Download Applied Quantitative Methods for Trading and Investment by Christian L. Dunis, Jason Laws, Patrick Naïm PDF

By Christian L. Dunis, Jason Laws, Patrick Naïm

ISBN-10: 0470848855

ISBN-13: 9780470848852

Until you already are within the exchange otherwise you are looking to write your personal buying and selling programmes, please supply this a go. This quantitative research dependent booklet is well past these with out very powerful facts and programming services. Sorry to inform you that as an MBA, CFA professional dealer, i may seize at such a lot 30% of the modeling ideas defined. definitely the CDROM bundled did aid. besides the fact that, I doubt what number readers may have that endurance and source to gather and enter the information wanted.

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Extra info for Applied Quantitative Methods for Trading and Investment

Sample text

Having constructed a synthetic EUR/USD series for the period up to 4 January 1999, the models were developed using the same in-sample data, 17 October 1994 to 18 May 2000, leaving the remaining period, 19 May 2000 to 3 July 2001, for out-of-sample forecasting. Applications of Advanced Regression Analysis 37 Forecasting techniques rely on the weaknesses of the efficient market hypothesis, acknowledging the existence of market inefficiencies, with markets displaying even weak signs of predictability.

16 In essence, the only difference from NNR models is the addition of a loop back from a hidden or the output layer to the input layer. The loop back is then used as an input in the next period. There is no theoretical or empirical answer to whether the hidden layer or the output should be looped back. However, the looping back of either allows RNN models to keep the memory of the past,17 a useful property in forecasting applications. This feature comes at a cost, as RNN models require more connections, raising the issue of complexity.

However, despite the limitations and potential improvements mentioned above, our results strongly suggest that regression models and particularly NNR models can add value to the forecasting process. For the EUR/USD exchange rate and the period considered, NNR models clearly outperform the more traditional modelling techniques analysed in this chapter. REFERENCES Balkin, S. D. and J. K. Ord (2000), “Automatic Neural Network Modelling for Univariate Time Series”, International Journal of Forecasting, 16, 509–515.

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Applied Quantitative Methods for Trading and Investment by Christian L. Dunis, Jason Laws, Patrick Naïm


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