New maths model to predict companies success, failure at share marketMarch 16th, 2008 - 1:48 pm ICT by admin
Washington, March 16 (ANI): Taiwanese researchers have created a mathematical model, based on the principle of natural selection, which can enable people interested in share market to predict whether any company is likely to fail.
The expert duo Ping-Chen Lin of the National Kaohsiung University of Applied Sciences in Kaohsiung and Jiah-Shing Chen of the National Central University in Jhongli says that a plethora of changing and interconnected factors make it difficult to predict any companys chances of success or failure.
The researchers say that they have now borrowed some of the principles of evolutionary biology to come up with a computer algorithm to make such predictions possible.
They feed different variables, such as earnings per share, liabilities and net income, into their genetic-based hybrid algorithm, which assigns a weighting to each value.
As output, the algorithm generates a new set of variables that are then selected for how well they fit the next set of financial results from the company.
The researchers say that those that fail are discarded or reduced in weight, while those that match the actual results more closely are fed back into the algorithm for the next round.
Using actual data from successful and failed companies and feeding this into the algorithm the researchers build up the fittest set of variables and weightings, which allows the algorithm to evolve so that it can then predict the financial future of any given company based on current income and expenditure, and tax obligations.
The researchers say that they have successfully blind tested the predictive power of their system on several companies.
“Our experimental results show that this hybrid approach obtains better prediction performance than when using a single approach effectively,” the researchers say.
A report detailing the new system appears in a forthcoming issue of the International Journal of Electronic Finance. (ANI)
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