Figure 1 shows the complete work flow of HIPM. Initially actual returns, [r.sub.t], are given as an input to linear prediction through which predictions [[??].sub.t] are obtained.
The performance of HIPM is checked here by using two error metrics, mean square error and mean absolute error.
In order to verify the performance of HIPM predictor, real world stock data has been used for experiments.
Figures 3 and 4 show the prediction output of HIPM (between 06-09-2013 and 31-12-2013) via multiplicative method and summation method for stock 2 and stock 3, respectively.
The performance of HIPM predictor can be better judged by Table 2, which displays values of error metrics obtained.
Caption: FIGURE 2: Recurrent neural network used in HIPM.
Caption: FIGURE 3: Multiplicative method: output of HIPM for stock 2.
Caption: FIGURE 4: Summation method: output of HIPM for stock 3.