Consider the function h = lambda x: x**2 + 4*x – 5 / x If we use fsolve from scipy.optimize to find the x where h(x) = 0, with a starting guess of x0=0.01, what is the output we obtain?

What is the output of the following code? newlist = np.zeros(4) for i in range(len(newlist)):    newlist[i] = i + newlist[i] print(newlist)   

Consider a portfolio with the following weights w,  expected return on each risky asset ER, and covariance matrix Cov below. w = np.array([0.05, 0.03]) ER = np.array([0.10, 0.02])Cov = np.cov([[0.004, 0.0156], [0.0156, 0.009]]) Which of the following expressions represents the portfolio volatility in Python?

How many atoms are in a face centered cubic lattice?

Consider the code below with numbered lines:1)def h(x): 2) return np.exp(-x**2 / 2) / np.sqrt(2 * np.pi) 3) 4)x = np.linspace(-4, 4, 51) 5)y = np.zeros(x) 6) 7)for i in range(len(y)-1): 8) y[i] = h(x[i]) 9)plt.plot(x, y) If we run the code above, we will receive an error. In which line lies the error?

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] ?