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

By Christian L. Dunis, Jason Laws, Patrick Naïm

This much-needed e-book, from a range of most sensible overseas specialists, fills a spot via supplying a guide of utilized quantitative monetary research. It makes a speciality of complex empirical tools for modelling monetary markets within the context of functional monetary purposes.
facts, software program and methods particularly aligned to buying and selling and funding will let the reader to enforce and interpret quantitative methodologies overlaying a number of types.
The surprisingly wide-ranging methodologies comprise not just the 'traditional' monetary econometrics but in addition technical research structures and plenty of nonparametric instruments from the fields of information mining and synthetic intelligence. even though, for these readers wishing to bypass the extra theoretical advancements, the sensible program of even the main complicated strategies is made as available as attainable.
The ebook should be learn through quantitative analysts and investors, fund managers, chance managers; graduate scholars in finance and MBA courses.

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For the logistic function the range is [0,1], avoiding the function’s saturation zones. , 1998). Crucial for backpropagation learning is the learning rate of the network as it determines the size of the weight changes. Smaller learning rates slow the learning process, while larger rates cause the error function to change wildly without continuously improving. To improve the process a momentum parameter is used which allows for larger learning rates. , 1998). 3 Neural network modelling procedure Conforming to standard heuristics, the training, test and validation sets were partitioned as approximately 23 , 16 and 16 , respectively.

1997), Modern Econometrics. An Introduction, Addison-Wesley, Harlow. Tyree, E. W. and J. A. html). , H. Poh and T. html). , Y. Li and C. L. Tan (1997), “Forecasting the Exchange Rates of CHF vs USD Using Neural Networks”, Journal of Computational Intelligence in Finance, 15, 2, 7–13. , B. E. Patuwo and M. Y. Hu (1998), “Forecasting with Artificial Neural Networks: The State of The Art”, International Journal of Forecasting, 14, 35–62. 2 Using Cointegration to Hedge and Trade International Equities A.

Vellido (eds), Business Applications of Neural Networks: The State-of-the-Art of Real-World Applications, World Scientific, Singapore, pp. vii–xxii. Maddala, G. S. (2001), Introduction to Econometrics, 3rd edition, Prentice Hall, Englewood Cliffs, NJ. Mehta, M. (1995), “Foreign Exchange Markets”, in A. N. ), Neural Networks in the Capital Markets, John Wiley, Chichester, pp. 176–198. Pesaran, M. H. and B. Pesaran (1997), “Lessons in Logit and Probit Estimation”, in Interactive Econometric Analysis Working with Microfit 4, Oxford University Press, Oxford, pp.

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