Therefore, they have not considered the evolutionary and industry-level aspects of M&A to support M&A decision-making. To counter this, we propose a systematic approach for identifying M&A patterns at the industry level based on the variation of historical M&A transactions. For this, first, historical data of M&A transactions providing information of the industry to which the firms belonged on the date of the transaction from the platinum database of the Securities Data Company (SDC) are collected at a regularly spaced time interval .
Second, Association Rule Mining (ARM) is modified to consider the direction of the M&A transaction to eliminate the important M&A transaction rules whose indices are higher than the threshold value. Third, network analysis is conducted to build a network of M&A transaction relationships and measure six quantitative indicators to confirm industry characteristics and important rules of M&A transaction through the concept of centrality. Finally, the important rules of M&A transactions are classified into dynamic and structural M&A patterns using indicator analysis and cluster analysis, respectively, to identify the trend development of M&A transactions.
Keywords: mergers and acquisitions, dynamic patterns of M&A, structural patterns of M&A, characteristics of industries, network analysis.
Introduction
Second, in terms of research findings, we focus on the patterns of mergers and acquisitions over time. Identifying patterns of M&A is an exploratory approach that can analyze historical M&A activity and reflect market conditions related to M&A strategies. Building an M&A strategy by identifying historical M&A activity is a low-risk strategy in that it provides specific direction for M&A based on completed M&A transactions.
In addition, this study provides detailed information on changes in M&A trends among the industries of interest by confirming changes in these M&A patterns over time. Finally, from a practical perspective, a more intuitive understanding of M&A patterns requires data visualization and the development of quantitative indices. Keeping the above conditions in mind, in this study we propose a systematic and exploratory approach to identifying M&A patterns at the industry level.
The principle of this study is that significant M&A patterns obtained from large quantitative databases can provide valuable information on M&A trend development for M&A decision-making, where corporations determine the main screening or selection criteria in the search process.
Related work
These studies proved that the speed of M&A integration can be positively related to success. Homburg and Bucerius (2006) studied profitability and the determinant related to M&A integration speed. Integration speed has a strong relationship with M&A success in the case of low external and high internal connectedness, while the impact is strongly negative for high external and low internal connectedness.
This study shows that if the external acquisition of innovative capabilities comes closer to the core business of a company, the role of integration. Chakrabarti et al (2005) proved that if the acquirer and target firms come from countries that are culturally diverse, post-merger performance is better in the long run. Ahammad et al (2014) highlighted that organizational culture differences negatively influence mediating relationships between knowledge transfer and performance across cross-border acquisitions.
Furthermore, Vaara et al (2014) found that prior experience strengthens the association between merger and acquisition failures and cultural differences.
Data and methodology
Data
Methodology
- Modified association rule mining
- Network analysis
- Cluster analysis
First, support X → Y is defined as the ratio of the number of transactions that include both items X and Y to the total number of transactions. Second, trust X → Y is the ratio of the number of transactions containing item Y to the transactions containing item X. Where 𝑁(𝑇) is the number of complete M&A transactions, and 𝑁𝑎𝑐(𝑋) is the number of Industry 2) 𝑆𝑢𝑝𝑝𝑜𝑟𝑡𝑡𝑎(Y): The number of Industry Y as a target divided by the total number of transactions.
We used degree centrality, which is defined as the number of links a node has. -degree is a count of the number of links directed to. the node and the out-degree is the number of links that the node directs to others. This is the index that represents the number of appearances of the acquirer in the network.
It is calculated as the sum of the values of each link from the given node to neighboring nodes. This is the index that represents the number of occurrences as the target within network. It is calculated as the sum of the values of each link from the neighboring nodes to the given node.
This is an index that represents the number of industries with which acquirers merge within a network. This is an index that represents the number of industries that cluster with a given industry, as a target, within a network. This is an index that represents the average value of the deal invested by a given industry in a target industry within the network.
It is calculated as the sum of the values of each link from the given node to neighboring nodes. This is the index that represents the average of the transaction value invested by the acquiring industry in the given industry within the network.
Empirical analysis
Data
Identification of patterns of M&A at the industry level
- Extraction of significant M&A transaction rules
- Generation of M&A transaction relationship network
- Identification of Dynamic & Structural patterns of M&A
- Dynamic patterns of M&A
- Structural patterns of M&A
In the case of M&A transaction rules: 1311 (crude oil and natural gas) → 1311 (crude oil and natural gas) can be considered the most important transaction rule for M&A has both high 𝑆𝑢𝑝𝑝𝑜𝑟𝑡𝑎𝑐 → 𝑡𝑎 (𝑋 → 𝑌) and high 𝐶𝑜𝑛𝑖𝑑𝑒𝑛𝑐𝑒𝑎𝑐→𝑡𝑎(𝑋 → 𝑌). M&A transaction rules with the highest 𝑆𝑢𝑝𝑝𝑜𝑟𝑡𝑎𝑐→𝑡𝑎(𝑋 → 𝑌) were 7372 (packaged software) → 7372 (packaged software 1) → 7372 (packaged software 1) and 311 (crude oil and natural gas 1) software rules, (1) 6798 (1). (real estate investment trusts) → 6512 (operators of non-residential buildings), 6021 (national commercial banks) → 6021 (national commercial banks), 3674 (semiconductors and related entities) → 3674 (semiconductors and related entities). The number of M&A transaction rules with a 𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒𝑎𝑐→𝑡𝑎(𝑋 → 𝑌) of 1 was 4 details, of which the following: 4226 goods (special ware → 4226) special warehousing and storage, not elsewhere classified), 3792 (travel trailers and campers) → 3792 (travel trailers and campers), 4481 (deep-sea transport of passengers, except by ferry) → 4481 (deep-sea transport of passengers, except by ferry), 3449 (miscellaneous structural metalwork) → 3449 .
Typical rules for M&A transactions belonging to cluster 2 are 2834 (pharmaceutical preparations) → 2834 (pharmaceutical preparations), 3812 (search, detection, navigation, guidance, aeronautical and nautical systems and instruments) → 3812 (search, detection, navigation, guidance, aeronautical and nautical systems and instruments), 4911 (electrical services) → 4911 (electrical services). Typical rules for M&A transactions are 5812 (eating places) → 5812 (eating places), 2836 (biological products except diagnostic substances) → 2836 (biological products, except diagnostic substances), 3559 (special industrial machinery, not elsewhere) → 3674 (semiconductors) and related entities). Typical rules for M&A transactions are 2711 (newspapers: publishing, publishing and printing) → 2711 (newspapers: publishing, publishing and printing), 1521 (general contractors-sing-family houses) → 1531 (general contractors-sing-family houses) , 4512 ( air transport, timetable).
Typical rules for mergers and acquisitions are 2511 (Wooden home furniture, except upholstered) → 2511 (Wooden home furniture, except upholstered), 8059 (Nursing and personal care facilities, not elsewhere classified) → 8059 (Nursing and personal care facilities, not elsewhere classified), 6553 (cemetery subdividers and developers) → 6553 (cemetery subdividers and developers). Typical rules for mergers and acquisitions belonging to cluster 7 are 2711 (newspapers: publishing, publishing and printing) → 2711 (newspapers: publishing, publishing and printing), 1521 (general contractors - single-family homes) → 1531 (operative builders), 4512 (air transport, planned) → 4512 (air transport, planned). Typical M&A transaction lines belonging to cluster 2 are 6798 (real estate investment trusts) → 7011 (hotels and motels).
Typical M&A transaction rules belonging to group 3 are 1311 (crude oil and natural gas) → 1311 (crude oil and natural gas), 7375 (information retrieval services) → 7372 (prepackaged software), 6519 ( lessor of real estate, not elsewhere classified ) → 6512 (operators of non-residential buildings). Typical M&A transaction rules belonging to group 4 are 4911 (electrical services) → 4911 (electrical services), 1389 (oil and gas field services, not elsewhere classified) → 1389 (oil and gas field services , not elsewhere classified), 2992 (lubricating oils and fats) → 2992 (lubricating oils and fats). Typical M&A transaction rules belonging to group 5 are 2834 (pharmaceutical preparation) → 2834 (pharmaceutical preparation), 4931 (electrical and other combined services) → 4924 (natural gas distribution), 1021 (copper ore) → 1311 ( oil and natural gas).
Typical M&A transaction lines belonging to cluster 6 are 5211 (traders of timber and other building materials) → 5211 (traders of timber and other building materials). Typical rules for mergers and acquisitions belonging to cluster 7 are 1522 (general contractors - residential buildings, other than single-family dwellings) → 1522 (general contractors - residential buildings, other than single-family dwellings), 1711 (plumbing, heating and air conditioning) → 1711 (plumbing, heating and air conditioning), 7379 (computer related services, not elsewhere classified) → 7379 (computer related services, not elsewhere classified). Typical lines for mergers and acquisitions belonging to cluster 8 are 6141 (Personal credit institutions) → 6141 (Personal credit institutions), 3949 (Sports and sporting goods, not elsewhere classified) → 3949 (Sports and sporting goods, not elsewhere classified), 5012 (automobile and other motor vehicles) → 3711 (motor vehicles and passenger car bodies).
Typical rules for M&A transactions belonging to cluster 9 are 3845 (electromedical and electrotherapeutic apparatus) → 3841 (surgical and medical instruments and apparatus), 1221 (bituminous coal and lignite surface mining) → 1221 (bituminous coal and lignite mining ( computer mining) processing and data preparation and processing) → 7374 (computer processing and data preparation and processing).
Conclusion
First, the proposed approach did not consider the other factors that could explain the characteristics of patterns of M&A. If it is possible to integrate the data set used in this study with financial, accounting and technological information, the characteristics of patterns of M&A will be more abundant. In order to cultivate a comprehensive decision support system for the whole M&A process, the further research using factors in finance, accounting and technology should be carried out because this study only focused on the patterns of M&A to M&A decision at the early stage of the M&A decision . M&A search process.
This relatively small number is due to the fact that transaction data on publicly disclosed deal values is sparse. Knowledge transfer and cross-border acquisition success: The impact of cultural distance and employee retention. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, No. 14, pp. 281-297).
Making mergers work for profitable growth: The importance of pre-deal planning and post-deal management.
