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 booklet, from a variety of most sensible foreign specialists, fills a spot by means of offering a guide of utilized quantitative monetary research. It makes a speciality of complicated empirical equipment for modelling monetary markets within the context of sensible monetary purposes.
info, software program and methods in particular aligned to buying and selling and funding will let the reader to enforce and interpret quantitative methodologies masking a variety of types.
The strangely wide-ranging methodologies comprise not just the 'traditional' monetary econometrics but additionally technical research structures and lots of nonparametric instruments from the fields of knowledge mining and synthetic intelligence. besides the fact that, for these readers wishing to bypass the extra theoretical advancements, the sensible software of even the main complex options is made as obtainable as attainable.
The ebook could be learn by way of quantitative analysts and investors, fund managers, possibility managers; graduate scholars in finance and MBA courses.

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1998). 2 Issues in neural network modelling Despite the satisfactory features of NNR models, the process of building them should not be taken lightly. There are many issues that can affect the network’s performance and should be considered carefully. The issue of finding the most parsimonious model is always a problem for statistical methods and particularly important for NNR models because of the problem of overfitting. Parsimonious models not only have the recognition ability but also the more important generalisation ability.

E. , G. M. Jenkins and G. C. Reinsel (1994), Time Series Analysis: Forecasting and Control, Prentice Hall, Englewood Cliffs, NJ. Campbell, I. , A. W. Lo and A. C. MacKinley (1997), “Nonlinearities in Financial Data”, in The Econometrics of Financial Markets, Princeton University Press, Princeton, NJ, pp. 512–524. Carney, J. C. and P. html). Clemen, R. T. (1989), “Combining Forecasts: A Review and Annotated Bibliography”, International Journal of Forecasting, 5, 559–583. Diekmann, A. and S. html).

The performance was measured statistically and financially via a trading simulation taking into account the impact of transaction costs on models with higher trading frequencies. The logic behind the trading simulation is, if profit from a trading simulation is compared solely on the basis of statistical measures, the optimum model from a financial perspective would rarely be chosen. The NNR model was benchmarked against more traditional regression-based and other benchmark forecasting techniques to determine any added value to the forecasting process.

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