Statistical procedure decision

Question

I have two problems in hand :

1. I have a dependant variable, lets say GDP, and many other independant variables. I need to know what procedure I can use to find which among the IVs are leading or lagging indicators. I have develop the model in SAS and Excel.

2. Based on some buy sell rules based out of x day ema and y day sma cross, I need to compute returns. I need to know which procedure I should use to find what values of x and y will give me the best returns (x and y being an array of prefixed values like (200,50)(300,30), etc.). Can a neural network be used here? If so can anyone give me a link to some documentation as to how to carry this out?

Answer 1

Ad 1: probably easiest is to calculate the linear correlation between the time series. Using both simultaneous and shifted time series will tell you something about lead/lag.

Ad 2: look into optimization, not neural networks. Initial and easiest approach is to use grid search: calculate the best returns for each combination of X and Y. Pseudocode:

x = [50:50:500]
y = [10:10:100]

for i in x:
    for j in y:
        return(i,j) = calculate_returns(x(i),y(j))
    end
end