unlike regression the DEA allows to incorporate multiple inputs and outputs (Cooper et al., 2000). Secondly, it constructs a piecewise linear frontier that envelops the most efficient observations (Cook, Seiford, 2009, Donthu, Yoo, 1998). In other words, the DEA offers an efficiency proxy compiled with the economic optimization mechanism of entities' set that transform comparable inputs into comparable outputs (Cook, Seiford, 2009). The DEA by identifying an efficient frontier and the distance of inefficient observations from that frontier can also highlight the sources of inefficiencies (Thanassoulis, 1993). Therefore, the efficiency assessment with DEA provides an overall, objectively determined, numerical score and a

ranking, something that is not available with the other methods. The DEA is a very useful tool to disentangle relationships which would otherwise remain hidden (Donthu, Yoo, 1998). In the settings of M&As, the DEA approach might help to understand how effectively the acquired technological inputs were used to achieve a certain level of M&A performance.

Secondly, several studies employ DEA on M&A efficiency. For instance, Bogetoft and Wang (2005) use labor-related input variables in a public administration setting. Liu Chen and Pai (2007) concentrate on financial indicators for telecom M&As. Rahman et al. (2016) focus on marketing-related input and output variables in measuring the post-merger performance in banks mergers. Wanke et al. (2017) also employed DEA studies on M&As efficiency in banks mergers. Overall, the DEA-based approach is applied in M&A context usually focusing on potential efficiency gains (Lozano, Villa, 2010; Halkos, Tzeremes, 2013; Peyrache, 2013; Lo et al., 2001). However, previous studies did not apply DEA models in the context of technology-driven M&A capturing firms and process specifics. In technology-driven M&As DEA models may allow to distinguish between more and less efficient deals by taking into consideration the differences between the technological characteristics of the targets in relation to the outputs of acquirers. Without a study that focuses on this context, the gap in whether the specific technical characteristics of acquiring companies harm the M&A's efficiency remains. Moreover, most of the studies examined only one type of M&A (e.g. Fresard et al., 2013) or compared two types (e.g. Gugler et al., 2003). There are limited studies that compare all the three types of M&As (Ivashkovskaya et al., 2020, p. 223; Kedia et al., 2011; Fan, Goyal, 2006), especially in technology-driven context. It is essential to address the research question using DEA because it investigates the origin of M&A inefficiencies. In doing so, we contribute to the literature stream on integrating knowledge sources, on research into efficiency (Obradovic et al., 2021), and the debate on complementary and substitutional effects (Caloghirou et al., 2004).

Technological efficiency of M&As in fintech industry context. The main attribute of recent fintech development is that it provides financial inclusion by allowing companies and consumers to be actively engaged in financial markets (Broadstock, 2021). Through access to investments opportunities, fintech inventions and services are expected to be one of the channels for economies and companies to transit into a green financial system and thus it will continue its expansion (Yang et al., 2021). The fintech research are generally very specific and focus on various types of technologies, its applications, and the efficiency of financial services (Wang, Tan, 2021; Wang et al., 2021). Studies and industry experts differentiate at least nine

categories of fintech companies that engage in financing, payment, asset management, insurance (insurtechs), loyalty programs, risk management, exchanges, regulatory technology (regtech), and other business activities (Haddad, Hornuf, 2019; KPMG, 2019). Technologies that fintech companies are usually based on are the artificial intelligence, big data, cloud computing, machine learning, blockchain, and other technologies (Haddad, Hornuf, 2021).

The literature based on the financial innovation in general covers the impact of fintech only on the financial service industry and small-medium enterprises (SMEs) (Haddad, Hornuf, 2021; Abbasi et al., 2021; Sheng, 2021). Research demonstrate that fintech development is positively associated with efficiency of financial industry. It fosters decrease of stock return volatility and the systemic risk exposure of financial institutions (Haddad and Hornuf, 2021). Additionally, fintech startups facilitate adoption of financial services and lead to SMEs' efficiency (Abbasi et al., 2021; Sheng, 2021). Therefore, it assumed that the fintech development may increase the efficiency of companies. Moreover, Haddad and Hornuf (2021) argue that acquisition of fintech companies may benefit financial institutions by providing new customers. However, in the essence, the empirical literature on fintech is still remain scarce and there is no research in the settings of M&As (Abbasi et al., 2021; Hua et al., 2019). To fill in this gap in investigating the impact of fintech M&As on stock returns, we contribute to the emerging literature stream on the financial innovation adoption on efficiency of companies -and, in particular, our study substantiates the sources of fintech effect on M&As, besides we provide instrumental approach of defining the fintech industry as a consolidation of financial and technological companies, and on question of disruptive or fostering nature of fintech (Ferrari, 2016).

Finally, we conclude that on the empirical level fintech acquisitions are foster companies from various industries regardless of technological environment and development. Our findings also have implications for the literature on acquisitions in general. Managerial hubris and agency problems (Roll, 1986; Hayward and Hambrick, 1997), imitation effects (Haunschild, 1993), inappropriate application of learning (Haleblian and Finkelstein, 1999), and underesti- mating the process impediments to post-acquisition integration (Jemison and Sitkin, 1986; Hitt et al., 1996; Singh and Zollo, 1997) are all valid and complementary explanations for why acquisitions consistently fail. The findings of our study suggest an additional explanation, one that is perhaps more sympathetic to managerial motivations and decisions: evaluating all acquisitions on the same performance metric may not be appropriate,

since acquisitions motivated by different objectives may differ in their timing and mode of impact on firm performance.

Theoretical implication of research

The developed models provide more comprehensive view of the efficiency measures of technology-driven mergers and acquisition in comparison to conventional approaches with limited information. Specifically, the proposed models take into account the influence of technological parameters of companies on the performance of mergers and acquisitions, which allows to draw conclusions about the efficiency of these transactions in developed and emerging capital markets and identify the determinants of such efficiency.

Practical implication of research

The proposed models for evaluating the effectiveness of mergers and acquisitions can be used for strategic management decisions. The identified determinants of efficiency allow management to develop strategies for the technological development of companies. The results of the work can be used to make decisions about investing in companies that acquire technology and knowledge as a result of mergers and acquisitions.

Brief literature review

Acquirer motives of M&A deal determine the possible target company, its valuation and the overall value gain (Renneboog, Vansteenkiste, 2019; Porrini, 2004). Several studies assess the relationship between motivation and the effectiveness of mergers and acquisitions (Phalippou et al., 2013; Shleifer, Vishny, 2003; Seth et al., 2002; Berkovitch, Narayanan, 1993; Morck et al., 1990; Roll, 1986). One of the primary reasons for an M&A deal is the potential synergy which may lead to increased competitive advantages (efficiency improvement) (Ivashkovskaya et al., 2020, p. 190; Fidrmuc and Xia, 2019). M&A deals with the aim to obtain synergy effects imply the transfer of intangible and tangible resources of companies (Bhattacharya, Li, 2020; Li, 2013; Phillips, Zhdanov, 2013; Cassiman, Veugelers, 2006).

In the context of increasing attention on technological and innovation development as a source of competitive advantage, the acquisition of external technologies is identified as a key reason for M&As (Bena, Li, 2014; Sevilir, Tian, 2012; Hussinger, 2012; Cloodt et al., 2006; Cassiman et al., 2005; Ahuja, Katila, 2001). At the same time, number of authors highlight and evaluate possible positive effects for the acquiring company that may enhance its technological capabilities (Hussinger, 2010), increase innovation activities (Jo et al., 2016), technological diversification and entrance to the new markets (Hagedoorn, Duysters, 2002b). Therefore,

technologically motivated M&A deals (TM&As) is strategically important with their potential synergy effect due to access to new technologies knowledge which can be crucial in the long run (Stiebale, 2016; Hussinger, 2010; Hagedoorn, Duysters, 2002a).

The M&As efficiency has been studied throughout various perspectives (King et al., 2020). According to Bauer and Matzler (2014) four theoretical approaches can be identified: financial (e.g. Renneboog, Vansteenkiste, 2019; Gubbi et al., 2010; Ivashkovskaya et al., 2009; Agrawal, Jaffe, 2000), strategic management (e.g. Bauer et al., 2016; Cassiman, Veugelers, 2006; Ahuja, Katila, 2001), organization behavior (e.g. Lin, Ho, 2021; Angwin et al., 2016; Kavanagh, Ashkanasy, 2006) and process (e.g. Zollo, Meier, 2008; Haspeslagh, Jemison, 1991; Jemison, Sitkin, 1986). At the same time, there is no consensus in assessing the M&As efficiency, in general (Zollo, Singh, 2004), and with the aim to obtain technologies and knowledge, in particular (Meglio, 2009)

The M&A efficiency examined by accounting measures is generally use the compare the metrics for acquiring and/or target companies before the acquisitions to the same (or weighted average) metrics for some period after the deal or to the sample of control-group (Sudarsanam, 2003). It is assumed that the ratios such as return on assets (ROA) may reflect obtained synergies from acquisitions in the improvement of long-term accounting performance (Hitt et al., 2001; Harrison et al., 2001). Several studies employ various accounting metrics arguing that this can examine different aspects of M&A efficiency - return on assets, return on equity (ROE), return on sales (ROS) and return on investments (ROI) (e.g Cording et al., 2010; Quah, Young, 2005; Corner, Kinicki, 2004). On the other hand, the accounting measures have been criticized for the narrow implication of M&As performance since they provide only past aggregate data which is difficult to isolate from other effects that may have impact on the firm over the years (Chenhall, Langfield-Smith, 2007; Lubatkin, Shrieves, 1986). Thanos and Papapdakis (2012) argue that none of the accounting measures on its own can exclusively reflects the M&As efficiency and multiple performance criteria must be applied.

The stock-market measures of M&A efficiency assumed that the stock price can be used as a direct measure of stockholder value (King et al., 2020). As a measure of short-term stock market performance, the cumulative abnormal returns (CARs) e generally employed and calculated as sum of the abnormal returns over a short event windows around the event. The dominant measure of long-term (starting from 180 days event windows) stock performance is buy-and-hold abnormal returns (BHARs), which is a geometric aggregation of the abnormal returns over the event period (Renneboog, Vansteenkiste, 2019; Barber, Lyon, 1997). Some

studies indicate limitation of the stock-market measures suggesting that the stock price volatility on the acquisition announcement may reflect the expectation of investors on the future prospects of the company, but no the actual economic outcome (Zollo, Meier, 2008). Moreover, by using this metric only M&A of listed companies can be examined (Schoenberg, 2006).

It is evidently the importance of studying M&A performance from a multiple criteria perspective, however, the M&A literature predominantly consists of one-dimensional construct of M&A efficiency measurement (King et al, 2020; King et al. 2004). The application of simultaneous multiple efficiency measures are associated with difficulties. Several studies suggest the use of a nonparametric method for M&A efficiency assessment- data envelopment analysis (DEA) (e.g., Wanke et. al, 2017; Rahman et al., 2016; Liu et al., 2007; Bogetoft, Wang, 2005). The DEA method estimates the relative efficiency for a deal by using a weighted measure of two set of input and output variables. Overall, the DEA-based approach is applied in M&A context usually focusing on potential efficiency gains (Lozano, Villa, 2010; Halkos, Tzeremes, 2013; Peyrache, 2013; Lo et al., 2001). In regards of TM&As efficiency, the DEA has not been employed. In technology-driven M&As DEA models allow to distinguish between more and less efficient deals by taking into consideration the differences between the technological characteristics of the targets in relation to the outputs of acquirers.

In accordance with previous empirical findings, this study is trying to address the efficiency evaluation by employing event study and DEA approach. Specifically, we are arguing that DEA can be applied to assess the effects of the target's R&D and patents on M&A financial performance capturing at the same time the ability of the acquirer to efficiently adopt the obtained knowledge and technologies.

Methodology

In order to examine the efficiency of technologically motivated mergers and acquisitions two-stage approach is implemented. Picture 1 summaries the streams of research. On the first step, we examine the methods of TM&As efficiency estimation. Accordingly, we propose the data envelopment analysis approach of relative efficiency, which allows combining accounting and market measures. Besides, we also examine the conventional method of efficiency estimation using event study which is generally applied in M&As' research.

On the second step, based on the methods of efficiency estimation, the scope of research and data availability, the determinants of efficiency are indicated. Specifically, we focused on

technological aspects of acquiring and target companies that demonstrate technological development level of companies. The specifics of industry and countries are also examined. The multiple robustness checks are conducted in order to provide solid conclusion regarding those specifics that have been examined.

a. The DEA approach on technological efficiency of M&As

In ICT industry

The efficiency of TM&As is estimated using DEA model. We analyze the impact of the technological characteristics, countries and industry specifics on the efficiency of technology acquisition. According to this approach, the DEA score is calculated by resolving optimization problem. We employ the input-oriented CCR DEA model:

V?

. Lr=1 urVrk

ma (1)

s.t.

2q = 1 uryrj

<1 (j = 1

Zf = i Vi Xij

ur > 0 (r = 1, ...,q), Vi > 0 (i = 1, ...,m);

The input-oriented CCR DEA model stated as follows: suppose we have n decision making units DMUj(j = 1, ...,n) (in our case it is M&A deals) with (x1j, ...,xmj) as an input vector and (y^-, —,yqj) as the output vector. Each have their weight vectors (v1,.,vm) and(u1, ...,uq), respectively. Assume that each DMUj uses x¿j amount of input i in order to produce yrj amount of output r, and that the input and output of DMUk(k = 1, ...,n) being evaluated are, respectively, (x1k, ...,xmk) and (y1k, .,yqk), where xik > 0 and yrk > 0. Let

_1

= tur and vi = tvi, where t = (^¿=1 vixik) (Zhang, Li, 2019).

The efficiency score 0j is sought to be maximized, under the constraints that using those

weights on each DMUk(k = 1, ...,n), no efficiency score exceeds one. For a set of n DMUs, the DEA model is solved n times, one for each DMU

The description of constructed inputs and outputs are presented on the Table 1.

Table 1. Description of inputs and outputs for DEA efficiency score of TM&As in ICT industry

_Variables_Descriptions_

_Input variables_

IA i,tar Logarithm of intangible assets of i-th target company one year prior its

acquisition.

R&A expenses over revenue of i-th target company one year prior its acquisition.

iitar Capital expenditure over revenue of i-th target company one year prior

its acquisition.

tar Market to Book ratio i of i-th target company one year prior its

acquisition.

_Output variables_

R&Dinti

i,tar

CAPEXint

MtBti

RevGrowth,

i,acq

ROA

i,acq

Logarithm of revenue growth of i-th acquirer one year after the acquisitions.

Return on assets of i-th acquirer one year after the acquisitions.

In order to assess the impact of R&D and other determinates of the acquiring company on the efficiency of the deals, that have been estimated by DEA method, following specification of the beta-regression model is applied (Ferrari, Cribari-Neto, 2010):

g(l*d =Po+ PiR&Dintiacq + p2lAiAcq + CAPEXintiacq GERDiacq + £i

where,

R&Dintiacq — R&D expenses over revenue of i-th acquirer at the year of acquisitions; I^i,acq — logarithm of intangible assets of i-th acquirer at the year of acquisitions;

(2)

CAPEXint,

i,acq

capital expenditure over revenue of i-th acquirer at the year of acquisitions;

GERDiacq — GERD as a percentage of GDP of of i-th acquirer at the year of acquisitions.

Types of TM&As

The relative efficiency of TM&A deals assessed by employment of DEA approach with consideration of both accounting and market measures. Table 2 presents the description of inputs and outputs that have been used in this research.

Table 2. Description of inputs and outputs for DEA efficiency score of TM&As.

_Variables_Descriptions_

_Input variables_

PatentSi tar Number of patents of i target company for the last available year prior

its acquisition

R&Dinti tar R&D expenses over Sales of i target company for the last available year

prior its acquisition

CAPEXinti tar Capital expenditure over Total Assets of i target company for the last

available year prior its acquisition

MtB tar Market to book ratio of i target company for the last available year prior

its acquisition

_Output variables_

CARiacq Cumulative abnormal return of i acquirer company on the

announcement of acquisitions

ROEi,acq Return on Equity of i acquirer company one year after the acquisitions

MtB i,acq Market to book ratio of i acquirer company one year after the

acquisitions

Three characteristics of an acquirer are employed in the main beta-regression model: number of patents, R&D intensity, and Capital intensity. The dummy variable of cross-border M&A deals were also considered in order to test country specifics. According to the three versions of DEA score three specifications of the main model are considered (Ferrari, Cribari-Neto, 2010):

g(Vi,R&D) = Po + PlR&Dintiacq + ^CAPEXintiacq + P3Int_Dum.rn.yi + £i (3)

g(l*i,pat) = Po + PiLnPatentSiacq + P2CAPEXintiacq + @3lnt_Dummyi + £t (4)

g(Vi,R&D_Pat) =p0+ PiLnPaten.tiAcq + p2R&Dinhacq + P3CAPEXinhacq ( .

+ p4Int_Dummyi + e^

where,

R&Dintiacq — R&D intensity measured by R&D expenses over sales of the i th acquirer at the year of acquisition,

CAPEXintiacq Capital intensity measured by capex over total assets of the i th acquirer at the year of acquisition,

LnPatentsiacq — logarithm of patent count plus 1 of the i th acquirer at the year of acquisition,

Int_Dummyi — cross-border dummy variable (0 - Domestic, 1 - Cross-border) at the year of acquisition.

In addition, we examine the efficiency within types of M&As. For each case of horizontal, vertical, and conglomerate M&As beta regressions on the characteristics of acquirers are applied:

g{Hi, Conglomerates) = Po + PiLnPatentSiAcq + ^R^Dint^ + P3CAPEXintUacq +

P4Int_Dummyi + s^ , g {^Horizontal) = Po + PiLnPatentsUacq + ^R^Dint^ + P3 CAPEXintiacq +

P4Int_Dummyi + s^ , g{f*i,vertical) = Po + PiLnPatentsiAcq + PzR&Dintiacq + P3CAPEXinti,acq +

P4Int_Dummyi + £i (8)

(6) (7)

b. Technological efficiency of M&As in Fintech Industry

To measure the efficiency, we employ event study method, which allows us to assess the reaction of the market to Financial Technology (Fintech) M&As announcement (McKinlay, 1997). In order to do this, we estimate abnormal returns following the deal announcement: cumulative abnormal returns (CARs) and buy-and-hold average abnormal returns (BHARs). For the estimation of CAR and BHAR, the market model, market adjusted model, and mean adjusted return model are used.

In order to identify whether the target company belongs to the Fintech sector, we use primary SIC codes of company related to both the financial and Information Technology (IT) sector at the same time. The following two-digit SIC codes (Standard Industrial Classification) were used for the financial sector:

1. SIC code 60 - Depository Institutions,

2. SIC code 61 - Non-depository Credit Institutions,

3. SIC code 62 - Security and Commodity Brokers, Dealers, Exchanges, and Services,

4. SIC code 63 - Insurance Carriers,

5. SIC code 87 - Engineering, Accounting, Research, Management, and Related Services.

As for IT sector three-digit SIC code 737 "Computer Programming, Data Processing, and other Computer Related Services" is considered.

All acquiring companies were also classifying into fintech and non-fintech companies. We assume that a acquiring company belongs to the fintech industry if it has SIC codes 60-67, 87-89, as well as SIC codes 7371-7374.

To test the effects of the described variables on fintech M&As performance, the following regression model was employed:

CARi = ß0+ ß1MBi + ß2lnTAi + ß3TaXi + ß4Deal Sizet

4

+ p5RDi+p6Industryi + PjDummyj. + s^

]=i

where a description of model variables are presented in the Table 3. Table 3. The List of Determinants of fintech M&As

(9)

Name

Description

Name

Description

Dummy 1

1 - target company related to "Money transfers and trade credit" section,

MBj

Market to book ratio of acquiring company

0 - otherwise

Dummy2 1 - target company related to "Brokers and dealers' services, trading exchange" section, 0 - otherwise

Dummy3 1 - target company related to "Accounting, research and advisory service" section, 0 - other

Dummy4 1 - target company related to "Consumer credit" section, 0 - otherwise

Industryi 1 - target company related to finance sector, 0 - otherwise

InTAi Logarithm of acquiring company's total assets

Deal Sizei The ratio of the transaction value to the market capitalization of the acquiring company

RDi Company expenses for R&D

Taxt Effective tax rate of the acquiring company

Main findings

In this research the new methods of M&As efficiency assessments with technological parameters of companies are proposed and analyzed. Specifically, the study provides several findings:

1. The new model of assessment of the M&As efficiency with consideration of technological parameters of companies, as well as specifics of ICT industry and countries. To obtain DEA estimates, the characteristics of the target companies used as input parameters: intangible assets, intensity of research and development, intensity of capital investments. As for the output parameters, we used characteristics of the acquiring companies: revenue growth and return on assets.

The results of the study showed that technological efficiency of mergers and acquisitions with ICT companies negatively affected by the level of research and development, both acquiring company itself and the country where it is operated. In fact, the technological efficiency of digital technologies obtained from the target company depends on how much the acquiring company requires new knowledge. When acquiring company has a high level of research and development expenses, it will be difficult for that company to acquire technologies that will lead to a sharp increase in competitiveness.

2. We also employ extended DEA approach by focusing on research and development expenses and patents of companies in international and local markets (more than 20 countries) and by TM&As' types (horizontal, vertical and conglomerate). To obtain DEA efficiency, characteristics of the target companies are used as input parameters: number of patents,

intensity of research and development, intensity of capital investments, market value to book ratio. Characteristics of the acquiring companies used as output parameters: cumulated abnormal returns, return on equity in a year after the deal, and market to the book ratio in a year after the deal.

Approbation of the developed model has demonstrated that the negative effect of the size of the technological base of the acquiring company is dominant in technological mergers and acquisitions. In other words, the effectiveness of transactions is reduced if the acquiring company invests significantly in its own research and development and owns a large number of patents. Moreover, for the horizontal type of transactions, with high probability of duplication of technologies and knowledge, such effects are higher. In turn, this may be due to the substitution effect resulting from the low level of technological openness between companies. In addition, the reason of obtained results may also be due to decrease in return on scale of the acquiring company. Another reason for negative effects is motivation in mergers and acquisitions, where a number of companies use deals to eliminate potential competitors and hence the deals may be ineffective.

3. The model for evaluation of the efficiency in mergers and acquisitions of financial technology that reflects the reaction of investors from developed (USA, Canada, France, Germany, Spain, Sweden, Switzerland, Netherlands, UK) and emerging (China and India) markets. An original method of industry identification is proposed. TM&A deals of financial technologies based on the codes of the standard industry classification (SIC). The target company is presumed to have activities in the financial technology industry if it belongs simultaneously to the sectors: information ("programming, data processing and other computer services") and financial (depository institutions; non-depository credit institutions; brokerage and dealer services, stock exchanges and other services; insurance; accounting, advisory services and other related services).

Based on the developed model, a positive cumulative abnormal return was found for acquiring companies' shares that acquire fintech companies in the short term, which may indicate an optimistic investor reaction on the announcement of M&A deals with the financial technology industry. However, in the long term, M&A deals to acquire fintech companies do not add value to the acquiring companies.

4. Determinants of efficiency have been identified: for three types of mergers and acquisitions: horizontal, vertical, and conglomerate; for deals with ICT companies; for deals

with financial technologies companies. Among the most significant determinants are research and development expenses. For example, the acquiring's R&D expenses has a positive effect on the cumulative abnormal return following the announcement of the deal with the fintech company. This factor is also significant for the types of M&As and M&A deals with ICT companies. Another factor that affects the efficiency of M&A deals in fintech companies is the effective tax rate. Several countries set tax preferences for companies in technology industry that may lead to a positive investor reaction to M&A deals with technology companies, which could reduce the tax burden. Among the country's specifics, it was found that M&A deals with financial technology companies bring higher return for acquiring companies from developed countries than for acquiring companies from emerging countries. This may be due to the fact that companies from developed countries operate in environments that allow the use of acquired technologies. Cross-border deals show higher cumulative abnormal returns for acquiring companies from developed countries, indicating a positive investor response to the expansion strategy. On the other hand, for acquiring companies from emerging countries, local deals are more profitable, while international acquisitions may be considered riskier for investors.

Заключение диссертации по теме «Финансы, денежное обращение и кредит», Очирова Елена Сергеевна

Заключение

В исследовании эффективности M&A с ИКТ-компа-ниями применялись оболочечный анализ данных и модель бета-регрессии. Результаты продемонстрировали, что эффективность таких сделок находится в отрицательной зависимости от уровня ИиР как у приобретателя, так и у страны его резиденции. В этом может проявляться эффект замещения технологий [King et al., 2008], обнаруженный в ряде исследований применительно к игрокам сектора ИКТ. Эффективность внедрения цифровых технологий, полученных у компании — объекта поглощения, зависит от мотивированности приобретателя в получении новых знаний подобным образом. Существует вероятность, что при высокой интенсивности ИиР у компании-покупателя приобретаемые технологии окажутся несовместимыми с другими ее уникальными разработками. Таким образом, компаниям с развитой научной базой сложно найти на рынке игрока, приобретение которого обеспечит ощутимый прирост их конкурентоспособности. Сформулированные в статье выводы могут быть использованы не только для принятия инвестиционных решений, но и при разработке стратегий цифро-визации, предполагающих приобретение технологий и знаний через механизм M&A.

Статья подготовлена в рамках Программы фундаментальных исследований Национального исследовательского университета «Высшая школа экономики».

Список литературы диссертационного исследования кандидат наук Очирова Елена Сергеевна, 2022 год

Библиография

Ahuja G., Katila R. (2001) Technological acquisitions and the innovation performance of acquiring firms: A longitudinal study. Strategic Management Journal, 22, 197-220. https://doi.org/10.1002/smj.157

Alam A., Uddin M., Yazdifar H. (2019) Institutional determinants of R&D investment: Evidence from emerging markets. Technological Forecasting and Social Change, 138, 34-44. https://doi.org/10.1016/j.techfore.2018.08.007

Andrade G., Stafford E. (2004) Investigating the economic role of mergers. Journal of Corporate Finance, 10 (1), 1-36. https://doi.org/10.1016/ S0929-1199(02)00023-8

Audretsch D.B., Belitski M. (2020) The role of R&D and knowledge spillovers in innovation and productivity. European Economic Review, 123, 103391. https://doi.org/10.1016/j.euroecorev.2020.103391

Belderbos R., Cassiman B., Faems D., Leten B., Van Looy B. (2014) Co-ownership of intellectual property: Exploring the value-appropriation and value-creation implications of co-patenting with different partners. Research Policy, 43(5), 841-852. https://doi.org/10.1016/j. respol.2013.08.013

Benou G., Madura J. (2005) High-tech acquisitions, firm specific characteristics and the role of investment bank advisors. The Journal of High Technology Management Research, 16(1), 101-120. https://doi.org/10.1016/j.hitech.2005.06.006

Bloningen B.A., Taylor C.T. (2000) R&D intensity and acquisitions in high technology industries: Evidence from the US electronic and electrical equipment industries. Journal of Industrial Economics, 68(1), 47-70. https://www.jstor.org/stable/117483

Bogetoft P., Wang D. (2005) Estimating the Potential Gains from Mergers. Journal of Productivity Analysis, 23, 145-171. https://doi. org/10.1007/s11123-005-1326-7

Onupoea E., ffpaHee K>., c. 31-38

Bravo-Ortega C., Marin A.G. (2011) R&D and Productivity: A Two Way Avenue? World Development, 39(7), 1090-1107. https://doi.

org/10.1016/j.worlddev.2010.11.006 Brown J.R., Fazzari S.M., Petersen B.C. (2009) Financing innovation and growth: Cash flow, external equity, and the 1990s R&D boom. The

Journal of Finance, 64(1), 151-185. https://doi.org/10.1111/j.1540-6261.2008.01431.x Capron L., Hulland J. (1999) Redeployment of brands, sales forces, and general marketing management expertise following horizontal acquisitions: A resource-based view. Journal of Marketing, 63(2), 41-54. https://doi.org/10.1177%2F002224299906300203

Cassiman B., Colombo M.G., Garrone P., Veugelers R. (2005) The impact of M&A on the R&D process: An empirical analysis of the role of technological- and market-relatedness. Research Policy, 34(2), 195-220. https://doi.org/10.1016Zj.respol.2005.01.002

Chakrabarti A., Hauschildt J. Süverkrüp C. (1994) Does it pay to acquire technological firms? R&D Management, 24, 047-056. https://doi.

org/10.1111/j.1467-9310.1994.tb00846.x Chan L., Lakonishok J., Sougiannis T. (2001) The Stock Market Valuation of Research & Development Expenditures. Journal of Finance,

56(6), 2431-2456. https://doi.org/10.1111/0022-1082.00411 Charnes A., Cooper W.W., Rhodes E. (1978) Measuring the efficiency of decision making units. European Journal of Operational Research,

2, 429-444. https://doi.org/10.1016/0377-2217(78)90138-8 Cloodt M., Hagedoorn J., Kranenburg H.V. (2006) Mergers and acquisitions: Their effect on the innovative performance of companies in

high-tech industries. Research Policy, 35(5), 642-654. https://doi.org/10.1016/j.respol.2006.02.007 Cohen W., Levinthal D. (1990) Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128-152. https://doi.org/10.2307/2393553

Desyllas P., Hughes A. (2008) Sourcing technological knowledge through corporate acquisition: Evidence from an international sample of high technology firms. The Journal of High Technology Management Research, 18(2), 157-172. https://doi.org/10.1016/j.hitech.2007.12.003 DeYoung R., Evanoff D.D., Molyneux P. (2009) Mergers and acquisitions of financial institutions: A review of the post-2000 literature.

Journal of Financial Services Research, 36(2-3), 87-110. https://doi.org/10.1007/s10693-009-0066-7 Duysters G., Hagedoorn J. (2001) Do Company Strategies and Stuctures Converge in Global Markets? Evidence from the Computer Industry.

Journal of International Business Studies, 32, 347-356. https://doi.org/10.1057/palgrave.jibs.8490956 Eisfeldt A.L., Papanikolaou D. (2014) The value and ownership of intangible capital. American Economic Review, 104(5), 189-194. DOI: 10.1257/aer.104.5.189

Emrouznejad A., Yang G.L. (2018) A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences, 61, 4-8. https://doi.org/10.1016/j.seps.2017.01.008

Griffith R., Harrison R., Van Reenen J. (2006) How Special Is the Special Relationship? Using the Impact of U.S. R&D on U.K. Firms Spillovers as a Test of Technology Sourcing. American Economic Review, 96, 1859-1875.

Griliches Z. (1958) Research Costs and Social Returns: Hybrid Corn and Related Innovations. Journal of Political Economy, 66(5), 419-431.

https://www.jstor.org/stable/1826669 Griliches Z. (1979) Issues in assessing the contribution of research and development to productivity growth. The Bell Journal of Economics,

10(1): 92-116. https://doi.org/10.2307/3003321. Hagedoorn J., Cloodt M. (2003) Measuring innovative performance: Is there an advantage in using multiple indicators? Research Policy,

32(8), 1365-1379. https://doi.org/10.1016/S0048-7333(02)00137-3 Hagedoorn J. Duysters G. (2002) External Sources of Innovative Capabilities: The Preferences for Strategic Alliances or Mergers and Acquisitions. Journal of Management Studies, 39, 167-188. https://doi.org/10.1111/1467-6486.00287

Haleblian J., Devers C.E., McNamara G., Carpenter M.A., Davison R.B. (2009) Taking stock ofwhat we know about mergers and acquisitions: A review and research agenda. Journal of Management, 35(3), 469-502. https://doi.org/10.1177%2F0149206308330554

Hall B.H. (1996) The private and social returns to research and development (NBER Working Paper No. R2092), Cambridge, MA: National Bureau of Economic Research.

Healy P.M., Palepu K.G., Ruback R.S. (1992) Does corporate performance improve after mergers? Journal of Financial Economics, 31(2),

135-175. https://doi.org/10.1016/0304-405X(92)90002-F Hitt M., Hoskisson R., Ireland R., Harrison J. (1991) Effects of Acquisitions on R&D Inputs and Outputs. The Academy of Management

Journal, 34(3), 693-706. https://doi.org/10.2307/256412 Hitt M., Hoskisson R., Johnson R., Moesel D. (1996) The Market for Corporate Control and Firm Innovation. The Academy of Management

Journal, 39(5), 1084-1119. https://doi.org/10.5465/256993. Holger E., Vitt J. (2000) The Influence of Corporate Acquisitions on the Behavior of Key Inventors. R&D Management, 30, 105-120. https:// doi.org/10.1111/1467-9310.00162

Hung S.C., Lee Y., Lin B.W. (2006) R&D intensity and commercialization orientation effects on financial performance. Journal of Business

Research, 59(6), 679-685. https://doi.org/10.1016/j.jbusres.2006.01.002 Iwasa T., Odagiri H. (2004) Overseas R&D, knowledge sourcing, and patenting: An empirical study of Japanese R&D investment in the US.

Research Policy, 33(5): 807-828. https://doi.org/10.1016/j.respol.2004.01.002 Jemison D., Haspeslagh P. (1991) Managing Acquisitions: Creating Value through Corporate Renewal, New York: Free Press.

Jovanovic B., Rousseau P.L. (2008) Mergers as reallocation. The Review of Economics and Statistics, 90(4), 765-776. https://doi.org/10.1162/ rest.90.4.765

King D.R., Slotegraaf R.J., Kesner I. (2008) Performance implications of firm resource interactions in the acquisition of R&D-intensive

firms. Organization Science, 19(2), 327-340. https://doi.org/10.1287/orsc.1070.0313 Koellinger P. (2008) The relationship between technology, innovation, and firm performance — Empirical evidence from e-business in

Europe. Research Policy, 37(8), 1317-1328. https://doi.org/10.1016/j.respol.2008.04.024 Kohers N., Kohers T. (2000) The value creation potential of high-tech mergers. Financial Analysts Journal, 56(3), 40-51. https://doi. org/10.2469/faj.v56.n3.2359

Lane P.J., Lubatkin M. (1998) Relative absorptive capacity and interorganizational learning. Strategic Management Journal, 19: 461-477. https://doi.org/10.1002/(SICI)1097-0266(199805)19:5%3C461::AID-SMJ953%3E3.0.CO;2-L

Liu H., Chen T., Pai L. (2007) The Influence of Merger and Acquisition Activities on Corporate Performance in the Taiwanese

Telecommunications Industry. The Service Industries Journal, 27(8), 1041-1051. https://doi.org/10.1080/02642060701673729 Lozano S., Villa G. (2010) DEA-based pre-merger planning tool. Journal of the Operational Research Society, 61(10), 1485-1497. https://doi. org/10.1057/jors.2009.106

Mansfield E. (1988) Industrial R&D in Japan and the United States: A Comparative Study. The American Economic Review, 78(2): 223-228.

https://www.jstor.org/stable/1818127 Ortega-Argiles R., Piva M., Potters L., Vivarelli M. (2010) Is corporate R&D investment in hightech sectors more effective? Contemporary

Economic Policy, 28, 353-365. https://doi.org/10.1111/j.1465-7287.2009.00186.x Peters R.H., Taylor L.A. (2017) Intangible capital and the investment-q relation. Journal of Financial Economics, 123(2): 251-272. https://

doi.org/10.1016/j.jfineco.2016.03.011 Peyrache A. (2013) Industry structural inefficiency and potential gains from mergers and break-ups: A comprehensive approach. European Journal of Operational Research, 230(2): 422-430. https://doi.org/10.1016/j.ejor.2013.04.034

Phillips R.L., Ormsby R. (2016) Industry classification schemes: An analysis and review. Journal of Business & Finance Librarianship, 21(1), 1-25. https://doi.org/10.1080/08963568.2015.1110229

Phillips G.M., Zhdanov A. (2013) R&D and the Incentives from Merger and Acquisition Activity. The Review of Financial Studies, 26(1):

34-78. https://doi.org/10.1093/rfs/hhs109 Prabhu J.C., Chandy R.K., Ellis M.E. (2005) The Impact of Acquisitions on Innovation: Poison Pill, Placebo, or Tonic? Journal of Marketing,

69(1), 114-130. https://journals.sagepub.com/doi/10.1509/jmkg.69.1.114.55514# Ranft A., Lord M. (2002) Acquiring New Technologies and Capabilities: A Grounded Model of Acquisition Implementation. Organization

Science, 13(4), 420-441. https://www.jstor.org/stable/3085975 Ranft A.L., Lord M.D. (2000) Acquiring new knowledge: The role of retaining human capital in acquisitions of high-tech firms. The Journal of High Technology Management Research, 11(2): 295-319. https://doi.org/10.1016/S1047-8310(00)00034-1

Ravenscraft D., Scherer F. (1982) The lag structure of returns to research and development. Applied Economics, 14 (6), 603-620. https://doi. org/10.1080/00036848200000036

Ravenscraft D., Scherer F. (1987) Life After Takeover. The Journal of Industrial Economics, 36(2): 147-156. https://doi.org/10.2307/2098409 Sirmon D.G., Hitt M.A., Ireland R.D., Gilbert B.A. (2011) Resource Orchestration to Create Competitive Advantage: Breadth, Depth, and Life Cycle Effects. Journal of Management, 37(5), 1390-1412. https://doi.org/10.1177%2F0149206310385695

Stoneman P., Kwon M.J. (1996) Technology adoption and firm profitability. The Economic Journal, 106(437), 952-962. https://doi. org/10.2307/2235366

Wagner M. (2011) To explore or to exploit? An empirical investigation of acquisitions by large incumbents. Research Policy, 40(9): 12171225. https://doi.org/10.1016/j.respol.2011.07.006 Wanke P., Maredza A., Gupta R. (2017) Merger and acquisitions in South African banking: A network DEA model. Research in International

Business and Finance, 41, 362-376. https://doi.org/10.1016/j.ribaf.2017.04.055 Worthington A.C. (2001) Efficiency in pre-merger and post-merger non-bank financial institutions. Managerial and Decision Economics, 22,

439-452. https://doi.org/10.1002/mde.1033 Xie E., Reddy K.S., Liang J. (2017) Country-specific determinants of cross-border mergers and acquisitions: A comprehensive review and future research directions. Journal of World Business, 52(2), 127-183. https://doi.org/10.1016/j.jwb.2016.12.005