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:
- 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?
- 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?
