Analysing Financial Performance with Quarterly Data Using Self- Organising Maps
نویسندگان
چکیده
The purpose is to analyse the quarterly financial performance of telecommunications companies with the help self-organising maps. In this report the three major actors on the telecommunications market is evaluated and benchmarked on the basis of seven key ratios, calculated for seven consecutive quarters between the first quarter of year 2000 to the third quarter of year 2001. To conduct benchmarking with several financial key figures in 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. We argue that by using self-organising maps and quarterly data managers and other interested parties receive up to date information on companies’ financial performance in an easily interpretable way.
منابع مشابه
Neural Networks: an Exploratory Data Analysis of Logistics Performance
Neural networks are a data processing technique that provides us a powerful tool to handle non-linear data and model complex relationships between data. Self-organising maps, a type of neural networks, has been used successfully as an exploratory data analysis method in applications like presenting the welfare states of the countries or analysing and representing financial data. Logistics inclu...
متن کاملFinancial Benchmarking of Telecommunications Companies
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 to...
متن کاملVisualisations for Comparing Self-organising Maps
Self-organising Maps (SOMs) are a very useful method for exploring and analysing large data collections: They project high-dimensional data into a low-dimensional output space so that it is easier to analyse for humans than the original data. For the purpose of analysis, plenty of visualisations exist which display different aspects and properties of the maps and the data. There are, however, v...
متن کاملPerformance Benchmarking of Non-banking Financial Institutions by Means of Self-organising Map Algorithm
We construct a benchmarking model in the form of a twodimensional self-organising map (SOM) to compare the performance of nonbanking financial institutions (NFIs) in Romania. The NFIs are characterized by a number of performance dimensions such as capital adequacy, assets’ quality and profitability. First, we apply Kohonen’ SOM algorithm (an unsupervised neural network algorithm) to group the N...
متن کاملForecasting Financial Markets with Classified Tactical Signals
The financial market dynamics can be characterized by macro-economic, micro-financial and market risk indicators, used as leading indicators by market professionals. In this article, we propose a method to identify market states integrating two classification algorithms: a Robust Kohonen Self-Organising Maps one and a CART one. After studying the market’s states separation using the former, we ...
متن کامل