Problem 1.

divide the data into training (80%) and test data (20%). 

Problem 2.

Build linear regression model for YY using X1,,X8X1,,X8 with training data. Make prediction in training and test data.

Problem 3.

Compute average daily profit in training data and test data using the following signal-based strategy.

if Y_predict >0, buy today and sell it tomorrow

else, do nothing.

Is the performance of your prediction model consistent? (Consistency means that it has similar performance in train and test.)

Problem 4.

In lecture, we claim that, if the average daily profit is higher than transaction cost, the strategy can be implemented (Now, we know that consistency of the model is also a necessary condition.)

But on some days, we do not trade stocks which are also counted in average daily profit. To evaluate the implementability more precisely, we need to compute average daily profit of those days we trade. Could you compute this adjusted average profit for training and test periods.