The prediction of economic and financial performance of companies using supervised pattern recognition methods and techniques

Transcription

1 The prediction of economic and financial performance of companies using supervised pattern recognition methods and techniques Table of Contents: Author: Raluca Botorogeanu Chapter 1: Context, need, importance and purposes of economic and financial performance prediction 1.1 The context of economic and financial peformance evaluation and specific indicators 1.2 The necessity and purposes of economic and financial performance evaluation 1.3 The issue of risk and uncertainty in economic and financial performance assessment 1.4 Progress of research on economic and financial performance evaluation Chapter 2: Statistical and mathematical models used in economic and financial performance evaluation 2.1. The necessity of using statistical and mathematical models in treating economic and financial performance 2.2 Models used in evaluating performance of companies 2.3 Methods and techniques of risk measurement and evaluation Chapter 3: Performance evaluation using pattern recognition methods and techniques 3.1 Pattern recognition in economic and financial performance evaluation 3.2 Establishing performance classes using cluster analysis 3.3 Performance classes prediction using supervised pattern recognition methods and techniques - Discriminant Analysis Chapter 4: Empirical study: Analysis, evaluation and prediction of economic and financial performance Conclusions

2 PAPER SYNTHESIS In the present financial theory, we confront with complex economic phenomena and activities which cannot be studied or analyzed profoundly because of the plurality of existing variables, ratios and information. Due to the dynamics of the economy, the uncertainty, risk and change, nowadays performance evaluation can be realized only in correspondence with the role one can have inside the analyzed company. Therefore, it is important to know and to relate in our studies regarding economic and financial results, to both current economic context and analysis s purpose itself. The reason I have chosen this theme for my study has been determined by the extraordinary possibilities that multi-dimensional analysis techniques offers in solving the various problems of performance classification, given the complexity of the information and variables. The thesis is divided into four chapters of which, in one aspect or another, three chapters are characterized by a marked applied feature, especially regarding performance evaluation and identification of economic and financial forecasting firm failure in its various manifestations, including bankruptcy. First chapter, entitled Context, need, importance and purposes of economic and financial performance prediction, presents among the current economic environment and specific indicators of performance assessment, purposes of different categories of stakeholders for evaluating economic and financial performance prediction. There are two broad categories of specific indicators for economic and financial performance assessment, traditional and contemporary, and they are used according to the performance measurement target that stakeholders may have. Here I have also mentioned the different classes of stakeholders, as users of a company s information. This chapter also includes the issue of risk and uncertainty versus decision making as long as the results obtained, and thus the indicators against which performance can be categorized, are made based on previous observations, whose values can not include risks that work is subject to in the future. Last subchapter presents the progress of research on economic and financial performance evaluation, by a temporal evolution of indicators and models as well. Most notorious and significant achievements are represented by Beaver and Altman who used univariate respectively multivariate discriminant analysis to evaluate performance, predicting unsuccess and failure. Chapter 2 is devoted to a brief description of the statistical and mathematical models used in economic and financial performance evaluation. It begins by showing the importance 2

3 of the statistical and mathematical models in treating the relationships between variables especially the data analysis techniques, which have the ability to synthesize information in certain indicators, decreasing of the initial causal space dimension, and to eliminate duplication of information. This is essential for achieving correct predictions and proper classification of companies as successful or insolvent. Afterwards I had described the main models used in performance evaluation through supervised pattern recognition methods. From the international authors, Beaver and Altman models remained as basic models for the ones developed afterwards. Beaver applied a uni-variate statistical analysis for the prediction of corporate failure using the selection of 30 ratios to analyze 79 failed firms within 5 years prior to default. He found as the strongest ratio to predict it the cash flow to total debt ratio. A careful consideration of the weaknesses of` Beaver s univariate model has led to the development of the Altman s Z-Score, which is based on the multiple discriminant analysis. The model combines five different financial ratios to determine the likelihood of bankruptcy amongst companies. Scores above 3 are considered to be healthy and, therefore, unlikely to enter bankruptcy. From the national side Anghel model remains as the most significant. At the end of the chapter I also analyzed aspects of risk measurement and evaluation, focusing on bankruptcy and financial distress as being the most concludent risks included in performance evaluation. Within Third chapter I have approached the theoretical fundamentals of pattern recognition, treating both unsupervised and supervised methods namely cluster analysis respectively discriminant analysis. Firstly I presented the possibilities and advantages of pattern recognition methods in the evaluation of performance by eliminating informational redundancies, reducing analysis time and allowing to include into anlysis a large volume of data. The first method described is the cluster analysis whose goal is the grouping or segmenting of a collection of patterns into subsets or clusters, such that those patterns within a cluster are more closely related to one another than those assigned to different clusters. Fundamental to all clustering techniques is the choice of distance or similarity measure between two patterns. Unlike previous method where the number of classes wasn t known a priori, the discriminant analysis is a technique for classifying a set of observations into predefined classes. In the following chapter this particular technique is used for classifing the successful companies from the insolvent ones, and that is the reason why I have described it s method. The purpose is to determine the class of an observation based on a set of variables known as predictors or input variables. The model is built based on a set of observations for which the classes are known, sometimes referred to as the training set. Based 3

4 on the training set, the technique constructs a set of linear functions of the predictors, known as discriminant functions which are used to predict the class of a new observation with unknown class. For a k class problem k discriminant functions are constructed. Given a new observation, all the k discriminant functions are evaluated and the observation is assigned to class i if the i th discriminant function has the highest value. Chapter 4 is the empirical study and it is the most important part of the thesis. It describes the research methodology, focusing on the supervised pattern recognition analysis. It also outlines the data sources and the definitions of variables used in research. The separate sections of the empirical study presents the principal components analysis, the cluster analysis and more detailed the discriminant analysis. The last section of this chapter contains an inte-grated interpretation of the findings. To start with, I have described the used database, 66 from the first and second class listed companies at Bucharest Stock Exchange, and the criteria used to validate it. Afterwards I have explained the 10 set of variables of the objects mentioned above, obtained as indicators of profitability, liquidity and indebtedness for the respective companies, for the year From the 10 indicators I can name: Return On Equity, Net Profit Margin, Current Liquidity Ratio, Price Earning Ratios and Earning per Share. Before proceeding to the analysis techniques the data had to be standardized, as the initial variables had different terms. This procedure can be done faster and easier by using the SPSS analytical software. From this point further, using both SPSS and SAS analysis softwares, I presented separately the three analysis methods mentioned earlier. Principal Components Analysis Analysis of the principal components is a simplified re-expressing of the initial causal space, the simplification being made on the conditions of maximization of the information quantity from the original space. I used the principal components method in order to analyze the economic and financial performance, for the purpose of determining for each company the class to which belongs. From the initial 10 indicators, I took into consideration only 9 ratios relevant for the activity since from the original EPS was retained less then 40% of the informational content. Further on, as Covariance Matrix and the Screen Plot shows, there were obtained 4 principal components, synthesizing 72% of the information contained in the original space. First principal component was strong influenced by ratios correspondent for economic performance, while the second component was representative for market influence. Since I wanted to obtain a classification according to the new characteristics and only one principal component didn t synthesized majority of the initial ratios, I appreciated the 4

5 performance of the companies by first two principal components. In the grafic shown below we can see the position of each company relative to the two principal components. Fondul Proprietatea, BVB are showing a better performance While MJM, IRC, UZT, IMP are positioned in the lower side. Cluster Analysis 5

6 As cluster analysis methodology had already been described I will focuse on the applied method. There are several ways to group cases based on their similarity coefficients, all based on a similar principle: there is a chain of similarity leading to whether or not a case is added to a cluster. For my study I chose Ward s method, which is considerably more complex than the simple linkage method. The aim in Ward s method is to join cases into clusters such that the variance within a cluster is minimised. To do this, each case begins as its own cluster. Clusters are then merged in such a way as to reduce the variability within a cluster, if this merger results in the minimum increase in the error sum of squares. After the 66-1 steps taken were obtained 3 clusters, first containing 59 firms- satisfying performances, second only 2 firms with high similarity in liquidity performance and the last including 5 unsuccessful firms. The results are similar to the representation from the principal component analyses since the 2 liquid firms are the same, and the other 5 as well. 6

7 Discriminant Analysis The discriminant analysis is one of the most important ways of estimating the probability of default or otherwise, evaluating the company s financial and economic performance. The main idea of it suppose that the groups are formerly established, so based of that assumption we can foretell where a new object belongs to. The case study is structure into two parts. First, we suppose that our firms are grouped in companies that have obtained high performance and companies that are below the benchmark. Secondly, I have considered another criteria in order to form the main groups in which companies are classified. As a consequence, we use the value of net income and the profit trend line, as a combined criteria, and we divided the pattern analysed into four subgroups: higher performance firms, performance companies, low performance companies and default companies. After realising this stage, I have analysed the results obtained in our case studies simultaneously, hereby I had the possibility of evaluating the performance of each model and establishing which one offers better results. As a conclusion, the first scenario is almost faultfree, because the inaccuracy probability is less then 10 percent, while the second scenario offers almost a 30% probability error. Even though the first study case reveals better results than the second one, I consider that a detailed classification of companies performance is worthy for the Romanian Capital Market. The main reason of choosing this scenario is based on the requirements established by the Bucharest Stock of Exchange regarding the companies listed, so we can not conclude that a listed company is going to bankruptcy in a short time period. Beside that, another part of my study emphasis the discriminant function s form because they are instruments which we use in order to realize future prediction sequences. The form of them is illustrated in the next table: 7

8 With the dicriminant function form known, we conclude our study with a summarisation analyse, where we compare the initial membership distribution of companies with the new distribution predicted and we analyse the probability of error for each companies included in our analyse. Conclusions Looking at the macroeconomic financial situation, it is generally recomanded to analyse the performance of a company in order to have a better perspective about the way you could invest you re capital resourses. At the same time, analysing one by one it s financial and economic indicators isn t an efficient way of evaluating its performance because while a raport could ilustrate that a company is profitable, other one could show exactly the different side of it. As a consequence, we have studied the financial and economic performance of 66 companies listed on the Bucharest Stock Exchange, using different econometrics programs. After that, we have analysed the probability of including a company in a performance group a priori established. The case study was based on two scenarios whose main difference was formed by the criteria used. Actually, the first one uses a simple criteria regarding the net income obtain by the company during 2010 year, while the second one uses both the net income obtained and the profit trend line as a combined criteria. We have observ that the first scenario is almost fault-free, because the inaccuracy probability is less then 10 percent, while the second scenario offers almost a 30% probability error. As a conclusion, even though the first study case reveals better results than the second one, I consider that a detailed classification of companies performance is worthy for the Romanian Capital Market. 8