Defining a trading strategy based on autocorrelation patterns a. For each of the two indices

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You have to use PYTHON to generate the regressions and graphs and all info in order to interpret and analyze.

The question is as follows:

1) Defining a trading strategy based on autocorrelation patterns a. For each of the two indices, estimate a regression of current returns on a constant and the 1-month lagged return on the same MSCI index. (You will estimate two regressions, one for each MSCI index). Present the regression results and discuss the economic and statistical relevance of these results.

b. Propose a profitable trading strategy for each of the two indices, using the sign and coefficient of the slope parameter from 3a). For instance, if the coefficient on the 1-month lagged return is positive what position should you take in the following month? Provide summary statistics of the returns generated from the trading strategies for each index.

c. Regress the returns of the strategy for each index on a constant. Are the residuals autocorrelated? Explain how you test for this. What are the implications for the parameter estimate and the standard error of the regression?

d. Compare and contrast your findings to those of the existing literature on autocorrelation in returns and time-series momentum in equity markets. Summarise the stand of the literature and discuss to what extent your findings are in line with, or in contrast to, existing studies. (Note: some literature has been suggested but we expect you to engage with a broader range of studies.)

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