696 research outputs found

    Determination of fuzzy relations for economic fuzzy time series models by neural networks

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    Based on the works /11, 22, 27/ a fuzzy time series model is proposed and applied to predict chaotic financial process. Thwe general methodological framework of classical and fuzzy modelling of economic time series is considered. A complete fuzzy time series modellling approach is proposed which includes: determining and developing of fuzzy time series models, developing and calculating of fuzzy relations among the observations, calculating and interpreting the outputs. To generate fuzzy rules from data, the neural network with SCL-based product-space clustering is used

    The Effectiveness of Hybrid Backpropagation Neural Network Model and TSK Fuzzy Inference System for Inflation Forecasting

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    Forecasting may predict the accurate future condition based on the previous circumstance. Problems that may occur are related to forecasting accuracy. This study proposes a combination of two methods: Neural Network (NN) and Fuzzy Inference System (FIS) to accuratelly forecast the inflation rate in Indonesia. Historical data and four external factors were used as system parameters. The external factors in this study were divided into two fuzzy sets. While time series variables were divided into three fuzzy sets. The combination of them generated a lot of fuzzy rules that may reduce the forecasting effectiveness. As a consequence, the less fit fuzzy rules formation would produce a low accuracy. Therefore, grouping all input variables into positive parameters and negative parameters are necessary for efficiency improvement. To evaluate the forecasting results, Root Means Square Error (RMSE) analytical technique was used. Fuzzy Inference System Sugeno was used as the base line. The results showed that the combination of the proposed method has better performance (RMSE=2.154901) than its base line

    Yapay sinir ağları ile yatırım değerlemesi analizi

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    This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.Bu çalışmada geleneksel yatırım değerleme metotlarından olan indirgenmiş nakit akım ve net bugünkü değer modeli ile yapay sinir ağları modelinin tahmin etme özelliğinin birleştirilmesi analiz edilmiştir. Değerleme modellerinin temel bileşenlerinden olan satış gelirleri, maliyetler, yatırım harcamaları ve bunların yıllar içerisindeki büyüme oranları sektörel dinamikler ve makroekonomik faktörlerle yakından ilişkilidir. Bununla birlikte, enflasyon oranı ve döviz kurları bu bileşenlerin değişim oranlarını etkilemektedir. Dolayısıyla enflasyon oranını ve döviz kurlarını tahmin etmek değerlemenin sonucu açısından kritik bir önem taşımaktadır. Bu çalışmada Türkiye enflasyonu ve USD/TRY döviz kuru yapay sinir ağları modeli ile tahmin edilmiş ve bu değişkenler indirgenmiş nakit akım modeli içerisine yerleştirilmiştir. Bu modelin sonuçları geleneksel yöntemler ile karşılaştırılmıştır

    Investment Valuation Analysis with Artificial Neural Networks

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    This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices

    Investment Valuation Analysis with Artificial Neural Networks

    Get PDF
    This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices

    Forecasting and Forecast Combination in Airline Revenue Management Applications

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    Predicting a variable for a future point in time helps planning for unknown future situations and is common practice in many areas such as economics, finance, manufacturing, weather and natural sciences. This paper investigates and compares approaches to forecasting and forecast combination that can be applied to service industry in general and to airline industry in particular. Furthermore, possibilities to include additionally available data like passenger-based information are discussed

    Financial crises and bank failures: a review of prediction methods

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    In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets

    Forecasting the stock market index using artificial intelligence techniques

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    The weak form of Efficient Market hypothesis (EMH) states that it is impossible to forecast the future price of an asset based on the information contained in the historical prices of an asset. This means that the market behaves as a random walk and as a result makes forecasting impossible. Furthermore, financial forecasting is a difficult task due to the intrinsic complexity of the financial system. The objective of this work was to use artificial intelligence (AI) techniques to model and predict the future price of a stock market index. Three artificial intelligence techniques, namely, neural networks (NN), support vector machines and neuro-fuzzy systems are implemented in forecasting the future price of a stock market index based on its historical price information. Artificial intelligence techniques have the ability to take into consideration financial system complexities and they are used as financial time series forecasting tools. Two techniques are used to benchmark the AI techniques, namely, Autoregressive Moving Average (ARMA) which is linear modelling technique and random walk (RW) technique. The experimentation was performed on data obtained from the Johannesburg Stock Exchange. The data used was a series of past closing prices of the All Share Index. The results showed that the three techniques have the ability to predict the future price of the Index with an acceptable accuracy. All three artificial intelligence techniques outperformed the linear model. However, the random walk method outperfomed all the other techniques. These techniques show an ability to predict the future price however, because of the transaction costs of trading in the market, it is not possible to show that the three techniques can disprove the weak form of market efficiency. The results show that the ranking of performances support vector machines, neuro-fuzzy systems, multilayer perceptron neural networks is dependent on the accuracy measure used
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