Keywords

fisher kernels, machine learning, support vector machines, stock price, sustainability

Start Date

1-7-2010 12:00 AM

Description

Many recent studies look at the issue of sustainability from the environmental and the social points of view. This paper takes a different approach and proposes to look at the economic stability of organizations in the water sector. It analyzes a set of 140 companies from around the world involved in this sector, and attempts to develop a methodology to find out, based on stock market data only, which ones will likely remain active in the foreseeable future, and which ones will likely "die" out. This methodology uses machine learning techniques to handle the stock price time series of companies over a period of variable length. These techniques make use of tools such as support vector machines and kernel learning, and more specifically of the Fisher kernel. This methodology has been previously tested on other sectors and has shown good results. This paper expands the testing to the water sector on an international basis and thus links it to the important area of sustainability. The paper’s results further confirm the potential of the methodology and provide prospective for future research.

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Jul 1st, 12:00 AM

Analysis of the Economic Sustainability of Companies in the Water Sector

Many recent studies look at the issue of sustainability from the environmental and the social points of view. This paper takes a different approach and proposes to look at the economic stability of organizations in the water sector. It analyzes a set of 140 companies from around the world involved in this sector, and attempts to develop a methodology to find out, based on stock market data only, which ones will likely remain active in the foreseeable future, and which ones will likely "die" out. This methodology uses machine learning techniques to handle the stock price time series of companies over a period of variable length. These techniques make use of tools such as support vector machines and kernel learning, and more specifically of the Fisher kernel. This methodology has been previously tested on other sectors and has shown good results. This paper expands the testing to the water sector on an international basis and thus links it to the important area of sustainability. The paper’s results further confirm the potential of the methodology and provide prospective for future research.