# Data Lab 4

1. Revisit the all-weather portfolio you crafted. Create the maximum Sharpe portfolio's daily return dataframe and then merge it with Fama French's five return factors.

2. Examine visually the correlation between portfolio and factor returns.

3. Regress the portfolio return on each factor and assess the portfolio's sensitivity to each factor. For the curious, optional challenge, how do you test whether the intercept (i.e., alpha) is significantly different from the risk-free rate for a single-factor regression?

4. Regress the portfolio return on all factors and assess the portfolio's sensitivity to factors. For the curious, optional challenge, how do you test whether the intercept (i.e., alpha) is significantly different from the risk-free rate for a multi-factor regression?

5. Optional Bonus. Construct a multi-factor pricing model for assets based on Arbitrage Pricing Theory. The Arbitrage Pricing Theory (APT) is a theory of asset pricing that holds that an asset’s returns can be forecasted with the linear relationship between an asset’s expected returns and the macroeconomic (e.g., GDP, changes in inflation, yield curve changes, changes in interest rates, market sentiments, exchange rates) or firm-specific statistical factors that affect the asset’s risk. Hint: You can draw these variables straight into your Jupyter notebook via Refinitiv API.

### Setting up the workspace

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### Question 1

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Run the app to see the outputs

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Run the app to see the outputs

Press the run button in the top right corner

Run the app to see the outputs

Press the run button in the top right corner

### Question 2

First, we visualize the correlation through a heatmap.

Run the app to see the outputs

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Secondly, we visualize the correlation between the portolio and the individual factors as a rolling time-series.

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### Question 3

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### Question 4

Run the app to see the outputs

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