Analysis of Financial Data from Portuguese Enterprises over 1991-1999 Period using Self Organizing Maps:
Study of their Competitive Position
Raquel Flórez-López
Department of Economics and Business Administration-University of León (Spain) Campus de Vegazana s/n – 24071 León (Spain) -Phone: +34-987.29.17.34
E-mail: [email protected]
Abstract. The European Economic and Monetary Union (EMU) process, which will finish in February 2002 with the general employment of an only common current, the Euro, is the culmination of the European integration process as far as the financial perspective is referred.
To decide the relation of countries included in this final phase it was established a relation of economical regulations known as Convergence Criteria or Maastricht Criteria, defined as some macroeconomics rules which must be fulfilled to guarantee the economic convergence among countries sharing the same currency. Nevertheless, these criteria are not enough to assure the effective convergence among countries as far as enterprises is referred, considering the incremental competitiveness characteristic of the EMU process. As consequence, the impossibility of using the exchange and interest differential rates among nations to cover their effective microeconomics competitive differences could distort a lot the effect of the Union in favour of some countries and against others. The employment of AI paradigmes together to classical analysis techniques, specifically the use of Self-Adaptive neural nets models based on Kohonen’s proposal, makes easier to analyse this economics and financial differences, getting a visual image of the particular positions through topological two -dimensional maps.
Keywords. Self-Organizing Maps, Pro fitability, Financial Data, Patrimonial Equilibrium, Relative Costs.
1. Self Organising Feature Map (SOFM)
Artificial Neural Networks (ANNs) constitute a Machine Learning paradigm that appeared like some attempts to establish mathematical forms about brain structure, characterised by learning through the experience and the knowledge extracting from multiple different events. The architectures used in ANNs can be divided into three categories, from the point of view of the data flow direction: feedforward networks, were the information flows in an only direction, feedback networks, where data can flow in different directions between layers and competitive, unsupervised or self- organizing models, based on competition among neighbouring cells in the net through mutual lateral interactions, so that neurons evolve to specific detectors of different signal patterns. The Self-Organizing Feature Map (SOFM) proposed by professor Kohonen in early 80’s belong to this category.
The system is structured in two layers with connection between them. While the input cells (neurons) are connected to the outputs by mean of feedforward links, final cells, organized like a plane or an hyper plane, got lateral inhibition links and reinforced self-connections. After the presentation of a pattern, only one of the neurons from the output layer could be the winner; then this cell’s weight and those belonging to neurones situated in the “neighbourhood zone” were adjusted to best fit
the pattern, being this adjust an inverse function of distance between the winner and the neighbour cell. Figure 1 shows the basic architecture of the Self Organizing Feature Map, model used in the empirical study performed in this paper.
The model presents two different stages:
1.- Learning or Training Stage: In the learning stage the network establishes the categories which will be used in the operation stage to classify new data.
Each time a pattern vector x(t) is presented to the system (a “step” in the network), the model calculates the distance between this vector and each one of the output neurones mij(t); there are many times to measure these distance d(x, mij), like the Euclidean distance, correlation, co sinus distance, Minkowski metric (like Manhattan distance), etc . After that, the nearest cell (“winner”) mc together to others belonging to the “neighbour zone” Nc(t) are updated in some means being compatible with the distance formula. There are different updating equations depended of the distance measure selected; the most popular, related with with the Euclidean distance is:
( ) ( )
) ( if )
( ) 1 (
) ( if ) ( ) ( , ) ( ) ( ) 1 (
t N i t
m t m
t N i t w t x t c i h t t m t m
c ijk
ijk
c ijk
k ijk
ijk
∈
= +
∈
−
⋅
−
⋅ +
=
+ α (1)
Being k the input variable considered, α(t) a parameter known as “learning rate” so that 0<α<1, which is related with the gain measure used in stochastic approximation processes, and h(i-c,t) the “neighbourhood function” which depends of
“neighbourhood radio”; both, learning rate and neighbourhood ratio tend to reduce when training advances. The presentation of the complete set of patterns to the network (usually in a random way to avoid slanted systems) is called one epoch of the learning. The training process used to finish after some pre-fixed steps or epochs (for example, a “thumb rule” stops the system at 100.000 steps). After finishing the learning phase it is possible to perform a second training stage, known as “fine tuning”, in order to get a better fit of the weight vectors, fixing the learning rate to a very small value (near 0.01) and the neighbour to 1.
2.- Operation or Working Stage: After the learning stage it begins the working stage, where the weight vectors remain fixed. This phase is quite simple, generating an only one output from each input vector. This result is obtained through the parallel calculus for each output neurone of the distance between the input data and its weight vector. The neurone with the maximum similarity is established as winner, assigning the input vector to this cell. Thus, each cell acts like a specific feature detector, where the winner points the feature or patron detected in the input data.
Fig.1. Generic Architecture of the Self Organizing Feature Map (SOFM)
INPUT LAYER OUTPUT
LAYER
m inputs
nx x ny
outputs
2 Empirical Analysis
2.1. EMU Process and SOFM architecture
The European Economic and Monetary Union (EMU) is an old aim of European countries in order to get a real stability monetary zone in Europe. This process that began in early 70’s, was finally culminated in 1990 with the Maastricht Treaty, where it accepted the ‘Delors Plan’ to facilitate the integration process, being its three main phases the following ones: Preparation stage (1-7-1990 to 1 -12-1993), characterised by the establishment of the Convergence Program, based on five macroeconomics Convergence Criteria which should be fulfilled for the countries that desired to participate in the launch stage: inflation, exchange rate, National Deficit, National Debt and the interest rate. Consolidation stage (1-1-1994 to 1-12-1998), transitory phase to the increase the convergence among countries in terms of their economic and monetary policies. Launch stage (1-1-1999 to 2002), characterised for the establishment of the final fixed exchange rates, the progressive implantation of the Euro as the European Currency, and the definition of a common Monetary Policy for the ECB, created in this phase together to the European System of Central Banks (ESCB), organism that assumes the responsibility of the common monetary policy.
Finally there were eleven countries which primary accessed to the third phase:
Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, Portugal and Spain (Greece was included in 2001).
Even when this process presents many microeconomics advantages (reduction of transaction and financial costs, increase of transparency as far as price policy is referred, raise of European markets’ stability, etc.) however it has many disadvantages also, due to the five Convergence Criteria’s main aim was to guarantee the similarity of European countries in macroeconomics terms, not considering the important microeconomics differences in terms of costs, benefits, profitability and patrimonial situation among European firms that will probably increase due to this harmonisation process. The objective of this paper is to investigate the economic and financial situation of different countries’ firms, focussing the attention in Portuguese ones, evaluating their relative position and their capability to affront these challenges, using both the classical analysis based on ratios and the Machine Learning technique known as Artificial Neural Networks (specifically Kohonen’s self organising model).
The database employed in the study of European enterprises as far as their relative competitive position is referred has been obtained form the ‘BACH Project Database’
(Bank for the Accounts of Companies Harmonized), which collects in a harmonised way the accounting states of non-financial companies in 13 countries broken down by major activity sector and size. The presented study has used 1991 to 1999 financial data from the nine countries integrated both in the Launch Stage of EMU and in BACH database: Germany, France, Italy, Spain, Belgium, the Netherlands, Portugal, Austria and Finland, as far as the overall manufacturing industry sector is referred, reducing the effect of different enterprises’ size through the employment of ratios’
technique. The period considered covers near all the Preparation and Consolidation Stage and the first year of the Launch Stage, which is important to analyse the evolution of different European microeconomics position during the global EMU period. In order to evaluate the competitive position of European enterprises, the
study was concentrated in their patrimonial equilibrium and their cost and profitability situation.
These items were analysed by means of nine financial ratios, grouped in three different categories, using a 2-dimensional SOFM model to get an overall view of firms’ relative situation in terms of these three classes. In that way, there are others techniques that could be employed to study thes e data (Rivera, Olarte y Navarro, 1993), like multidimensional scales, discriminant analysis or cluster analysis, but these techniques have some important disadvantages that should not be forgotten;
thus, multidimensional scales are critical about distance selection and total pre- defined dimensions, and produce very overlapping segments in presence of outliers, discriminant analysis need the fulfilment of very restrictive hypothesis about data (normality, homocedasticity) and cluster analysis uses to detect a very reduced number of groups if there is some very distinctive characteristic. In addition, none of these techniques lets to obtain an overall 2-dimensional map. SOFM model avoids these difficulties, but requires a higher analysis of internal parameter, and is not mathematically optimal, but based on pragmatic learning techniques.
The employment of the three SOFMs involved the definition of some critical variables as far as both neural architecture and learning process is referred. In that way, inputs variables were financial quotients and outputs variables were the analysed nine countries and nine years (81 records), that was positioning using their relative similitude in terms of that financial information.
To determinate the number of output units, it was considered that it should be equal or higher that the number of patterns in order to avoid excessive data overlapping, and to decide the final structure it was used the technique known as “Sammon’s mapping”, a cluster method that tries to approximate the geometric relationship existing among patterns in a 2-dimensional space, representing relative distances among clusters. The final map has a 9x11 structure (99 neurons, 122% over the number of patterns), which generates a maximum neighbourhood ratio of 12.80 (Euclidean distance).
Initial weights were randomly selected in the [0, 0.01] interval [(Kohonen, 1997), (Martín y Sanz, 1997)], and it was employed a “two-phases” learning: the “rough learning stage” (stopped about 100.000 iterations) and the “fine adjustment stage”
(conformed of about 1.000 iterations). The function used to adjust the neighbourhood ratio for the first stage was the formula ,
Rf
f t
R t R R t
R()= 0+( − 0) where R0 was the initial total radio of the net (12.8), Rfrepresented the final radio (1), t the actual step and tRf the total number of steps (Martín y Sanz, 1997). The learning rate was progressively decreased from 0.9 to 0.01 in order to obtain a better characterisation.
2.2. Analysis of Relative Patrimonial Equilibrium of Portuguese Enterprises
The relative patrimonial equilibrium of Portuguese firms is based on the analysis of four different quotients, used to obtain the SOFM about the relative patrimonial evolution of European enterprises exposed in Figure 2:
1. Current Assets / Short-Term Payable Liabilities (CA/S-TL): Informs about the capability of the Current Assets to pay all the short -term debts.
2. Net Assets / Payable Liabilities (NA/PL): Together with the before quotient, this ratio informs about the ‘safety solvency’ of the enterprise.
3. Working Capital / Current Assets (WC/CA): Informs about Working Capital importance (Current Assets–Short Term Payable Liabilities) over Current Assets.
4. Working Capital / Stocks (WC/S): Stocks is the component of Current Assets more removed from liquidity, so it is the element more recommended to be financed with Permanent Resources. It is desired high and positive values for this ratio.
1 2 3 4 5 6 7 8 9 10 11
1 S93 I93-I94 I92-I95 I96-I97 I98-I99
A91-
A92 F93
2
S92-
S94 I91 A96
A93-
A95 A94 F91-F92
3 B99
B92- B95
S91-
B93 S95 A97 A98 G93
4 B97 B98 B96 B91
G94- G95
5 S96 B94
Fr91- Fr93 Fr92
G91- G92 G96
6 N92
S97- S99 S98
Fr94- Fr95
A99- Fr96 Fr97
G97- G98
7 N91 N98 P92 P97
Fr98- Fr99
-F95 F94 G99
8
N93- N95
P91-
P94 P93 F96 F97
9 N97 N96 N94 N99 P98
P95-
P99 P96 F99 F98
Fig.2. Relative position of Portuguese firms in terms of Patrimonial Equilibrium (A=Austria; B=Belgium; F=Finland; Fr=France; G=Germany; I=Italy;
N=the Netherlands; P=Portugal; S=Spain)
As it can be observed, Portugal is situated in a final position near to France’s state and nor far from Finland situation in middle ‘90s. The country has evolved from a position characterised for a medium-low CA/S-TL ratio (135-140%) to a better state (146.97%); in terms of NA/PL quotient, the situation has improved too but in a less important way (1.85 to 2), and the WC/AC indicator has increased too from 29.79 to 31.96%). Finally, the WC/S ratio rises from 79.42% to 105.96% too. It must been pointed that 1992 represent the only year where ratios number 1, 3 and 4 decreases a little with reference to 1991 values, but this tendency is quickly inverted in next years.
In addition, Portuguese Working Capital presents a medium position, between 12- 17%, improving in the considered period.
It is quite interesting to observe too that other countries like the Netherlands or Spain seem to follow the Portuguese evolution, moving from the left part of the map (with the lowest values for the 4 ratios) to positions situated in a more down -right zone (with higher quotients) which can give Portuguese enterprises a competitive advantage in terms of patrimonial equilibrium. This evolution from low or medium ratios’ values to higher rates can be observed fo r the majority of the countries analyses, except Belgium which presents worse results in 3 ratios (numbers 1, 3, 4).
Finally, it can be observed the formation of at least three groups inside the map.
The first one is composed by Spain, Belgium and Italy, characterised by a reduced CA/S-TL ratio (115-135%), a low-medium NA/PL value (1.35-1.8), a weak situation in terms of WC/AC (13-25%) and a quite small WC/S quotient (40-80%).
The Netherlands, Portugal and France present medium-high values in NA-PL ratio (1.55-2.25) and a medium or medium-high situation in relation to the others 3 quotients: CA/S-TL (116-160%), WC/AC (14-32%) and WC/S (71-117%).
The final group (Austria, Germany and Finland) is characterised by a high value of the CA/S-TL (140-190%) and WC/AC indicators (1.4 to 2) and a quite high WC/S rate (near 230% in some years). The NA/PL ratio has a quite heterogeneous situation, been quite reduced for countries situated in the top-right corner of the map (about 1.35) but quite high for nations in the bottom-right zone (near 2).
2.3. Analysis of Cost Situation of Portuguese Enterprises
The analysis of the situation of Portuguese enterprises in cost terms results very interesting to determinate its capacity to assume the lower margins that the increase of competence would probably generate, being considered three different quotients:
1. Staff costs/AAV: This variable, which could be interpreted like the labour unitary cost, is one of the most used indicators to measure the competitiveness of firms. AAV represents the Adjusted Added Value, that informs about value generated for the enterprise in its productive operations, and which has had a very positive evolution in Portugal for the last years, having one of the highest value of Europe in 1999.
2. Cost of materials and consumables/Net turnover: This ratio informs about the percentage of turnover used in tangible consumes needed to the production cycle.
3. Interest and similar charges/AAV: This quotient reports to the portion of AAV used to reward external creditors of the enterprise. In that way, the financial policy of the firm will determine in a very important way the evolution of this indicator.
Nevertheless, data from BACH database did not permit to calculate this third indicator for the Netherlands (period 1993-1994) and Austria (period 1991-1999), so it was not introduced in Kohonen’s map (Figure 3). Analysing this indicator, it can be observed that it has decreased in all countries considered, being finally situated in its majority in an interval of 7-10% (Portugal has a rate in 1999 of 9.09%).
1 2 3 4 5 6 7 8 9 10 11
1 G93
G92-
G94 G97 G98 G99 S91 S93
2 G91
G95-
G96 Fr96 Fr93 S92
3
A92-
A93 A96 F91
Fr94-
Fr97 Fr92 I91-I92
B92- B93
4 A94 A95 A97 Fr91 Fr95 S94 I93 B94 B91
5 A91 A99 A98 P93 N93 Fr99 Fr98
B95- B96
6 F92 P92 N94 I94 S96 B99-I69
7 F95 F96 F94
P96- N91-
N92 S95 B97 B98-I97
8 F97 F93 N95 N96
N97-
N98 I98-I99 S97
9 F98-F99
P91-
P94 P95 P97 P99
P98- N99 I95
S98- S99
Fig. 2. Relative position of Portuguese firms in terms of Costs
As it can be observed, Portugal is situated again not far from French, Finish and Italian positions and very near to the Netherlands’ situation. The analysis of
Kohonen’s map weights let observed that Portugal has evolved from a a medium-low labour cost (58-60%) and a medium-high cost of consumables (68-69%) to a new situation where the labour costs have decreased a little (54-56%) but the consume of materials have increased (72-73%), that is to say, some resources initialised employed to pay salaries and similes have been re-designated to purchase (and consume) materials. In conclusion, the progresses that Portuguese firms have done in terms of labour costs have been “destroyed” for the increase of material costs. Anyway, this increase of materials’ consume is characteristic of many European countries in the period, so Portuguese firms have followed the general behaviour.
Finally, the observation of map lets examine some different zones inside it (it must be appointed that in this case the highest labour costs are situated at the bottom of the map, reducing when it moves to upper positions and the lowest material cost is located at the left-top corner, increasing when it moves to the bottom-right zone).
In that sense, it is possible to identify a first zone, composed by Germany and Austria, with very high labour costs that have been reduced a little in the period (from 75-80% to 68-70%) but quite low material costs (52-53% in 1991 to 56-58% in last years). As consequence, these countries based their competitive advantage in terms of cost in a very efficient employ of consumables. Finland can be identified as a separate region, characterised by a low labour rate, that have decrease in the period from 68%
to 51% and a reduced cost of consumables (evolving from 59% to 54%). As it can be observed, this country has evolving in a very positive forms (as far as costs is refered), so that it presents the best situation to compete in costs all along the European countries. France constitutes another independent group, with a medium labour rate that have decreased the last years, been finally situated in the 64.51%. In terms of material cost, it presents a quite stable position all over the period, evolving from 69.78% to 70.68%, that can be considered a medium high quotient. So, France has a medium position in terms of cost efficiency, so probably this nation should take some measures to improve its ratios. Portugal and the Netherlands conform another region, characterised by medium-low labour rates and medium-high costs of consumables, like it has been commented previously. Finally, Belgium, Italy and Spain conform a bigger heterogeneous group characterised by an important movement in the period from labour rates of about 70% to quotients near 60%, and from material cost that varies from 70% to 75% for different countries and years.
These nations present the lowest labour costs, which can be its main competitive advantage, but its material cost are very high and increasing for the last year, so they must be careful in this point for not to lose the previous advantage.
2.4. Analysis of the Profitability Capacity of Portuguese Enterprises
The profitability, or firm's capacity to generate profits, being basic to get a positive result from the increase of competence that the final phase of EMU is going to generate. The main variables utilized to illustrate this analysis were:
1. Economic profitability (1): This ratio represents the ‘margin effect’ of the economic profitability, calculated like “Profit before financial result and taxes/Net turnover”. The higher the ratio, the greater the profit value per each product sold.
2. Economic profitability (2): This indicator is known as the ‘rotation effect’, defined as “Net turnover/Net Assets”. If this ratio gets a high level, it will be indicative of an elevate number of times in which the inverted money is sold.
3. Financial profitability: This quotient informs about the profitability gets for business owners, so this magnitude has into account the composition of the Liabilities of the firm. This variable is calculated like “Profit before taxes/Own resources (capital and reserves in BACH database)”.
4. Cost of payable liabilities: This magnitude informs about the specific cost of the non-owned liabilities and depends both of the interest rate and of the financial politics of the enterprise. It may be commented that this fourth ratio could not be calculated for the Netherlands (1993-1994), Finland (1993) and Austria (1991-1998), so the final map (Figure 3) did not considered this magnitude.
1 2 3 4 5 6 7 8 9 10 11
1 G91 G97 Fr99 I91 G92 G94 G93 S93
2
G98-
G99 G96 G95 Fr93 S92
3 A97 A95
Fr97- Fr98 Fr95
A96- Fr96 Fr92
B91-
B92 B-93 I93
4 A98 I99 A34-I98 Fr91 Fr94 A33 I92
5 A99 N94 S98-I95 A92 I97 B96-I96 S94 S91
6 A91
B97-
S99 B98 S94
B94- B95-
S96 S95 I94
7 N95
N96-
N99 N91 B99 P96
P92- P93
8 N97 P99 N93 N92 P98 P94 F93
9
F97-
F98-F99 N-98 F95 F96 F94 P97 P95 P91 F91 F92
Fig. 3. Relative position of Portuguese firms in terms of Profitability
It can be detected again different groups in the map, specially for the last years of the analysed period. In that way Austria and Germany appear together, characterised by medium-high EP1 (3.5%-6% at the final years), a quite high rotation or EP2 (1.15- 1.25 in 1998-1999) and a very high financial rate at the end of the period (16%-23%).
France and Italy constitute a new sub-group, with medium values for EP1 (4%-4.5%
in 1998 and 1999), medium-high (1.01) and quite high (1.22) values for Italy and France at the EP2 indicator in 1999, respectively, and medium-high values for the FP quotient (14%- 15%). The third group, composed by Spain and Belgium basically present a more significant evolution, specially in the Spanish case, beginning the period with quite small figures but evolving until getting a medium or high-medium situation in the three indicators. Finland constitute an own group, with high values for EP1 and FP1 but quite reduced figures for FP1 (only 0.68 in 1999), so this third magnitude should be revised for Finish firms in order to improve it.
Finally, Portugal and the Netherlands present again some similar positions, so that even when the Netherlands begins the period with a better situation than Portugal, Portuguese enterprises have done a big effort in this items, ending the stage with very similar ratios to Dutch ones, being characterised by medium-high values for EP1 (6.73%), a medium rotation factor (1.00) and a medium-high value for FP1 (13.68%).
In conclusion, Portugal is situated in a medium position from the profitability point of view, so it probably should make some efforts to improve its situation in order to be stronger in competitive terms.
3. Conclusions
In view of the previous analysis it can be concluded that European enterprises do not present an homogeneous situation in competitive terms to face the new environment that EMU culmination is going to generate. This could distort some of the objectives of this process, in benefit of some countries and against others.
In terns of the relative Portuguese firms’ situation, it should be pointed:
1.Portugal present a good patrimonial position, near France and Finland’s situation and far from countries traditionally considered as nearest, like Spain.
2. In terms of costs, Portuguese enterprises have a medium-low position, being the labour costs their main competitive advantage in this analysis.
3. The profitability situation of Portugal can be defined as “medium”, so the country should consider the imp rovement of these ratios. In both two last quotients, Portuguese firms are near Dutch ones and not far from French enterprises either.
In conclusion, Portuguese situation against new challenges is quite good in overall terms, stronger than enterprises fro m countries like Spain or Belgium, weaker than German, Austrian and Finish positions and near to France and the Netherlands’ ones.
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