Financial Benchmarking of Telecommunications Companies

Jonas Karlsson Åbo Akademi University, Department of Information Systems, Lemminkäisenkatu 14, FIN-20520 Turku, Finland E-mail: Jonas.Karlsson@abo.fi

Barbro Back Åbo Akademi University, Department of Information Systems, Lemminkäisenkatu 14, FIN-20520 Turku, Finland E-mail: Barbro.Back@abo.fi

Hannu Vanharanta Pori School of Technology and Economics, Pohjoisranta 11, FIN-28100 Pori, Finland E-mail: Hannu.Vanharanta@pori.tut.fi

Ari Visa Tampere University of Technology, Department of Information Technology, Korkeakoulunkatu 10, FIN-33100 Tampere, Finland E-mail: Ari.Visa@tut.fi

Turku Centre for Computer ScienceTUCS Technical Report No 395 February 2001 ,6%1 952-12-0788-4 ISSN 1239-1891

Abstract The aim of this paper is to evaluate the financial performance of telecommunications companies with the help self-organising maps. A total of 88 companies is evaluated and benchmarked on the basis of seven key ratios, calculated for five consecutive years 1995-99. To conduct benchmarking with several financial key figures in a changing business environment is difficult and time consuming for today’s managers and there exists a need for an easy and quick tool to accomplish this task. Furthermore, financial analysts and executives need efficient tools for their work to make comparisons and to trace company performance over time. In this study, we have collected financial data from the Internet and used self-organising maps, a form of neural networks, to analyse telecommunication companies’ financial performance. The self-organising map method turned out to be a good analysing, clustering and visualising tool for depicting patterns and company movements over the years, within the business line of telecommunications companies. Keywords: Financial benchmarking, Self-organising maps, Neural

networks, Financial performance, Telecommunication

TUCS Research Group Computational Intelligence in Business

1

1. Introduction At the same rate as new products are launched onto the market and new competitors arise, there is an increasing pressure on the companies to be competitive enough to stay alive. There is a need to develop high-quality products, implement efficient working methods, and to have a clear hunch about who the major competitors are, what they are good at, and how they are doing financially. Furthermore, other stakeholders such as investors and financial analysts have a vast interest in how companies in the same line of business are doing financially. To be able to understand this, it is important to have efficient tools. Benchmarking is one way to accomplish necessary improvements in the company’s activities by measuring itself against other companies. [Bendell et al. 1998, 66] Benchmarking can be divided into qualitative and quantitative benchmarking. Qualitative benchmarking involves to measure functions and methods of one company against other companies. Quantitative benchmarking, on the other hand, involves benchmarking the performance and profitability of one company against others. Quantitative benchmarking can also be referred to as financial benchmarking. Financial benchmarking is usually conducted by analysing the financial statements or financial key ratios of the selected companies. [Karlöf 1997, 40-41] One problem that is encountered immediately is, however, that within a database there can be very much information, both relevant and irrelevant. How should the collection be done in order to gather only relevant information? To manually browse through, and check the database, is cumbersome work that could take a great amount of time to accomplish. Furthermore, there is a risk of missing relevant information or possible hidden patterns. [Stein, 1993] By using ordinary spreadsheet programs one can easily compare two to six companies at a time according to one ratio at a time. However, if one wants to obtain an overview of the competitors on the market, or want to take into account several ratios at the same time, spreadsheet programs are no longer of any use. Neural networks, in the form of self-organising maps, provide a new tool for clustering and visualisation of large amounts of information. This technique analyses the different characteristics of the input, and groups samples with similar characteristics together. In this report, we do a financial benchmark for telecommunications companies using self-organising maps. Thus, in the financial comparison conducted in this report the self-organising map will analyse selected financial key ratios of companies, grouping companies with similar financial performance together. The term self-organising map has become a very popular topic in today’s information technology society. Since its invention in 1981 over 4300 research papers have been written on the subject of self-organising maps [Kohonen, 2000]. Some examples of more resent research papers include cloud classification [Ambroise et al., 2000], image object classification [Becanovic, 2000], breast cancer diagnosis [Chen et al., 2000], classifying and clustering Internet traffic [Raivio et al., 2000], and extracting knowledge from text documents [Visa et al., 2000]. Generally these reports can be divided into two

2

groups, analyses and surveys. The analyses take a more technological approach towards the algorithm and function of the self-organising map, while surveys take the practical application of the self-organising map into consideration [Kaski, 1998]. There are a very small number of reports concentrating on financial analysis and large databases available at present. An example of a financial survey is the study conducted by Back et al. (1998). In this study self-organising maps were used to benchmark and analyse 120 companies in the pulp and paper market. The main objective of the study was to determine whether the self-organising map could be used for managing complexity in large databases. The result of the study indicates that there are benefits from using the self-organising map as a tool for organising large, standardised databases, and also to identify clusters of companies with similar characteristics. Not many financial analyses of the telecommunications market have been conducted using the self-organising map in the same fashion as the study of pulp and paper companies. In this way, this report takes a step into a newer and untried field, furthermore, this study is more concentrated at analysing the companies and their movement over the years, instead of evaluating whether self-organising maps are a suitable tool for this kind of comparisons. One should also keep in mind that the companies in the pulp and paper market are often well-established, 100-year old companies with a permanent customer base. In the telecommunications sector, most of the companies are new actors on the market (under 10 years old), often with poor profitability due to heavy investments needed in order to be competitive. This report is based on the findings of Karlsson (2000). The rest of the paper is organised as follows: Section 2 describes the methodology and the choice of financial ratios and companies. Section 3 presents the construction of the self-organising maps and Section 4 presents a detailed analysis of the maps. The conclusions of this paper are presented in Section 5.

3

2. Methodology In this section we provide a description of the self-organising map, introduce the companies included in the study as well as describe the choice of the financial key ratios.

2.1 The Data Mining Tool The self-organising map technique is a form of a neural network that can be used as a clustering and visualisation tool. The self-organising map technique creates a two-dimensional map from the input data. This map resembles a landscape where it is possible to identify borders that define different clusters. These clusters consist of input variables with similar characteristics, i.e. in this report they consist of companies with similar financial performance. [Kohonen 1997, VIII] With every node i on the map, a parametric reference vector mi is associated. The nodes in the array represent the neurons of the neural network. Data vectors x, representing the high dimensional input data space, are mapped onto the two-dimensional array. Vector x can be compared to every mi by, for example, using the smallest Euclidian distance

ΛΠ[ − in order to define the best-matching node. [Kohonen 1997, 86]:

,ΠΛΘ Λ

ΛΦ Π[Π[ −=− [2.1]

where the index c refers to the winning unit. The self-organising map algorithm consists of two steps that are iterated for every sample: Step 1: When a sufficient number of input vectors x have been presented, and the

reference vector defined, the calibration of the map begins. This is done by finding where the best matching neurons on the map lie, and labelling the map units correspondingly [Kohonen 1997, 90]. For each input vector used in the training of the self-organising map the best matching neuron c in the map is located. Usually, the best matching neuron is defined by the distance function used, in most cases the earlier described Euclidian distance. In other words, the best matching neuron is the neuron with the weight vector mi situated closest to the input vector. [Kohonen 1997, cited in Kangas 1994, 15]

Step 2: During the learning process those nodes will activate each other to learn

something from the same input x. This will result in a local smoothing effect on the weight vectors of neurons in this neighbourhood. [Kohonen 1997, 87]

The process can be described as following: first the input vectors, xi are mapped randomly onto the two-dimensional array, the map. The input vector is compared to all reference vectors mi, in order to find the best-matching node, c (Step 1). The best-matching neuron affects the neurons within the neighbourhood area hci, i.e. they learn from the best-matching neuron (Step 2).

4

The weight vector values mi are tuned so that the match of the modified weight vectors in the neighbourhood of the best matching neuron and the input vector x is improved [Kangas 1994, 15]. When using the Euclidian distance function, the adaptation process has the following general form [Kohonen 1997, 87]:

[ ] ,)()()()()1( ΩΠΩ[ΩΚΩΠΩΠ ΛΦΛΛΛ −+=+ [2.2]

where t = 0, 1, 2,… is an integer, the discrete-time coordinate. The function hci(t) is the neighbourhood of the winning neuron c, and acts as a smoothing kernel defined over the lattice points. [Kohonen 1997, 87] The neighbourhood function hci(t) defines the activity of neighbouring neurons. Usually the value of the neighbourhood function depends only on the distance between the neurons and time. The neighbourhood function can be defined as:

,)(2

Η[Σ)()(2

2

−−

∗=Ω

ΥΥΩΩΚ

ΛΦ

ΦΛ σα [2.3]

where α(t) and )(Ωσ are suitable decreasing functions of time, r i and rc are the coordinates of units i respective c. [Kohonen 1997, cited in Kangas 1994, 15]

These two steps are iterated and cycled through each input vector until a certain stopping criterion is reached. The stopping criterion is usually a pre-defined number of iterations. The software we will use when training and creating the self-organising map is called The Self-Organising Map Program Package Version 3.1 (SOM_PAK), and is based on the Kohonen self-organising algorithm. The software package has been developed to demonstrate the implementation of the self-organising algorithm, and was written by the SOM programming team at the Helsinki University of Technology. The SOM_PAK and more information regarding the software can be acquired from the homepage of the Neural Networks Research Centre (http://www.cis.hut.fi/research/). In the following section, this software will be referred to as SOM_PAK. To visualise the final self-organising map we will use the unified distance matrix method (U-matrix). The U-matrix method can be used to discover otherwise invisible relationships in a high-dimensional data space. It also makes it possible to classify data sets into clusters of similar values. The simplest U-matrix method is to calculate the distances between neighbouring neurons, and store them in a matrix, i.e. the output map, which then can be interpreted. If there are “walls” between the neurons, the neighbouring weights are distant, i.e. the values differ significantly. The distance values are also displayed in colour when the U-matrix is visualised. Hence, dark colours represent great distances while brighter colours indicate similarities amongst the neurons. [Ultsch 1993]

The software we have used to visualise the final constructed self-organising maps and the feature planes in this report is a program called Nenet version 1.1a. This software is

5

developed by the Nenet team at the Helsinki University of Technology. Nenet is a user-friendly program designed to illustrate the use of self-organising maps, and provides an easy way to visualise the output maps with not only the U-matrix method but also the Interpolated 2D U-matrix method, and as parameter level maps. Because the software is a commercial product the version used in this report is a demo version. More information about the software and a downloadable demo version can be obtained from the Nenet homepage (http://koti.mbnet.fi/~phodju/nenet/Nenet/General.html).

2.2 Choice of Companies We selected the companies from five different regions: Asia, Canada, Continental Europe, Northern Europe (the Nordic Countries) and the USA. It would have been interesting to include companies from Africa or South America, but these companies had very little or no financial information at all on the Internet, so these companies were excluded. The 88 selected companies are presented in Table 2-1. There is also an average of every region included as an additional “company”, bringing the total number of companies to 93. The averages will make a comparison between the different regions possible.

2.3 Choice of data and information The starting point of this report was to use only the Internet as a source of financial data. Hence, the data searching part of this research was executed by searching for financial statements on the homepages of the companies, as well as in different databases on the Internet. Many of the companies did not have financial information for more than three years on their homepages on the Internet. Therefore, in most cases, this was complemented with financial information from databases such as the U.S. Securities and Exchange Commission (http://www.sec.gov) for American companies, the System for Electronic Document Analysis and Retrieval (http://www.sedar.com) for Canadian companies and Japan Financials (http://japanfinancials.com) for Japanese companies. Since no good database was found for the Continental European companies the financial information has been complemented with annual reports received via regular mail.

6

1ΞΠΕΗΥ &ΡΠΣ∆Θ∴ <Η∆Υς 1ΞΠΕΗΥ &ΡΠΣ∆Θ∴ <Η∆Υς

47 ,∋7 1995-99

1 %ΗΘΗΙΡΘ 1995-99 48 ,ΘΩΗΟΟΛΦ∆ΟΟ 1995-98

2 ∋ΡΥΡ 1995-99 49 ,ΘΩΗΥΓΛϑΛΩ∆Ο 1995-99

3 (ΥΛΦςςΡΘ 1995-99 50 /6, /ΡϑΛΦ 1995-99

4 +3< 1995-99 51 /ΞΦΗΘΩ 1995-99

5 1ΗΩΦΡΠ 1995-99 52 0&, :ΡΥΟΓ&ΡΠ 1995-99

6 1ΡΝΛ∆ 1995-99 53 0ΡΟΗ[ 1995-99

7 6ΡΘΗΥ∆ 1995-99 54 0ΡΩΡΥΡΟ∆ 1995-99

8 7ΗΟΗ ∋ΗΘΠ∆ΥΝ 1995-99 55 1Η[ΩΗΟ 1995-99

9 7ΗΟΗ1ΡΥ 1995-99 56 3ΡΖΗΥΩΗΟ 1995-99

10 7ΗΟΛ∆ 1995-99 57 3ΡΖΗΥΖ∆ΨΗ 1995-99

11 1ΡΥΓΛΦ ∃ΨΗΥ∆ϑΗ 1995-99 58 4Ξ∆ΟΦΡΠΠ 1995-99

59 6%& 1995-99

60 6ΣΥΛΘΩ 1995-99

12 ∃ΟΦ∆ΩΗΟ 1995-99 61 7ΗΟΟ∆Ες 1995-99

13 ∃ςΦΡΠ 1995-99 62 8ς:ΗςΩ 1995-99

14 %ΥΛΩΛςΚ 7ΗΟΗΦΡΠ 1995-99 63 9Λ∆ΩΗΟ 1995-99

15 &∆ΕΟΗ & :ΛΥΗΟΗςς 1995-99 64 ;ΛΥΦΡΠ 1995-99

16 &ΡΟΩ 7ΗΟΗΦΡΠ 1995-99 65 86∃ ∃ΨΗΥ∆ϑΗ 1995-99

17 ∋ΗΞΩςΦΚΗ 7ΗΟΗΝΡΠ 1995-99

18 )Υ∆ΘΦΗ 7ΗΟΗΦΡΠ 1995-99

19 0∆ΥΦΡΘΛ 1995-99 66 %ΗΟΟ 0ΡΕΛΟΛΩ∴ 1995-99

20 0∃7∃9 1996-99 67 &ΟΗ∆ΥΘΗΩ 1995-99

21 0ΡΕΛΟΦΡΠ 1996-99 68 0ΛΩΗΟ 1995-99

22 2ΟΛΨΗΩΩΛ 1995-99 69 1ΡΥΩΗΟ 1ΗΩΖΡΥΝς 1995-99

23 2Υ∆ΘϑΗ 1995-98 70 6∆ςΝΩΗΟ 1995-99

24 3ΚΛΟΛΣς 1995-99 71 7ΗΟΞς 1995-99

25 3ΡΥΩΞϑ∆Ο 7ΗΟΗΦΡΠ 1995-99 72 &∆Θ∆Γ∆ ∃ΨΗΥ∆ϑΗ 1995-99

26 5ΡςΩΗΟΗΦΡΠ 1995-98

27 6∆ϑΗΠ 1996-99

28 6ΛΗΠΗΘς 1995-99 73 ∋∋, 1995-99

29 6ΖΛςςΦΡΠ 1995-99 74 ,ΘΓΡς∆Ω 1995-99

30 7ΗΟΗ:ΗςΩ 1995-99 75 ,Ζ∆ΩςΞ 1995-99

31 9ΡΓ∆ΙΡΘΗ 1996-99 76 −∆Σ∆Θ 5∆ΓΛΡ 1995-99

32 (ΞΥΡΣΗ ∃ΨΗΥ∆ϑΗ 1995-99 77 −∆Σ∆Θ 7ΗΟΗΦΡΠ 1995-99

78 .ΡΝΞς∆Λ 1995-99

79 .∴ΡΦΗΥ∆ 1995-99

33 3&ΡΠ 1995-99 80 0∆ΩςΞςΚΛΩ∆ 1995-99

34 ∃∋& 1995-99 81 0ΛΩςΞΕΛςΚΛ (ΟΗΦΩΥΛΦ 1995-99

35 ∃ΟΟΩΗΟ 1995-99 82 1(& 1995-99

36 ∃ΘΓΥΗΖ &ΡΥΣ. 1995-99 83 177 ∋Ρ&Ρ0Ρ 1995-99

37 ∃7&7 1995-99 84 2., 1995-99

38 ∃ΞΓΛΡΨΡ[ 1995-99 85 6∆ΠςΞΘϑ 1995-99

39 %ΗΟΟ ∃ΩΟ∆ΘΩΛΦ 1995-99 86 6∆Θ∴Ρ 1995-99

40 %ΗΟΟ6ΡΞΩΚ 1995-99 87 6Κ∆ΥΣ 1995-99

41 &ΗΘΩΞΥ∴7ΗΟ 1995-99 88 6ΡΘ∴ 1995-99

42 &ΛςΦΡ 1995-99 89 7ΗΟςΩΥ∆ 1995-99

43 &ΡΠς∆Ω 1995-99 90 7ΡςΚΛΕ∆ 1995-99

44 &ΡΠΨΗΥςΗ 1995-99 91 8ΘΛΓΗΘ 1995-99

45 (ΟΦΡΩΗΟ 1995-99 92 9ΛΓΗςΚ 1Λ∆ϑ∆Π 1996-99

46 ∗7( 1995-99 93 ∃ςΛ∆ ∃ΨΗΥ∆ϑΗ 1995-99

1ΡΥΓΛΦ &ΡΠΣ∆ΘΛΗς

&ΡΘΩΛΘΗΘΩ∆Ο (ΞΥΡΣΗ∆Θ &ΡΠΣ∆ΘΛΗς

∃ΠΗΥΛΦ∆Θ &ΡΠΣ∆ΘΛΗς

∃ςΛ∆Θ &ΡΠΣ∆ΘΛΗς

&∆Θ∆ΓΛ∆Θ &ΡΠΣ∆ΘΛΗς

7∆ΕΟΗ >2-1≅: 7ΚΗ ςΗΟΗΦΩΗΓ ΦΡΠΣ∆ΘΛΗς.

7

2.4 Choice of Ratios To conduct the financial benchmarking of telecommunications companies, the companies’ financial statements have been used as information source. From these financial statements seven key ratios have been calculated for each company and used as input data when training the self-organising map. These key ratios will be briefly presented in the following section. The selection of relevant key ratios was based on an empirical study by Lehtinen (1996) where the international accounting differences were analysed in more detail, especially as to the reliability and validity of the ratios. Seven financial key ratios, which fulfilled the criteria of good validity and reliability, were selected and calculated for each of the companies. The key ratios can be divided into four different classes: profitability ratios, liquidity ratios, solidity ratios and efficiency ratios. In financial benchmarking it is common to choose ratios that measure different aspects on financial behaviour. In this financial benchmark more emphasis was put on the profitability since it is regarded to be a driving force behind the companies operating in the telecommunications market. Three profitability ratios were selected, Operating Margin, Return on Total Assets and Return on Equity. In the class liquidity only one ratio was selected, the Current Ratio. The solidity of a company was regarded to be nearly as important as the profitability and thereby two ratios were selected, Equity to Capital and Interest Coverage. In the final class, efficiency, only one ratio was selected, Receivables Turnover ratio. The formulas for the calculation of the selected key ratios are presented below:

1. 2ΣΗΥ∆ΩΛΘϑ 0∆ΥϑΛΘ

2. 527∃

3. 52(

4. &ΞΥΥΗΘΩ 5∆ΩΛΡ

5. (ΤΞΛΩ∴ ΩΡ &∆ΣΛΩ∆Ο

6. ,ΘΩΗΥΗςΩ &ΡΨΗΥ∆ϑΗ

7. 5ΗΦΗΛΨ∆ΕΟΗς 7ΞΥΘΡΨΗΥ

.100∗6∆ΟΗς1ΗΩ

ΡΙΛΩ3Υ2ΣΗΥ∆ΩΛΘϑ

.100)(

∗+∃ΨΗΥ∆ϑΗ∃ςςΗΩς7ΡΩ∆Ο

([ΣΗΘςΗ,ΘΩΗΥΗςΩ,ΘΦΡΠΗ7ΡΩ∆Ο

.100)(

∗+ ∃ΨΗΥ∆ϑΗ(∆ΥΘΛΘϑςΩ∆ΛΘΗΓ5Η&∆ΣΛΩ∆Ο6Κ∆ΥΗ

,ΘΦΡΠΗ1ΗΩ

.ς/Λ∆ΕΛΟΛΩΛΗ&ΞΥΥΗΘΩ

∃ςςΗΩς&ΞΥΥΗΘΩ

.100)(

∗+∃ΨΗΥ∆ϑΗ∃ςςΗΩς7ΡΩ∆Ο

(∆ΥΘΛΘϑςΩ∆ΛΘΗΓ5Η&∆ΣΛΩ∆Ο6Κ∆ΥΗ

.([ΣΗΘςΗς,ΘΩΗΥΗςΩ

,ΘΦΡΠΗ1ΗΩ7∆[Ης,ΘΦΡΠΗ([ΣΗΘςΗς,ΘΩΗΥΗςΩ ++

.)( ∃ΨΗΥ∆ϑΗΦΗΛΨ∆ΕΟΗ5Η∃ΦΦΡΞΘΩς

6∆ΟΗς1ΗΩ

3ΥΡΙΛΩ∆ΕΛΟΛΩ∴

5∆ΩΛΡς

/ΛΤΞΛΓΛΩ∴

5∆ΩΛΡς

6ΡΟΛΓΛΩ∴

5∆ΩΛΡς

(ΙΙΛΦΛΗΘΦ∴

5∆ΩΛΡς

8

3. Training the Maps During the training process, several tests were carried out in order to determine suitable parameters. The hexagonal lattice type was preferred for the visualisation of the output map. Furthermore, the map ought to be of a rectangular form, rather than square, in order to achieve a stable orientation in the data space [Kohonen 1996, 3-4]. Commonly the x-axis should be about 30 per cent greater than the y-axis, thus forming a rectangular output map. Another recommendation is that the training length of the second part should be at least 500 times the number of network units [Kohonen 1997, 88]. To ease the neural networks learning process and improve the quality of the map the input was standardised. For example, one of the selected key ratios would have a range of 0 to 1, while another key ratio has a range of –100 to 100, then the contribution of the second input will likely be given more weight than the first one. Because of this it is essential to standardise the input data so that their value reflects their importance, or at least that the value is similar in relation to the other input data [Bishop 1995]. In this research the standardisation has been done by scaling the input variables by the variance according to the following formula:

.)(α

2σΛΛΘ

ΛΘ

[[[

−= [3.1]

During the training process the self-organising map was still placing too much weight on the extreme values, even after the standardisation. To cope with this in order to receive an interpretable map Johnson and Wichern (1997) suggests that one should modify the input data by putting a limit to how great values the extreme observations were allowed to take. In this research the extreme values have been limited to –50 respectively 50. The constructed map was trained using input data for the years 1995-99, so only one map was created and analysed. The reason for why all years were used to train one map, and not one map for each year, is that now the same clusters appear for all years, and in the same places. If one map would be trained for each year, different clusters would probably appear, and would have to be analysed and interpreted separately. The trained map is presented in Figure 3-1, and is of the size 9 x 6 neurons. On this size of map, it is still rather easy to define the different clusters by looking at the colour shades of the borders between the hexagons. The brighter colours of the hexagons imply similar characteristics, while darker colours represent greater distances. The coloured borders between the hexagons are of great value when trying to determine and interpret clusters. Furthermore, it is also possible to visualise company movements in an interpretable fashion on this somewhat larger map.

9

)ΛϑΞΥΗ >3-1≅: 7Υ∆ΛΘΗΓ Π∆Σ ΖΛΩΚ ΩΚΗ ςΛ]Η 9 [ 6 ΘΗΞΥΡΘς.

Using the feature planes it is possible to visualise the values of a single vector column, i.e. in this research the maps for one financial key ratio. These feature planes can be analysed in order to discover how well the companies have been doing regarding one single financial ratio [Kohonen 1996]. Thus, with these feature planes it is rather easy to see where the companies with good profitability are located on the map, and in the same fashion where the companies with poor profitability are located. The feature planes provided excellent assistance in determining and analysing the company clusters on the self-organising map.

,ΘΩΗΥΗςΩ &ΡΨΗΥ∆ϑΗ(ΤΞΛΩ∴ 7Ρ &∆ΣΛΩ∆Ο

5ΗΦΗΛΨ∆ΕΟΗς 7ΞΥΘΡΨΗΥ

&ΞΥΥΗΘΩ 5∆ΩΛΡ

2ΣΗΥ∆ΩΛΘϑ 0∆ΥϑΛΘ 527∃ 52(

([ΦΗΟΟΗΘΩ

9ΗΥ∴ ∗ΡΡΓ

∗ΡΡΓ

∃ΨΗΥ∆ϑΗ

%∆Γ

9ΗΥ∴ %∆Γ

7ΗΥΥΛΕΟΗ Figure [3-2]: The Feature planes.

10

The feature planes show a map for each of the financial key ratios, where warmer colours, i.e. red, represent high values, which in our case implies good values, and darker colours show low values, which in our case implies poor values. A high value does not necessarily mean a good value, but it does in the case of our selected key ratios.

3.1 Defining the Clusters By analysing the output map more carefully, six major clusters of companies were identified. To identify the clusters we used both the U-matrix map [Ultsch, 1993] and the feature planes [Kohonen, 1996]. By analysing the colours of the borders between the hexagons, as well as the colour of the hexagon itself it is possible to find similarities as well as differences. Furthermore, the values of the neurons have been evaluated in order to determine that the clusters are right. The identified clusters are presented in Figure 3-1 in the form of an U-matrix map:

)ΛϑΞΥΗ >3-3≅: 7ΚΗ ΛΓΗΘΩΛΙΛΗΓ ΦΟΞςΩΗΥς ΡΘ ΩΚΗ ςΗΟΙ-ΡΥϑ∆ΘΛςΗΓ Π∆Σ.

In Figure 3-1 six different clusters of companies were identified. These clusters have been labelled: A1, A2, B, C1, C2 and D. The conclusions of the interpretations is as follows:

- Group A1 and Group A2 represent the best in class companies. For the companies situated in subgroup A1, profitability is very good, with very high values in the financial ratios Operating Margin, ROTA, and ROE. Solidity is decent, i.e. the values of the Equity to Capital ratio and the Interest Coverage ratio vary from good to average. Generally, liquidity is slightly worse, i.e. lower values in Current Ratio. For instance, companies like British Telecom (97-99), Nokia (97-99), Samsung (95, 99), Sonera (96-97, 99), and Vodafone (97, 99) are situated in this group.

- Group A2 is the second subgroup of the best in class group. The companies

situated in this group are characterised by slightly lower profitability than Group A1, but instead liquidity and solidity are much better. These companies generally have the best values in Current Ratio on the map.

11

Furthermore, these companies possess very high values in the Equity to Capital ratio, and are almost the only companies to show great values in the Interest Coverage ratio. Efficiency is average. This group includes companies like Benefon (95-97), Motorola (95), Sonera (98), and Vodafone (95-96).

- Group B is where the companies with slightly poorer performance than

those in Group A1 and A2 are situated. These companies are distinguished by good profitability, and especially the ROE ratio is excellent. These companies also have somewhat worse liquidity and solidity than the companies in Group A. Instead, the Receivables Turnover ratio is generally better. Examples of companies are Alcatel (97-98), AT&T (95-99), Ericsson (95-99), Lucent (96-99), Nokia (95-96), and Vodafone (98).

- Group C1 is the better of two subgroups in Group C. Here the companies

possess decent profitability, good liquidity, and also good values in the Equity to Capital ratio. But the values in Interest Coverage and Receivables Turnover are worse. In this group, companies such as Alcatel (99), NTT Docomo (95-99), Sonera (95), Sony (96-99), and Sprint (96-98) can be found.

- Group C2 is the slightly poorer of the two middle groups. These companies

have decent profitability, but poor liquidity. Interest Coverage and Receivables Turnover are also poor, but Equity to Capital, on the other hand, is very good. Companies situated in this group include for example British Telecom (95-96), Matsushita (95-99) Motorola (96-99), and Telia (95-99).

- Group D is the poorest group. The companies with poor financial

performance can be found in this group. Distinguishing features are commonly poor profitability and solidity. Liquidity is average and Receivables Turnover varies from very good to poor. Generally this group contains service providers from Europe and the USA, but also some Japanese companies, mostly for the years 1998-99. Examples of companies in this group include Alcatel (95), Benefon (98-99), Orange (95-98), MCI WorldCom (96-99), Sony (95), and Sprint (99).

The characteristics of the identified clusters are summarised in Table 3-1. Each of the selected financial key ratios have been valued from excellent to very poor within each of the analysed clusters of companies. This valuation is based on the interpretation of the feature planes. As mentioned earlier the red colours on the feature planes indicate high values in the respective key ratio, while dark colours represent lower values.

12

2ΣΗΥ∆ΩΛΘϑ 527∃ 52( &ΞΥΥΗΘΩ (ΤΞΛΩ∴ ΩΡ ,ΘΩΗΥΗςΩ 5ΗΦΗΛΨ∆ΕΟΗς

0∆ΥϑΛΘ 5∆ΩΛΡ &∆ΣΛΩ∆Ο &ΡΨΗΥ∆ϑΗ 7ΞΥΘΡΨΗΥ

∗ΥΡΞΣ ∃1 ([ΦΗΟΟΗΘΩ ∗ΡΡΓ 9ΗΥ∴ ∗ΡΡΓ 9ΗΥ∴ 3ΡΡΥ ∗ΡΡΓ 3ΡΡΥ 9ΗΥ∴ 3ΡΡΥ

∗ΥΡΞΣ ∃2 9ΗΥ∴ ∗ΡΡΓ ∗ΡΡΓ ∗ΡΡΓ ∗ΡΡΓ ([ΦΗΟΟΗΘΩ ∗ΡΡΓ 9ΗΥ∴ 3ΡΡΥ

∗ΥΡΞΣ % 9ΗΥ∴ ∗ΡΡΓ ∃ΨΗΥ∆ϑΗ ([ΦΗΟΟΗΘΩ 9ΗΥ∴ 3ΡΡΥ ∃ΨΗΥ∆ϑΗ 3ΡΡΥ ∃ΨΗΥ∆ϑΗ

∗ΥΡΞΣ &1 ∗ΡΡΓ ∃ΨΗΥ∆ϑΗ ∃ΨΗΥ∆ϑΗ ∃ΨΗΥ∆ϑΗ ∃ΨΗΥ∆ϑΗ 9ΗΥ∴ 3ΡΡΥ 9ΗΥ∴ 3ΡΡΥ

∗ΥΡΞΣ &2 ∗ΡΡΓ ∃ΨΗΥ∆ϑΗ ∃ΨΗΥ∆ϑΗ 3ΡΡΥ 9ΗΥ∴ ∗ΡΡΓ 9ΗΥ∴ 3ΡΡΥ 9ΗΥ∴ 3ΡΡΥ

∗ΥΡΞΣ ∋ 3ΡΡΥ 3ΡΡΥ 9ΗΥ∴ 3ΡΡΥ 3ΡΡΥ 3ΡΡΥ 9ΗΥ∴ 3ΡΡΥ 3ΡΡΥ 7∆ΕΟΗ >3-1≅: 7ΚΗ ΦΚ∆Υ∆ΦΩΗΥΛςΩΛΦς ΡΙ ΩΚΗ ΓΗΙΛΘΗΓ ΦΟΞςΩΗΥς.

Based on Table 3-1, it is clear that both subgroups of Group A are good groups, but in slightly different manner. Both groups show good profitability, even though subgroup A1 is performing slightly better. On the other hand, subgroup A2 is characterised by better liquidity and solidity. From this table it also becomes obvious that the key ratio Receivables Turnover is not a very suitable measure when performing this type of financial study of the telecommunications market. With values ranging from average to very poor, it is likely that not much weight has been placed on this ratio when training the map. In fact, most of the companies experienced good Receivables Turnover. However, the values of this ratio are very low compared to, for example the ROE ratio. This is probably the reason for why the self-organising map has placed so little weight on this ratio. The conclusion is that although the Receivables Turnover ratio was considered to have great reliability and very good validity, it was not a useful financial key ratio in this report. The same problem can also be found with the Interest Coverage ratio, relatively low values give low weight in the training process.

4. Benchmark Analysis of the Companies Over Time In section 3, the clusters of companies on the self-organising output map were identified and analysed, in this section a more detailed analysis is conducted concerning company movement during the years 1995-99. Furthermore, the competing companies in the specific markets will be benchmarked against each other. The companies on the analysed self-organising maps will be labelled numerically according to Table 2-1 with the company numbers, except for the map with country averages. Section 4.1 presents country averages, section 4.2 presents the largest manufacturers and section 4.3 presents the largest service providers. In section 4.4 the Northern European companies will be benchmarked against each other, in section 4.5 the Continental European companies, in section 4.6 the American companies, in section 4.7 the Canadian companies and finally the Asian companies in section 4.8.

4.1 Country Averages In this section, the company averages will be benchmarked against each other, the accounting diversities between the five different regions will be analysed to see whether the locations on the map can be explained. The figures of the five regions are calculated as an average of the companies operating within the regions, i.e. the Nordic average consists of the Northern European companies.

13

)ΛϑΞΥΗ >4-1≅: 7ΚΗ ΠΡΨΗΠΗΘΩ ΡΨΗΥ ΩΚΗ ∴Η∆Υς ΙΡΥ ΩΚΗ ΦΡΞΘΩΥ∴ ∆ΨΗΥ∆ϑΗς.

The map shows that the company averages are actually positioned in the average groups, as could be estimated in advance. No company average is situated in the best or poorest groups. The only average to enter the somewhat poorer middle group, Group C2, is the Canadian Average for the years 1995-98. What is interesting with this average is that it is characterised by very high values in Equity to Capital. All of the Canadian companies have very high share capital in proportion to total assets. Hence, the Canadian companies seem to finance their operations rather heavily using their equity. The presumption was that the American and Canadian companies would have rather similar accounting methods and capital structures. Upon closer examination, it was discovered that many of the American companies also have very high Equity to Capital ratios. However, there exist large variations amongst these companies, many companies having very low Equity to Capital ratios, thus decreasing the American average. Another difference between the Canadian and American averages is that only service providers were selected from Canada, hence Canadian profitability is usually very low. The American average is situated a bit further away from the other company averages in the middle group (pink circles). One reason for this may be the unrestrictive accounting principles used in the United States of America. The Asian average seems to have similar characteristics as the Nordic and European average, situated in the middle group C1, even though a slight backtracking occurred in 1999, probably due to the Asian crisis (red arrow). As for the Asian crisis, the major crisis took place in 1997-98, but it is likely that the crisis only affects the financial figures for 1998-99. The impact of the crisis on the Asian telecommunications companies will be analysed in more detail in Chapter 6.8. In advance, it was anticipated that the Asian average would be situated in a middle to poor group, due to very strict accounting standards, but no major differences can be found in comparison with, for example, the Nordic average.

14

As expected, the Nordic and European averages display rather similar characteristics, and are consequently situated in the same group, Group C1. The only major movement to speak of is that the European average was situated close to Group D in 1995-96. However, in 1997-99, the financial figures have obviously improved, since the European average is now situated close to Group A1 (blue arrows).

4.2 The Largest Manufacturers In Figure 4-2, a visualisation of the largest manufacturers in this study is presented. This benchmark is international, hence companies from different countries are benchmarked against each other. This means that some accounting differences might influence the resulting output map.

)ΛϑΞΥΗ >4-2≅: 7ΚΗ ΠΡΨΗΠΗΘΩ ΡΨΗΥ ΩΚΗ ∴Η∆Υς ΡΙ Ο∆ΥϑΗςΩ Π∆ΘΞΙ∆ΦΩΞΥΗΥς.

As Figure 4-2 shows, Nokia (No. 6) is the only mobile phone manufacturer that experiences a steady increase in financial performance. Nokia is situated in Group B during the years 1995-96, and took a leap into Group A1 in 1997-99 (blue arrow). The reason for the success is a constant increase in all of the selected key ratios. Judging by the feature planes, Nokia was experiencing excellent profitability in 1999, and very good liquidity and solidity. Only the value of Receivables Turnover was somewhat lower than for the previous years. Motorola (No. 54), on the other hand, shows an almost steady decrease in performance. In 1995 the company was situated in Group A2, but has since then moved into the slightly poorer of the middle groups (red arrows). This is probably due to increasing competition on the telecommunications market. Examining the financial statements of Motorola reveals, for example, that net income has been decreasing steadily since 1995. Studying the selected key ratios shows that Motorola exhibits very good Equity to Capital but, on the other hand, its profitability has decreased during the last four years.

15

The third of the major mobile phone manufacturers, Ericsson (No. 3), is firmly situated in Group B during the years 1995-99 (purple circles). Ericsson is one of few of the selected companies that display high Receivables Turnover ratios. Furthermore, the Operating Margin and ROE ratios are excellent. The values in liquidity and solidity are slightly poorer. It is also interesting to take a look at the performance of the Finnish mobile phone manufacturer Benefon (No. 1). During, three consecutive years (1995-97) the company was firmly situated in Group A2, experiencing great liquidity, solidity, and profitability. In 1998-99, the company experienced the effects of both the Asian crisis and the Russian crisis, crashing into the poorest group, Group D (orange arrows). During these years, profitability is terrible and Interest Coverage is disastrous. However, liquidity and efficiency are good, and Equity to Capital is excellent. Sony (No. 88), on the other hand, has experienced a slight increase in their performance. In 1995 the company was situated in the poorest group, but eventually ended up in the slightly better middle group, C1, in 1999 (turquoise arrows). What is interesting about Sony’s performance is that they do not seem to experience any effects of the Asian crisis, except for a slight backtracking in 1999. One reason for why Sony is not experiencing any greater effects of the crisis could be the fact that they are a large international company, thus the Asian market comprises only a small part of their operations. Overall, Sony is performing much like the average Asian company. Lucent (No. 51) is a company with similar performance to Ericsson, situated in the same neurons for the years 96-98. In 1995, the company was situated in Group D, probably as a result of the fact that they were in their first years of operations. In 1996, they moved into Group B, and have been situated in the same area since then (yellow arrow). In 1999, Lucent experienced an increase in all of the selected key ratios, except Receivables Turnover. At this stage, Lucent had excellent ROE and good Operating Margin, ROTA, Equity to Capital, and Interest Coverage ratios. As a result of this, a small movement towards Group A1 is shown on the output map. Analysing this map reveals that most of the manufacturing companies are situated either in the middle groups or in Group B. Only a few companies manage to place themselves in Group A1 or A2. Similarly, only a few companies have been placed in the poorest group, amongst them Benefon. Most of the Asian companies are situated close to or in Group D. This is likely to be a consequence of the strict accounting methods in Asia.

4.3 The largest Service Providers In this section, the largest service providers are benchmarked against each other. Many of the service providers are new, growing companies that have recently started selling their services on the market. As a result of this, many of the companies are experiencing low profitability. This is probably a result of the high cost of developing their own infrastructure, i.e. building the telecommunications network, hiring personnel, and developing new services. Therefore, it was presumed that many of the companies would be situated in the group of poorly performing companies.

16

)ΛϑΞΥΗ >4-3≅: 7ΚΗ ΠΡΨΗΠΗΘΩ ΡΨΗΥ ΩΚΗ ∴Η∆Υς ΡΙ ΩΚΗ Ο∆ΥϑΗςΩ ςΗΥΨΛΦΗ ΣΥΡΨΛΓΗΥς.

An excellent example of the earlier mentioned hypothesis, that some of the service providers would be situated in the worst group, is the company Orange (No. 23). Orange is a fairly new service provider that is still in the phase of developing their own infrastructure. Examining the map reveals that Orange is situated in the poorest group, Group D, during the years 1995-98 (brown circle). Unfortunately, data for 1999 was not officially published on the Internet at the time this study was carried out. By studying the selected key ratios, it becomes clear that profitability is low, even though improvements have been made. During the last years, liquidity is below average and solvency is terrible. The reason for the low solvency and the ROE ratios is that the figure of retained earnings1 is negative, a figure as large as £ -1.1 billion! This suggests the possible acquisition of Orange, since it is clear that they cannot continue to operate in the same fashion, as they have to date. Actually, in May 2000, France Telecom acquired Orange. This is probably also the reason for why financial information for 1999 has not been published as of today. The acquisition of Orange makes France Telecom (No. 18) the second largest service provider in Europe, with 22 million customers. France Telecom is a Group C1 company, although they are situated close to both Group B and Group A1 (white arrows). France telecom shows rather similar values in the selected key ratios over the years, with high values in Operating Margin, ROE, and Equity to Capital ratios. For Sonera (No. 7), everything looks nearly as bright as for Nokia, situated in Group C1 in 1995, and entering Group A1 in 1996-97 (turquoise arrows). In 1998, Sonera experienced a slight backtracking and moved into Group A2, but eventually returned to the best in class group in 1999. This slight backtracking becomes obvious when

1 ∃ΦΦΞΠΞΟ∆ΩΗΓ ΟΡςς (ΣΥΡΙΛΩ ∆ΘΓ ΟΡςς ∆ΦΦΡΞΘΩ)

17

examining the financial key ratios closer; in 1998 the Operating Margin ratio decreased slightly while the Interest Coverage ratio increased significantly. In 1999, the Operating Margin ratio increased while the Interest Coverage ratio decreased. Vodafone (No. 31) is constantly situated in the better groups. In 1995-96 they were firmly situated in Group A2, and moved into Group A1 in 1997 (yellow arrows). Analysing the financial key ratios slightly closer reveals that they showed a slight increase in ROE, but instead, a decrease in solidity. In 1998, Vodafone moved into Group B, much due to a continuous decrease in solidity, although they experienced an uninterrupted increase in ROE. In 1999, the ROE ratio continued to increase, at the same time as solidity improved, resulting in the company re-entering Group A1. British Telecom (No. 14) is another English service provider that shows some movement. This company is situated in Group C2 during the years 1995-96, moving into Group A1 in 1997-99 (pink arrows). Examining the financial ratios closer reveals that British Telecom shows a slight increase in all financial ratios, during the years 1995-99. When studying the American companies, it is obvious that AT&T (No. 37) is a Group B company, situated in the same group during the years 1995-98. However, they show somewhat decreasing performance, moving into Group C1 (red arrows) in 1999. Actually, this slight decrease seems to be characteristic for larger American companies. Apart from AT&T, Motorola also, as mentioned earlier, experienced somewhat poorer performance, and Sprint too backtracks into the poorest group during the years. One thing that is very interesting is the clustering of the two companies Telia (No. 10) and TeleNor (No. 9). These two companies negotiated possible merger with each other for quite a long time, but eventually they broke off the negotiations. What is interesting is that these two companies seem to have almost identical financial performance. Both companies are situated in the same neuron for the years 1995-96 and 1998, and next to each other during the other years in Group C2 (green circle). Examining the selected key ratios reveals that Telia shows slightly better profitability and liquidity, while TeleNor shows slightly better solvency and efficiency. Another interesting observation is the performance of NTT Docomo (No. 83), which is the largest service provider in Japan. NTT actually managed to improve their financial performance slightly during 1997-99 (blue arrows). When examining the financial key ratios a little bit closer it becomes clear that NTT has managed to increase not only their profitability, but also liquidity and solidity during the Asian crisis. One reason for this is the dramatic increase in their customer base, which has occurred due to new and popular services. At the same time, as NTT has improved, Japan Telecom (No. 77) has moved towards the poorer end of the map. This is probably a result of the Asian crisis, as well as customers and market share lost to NTT.

18

4.4 The Northern European Companies In the following section, the Northern European companies will be benchmarked against each other. It is likely that the accounting principles will not have a large impact on this benchmark, since the companies in this region has similar laws and principles. Figure 4-4 visualises the movement over the years for the Northern European companies included in the research. The companies Benefon, Ericsson, Nokia, Sonera, Telia and TeleNor have been benchmarked earlier and will not be examined in detail in this section. Instead, more emphasis will be placed on their regional competitors.

)ΛϑΞΥΗ >4-4≅: 7ΚΗ ΠΡΨΗΠΗΘΩ ΡΨΗΥ ΩΚΗ ∴Η∆Υς ΡΙ ΩΚΗ 1ΡΥΩΚΗΥΘ (ΞΥΡΣΗ∆Θ ΦΡΠΣ∆ΘΛΗς.

Regarding the improvements in the performance of Sonera it is also interesting to see how the local competitor Helsingin Puhelin Yhtiöt (HPY) is performing. This company is also known as Elisa Communications, and owns Radiolinja, which is the second largest provider of mobile services in Finland. Like Sonera, HPY (No. 4) has experienced a continuous increase in its financial performance, much in the same rate as the usage of mobile phones has increased. HPY was situated in Group C1 in 1995-97 but moved into Group B for the years 1998-99 (yellow arrows). This movement is a result of an increase in the key ratios ROTA, ROE, and Interest Coverage, even though HPY shows a slightly lower Operating Margin ratio than for the first three years. Doro (No. 2) is a Swedish company specialised in telecommunications equipment. This company too has experienced an almost continuous increase in financial performance. Except for a slight backtracking in 1996 within the Group C1, Doro have moved into Group B where they are firmly situated in the same area as Ericsson (blue arrows). The main reason for this movement is a heavy increase in the ROE ratio, but also a continuous increase in the other profitability ratios can be found.

19

Tele Denmark (No. 8) is firmly situated in the same area during the years 1995-99, currently situated in Group A1, close to both Group B and Group C1 (yellow arrows). During this period of five years they experienced hardly any larger changes in their financial key ratios. NetCom (No. 5) is a rather new service provider from Sweden that also operates in Denmark, Norway, and in Europe. Known brands are Comviq and Tele2. The fact that this company is a rather new service provider is also visualised in the financial figures, NetCom is situated in Group D for all years included in this study (red arrows). Actually, the company has shown a positive financial result for the last two years, but terrible values in the ROE ratio are still placing them in the worst group. An important question arises whether the Return on Equity is such a suitable financial ratio for this type of comparison of telecommunications companies. In many cases a company with negative profitability for several years will also have negative equity, because of the cumulated retained earnings (losses). This results in a phenomenon where the ROE ratio turns into a very high positive ratio, since a negative number divided by another negative number becomes a positive number. This does not give a fair view of the companies’ performance. In this report this problem has been dealt with by altering this number into a negative one in the case where such a problem exists. This is not recommended but in this research it gives a more accurate view of the companies’ financial performance. Since rather many companies, mostly service providers, had this problem, the conclusion is that this financial key ratio is not very usable in this type of financial research. The findings of Chapter 3 shows that the ROE ratio is one of the most valid key ratios used in this report; it is also the ratio most widely used by the telecommunications companies in their financial statements. However, the extreme values and the great variations in this ratio make it possible that too much weight will be put on it when training the self-organising map.

4.5 The Continental European Companies In the following section the Continental European companies will be benchmarked against each other, this benchmark will include both manufacturers and service providers. The Northern European companies will not be included since a benchmark for these was conducted earlier. In this benchmark of the Continental European companies some companies are included with missing information for either 1995 or 1999. Therefore, these companies will appear for four different years.

20

)ΛϑΞΥΗ >4-5≅: 7ΚΗ ΠΡΨΗΠΗΘΩ ΡΨΗΥ ΩΚΗ ∴Η∆Υς ΡΙ ΩΚΗ &ΡΘΩΛΘΗΘΩ∆Ο (ΞΥΡΣΗ∆Θ ΦΡΠΣ∆ΘΛΗς.

The Continental European companies seem to be spread rather wide over the map, with companies in all groups. There is no astonishing clustering of the companies. A few companies and their respective movement will be evaluated in more detail. Alcatel (No. 12), a French manufacturer of telecommunications equipment, shows a rapid increase in financial performance for the years 1995-98 (dark blue arrows). Located in Group D in 1995 Alcatel moves into Group B in 1997-98, primarily because of a rapid increase in the ROE and Interest Coverage ratios. In 1999 Alcatel falls to Group C1, again mostly due to a heavy decrease in the values in the ROE and Interest Coverage ratios. It is interesting to see that Philips (No. 24) has a similar movement to Alcatel. Philips is situated in Group C1 in 1995, but backtracked close to Group D in 1996 (red arrows). Examining the financial key ratios reveals that Philips encounters a slight decrease in the Operating Margin and ROTA ratios, but the decrease in the ROE ratio is significant. In 1997 Philips’ financial performance improved greatly, taking a leap into Group B, resulting from an increase in profitability, liquidity and solidity. In 1998 Philips improves a little bit more, within Group B, despite a decrease in both the Operating Margin and ROTA ratios. However, the reason for the improvement can be found in the ROE ratio, which increases dramatically. In 1999 Philips takes a dive into Group C2, despite a remarkable increase in the Operating Margin and ROTA ratios. The reason for this backtracking is, not surprisingly, a significant decrease in the ROE ratio. Philips is an excellent example of how the ROE ratio dictates the company movement on the self-organising map in this research. Siemens (No. 28) is clearly a Group C1 company, situated in the same group for all of the years 1995-99. Evaluating the financial key ratios reveals that Siemens shows good profitability, excellent liquidity, and good solidity. In 1999 Siemens improves its financial performance slightly, moving a few neurons closer to Group B (yellow

21

arrows). This movement is probably due to a somewhat increasing ROE ratio, but the other ratios are also on a good level. Sagem (No. 27) is another French manufacturer benchmarked in this research. Sagem is actually one of the companies included with missing information for the year 1995. Furthermore, the figures for interest expenses could not be found in the annual statements. Therefore, the values used in this research are estimated values. This should not have so much impact on the result since not very much weight was placed on the Interest Coverage ratio by the self-organising map. In 1996 Sagem was situated in Group C1, and moved into Group A2 in 1997 (turquoise arrow). This movement is mainly a result of increased liquidity and solidity. The final company to be benchmarked in this section is Deutsche Telekom (No. 17), the largest service provider in Germany. Deutsche Telekom is situated in Group B in 1995, but moves back to Group C1 in 1996-99. The reason for this movement is a significant decrease in the profitability ratios, especially in the ROE ratio. For the rest of the years the figures remains on the same level, with small variations.

4.6 The American Companies In the next section the American companies will be benchmarked against each other. The assumption was that most of the American companies would be classified as middle to good companies, because of the easy accounting principles. As the Figure 6-7 below illustrates, not many of the companies can be found in the middle groups; instead most of them are situated either in the worst or the better groups.

)ΛϑΞΥΗ >4-6≅: 7ΚΗ ΠΡΨΗΠΗΘΩ ΡΨΗΥ ΩΚΗ ∴Η∆Υς ΡΙ ΩΚΗ ∃ΠΗΥΛΦ∆Θ ΦΡΠΣ∆ΘΛΗς.

Most of the companies in the Group D are service providers. As stated earlier, the poor performance is probably a result of the high costs of entering the market. One example

22

is MCI WorldCom (No. 52), who in 1995 in fact was situated in Group B, with good financial ratios, and fantastic values in the ROE ratio. In 1996-99 MCI WorldCom fell to Group D (red arrows) as a result of poor profitability and especially very poor values in the ROE ratio. In 1999 MCI WorldCom actually had very good profitability, but the ROE ratio was still very poor, resulting in the company remaining in Group D. One manufacturer situated in Group D is Audiovox (No. 38) in 1995-96. In 1997 they jumped into Group B (blue arrows), which is a result of an improved financial result. In 1998 Audiovox experiences a decrease in most of the financial key ratios, and in the ROE ratio the decrease is significant. The result of this is a backtracking into Group C1. Analysing the annual statements reveals that no direct decrease in profitability occurs. However, in 1997 they sold some investments, which made the financial result positive. In 1999 Audiovox re-enters Group B due to an increase in all of the financial key ratios. As mentioned earlier both Motorola and AT&T show slight backtracking, which is also the case for the service provider Sprint (No. 60). In 1995 Sprint is situated in Group C1, and is moving into Group A1 in 1996 due to an increase in most of the selected key ratios (yellow arrows). In 1997-98 Sprint moves back into Group C1, and towards Group D. This is mainly a consequence of a decrease in the profitability. In 1999 the financial result was actually negative, resulting in entering the Group D. Qualcomm is an American mobile manufacturer that is performing well, situated in Group B for all years (pink arrows). Qualcomm shows good values in all financial key ratios, and for the ROE ratio the values are excellent. Qualcomm seems to have similar characteristics to Ericsson, situated in the same two neurons for all of the years 1995-99. This is particularly interesting since Ericsson this summer (2000) acquired the CDMA wireless infrastructure business from Qualcomm, including its R&D resources, related assets, premises and personnel.

4.7 The Canadian Companies In the following section the Canadian companies will be benchmarked against each other. This is a region consisting of very few companies, which are all service providers. In a way this benchmark may not be too important, but it is interesting to see how the Canadian companies differ from the American as well as visualising why the country average is situated in the slightly worse middle group.

23

)ΛϑΞΥΗ >4-7≅: 7ΚΗ ΠΡΨΗΠΗΘΩ ΡΨΗΥ ΩΚΗ ∴Η∆Υς ΡΙ ΩΚΗ &∆Θ∆ΓΛ∆Θ ΦΡΠΣ∆ΘΛΗς.

As the Figure 4-7 visualises, almost all of the Canadian companies are situated close to each other in Group C2, with the exception of Bell Mobility (No. 66) and Clearnet (No. 67). These two companies are moving all over the map with great variations in their financial performance. Nearly all of the other companies are situated within the same area, with three companies in the same neuron. It seems that most of the Canadian service providers have very similar characteristics. Bell Mobility is the leading wireless provider in Canada, and has moved into Group A1 after a short backtracking into Group D in 1997 (dark blue arrows). The backtracking into Group D is a result of a negative ROE ratio. This again, is a consequence of a negative net income in 1997, while all other ratios are positive and good. Once again, it becomes obvious that the self-organising map has placed most weight on the ROE ratio, probably as a result of the variations and extreme values found there. Again the question arises whether the ROE ratio is to be considered as a valid ratio for this type of research. Clearnet is a rather newly started service provider, which is suffering from very poor equity, which leads to terrible values in the ROE and Equity to Capital ratios. The financial result has been negative for all of the years between 1995-99, placing Clearnet in Group D (red arrows). The constant decrease in financial performance resulted in Clearnet being acquired by Telus (No. 71) in August 2000, resulting in the largest telecommunications company in Canada. Telus is a company with rather firm financial performance, situated in Group C2 for the years 1995-99 (yellow arrows) with good values in the profitability ratios and exceptionally high values in the Equity to Capital ratio.

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4.8 The Asian Companies and the Asian Crisis In the following section the Asian companies will be benchmarked against each other. One objective of this section is to evaluate whether the Asian crisis has affected the Japanese telecommunications companies and to what extent. Included in this benchmark are also two Indian companies and one Australian, but these companies were probably not affected by the crisis. It can also be mentioned that recession has existed in Asia for the last ten years. To a large extent this means that the real effects on the financial performance of the companies are not visualised in this research. However, the major economic crisis occurred in 1997-98, and these effects will likely be visualised by the following benchmark. [Corsetti, 1998] But although recession has existed, the Asian market is a sleeping giant with a huge amount of potential telecommunications customers. In Finland roughly 70 per cent of the population have a wireless phone, but only 5 per cent in Asia. Still there live 3.3 billion people in Asia, representing 58 per cent of the world’s population. The number of wireless users in Asia is expected to double in the next three years, to 350 million users [Ihlwan et al, 2000]. In January 2000 there were around 400 million mobile users in the whole world. When looking at these figures it becomes a little bit more interesting to look at the financial performance of Asian companies, and to think about whether known companies like Nokia and Ericsson will penetrate the Asian market.

)ΛϑΞΥΗ >4-8≅: 7ΚΗ ΠΡΨΗΠΗΘΩ ΡΨΗΥ ΩΚΗ ∴Η∆Υς ΡΙ ΩΚΗ ∃ςΛ∆Θ ΦΡΠΣ∆ΘΛΗς.

As the map illustrates most of the Japanese companies are situated in either the middle groups or the poor group. The Australian company Telstra (No.89) and the Indian companies Indosat (No. 74) and Videsh (No. 92) are situated in Group A. The reason for Telstra’s success is probably that they are such a large service provider in Australia that the situation resembles a monopoly. Regarding the Indian companies a suspicion exists that their success could depend on the accounting principles used in India.

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Kokusai (No. 78) is a Japanese mobile manufacturer situated in Group A2 for the years 1995-97, showing great values in all key ratio figures. In 1998-99 they clearly experience the effects of the Asian crisis, crashing into the worst group in 1999 (red arrows). At this stage they show terrible profitability with low values in all profitability ratios. For this smaller company, which only has Asia as the market for their products, the financial crisis was devastating. OKI (No. 84) is another Japanese company that backtracks into Group D. Starting in Group B in 1995 OKI first moves into middle group C1 before crashing into Group D in 1997-99 (yellow arrows). The actual Asian crisis occurred in 1997-98, but for most companies the effects are visualised in the financial figures for 1998-99. However, it seems that OKI experienced the effects already in 1997 with a decreasing profitability, although it is still positive at this stage. This movement on the map is most likely a cause of a drastic decrease in the ROE ratio. In 1998-99 OKI shows a negative profitability, decreasing the value in all of the selected financial key ratios. Matsushita (No. 80) is the second largest manufacturer of electronic equipment in the world, more known for their brand Panasonic. Matsushita does not experience any greater effects from the Asian crisis, even though they backtrack slightly in 1999. Overall, Matsushita is situated in the same two neurons for all five years, in Group C2 (blue circles). This company shows rather similar values in all of the selected key ratios, except for the Equity to Capital, they constantly show great values. The reason why Matsushita is not so much affected by the crisis is probably that they are a large, international company like Sony. The second largest service provider in Japan is Japan Telecom (No. 77). This company obviously experiences the effects of the Asian crisis, performing a steady backtracking since 1998 (turquoise arrows). Looking at the financial key ratios reveals that the profitability ratios have decreased considerably while the rest of the ratios seem to be at the same level as for the years 1995-97. Samsung (No. 85) seems to be the only company that is able to recover well from the Asian crisis. Samsung is situated in Group A1 in 1995, but falls back to Group C2 in 1996 as a result of a significant decrease in the profitability, resulting in a decrease in all of the profitability ratios (pink arrows). In 1997-98 the business is even worse, moving Samsung into Group D with a negative result and very poor values in the selected key ratios. This backtracking is likely to be an effect of the Asian crisis. In 1999 Samsung is back in business. Great profitability and a strong increase in all key ratios move them back into Group A1.

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5. Conclusion Six different clusters of companies were found and analysed in more detail in the self-organising map; two groups with very good financial performance, one with above average performance, two average groups, and one with very poor financial performance. When visualising the company’s movement over the years, these clusters were used to determine whether a company was performing well or poorly. The movement of a company on the map indicates a change in its financial performance. Different benchmarking studies were performed over companies in different regions, and their performance were measured against each other. The best in class companies were identified, as well as those struggling to stay alive. In these benchmarking studies the companies’ movement over the years is visualised and evaluated. The movement of a company indicates whether a company has performed well or poorly. The results of the study indicate that the Northern European companies have performed well, both Nokia and Sonera can be found in the best group. Regularly the Continental and Northern European companies are performing equally well, what is surprising is that the Asian companies also seem to have similar financial performance, except for somewhat decreasing financials during the Asian crisis in 1998-99. The American and Canadian companies show similar financial characteristics, although most of the Canadian companies are located in the slightly poorer of the middle groups. The American companies on the other hand are spread out over the whole map. One interesting thing is that the worst group, Group D, consists mainly of service providers from Continental Europe and America, except for a few Japanese companies during the years 1998-99. The reason for why the service providers usually are performing poorly financially is probably that most of the companies are very young and still developing their own infrastructure. The output map received and analysed in this study indicates that the self-organising map technique is a useful tool in structuring large amounts of input data, and also to find patterns that otherwise might be difficult to discover.

7. Acknowledgements The financial support from TEKES (grant number 40943/99) is gratefully acknowledged.

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Databases Used: Global Corporate Information Service: http://www.gsis.com Hoover’s Online (U.K): http://www.hoovers.co.uk HUGIN Online International: http://www.huginonline.com Japan Financials: http://japanfinancials.com The Canadian Depository of Securities (SEDAR): http://www.sedar.com U.S. Securities and Exchange Commission (EDGAR); http://www.sec.gov,

http://www.freeedgar.com

Turku Centre for Computer Science Lemminkäisenkatu 14 FIN-20520 Turku Finland http://www.tucs.abo.fi/

University of Turku • Department of Mathematical Sciences

Åbo Akademi University • Department of Computer Science • Institute for Advanced Management Systems Research

Turku School of Economics and Business Administration • Institute of Information Systems Science