RLSTM: A New Framework of Stock Prediction by Using Random Noise for Overfitting Prevention

<table class="table-group" id="tab7"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left"> </td><td class="align_center">Prediction module</td><td class="align_center">Prevention module</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">SSEC</td><td class="align_center">0.4705</td><td class="align_center">0.0050</td></tr><tr><td class="align_left">S&amp;P500</td><td class="align_center">0.3919</td><td class="align_center">0.0088</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Absolute mean of weights of each module to forecast different stock market index.</div>

Computational Intelligence and Neuroscience

tab7

Table 7

Table 7: RLSTM: A New Framework of Stock Prediction by Using Random Noise for Overfitting Prevention 