Assume we live in a world where the CAPM holds and it is given by  ER = lambda beta: beta * 0.05 + 0.02If you know this linear relationship between beta and ER, you can compute the ER for a given beta.What is the ER on a stock with beta = 0.5?

What is the last element of x?x = np.linspace(0, 0.4 + 0.05, 9)

Assume we download the stock price of Tesla and compute its return using the command  startdate = ‘2019-01-01’ enddate = ‘2021-01-01’ tesla = web.get_data_yahoo(“TSLA”, startdate, enddate)R_tesla = tesla[‘Adj Close’].pct_change().dropna() Which of the following commands is used to compute the volatility of Tesla returns?  

Consider an economy with three possible states: bad, normal, and good. The probability of each state is given in the array of probabilities “p”  below. The payoff of a risky stock in each state is given in the array R.  p = np.array([0.1, 0.6, 0.3]) R = np.array([[0.05], [0.03], [-0.02]]) If we want to compute the expected return on this risky asset, what is the command line we should execute in Python?

Consider the array below: x = np.array([9, 0.05, -90.43, 1.78, 5, 6.3]) What is the element x[3] ?

Which of the following molecule has the highest boiling point? (The numbers shown in the choices are supposed to be subscripts.)

Assume we download the stock price of Tesla using the command  startdate = ‘2019-01-01’ enddate = ‘2021-01-01’ tesla = web.get_data_yahoo(“TSLA”, startdate, enddate)tesla Date Open High Low Close Adj Close Volume 2019-01-02 61.220001 63.026001 59.759998 62.023998 62.023998 58293000 2019-01-03 61.400002 61.880001 59.476002 60.071999 60.071999 34826000 2019-01-04 61.200001 63.599998 60.546001 63.537998 63.537998 36970500 2019-01-07 64.344002 67.348000 63.549999 66.991997 66.991997 37756000 2019-01-08 68.391998 68.802002 65.403999 67.070000 67.070000 35042500   Which of the following commands gives you the “Adj Close” column?

RMSProp is an example of a/an

Which of the following is NOT affected by the intermolecular forces?

To evaluate the performance of our ML model, we can look to some metrics on the validation set. For instance, we can check the L2 norm of the validation set, which is simply the