The main aim of this study is to predict the daily stock exchange price index of the Athens Stock Exchange (ASE) using back propagation neural networks. We construct the neural network based on the minimum embedding dimension of the corresponding strange attractor. Multistep prediction for nine days ahead is achieved with this particular network indicating the increased possibility of this technique for immediate forecasts for very timeshort data sets, mostly daily and weekly.

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Regional form of Organization of the health care that are called today DyPE, have as a main goal to promote more rational resource allocation through decentralization in the decision making process. The concern for more effective and efficient use of resources devoted into the health care sector renders hospitals a critical vehicle of the quest for superior economic performance, especially if we take into our consideration their mounting over time deficits. Economic performance is primarily traced through a set of specific financial ratios, which embrace important elements that constitute the substance of the financial well-being of hospitals as economic units. An array of financial ratios is critically reviewed and a combination of them is proposed as a means of effective financial management. The later is necessary to ameliorate the funding strain imposed on the health care system and especially on hospitals. The financial performance is determined by the return on capital (profitability) in connection with the risk involved. Both factors determine the value created, which in turn affects the amount of financing attracted in the sector. The financial information available to the supervising regional bodies (DyPE), don't considered sufficient for their management to assess financial management of hospitals effectively. The lack of the appropriate economic data is due to the fact that double entry accounting has not yet fully adopted by the economic units that report to the corresponding DyPE. So, double entry accounting is prerequisite for reporting and monitoring acceptable financial performance. The later is vital in securing that the financial needs of the health sector that are growing at an ever accelerating pace, are met.

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PurposeThis paper sets out to apply chaos theory to the prediction of stock returns using Greek and Turkish stock index data. The aim of the analysis is to empirically show whether the markets have informational efficiency, in a comparative perspective.Design/methodology/approachThe research employs Grassberger and Procaccia's methodology in the time series analysis in order to estimate the correlation and minimum embedding dimensions of the corresponding strange attractor. To achieve out of the sample multistep ahead prediction, the paper gives the average for overall neighbours' projections of k‐steps into the future.FindingsThe results display the fact that the chaos theory is suitable to examine the time series of stock index returns. The empirical findings show that the stock markets are efficient in Greece, though in Turkey the market is predictable. The main practical implication of the findings is that the technical analysis works in Turkish markets and it is possible to beat the market, while in Greece the fundamental analysis works for equity trading.Originality/valueThe research results have both methodological and practical originality. On the theoretical side, the research shows how the chaos theory can be applied in financial time series analysis. The model is employed with data from Greece, as an EU member; and Turkey, as a candidate to the EU. The fact that the model works in Turkey implies that chaos theory can be used in emerging economies as a prediction model. On the practical side, the paper contributed to the previous literature by providing empirical evidence on market efficiency using a stochastic model.