Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market

88

6. Conclusions

Two prediction models developed in this study. The first

model was developed with the well-known back propa-

gation feed forward neural network. The second model

used here is based on polynomial classifiers which are

being used for the first time in stock prices prediction.

The inputs to both models were identical, and bo th mod-

els were trained and tested on the same data in three dif-

ferent training scenarios and two prediction modes. The

data used here is the historical p rices for two of the lead-

ing stocks in Dubai F i n ancial Market.

In general, both models achieved outstanding resu lts in

terms of mean absolute error percentage (MEAP). Both

models achieved around 1.5% MEAP in predicting the

next day, 2.5% MEAP in predicting the second day, and

around 4% MEAP in predicting the third day. The pre-

diction accuracy of the two models was certainly re-

markable, where around 60% of the predicted prices of

the first day, 50% of the predicted prices of the second

day, and 35% of the predicted prices of the third day,

were all within –1% to 1% of the actual prices of the

three days.

When comparing the neural network and polynomial

classifiers prediction models, it was found that first order

polynomial classifier performed comparable to or slight-

ly better than the neural network. Whereas the second

order polynomial classifier could barely achieve similar

results on the stocks used in this study. Further work can

be done using other stocks in similar emerging markets

and mature markets, to verify this conclusion.

On the other hand it should be noted that PC is a lot

more computationally efficient than ANN since its

weights can be obtained directly an d non-iteratively fro m

a closed formula as shown in Section 3.1.

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