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

By Christian L. Dunis, Jason Laws, Patrick Naïm

Until you already are within the alternate otherwise you are looking to write your personal buying and selling programmes, please provide this a move. This quantitative research established publication is easily past these with out very powerful data and programming functions. Sorry to inform you that as an MBA, CFA professional dealer, i may snatch at such a lot 30% of the modeling ideas defined. definitely the CDROM bundled did aid. even though, I doubt what number readers could have that persistence and source to assemble and enter the knowledge wanted.

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White (1989), “Multilayer Feedforward Networks Are Universal Approximators”, Neural Networks, 2, 359–366. Kaastra, I. and M. Boyd (1996), “Designing a Neural Network for Forecasting Financial and Economic Time Series”, Neurocomputing, 10, 215–236. 40 Applied Quantitative Methods for Trading and Investment Kingdon, J. (1997), Intelligent Systems and Financial Forecasting, Springer, London. Lisboa, P. J. G. and A. Vellido (2000), “Business Applications of Neural Networks”, in P. J. G. Lisboa, B. Edisbury and A.

Some of the more important measures include the Sharpe ratio, maximum drawdown and average gain/loss ratio. The Sharpe ratio is a 18 The MAE and RMSE statistics are scale-dependent measures but allow a comparison between the actual and forecast values, the lower the values the better the forecasting accuracy. 19 When it is more important to evaluate the forecast errors independently of the scale of the variables, the MAPE and Theil-U are used. They are constructed to lie within [0,1], zero indicating a perfect fit.

E. of regression Sum squared resid. Log likelihood Restr. log likelihood LR statistic (6 df) Prob(LR statistic) Obs. with dep = 0 Obs. D. dependent var. Akaike info. criterion Schwarz criterion Hannan–Quinn criterion Avg. log likelihood McFadden R-squared Total obs. Prob. 025760 1440 Theoretically, the advantage of NNR models over traditional forecasting methods is because, as is often the case, the model best adapted to a particular problem cannot be identified. It is then better to resort to a method that is a generalisation of many models, than to rely on an a priori model (Dunis and Huang, 2002).

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