Hi people,

Can anyone please offer some insight into this for me?

I'm coming from a functional magnetic resonance imaging research background where I analyzed a lot of time series data, and I'd like to analyze the time series of stock prices (or returns) by: 1) modeling a successful stock in a particular market sector and then cross-correlating the time series of this historically successful stock with that of other newer stocks to look for significant relationships; 2) model a stock's price time series and use forecasting (e.g., exponential smoothing) to predict future values of it. I'd like to use non-linear modeling methods (ARIMA and ARCH) to do this.

Several questions: How often do ARIMA and ARCH modeling methods (given that the individual who implements them does so accurately) actually fit the stock time series data they target, and what is the optimal fit I can expect? Is the extent to which this model fits the data commensurate with the extent to which it predicts this stock time series' future values?

Rather than randomly selecting stocks to compare or model, if profit is my goal, what is an efficient approach, if any, to selecting the stocks I'm going to analyze?

Which stats program is the most user-friendly for this?

Any thoughts on this would be great and would go a long way for me.

Thanks, Brian