I'm looking for insights on how to test the accuracy of a model I built to predict next days stock price - Open, High, Low and Close next day prices.
Is there a unique indicator of accuracy or standard approach that should be used to represent the accuracy of this system ?
This is a predictive model for prices, not a trading system.
Input to the model is the historic time series - end of day stock prices-Open, High, Low, Closing.
I've run some initial stats by calculating the ratio of predicted prices to the actual end of day prices i.e if PC represents predicted close price for next day I am simply taking the ratio of PC / C where C is the actual close price for the predicted day and representing it as a %.
Have researched some forecasting system models online and understand that the common approach is to use Mean Absolute Percent Error, Mean Absolute Deviation, R-Squared, Correlation, Cumulative Forecast Error. As an example my prediction model is giving me values for Correlation and R-Squared 0.995 0.9924 0.9912 0.9885 & 0.9905 0.983 0.981 0.976 respectively for the O H L C predictions..would these be considered good values ?
Any suggestions on what would be the best approach?