The International Technology Management Review

Volume 8, Issue 1, 2019, Pages 16 - 21

Intra-industry’ Effects from Mergers on Financial Statements, in and out of Technology-intensive Industries: Evidence from Greece

Authors
Michail Pazarskis1, *, George Drogalas2, Andreas Koutoupis3, Grigorios Lazos4
1Department of Economics, International Hellenic University, End of Magnesias Street, Serres, GR-621 00, Greece
2Department of Business Administration, University of Macedonia, 156 Egnatias Street, Thessaloniki, GR-546 36, Greece
3Department of Accounting and Finance, University of Thessaly, University of Thessaly, Larissa, GR-411 10, Greece
4Program of Business Administration, Hellenic Open University, 35 26th Oktovriou Street, Thessaloniki, GR-546 27, Greece
*Corresponding author. Email: pazarskis@gmail.com
Corresponding Author
Michail Pazarskis
Received 17 August 2019, Accepted 19 September 2019, Available Online 16 November 2019.
DOI
https://doi.org/10.2991/itmr.k.191104.001How to use a DOI?
Keywords
Mergers; financial statements; ratios; technology-intensive industrial sector; crisis; Greece
Abstract

The study examines the impact of mergers on accounting performance of Greek listed firms involved in mergers. More specifically, we studied a sample of thirty-two absorbed listed firms in four sectors (primary sector, technology-intensive industrial sector, commercial and services sector, construction sector) during the period of economic crisis by using thirty-two accounting measures and ratios extracted from corresponding financial statements. The results of the study indicated that there is no statistically significant improvement or worsening for none of the examined variables in the post-merger period for the merged firms in the four examined sectors. However, as the whole economic image of the Greek economy is not very encouraging with the economic crisis, we concluded that mergers lead the involved firms to avoid any business losses in such a difficult economic period. Last, the results among four industries showed that none of the examined quantitative variables has a statistically significant change, and thus, did not reveal a different performance of one industry, as economic crisis had horizontal effects all over the Greek business.

Copyright
© 2019 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION

Merger is the action of unity from two or more firms under the control of one management. The merger eliminates one or more firms as independent legal entities and transfers their assets to a company that absorbs it [1]. The factors that may lead to mergers are market conditions, developments in new technologies, changes in government regulation, internationalization of markets, etc. [25]. Thus, there are micro and macro factors that affect the activity of mergers [6].

They are also the subject of extensive study for their success and firms’ profitability, as they are important transactions, not only for the merged firms, but also for all stakeholders (shareholders, managers, employees, competitors, consumers and government), as well as the whole economy and society [712]. Mergers are one of the basic methods of restructuring by which each firm can acquire new resources, which they will use to increase their incomes and improve their market competitiveness.

Greece, after the global economic crisis in 2008, experienced an extended economic crisis almost for a decade (mainly from 2010 to 2018). During this period Greece was under the supervision of the ‘troika’ (European Union-EU, International Monetary Fund-IMF and European Central Bank) [13]. Economic crisis provided a ‘toxic’ environment for firms’ activities in Greece, with a shrinking of their liquidity and profitability. However, financial statements’ analysis provides in-depth analysis that reveals every problem in accounting performance for the examined firm and facilitates various comparisons of different samples [9,1416].

Thus, the aim of this study is to examine the accounting performance of firms following mergers into different business industries and reveal any possible particularities, by deploying a plethora of quantitative variables from financial statements (thirty two accounting measures and ratios, extracted from them) for all listed companies at the Athens Exchange in the period of 2011–2016. During this period of economic crisis (2011–2016) in Greece, the chosen sample of 32 listed firms is examined on the basis of four main categories (according the industry type of the sample firms): primary sector (six firms), industrial sector (10 firms), commercial and services sector (eight firms), construction sector (eight firms).

The structure of the paper is as follows: Section 2 discusses the relevant literature review. Section 3 presents the research methodology and the examined data. Section 4 analyses the results of the study. Last section presents the conclusions of the study.

2. LITERATURE REVIEW

Examining the impact of industry differentiation and mergers, Healy et al. [14] tested for differences a group of merged firms with a group of non-merged firms. They found better accounting performance after mergers for the merged firms, and this implies for industry differentiation’s consequences in mergers. In another study, Ramaswamy and Waegelein [17] reported that firms with mergers in dissimilar industries may achieve better results in terms of efficiency and performance. For an emerging market, Al-Hroot [18] argued that each industrial sector’s firms, as he had examined in his study firms after mergers in the Jordanian market, reacted differently on a merger event. Similar results were found by Rao-Nicholson et al. [19] as they also claimed for differences in every industry sector at the ASEAN countries and Ahmed and Ahmed [20] for the market of Pakistan.

For the Greek market and before the outbreak of the economic crisis, Agorastos et al. [16] claimed that the accounting performance of the acquiring firms in the post-merger period was different due to their industry type. Pantelidis et al. [21] in the beginning of the economic crisis (examined years with merger activity 2008–2009) in Greece proposed, in general, different results at the post-merger performance for their sample of examined acquiring firms of each industry. Alexandrakis et al. [22], studying Greek mergers in different business industries, argued that their results revealed for the examined firms of each industry different results per industry to profitability and operating efficiency. Finally, Pazarskis et al. [23] examined a knowledge-intensive industry in Greece, the information technology industry, using accounting variables and found a partial a worsening in their performance after mergers.

3. RESEARCH DESIGN

3.1. Sample Selection and Industry Type (Qualitative Variables)

All mergers events from listed firms in the Athens Stock Exchange (ASE) in the period from 2011 to 2016 are tracked. The reason that listed firms are studied is their size and data availability. From this preliminary sample of all mergers then are excluded:

  1. (a)

    firms with banking and financial activities (due to the particularities of their financial statements),

  2. (b)

    firms that their financial statements are no longer published (no available) in the ASE website,

  3. (c)

    firms that presents multiple mergers (more than in one per year) that are excluded as no comparisons of financial statements from year to year can be made.

The final sample includes thirty two mergers of listed firms during the period of economic crisis in Greece.

Next, we have categorized the sample firms according their industry type in four main categories:

  1. (i)

    primary sector (six firms),

  2. (ii)

    technology-intensive industrial sector (10 firms),

  3. (iii)

    commercial and services sector (eight firms),

  4. (iv)

    construction sector (eight firms).

The merger events’ participation in the sample per year and per industry is shown in Table 1.

Year Mergers per year Primary sector Technology-intensive industrial sector Commercial and services sector Construction sector
2011 6 3 3 0 0
2012 3 1 1 0 1
2013 4 0 0 2 2
2014 4 0 3 0 1
2015 6 1 1 3 1
2016 9 1 2 3 3
Total 32 6 10 8 8
Table 1

Merger events by year and categorized according their industry type

3.2. Accounting Measures and Ratios (Quantitative Variables)

For the sample firms their financial statements are collected from the ASE website. From them 16 accounting measures were extracted. To gain a better understanding of merger, we have calculated our 16 ratios (related to our selected accounting measures). The 32 quantitative variables of the study (accounting measures and ratios) that have been selected for the data of our sample are tabulated in Table 2.

Variables Accounting measures and ratios Accounting measures and ratios’ definitions
AccDat01 Inventories Inventories
AccDat02 Debtors Debtors
AccDat03 Long term loans Long term loans
AccDat04 Short term loans Short term loans
AccDat05 Current liabilities Current liabilities
AccDat06 Total liabilities Total liabilities
AccDat07 Shareholders funds Shareholders funds
AccDat08 Total assets Total assets
AccDat09 Depreciations Depreciations
AccDat10 Interest expenses Interest expenses
AccDat11 Sales Sales
AccDat12 Gross profit or loss Gross profit or loss
AccDat13 EBITDA Earnings before interest, taxes and depreciation
AccDat14 EBIT Earnings before interest and taxes
AccDat15 Before-tax profit or loss Before-tax profit or loss
AccDat16 Net income Net income
Ratio01 Current ratio Current assets/Current liabilities
Ratio02 Liquidity ratio (Current assets – Stocks)/Current liabilities
Ratio03 Collection period (Debtors/Sales) × 360
Ratio04 Inventories turnover Net sales/Inventories
Ratio05 Credit period (Creditors/Sales) × 360
Ratio06 Debt ratio Total liabilities/Total assets
Ratio07 Debt-equity ratio Total liabilities/Shareholders funds
Ratio08 Shareholder equity ratio Shareholders funds/Total assets
Ratio09 Sales to total liabilities ratio Sales/Total liabilities
Ratio10 Asset turnover ratio Sales/Total assets
Ratio11 Gross margin Gross profit/Sales
Ratio12 EBIT margin Earnings before interest and taxes/Sales
Ratio13 EBITDA margin Earnings before interest, taxes and depreciation/Sales
Ratio14 Net assets turnover Sales/(Shareholders funds + Non-current liabilities)
Ratio15 Interest cover Earnings before interest and taxes/Interest expenses
Ratio16 Gearing Long term debt/Shareholders funds

Note: Stocks are outstanding shares. Shareholder funds are all assets less all liabilities.

Table 2

Classification of accounting measures and ratios (quantitative variables)

3.3. Methodology

The sample includes mergers for 6 years (2011–2016) and is examined for 1 year before and after merger, thus our data analysis covers from the year 2010 (the beginning of the economic crisis in Greece) up to the year 2017 (the end of the economic crisis in Greece). More analytically, we explore accounting performance based on a ‘change model’ that compares post-merger data (1 year after merger, thus t + 1) and pre-merger data (1 year before merger, thus t − 1) and is applied as a modified methodology of Ramaswamy and Waegelein [17], Francis and Martin [24] and Pantelidis et al. [13]. In this study, we have chosen to calculate the mean from the sum of each ratio than the median for more accurate results, as many other past studies [15,25]. Furthermore, the merger year (t = 0) is not included in our data analysis as this the year happens many of one-time expenses related to merger event [14,17]. Next, we subtracted our sample to four sub-samples to examine the accounting performance in every of our four industries. To test our ‘change model’ in accounting performance, we compare year t + 1 to year t − 1 by using two independent sample mean t-tests for unequal variances. Thus, this test is applied for merged firms in every industry category: primary sector, industrial sector, commercial and services sector, construction sector.

Furthermore, to test the rate of change of accounting performance for the merged (absorbed) firms, we examine our variables in relation to the industry type of each firm by applying a modified methodology of Sharma and Ho [15], Ramaswamy and Waegelein [17] and Francis and Martin [24]. In particular, we calculate first the change in accounting performance of the absorbed firm in every quantitative variable from the post-merger value minus the pre-merger value. Then, the calculated change in every quantitative variable is divided by the pre-merger value and this is done for every firm of our sample (dAccDat01-16, dRatio01-16). Next, we compare the rate of change of accounting performance of merged firms regarding to the four industry categories of our sample (primary sector, industrial sector, commercial and services sector, construction sector). Because these four data sets have not a normal distribution, we use the Kruskal–Wallis test for our analysis [13].

4. RESULTS

4.1. Intra-industry Results

In our study, all mergers events from listed firms in the ASE in the period from 2011 to 2016 are tracked. After several eliminations of our preliminary sample (due to banking and financial activities’ firms, data availability, etc.), the final sample includes thirty two mergers of listed firms during the period of economic crisis in Greece, which are further subtracted according their industry category. Firstly, the comparison results (t-tests) for accounting measures and ratios from pre- and post-merger period in the primary sector are presented in Table 3. We observe that there is no statistically significant change after mergers in accounting performance of the merged firms in the primary sector. Pantelidis et al. [13] argued also for similar results with no significant change after mergers per industry, as well as Pazarskis et al. [1] have been drawn same conclusions for the commercial and services sector after mergers. Finally, some other researchers concluded that there is a worsening in performance after mergers in this sector [16,22].

Variables Mean pre-merger Mean post-merger t-value p-value Confidential index
AccDat01 294.942 231.302 −0.19 0.851 (−806.901; 679.621)
AccDat02 174.037 148.94 −0.13 0.903 (−477.449; 427.256)
AccDat03 210.597 93.383 −0.60 0.569 (−592.868; 358.44)
AccDat04 245.858 427.057 0.41 0.695 (−866.874; 1229.273)
AccDat05 291.583 349.658 0.14 0.890 (−868.179; 984.329)
AccDat06 832.679 916.493 0.08 0.939 (−2322.219; 2489.847)
AccDat07 445.711 443.963 −0.00 0.998 (−1326.133; 1322.639)
AccDat08 1278.39 1354.696 0.05 0.964 (−3652.222; 3804.834)
AccDat09 28.925 31.94 0.08 0.940 (−85.059; 91.089)
AccDat10 17.232 15.289 −0.13 0.903 (−36.891; 33.005)
AccDat11 1475.81 1809.809 0.15 0.884 (−4704.843; 5372.841)
AccDat12 146.88 106.373 −0.24 0.819 (−436.565; 355.551)
AccDat13 82.828 46.836 −0.37 0.720 (−259.72; 187.737)
AccDat14 58.139 15.46 −0.69 0.514 (−193.145; 107.787)
AccDat15 37.997 8.157 −0.52 0.625 (−171.545; 111.865)
AccDat16 20.475 3.834 −0.43 0.681 (−108.551; 75.267)
Ratio01 0.933 0.887 −0.17 0.868 (−0.69; 0.598)
Ratio02 0.486 0.385 −0.78 0.460 (−0.402; 0.199)
Ratio03 148 90.9 −0.87 0.426 (−225.1; 111.7)
Ratio04 9.3 5.18 −0.68 0.521 (−18.58; 10.32)
Ratio05 305 263 −0.36 0.729 (−308; 224)
Ratio06 0.724 0.778 0.51 0.624 (−0.191; 0.299)
Ratio07 3.6 −8.1 −1.02 0.354 (−41.2; 17.8)
Ratio08 0.276 0.249 −0.31 0.766 (−0.2244; 0.1707)
Ratio09 1.247 1.399 0.29 0.781 (−1.047; 1.351)
Ratio10 0.537 0.647 0.50 0.626 (−0.382; 0.602)
Ratio11 0.230 0.153 −0.64 0.541 (−0.353; 0.200)
Ratio12 0.0207 −0.082 −1.22 0.275 (−0.3194; 0.1133)
Ratio13 0.05 −0.036 −0.94 0.376 (−0.2959; 0.1247)
Ratio14 1.218 1.9 0.94 0.385 (−1.097; 2.460)
Ratio15 1.09 −0.44 −1.09 0.307 (−4.76; 1.70)
Ratio16 0.997 −0.47 −0.84 0.442 (−5.99; 3.05)

Notes:

1.

The variables AccDat01–AccDat16 are in millions euro.

2.

***, **, * indicate that the change of the mean is significantly different from zero at a significance level of 0.01. 0.05. and 0.10, respectively, as calculated by comparing the average of two independent subassemblies (two independent sample mean t-tests) at ratios of sample. More specifically, for the three above cases the classification levels relative to the value of the p-value are the following: p < 0.01 indicates strong evidence against H0 (denoted by ***). 0.01 ≤ p < 0.05 indicates moderate evidence against H0 (denoted by **). 0.05 ≤ p < 0.10 indicates minimum evidence against H0 (denoted by *). 0.10 ≤ p indicates no real evidence against H0.

Table 3

Comparison results (t-tests) for accounting measures and ratios from pre- and post-merger period in the primary sector

Next, for the firms that are in the technology-intensive industrial sector (10 firms) from comparison results (t-tests) for accounting data and ratios from pre- and post-merger period, we observe for the quantitative variables that none of them are statistically significant (p > 0.1). These results are presented in Table 4. Similar conclusions have been drawn earlier studies based on stock market or accounting performance measures that supported no significant results after mergers per industry [13,16]. On the other hand, different conclusions that there is (a) an improvement at performance in different industry than technology-intensive industrial sector were found by Pazarskis et al. [1], or (b) an improvement at performance of the technology-intensive industrial sector were found by Alexandrakis et al. [22].

Variables Mean pre-merger Mean post-merger t-value p-value Confidential index
AccDat01 22.787 37.33 0.64 0.529 (−33.144; 62.23)
AccDat02 112.318 91.014 −0.26 0.797 (−191.824; 149.216)
AccDat03 42.576 44.225 0.03 0.973 (−99.672; 102.971)
AccDat04 35.284 47.82 0.37 0.716 (−58.204; 83.277)
AccDat05 93.381 84.393 −0.12 0.908 (−168.555; 150.579)
AccDat06 243.312 213.892 −0.16 0.878 (−423.59; 364.751)
AccDat07 141.092 162.09 0.16 0.878 (−260.218; 302.214)
AccDat08 348.74 375.982 0.08 0.934 (−646.425; 700.91)
AccDat09 7.549 7.138 −0.06 0.956 (−15.767; 14.944)
AccDat10 6.555 6.944 0.06 0.955 (−13.697; 14.477)
AccDat11 220.085 230.454 0.06 0.951 (−335319; 356058)
AccDat12 29.892 40.476 0.43 0.673 (−40.838; 62.007)
AccDat13 34.024 31.193 −0.10 0.923 (−62.708; 57.046)
AccDat14 26.406 24.025 −0.11 0.916 (−48.804; 44.043)
AccDat15 17.668 16.027 −0.10 0.919 (−34.802; 31.521)
AccDat16 13.179 12.265 −0.08 0.941 (−26.194; 24.367)
Ratio01 2.29 1.659 −0.86 0.406 (−2.229; 0.961)
Ratio02 1.89 1.215 −0.92 0.374 (−2.263; 0.917)
Ratio03 149 128 −0.49 0.629 (−110.0; 68.1)
Ratio04 9.8 5.07 −1.12 0.283 (−13.91; 4.44)
Ratio05 176 177 0.03 0.975 (−95.5; 98.5)
Ratio06 1.89 0.595 −1.39 0.192 (−3.335; 0.753)
Ratio07 7.1 3.21 −0.76 0.463 (−15.0; 7.26)
Ratio08 0.500 0.405 −0.97 0.346 (−0.3016; 0.1111)
Ratio09 2.31 2.27 −0.09 0.930 (−1.017; 0.933)
Ratio10 1.028 0.81 −0.76 0.462 (−0.839; 0.403)
Ratio11 0.289 0.308 0.22 0.826 (−0.1532; 0.19)
Ratio12 0.0779 0.0979 0.65 0.525 (−0.0443; 0.0842)
Ratio13 0.126 0.1368 0.29 0.775 (−0.067; 0.0886)
Ratio14 1.92 1.68 −0.36 0.725 (−1.648; 1.167)
Ratio15 12.5 6.76 −0.59 0.566 (−27.0; 15.5)
Ratio16 0.568 0.83 0.61 0.547 (−0.627; 1.149)
Table 4

Comparison results (t-tests) for accounting measures-ratios from pre- and post-merger period in the technology-intensive industrial sector

Regarding the commercial and services sector (eight firms), Table 5 presents the results for years 2011–2015 based on t-test. There is no significant change of the examined number of the quantitative variables. Similar conclusions with no significant results after mergers per industry have been drawn earlier studies examining several accounting performance measures for the commercial and services sector after mergers [1,13]. Finally, some other researchers concluded that there is a worsening in performance [16,22].

Variables Mean pre-merger Mean post-merger t-value p-value Confidential index
AccDat01 56.564 63.446 0.20 0.846 (−68.244; 82.009)
AccDat02 114.666 122.864 0.07 0.946 (−250.278; 266.674)
AccDat03 335.122 310.851 −0.07 0.946 (−786.801; 738.26)
AccDat04 117.566 92.205 −0.36 0.725 (−178.808; 128.085)
AccDat05 290.005 315.865 0.07 0.945 (−766.853; 818.572)
AccDat06 844.928 811.721 −0.04 0.970 (−1909.056; 1842.642)
AccDat07 436.539 463.425 0.06 0.954 (−958.201; 1011.974)
AccDat08 1281.48 1270.709 −0.01 0.994 (−2834.83; 2813.29)
AccDat09 7.524 120.017 1.03 0.336 (−144.911; 369.896)
AccDat10 7.279 22.112 0.91 0.395 (−23.844; 53.511)
AccDat11 680.456 702.985 0.03 0.973 (−1397.484; 1442.541)
AccDat12 140.617 239.584 0.59 0.571 (−277.446; 475.379)
AccDat13 203.832 193.735 −0.04 0.966 (−507.404; 487.21)
AccDat14 96.591 73.735 −0.26 0.800 (−214.865; 169.154)
AccDat15 58.212 50.155 −0.13 0.901 (−146.246; 130.133)
AccDat16 37.588 23.516 −0.34 0.742 (−106.84; 78.696)
Ratio01 1.41 1.69 0.33 0.748 (−1.577; 2.137)
Ratio02 1.005 0.787 −0.55 0.592 (−1.075; 0.638)
Ratio03 92.3 79.6 −0.39 0.704 (−83.3; 58.0)
Ratio04 10.7 7.81 −0.52 0.616 (−15.12; 9.37)
Ratio05 142.6 121.2 −0.57 0.581 (−104.4; 61.5)
Ratio06 0.755 0.805 0.32 0.752 (−0.285; 0.384)
Ratio07 123 0.52 −1.38 0.210 (−331.7; 87.2)
Ratio08 0.245 0.232 −0.08 0.934 (−0.331; 0.306)
Ratio09 2.05 3.56 0.84 0.427 (−2.72; 5.74)
Ratio10 0.665 0.690 0.29 0.773 (−0.1596; 0.21)
Ratio11 0.244 0.282 0.53 0.603 (−0.1183; 0.196)
Ratio12 0.075 0.064 −0.22 0.830 (−0.1246; 0.1019)
Ratio13 0.134 0.131 −0.06 0.956 (−0.1368; 0.1299)
Ratio14 1.68 0.86 −0.78 0.454 (−3.17; 1.53)
Ratio15 3.96 −3.2 −1.35 0.202 (−18.69; 4.4)
Ratio16 36.3 –0.18 −1.30 0.233 (−102.7; 29.7)

Notes:

1.

The variables AccDat01–AccDat16 are in millions euro.

2.

***, **, * indicate rejection of the null hypothesis at a significance level of 0.01, 0.05, 0.1, respectively.

Table 5

Comparison results (t-tests) for accounting measures and ratios from pre- and post-merger period in the commercial and services sector

Finally, for the construction sector (see Table 6), which includes eight firms, we observe that there is no statistically significant change after mergers in accounting performance of the merged firms (p > 0.1). Different conclusions have been drawn earlier studies based on accounting performance, which found a comparative better performance of the constructions sector: Pantelidis et al. [21] in the beginning of the economic crisis found that return on total assets presents a significant change due to mergers events, which it signalizes a better performance among the acquiring firms in their examined sample period for the firms of the constructions industry. Also, Pazarskis et al. [1] during the economic crisis claimed for a better performance after mergers of the constructions industry, as they found an improvement in five out of 12 examined financial ratios in their study. On the other hand, different conclusions were drawn before the outbreak of the economic crisis by Agorastos et al. [16], as they claimed that there is a worsening for the acquiring firms from constructions industry at their post-merger performance.

Variables Mean pre-merger Mean post-merger t-value p-value Confidential index
AccDat01 160.596 202.783 0.20 0.843 (−426.485; 510.858)
AccDat02 161.391 149.571 −0.11 0.916 (−259.156; 235.515)
AccDat03 256.53 202.91 −0.29 0.775 (−465.906; 358.66)
AccDat04 172.04 260.881 0.46 0.654 (−344.471; 522.152)
AccDat05 143.475 166.276 0.23 0.823 (−201.178; 246.78)
AccDat06 650.602 706.005 0.11 0.914 (−1067.128; 1177.935)
AccDat07 505.363 514.17 0.03 0.978 (−706.181; 723.795)
AccDat08 1155.965 1220.175 0.09 0.933 (−1620.023; 1748.444)
AccDat09 34.864 29.772 −0.17 0.865 (−71.146; 60.962)
AccDat10 95.9 21.618 −1.11 0.318 (−246.689; 98.125)
AccDat11 742.357 1245.289 0.66 0.525 (−1216.647; 2222.511)
AccDat12 63.367 91.941 0.43 0.675 (−123.091; 180.238)
AccDat13 59.469 83.717 0.42 0.686 (−108.949; 157.446)
AccDat14 24.888 51.814 0.79 0.455 (−53.495; 107.345)
AccDat15 −3.333 22.534 1.21 0.262 (−23.512; 75.247)
AccDat16 −9.486 25.513 1.49 0.170 (−18.049; 88.046)
Ratio01 1.61 3.35 0.76 0.481 (−4.12; 7.59)
Ratio02 1.39 3.14 0.75 0.486 (−4.24; 7.75)
Ratio03 166 101.3 −0.98 0.354 (−216.7; 87.0)
Ratio04 8.73 37.6 0.87 0.426 (−56.8; 114.5)
Ratio05 222 −50 −1.11 0.319 (−905; 361)
Ratio06 0.583 0.606 0.19 0.854 (−0.254; 0.301)
Ratio07 1.88 2.15 0.36 0.729 (−1.425; 1.960)
Ratio08 0.417 0.394 −0.19 0.854 (−0.301; 0.254)
Ratio09 1.88 2.34 0.38 0.715 (−2.49; 3.41)
Ratio10 0.501 1.15 0.94 0.390 (−1.127; 2.427)
Ratio11 0.146 0.0516 −0.85 0.436 (−0.382; 0.193)
Ratio12 0.057 0.218 0.84 0.426 (−0.282; 0.605)
Ratio13 0.125 0.294 0.89 0.397 (−0.268; 0.607)
Ratio14 0.938 2.71 1.10 0.321 (−2.36; 5.90)
Ratio15 −0.61 2.09 1.76 0.112 (−0.76; 6.16)
Ratio16 0.632 0.465 −1.02 0.336 (−0.540; 0.205)
Table 6

Comparison results (t-tests) for accounting measures and ratios from pre- and post-merger period in the construction sector

4.2. Comparison Results from all Industries

Several studies examined impact of industry differentiation to accounting performance after mergers [1,13,16,17,19,21,22]. To test mergers of Greek listed firms in different business industries and to compare them to find which one presents better relative results among others, we test the rate of change of accounting performance for the merged (absorbed) firms. More analytically, we examine our variables in relation to the industry type of each firm by applying a modified methodology of Sharma and Ho [15], Ramaswamy and Waegelein [17] and Francis and Martin [24]. Thus, we calculate first the change in accounting performance of the absorbed firm in every quantitative variable from the post-merger value minus the pre-merger value and then, the calculated change in every quantitative variable is divided by the pre-merger value and this is done for every firm of our sample. The comparison results (Kruskal–Wallis test) for accounting data and ratios by industry is presented in Table 7. The results showed that none of the examined variables has a statistically significant change (p > 0.1). Our results are aligned with these of Pantelidis et al. [13] and differ from described past studies [19,20], as well as from several earlier studies for the Greek market and mergers before or during the economic crisis in Greece [1,16,21,22].

Variables Primary sector Technology-intensive industrial sector Commercial and services sector Construction sector p-value
dAccDat01 −0.1251 0.2478 0.1503 0.1999 0.678
dAccDat02 −0.164173 0.008126 −0.00402 −0.055673 0.870
dAccDat03 −0.52902 0.26471 −0.05175 −0.15389 0.333
dAccDat04 −0.12154 0.16939 −0.05738 0.45405 0.576
dAccDat05 −0.008284 0.07892 −0.128897 −0.030437 0.706
dAccDat06 −0.0509075 −0.0004085 −0.0179374 0.0956588 0.552
dAccDat07 −0.202972 0.018222 −0.148068 −0.006925 0.743
dAccDat08 −0.11795 0.02782 −0.04795 0.05105 0.279
dAccDat09 −0.11066 −0.05929 0.08495 −0.14548 0.475
dAccDat10 −0.11409 −0.07228 −0.14283 −0.17226 0.400
dAccDat11 0.084221 −0.004569 0.044256 0.241735 0.761
dAccDat12 −0.2194 0.1239 0.1845 0.1709 0.436
dAccDat13 −0.06301 −0.008016 −0.012638 0.183711 0.678
dAccDat14 0.0568 −0.06477 −0.50254 −0.30008 0.405
dAccDat15 −0.08875 −0.24337 −0.52177 −1.2521 0.645
dAccDat16 −0.11703 −0.05575 −0.65192 −1.17943 0.560
dRatio01 −0.005787 0.032259 0.024128 0.076823 0.963
dRatio02 −0.1317 0.01718 −0.46039 0.07047 0.717
dRatio03 −0.27211 −0.15347 −0.01825 −0.18839 0.455
dRatio04 0.3978 −0.1414 −0.1426 −0.1345 0.574
dRatio05 −0.05994 0.0351 −0.15673 0.14557 0.681
dRatio06 0.09596 −0.02098 0.027 0.05604 0.871
dRatio07 −0.004994 −0.08078 −0.190582 0.126071 0.275
dRatio08 −0.12319 −0.06424 −0.09227 −0.04014 0.867
dRatio09 0.31469 0.06916 0.15556 −0.244 0.576
dRatio10 0.24353 0.025495 0.02029 0.004308 0.310
dRatio11 −0.25752 0.08201 0.02603 −0.52789 0.223
dRatio12 −0.09126 −0.16732 −0.50009 −0.69947 0.465
dRatio13 −0.25386 −0.01348 −0.08836 −0.04294 0.799
dRatio14 0.39139 0.01555 −0.07601 0.28586 0.218
dRatio15 −0.08588 −0.07394 −0.84225 0.15012 0.356
dRatio16 −0.119 0.1212 −0.2288 −0.2577 0.158

Notes:

1.

The variables AccDat01–AccDat16 are in millions euro.

2.

***, **, * indicate rejection of the null hypothesis at a significance level of 0.01, 0.05, 0.1, respectively.

Table 7

Comparison results (Kruskal–Wallis test) for accounting measures and ratios by industry

5. CONCLUSION

Greece, after the U.S. economic crisis in 2008, experienced an extended economic crisis almost for a decade (from 2010 to 2018). The present study analyse financial statements from listed firms after their mergers in Greece. However, financial statements’ analysis provides in-depth analysis that reveals every problem in accounting performance for the examined firm and facilitates various comparisons of different samples.

This study compares accounting performance of Greek listed firms that are in the same industry in the ASE before and after mergers, by deploying a plethora of quantitative variables, in order to reveal any intra-industry particularities. The examined period of all merger events is from 2011 to 2016 (period of economic crisis in Greece). Furthermore, our study examined impact of industry differentiation to accounting performance after mergers. To test mergers of Greek listed firms in different business industries and to compare them to find which one presents better relative results among others, we test the rate of change of accounting performance for the merged (absorbed) firms. Last, the study used according the relevant literature a parametric (t-test) and a non-parametric test (Kruskal–Wallis test).

The results indicate that there is no statistically significant improvement or worsening for none of the examined quantitative variables in the post-merger period for the merged firms in the four examined sectors (primary sector, technology-intensive industrial sector, commercial and services sector, construction sector). However, as the whole economic image of the Greek economy is not very encouraging with the economic crisis, we could concluded that mergers lead the involved firms to avoid any business losses in such a difficult economic period. Finally, the results among four industries showed that none of the examined quantitative variables has a statistically significant change, and thus, did not reveal a different performance of one industry, as economic crisis had horizontal effects over all the Greek business.

As a future research of the study is proposed an analysis of financial statements extracted from a sample of non-listed Greek with this one of listed firms. This could further be tested on the existence of differences from two different samples, entered in and were out of a period of economic crisis. Furthermore, an international comparison of different sub-samples from different countries could be helpful to understand the particularities among different business areas in every country. Finally, the present study could be examined on a different time-frame period and further could be compared with the present one’s results for finding any potential differences.

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Journal
The International Technology Management Review
Volume-Issue
8 - 1
Pages
16 - 21
Publication Date
2019/11/16
ISSN (Online)
1835-5269
ISSN (Print)
2213-7149
DOI
https://doi.org/10.2991/itmr.k.191104.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Michail Pazarskis
AU  - George Drogalas
AU  - Andreas Koutoupis
AU  - Grigorios Lazos
PY  - 2019
DA  - 2019/11/16
TI  - Intra-industry’ Effects from Mergers on Financial Statements, in and out of Technology-intensive Industries: Evidence from Greece
JO  - The International Technology Management Review
SP  - 16
EP  - 21
VL  - 8
IS  - 1
SN  - 1835-5269
UR  - https://doi.org/10.2991/itmr.k.191104.001
DO  - https://doi.org/10.2991/itmr.k.191104.001
ID  - Pazarskis2019
ER  -