2
$\begingroup$

In the artcicle Forecasting and Trading the High-Low Range of Stocks and ETFs with Neural Networks HCNN is used for forecasting of nine time-series, namely:

  • returns of the lows
  • returns of the highs
  • five-period exponential moving average of the lows
  • five-period exponential moving average of the highs
  • five-period lower bollinger band of the closes
  • five-period upper bollinger band of the closes
  • returns of the open
  • same-day return open to low
  • same-day return open to high

The proposed state transition function is: $$ s_{t+1}=\tanh(W \cdot s_t) $$ The outputs (predictions) of HCNN are the first nine elements of state vector $s_{t+1}$ so the outputs are within the range $[-1,1]$. The questions are:

  1. Would it be correct to assume that "returns on ..." are represented as negative and positive deltas, i.e. 10% increase in price is 0.1 while 5% loss is -0.05?
  2. Five-period exponential moving average of lows will be in general well beyound $[-1,1]$ range of $\tanh$. Shouldn't these values be scaled into the $[-1,1]$ range? How?
$\endgroup$

0

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.