Abstract
Corporate net value is efficiently described on its stock price, offering investors a chance to include a potentially surplus value to the net worth of the overall investment portfolio. Financial analysis of corporations extracted from the accounting statements is constantly demanded to support decisions making of portfolio managers. Econometrics and Artificial Intelligence methods aim to extract hidden information from complex accounting and financial data. Support Vector Machines hybrids optimized in their components by Genetic Algorithms provide effective results in corporate financial analysis.
Highlights
Stocks volatility in financial markets and the real economic conditions in a company—partially expressed in accounting statements—demand effective financial analysis
The Hybrid neuro-genetic Support Vector Machine with 1000 epochs and no hidden layer converged successfully as well, since the initially characterized healthy companies by bank executives were classified as healthy in a proportion of 100%, and the companies grouped in the distress category at the first place were classified as in distress at a rate of 100%
The Support Vector Machines of 1000 epochs in On-Line Learning had the optimal convergence, with a classification outcome identical to the bank experts decisions, a very high fitness of the model to the data, the lowest MSE and in the fastest processing time, on every case: the in-sample, the out-of-sample and the overall results, and this holds for the rest of the models
Summary
Stocks volatility in financial markets and the real economic conditions in a company—partially expressed in accounting statements—demand effective financial analysis. Portfolio managers require precise information on the economic health of corporation to secure lucrative portfolios to their investors. Econometric models mostly of Moving Average (MA), Auto Regression Moving Average (ARMA), Capital Asset Pricing Model (CAPM), or Arbitrage Pricing Theory (APT) and Artificial Intelligence in a nonlinear approach are capable to support corporate financial analysis [1]-[9]. Support Vector Machines in a hybrid with Genetic Algorithms optimization provide efficient results of financial analysis in companies
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