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

By Christian L. Dunis, Jason Laws, Patrick Naïm

ISBN-10: 0470848855

ISBN-13: 9780470848852

Except you already are within the alternate otherwise you are looking to write your individual buying and selling programmes, please supply this a move. This quantitative research dependent publication is well past these with no very powerful statistics and programming functions. Sorry to inform you that as an MBA, CFA professional dealer, i'll clutch at so much 30% of the modeling concepts defined. definitely the CDROM bundled did aid. despite the fact that, I doubt what percentage readers may have that endurance and source to gather and enter the knowledge wanted.

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Additional resources for Applied Quantitative Methods for Trading and Investment

Sample text

Each of the models evaluated, except the logit model, are nominated “best” at least once. 6901; if this model is believed to be the “best” model there is likely to be no added value using more complicated forecasting techniques. 0056. The NNR model has the lowest RMSE statistic, however the value is only marginally less than the ARMA model. 00% of the time. 26) t=1 Total trading days Avg. gain in up periods Avg. loss in down periods Avg. 31) [(W T × AG2 ) + (LT × AL2 )] MaxRisk is the risk level defined by the user; this research, 10% Profits T -statistics Source: Dunis and Jalilov (2002).

Criterion Schwarz criterion F -statistic Prob(F -statistic) Prob. 373884 variable equal to one is produced if the return is positive, and zero otherwise. The same transformation for the explanatory variables, although not necessary, was performed for homogeneity reasons. A basic regression technique is used to produce the logit model. The idea is to start with a model containing several variables, including lagged dependent terms, then through a series of tests the model is modified. 3937X6,t + εt where X1,t , .

For a full discussion on the procedure refer to Box et al. (1994), Gouri´eroux and Monfort (1995), or Pindyck and Rubinfeld (1998). 5) 8 The general class of ARMA models is for stationary time series. If the series is not stationary an appropriate transformation is required. 9 (1,40) combination moving average Excel spreadsheet (in-sample) where Yt is the dependent variable at time t; Yt−1 , Yt−2 , . . , Yt−p are the lagged dependent variables; φ0 , φ1 , . . , φp are regression coefficients; εt is the residual term; εt−1 , εt−2 , .

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


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