Int. J. of Computers, Communications & Control, ISSN 1841-9836, E-ISSN 1841-9844 Vol. III (2008), Suppl. issue: Proceedings of ICCCC 2008, pp. 322-326
Data-Mining Techniques for Supporting Merging Decisions Lucian Hâncu Abstract: The Mergers and Acquisitions transactions have increased during the last years, as business entities face multiple threats caused by globalization and are unable to exploit the opportunities offered by the global market. These transactions come as a solution for consolidating the position of one entity on the local, national or global market, but usually surprise the competition, which does not have any prepared strategy for surviving the rise of a stronger competitor. In order to help the entities decide to which company should merge, or to be aware of the fact that a competitor could merge in the near future, we have built a technique for supporting merging decisions, based on the financial statements analysis and the Web usage logs extracted from our multi-server search application. The model suggests the merge with an entity which share the same activity code with the initial entity, or has a related activity code, according to the Business Dependency Map (derived from the Web search logs). Keywords: Financial-statement analysis; Web usage mining; Merge decisions.
1
Introduction
The numerous examples of recently completed mergers (Arcelor and Mittal for the global steel industry [1], or Catex Calarasi and Serca [2] for the national textile industry) illustrate an increasing interest in the merging and acquisitions transactions proved by companies all over the world. The mergers or the acquisitions come as a solution for consolidating a market position (in the case the two companies share the same business activity) or to have access to new markets, when the two companies are business partners and have different activity domains. The last decade’s development of the Web technologies made available a large amount of valuable material that can be easily browsed and digested by humans, or indexed by search engines. The online availability of the Romanian companies’ financial statements significantly eases the development of competitive analyzes, with the aim of improving the business community’s knowledge of the competition and to predict its moves. In this article, we consider the case of analyzing the Romanian entities’ profitability of the capitals. We compute the profitability by automatically analyzing the online financial statements. We suggest merger decisions for companies whose profitability is below the average profitability on the sector. Furthermore, we make use of Web usage mining techniques [7] in order to derive dependencies between various sectors of the economy and suggest mergers between companies from related business activity sectors. The paper is organized as follows: the subsequent section presents the method of analyzing the publicly available financial statements of the Romanian companies, whereas the third section discusses the method of building a map of dependencies between the various sectors of the economy. The fourth section highlights the results of classifying the entities according to the profitability of the capitals, the derived map of business dependencies and our technique of suggesting merging decisions. The last section points out the conclusions of our research and directions for future development of our methods.
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Financial statements analysis
During our previous research, we have investigated various methods of collecting [5] and classifying [6] the financial statements of the Romanian entities. Official sources (like the Ministry of Finances and the Registry of Commerce) publish online financial information, which is retrievable by automatic filling of the Web search forms. Therefore, we can easily download large amounts of financial data for the purpose of further analyzes. In this article, we analyze the Romanian entities’ profitability of the capitals (gross profit divided by the total capitals), as the main indicator for predicting future merging operations. We aim to suggest the merger between two companies, either sharing the same activity code (according to the Romanian CAEN classification [3]) or having dependent activity codes (in the subsequent section, we shall present a technique for deriving these dependencies). The consequence of the merger would be the improvement of the resulting company’s profitability of the capitals. In order to accomplish our purpose, we compute each entity’s profit margin and the average of this indicator on each one of the available CAEN activity codes. We consider that a business entity requires a merging if its Copyright © 2006-2008 by CCC Publications - Agora University Ed. House. All rights reserved.
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indicator is far below the average indicator of its activity group. In addition, the mentioned company should merge with an entity whose profitability of the capitals exceeds the average measure on the sector.
3
Dependencies between entities
A major disadvantage of the financial statements’ analysis is that it does not consider the dependencies between the entities of the economy. Instead, it analyzes each single entity separately from its competitors and from its clients or suppliers. For improving our merger suggestions, we should also predict whether a sector of the economy is related to other sector, and propose mergers candidates from linked sectors. Hence, we need to compute the dependency map of the various sectors of the economy: we call that the entity A depends on the entity B, if either A is a client of B or A is a supplier for B). These dependencies are crucial for suggesting mergers between companies, as the merging usually takes place between two competitor companies (in order to strengthen the position of the resulting company on the market), or between a producer and a customer (in order to have access to the customer’s market and reduce costs). Although the list of the clients and the suppliers of a company is a private asset of that company and hard to be obtained from any third source, we can still predict the sectors of the economy in which the company acts as a supplier or as a client. We shall illustrate the technique by an example: let us consider the case of an aluminum smelter and try to guess possible mergers for our business entity. The aluminium smelter gathers material from a bauxite mine and it delivers the aluminum to an airplane producer. Therefore, it could be interested in the merge with either a bauxite mine or an airplane producer, as a measure for reducing the costs of the acquired bauxite or the costs of producing the airplane and obtain larger profit margins. Based on our example, we shall conclude that the aluminum smelting activity (whose activity code is 2742, according to the CAEN Rev-1 classification [3]) depends on the bauxite mining activity (CAEN code: 1320) and the airplane building activity (CAEN code 3530) depends on the aluminum smelting activity. Our method of predicting the dependencies between various sectors of the Romanian economy consists in analyzing the Web logs of our Business Information Search Engine Application d394.eu [4]. The Web logs contain a daily-based and server-based list of performed queries: a query consists in one or more financial codes of Romanian companies. Therefore, a dependency matrix can be easily computed by analyzing the logs and classifying the financial codes of the companies into their activity codes, according to [3]. In the second part of the next section, we present our results of analyzing the Web search logs and their usage in suggesting merger operations between the Romanian entities.
4 4.1
Results Results from the analysis of the financial statements
The gathering of the publicly available financial statements of the Romanian entities resulted in the building of a database containing 518.409 active entities, which have regularly published their financial statements during the last 5 financial years. By analyzing the available information and computing the profitability of the capitals (gross profit divided by the total capitals), we have calculated the average indicator for each activity code (according to the CAEN classification) and outlined the best profitability indicator of each CAEN code. The results, depicted in [Table 1], show the CAEN codes having an average profitability of the capitals above 1,200%. The table also enlightens the highest profitability of the capital for each activity code at the end of the 2006 financial year. The data impresses, as there is an important difference between the average profitability of the capital of the most part of activity codes (less than 100%) and the average profitability of the ones depicted in the table (greater than 1,200%). The analysis reveals that the Romanian economy has some entities with a very high indicator of profitability of the capitals (for instance, 847,732.90% - the profitability of an entity whose activity code is 6312 "Deposits"). The fact appears as there are several privately-owned companies who exhibit only a minimum required capital of 5 RON. Our study also shows that privately-owned businesses can provide a much higher profitability when compared to other investment alternatives (stock exchange or mutual funds). Even so, their lack of capitalization makes them vulnerable to hostile takeovers and decreases the corresponding investors’ attractiveness.
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CAEN Code 0122 2411 2615 2861 4511 6010 6220 6312 6523 7320 7415 8022 9212
Average Profitability of Capitals (%) 1533.93 1616.52 4366.41 2348.89 1895.94 1913.49 2701.85 4441.15 1574.28 1204.55 2287.31 2259.93 1351.68
Highest Profitability per CAEN Code (%) 239518.00 65595.83 125117.20 21896.67 395142.50 156028.50 98701.00 847732.90 109585.60 52688.89 158053.40 13088.93 64280.20
Table 1: The sectors with the highest profitability of capital (December 2006)
4.2
Analyzing Web Logs
We have analyzed our daily-based and server-based collection of Web search logs, gathered from our MultiServer Search Application. The collection contains 267.713 log entries, both successfully and unsuccessfully searches (server errors or client mistakes). The map of Business Dependencies (see Figure 1) results as follows: for each two subsequent entries in a log file we consider that the activity code (according to the Romanian CAEN classification) of the second entry depends on the activity code of the first entry, therefore it would be probable that an entity from the first CAEN class would be either a client or a supplier of an entity from the second CAEN class. The more intense is the color corresponding to the dependency between the two CAEN classes - the highest is the probability that the second class (depicted as columns of the Figure 1 map) depends on the first class (depicted as rows in the map). The computed business map shows an increased business activity corresponding to the region of the 4*** and 5*** CAEN groups (see the excerpts of the Figure 2). To some extent, our automatically-generated map reflects the reality of the Romanian business economy: the 4*** group (which is the fifth group from left to right and from top to bottom, according to the margins of Figure 1) denotes utility supplier entities (electricity, gas), whereas the 5*** group groups the entities from the commerce sector. It is straightforward that almost every business sector depends on the entities of the 4*** CAEN class; it is also highly expected that there is an increased dependence between the entities from the 5*** CAEN class (depicted as the red cells in the above enumerated figures). An interesting result is that there are no dependencies between some regions of the maps: we explain this finding as the entities of some business activity codes do not have intense business activity with other sectors of the economy. For instance, the 0*** CAEN class (agricultural sector) and the 9*** CAEN class (the last one depicted in the Figure 1 - from left to right and from top to bottom) are scarcely represented on the map. We should also take in consideration the fact that we have built the model based on Web log entries, which means that some entities of the economy could be easily left outside the log entries, as users did non search for information on those entities.
4.3
Suggesting mergers
Once we collect the financial information, analyze it and generate the business map for supporting the mergers, we simply find an entity with which our A company should merge. We point out that the purpose of the merge should be the increase of the profitability of the capitals of the first company (we call it company A). For that company, we compute the list of the candidate merger entities: the list contains the entity which has the highest profitability of the capitals in the same activity code as our A company and the companies with the highest profitability of the capitals in the sectors which come in a dependency relation with the sector of the company A, according to the Business Dependency Map shown in Figure 1. The company which exhibits the highest profitability of the capitals from the candidate companies becomes the suggestion for the merger transaction. The fact that the candidate companies are top performers in their sectors of activity (in terms of profitability of capitals) assures the improvement of the indicator on the resulting company, as compared with the initial company
Data-Mining Techniques for Supporting Merging Decisions
Figure 1: The Map of Business Dependencies
Figure 2: Map of Business Dependencies: Excerpts from Figure 1
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A. In terms of profitability of the capitals, the suggested merger candidate (we shall name it B) will rather not be interested in a merge operation. The merge could be an advantage for both companies if the resulting company’s position in the market is significantly strengthened, when compared to the individual companies before the merge.
5
Summary and Conclusions
We presented a technique for suggesting merging operations based on the financial statements of the Romanian companies and the Web logs collected from our Business Information Server Search Application D394. We chose the profitability of the capitals as the definite criterion for suggesting which company should merge with which company. The indicator was computed using the financial statements available at the end of 2006. The model can be easily extended to a multi-year analysis of the financial statements. We also plan to use other indicators (like the position of the companies on the market, or the total intangible assets of each company) to improve the merger suggestion model. The use of the total intangible assets enhances the model to suggest also possible acquisitions or absorptions on the market. A model of predicting merger or acquisition operations will definitely be useful for the business community, as a merger announcement usually comes as a surprise in the market, with little chance of reaction for the competing entities.
References [1] R. Miller, ”Global Steel is Coming Together”, Business Week, September 13, 2006. [2] G. Sarcinschi, ”O firma a ’regelui confectiilor’ absorbita de Catex Calarasi”, Business Standard, December 2, 2007. [3] National Institute of Statistics, ”The classification of the National Activities - CAEN Rev-1, http://www.ins.ro, 2002. [4] SoftProEuro, ”Declaratia 394”, http://www.d394.eu. [5] L. Hancu, ”Enhancing the Invisible Web”, KEPT 2007 International Conference, Cluj-Napoca, Romania, June 2007. [6] L. Hancu, ”The Pre-Accession Competitiveness of the Romanian Software Companies”, International Conference Competitiveness and European Integration, Babes Bolyai University, Cluj Napoca, Romania, October 2007. [7] M. Konchady, ”Text Mining Application Programming - Programming Series”, Charles River Media, May 2006, pp. 197-202.
Lucian Hâncu SoftProEuro s.r.l. Cluj-Napoca E-mail:
[email protected]
