Consider the numbered lines below:1)def price(PMT, IY, g) 2)…

Consider the numbered lines below:1)def price(PMT, IY, g) 2) return PMT / (IY – g) 3) 4)g = np.linspace(0, 0.09, 21) 5)IY = 0.1 6)PMT = 10 7)PV = np.zeros_like(g) 8)for i in range(len(g)): 9) PV[i] = price(PMT, IY, g[i]) 10)plt.plot(g, PV) 11)plt.xlabel(‘Growth Rate’) 12)plt.ylabel(‘Perpetuity with Growth Price’);If we execute the code above, we receive an error. In which line lies the error?

Assume we download the stock price of Tesla and compute its…

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 not a valid command in Python?  

Consider the following code with numbered lines: 1)betai = n…

Consider the following code with numbered lines: 1)betai = np.array([-2, -1.5, -1, -0.5, 0., 0.5, 1, 1.5, 2]) # Features 2)ERi = np.array([-0.08, -0.06, -0.03, -0.01, 0.02, 0.04, 0.07, 0.1 , 0.12]) # Labels 3)hidden = tf.keras.layers.Dense(units=1, input_shape=[1]) 4)model = tf.keras.Sequential([hidden]) 5)loss = ‘mse’ 6)optimizer = ‘Adam’ 7)model.compile(loss=loss, optimizer=optimizer) 8)history = model.fit(betai, ERi, epochs=10000, verbose=False) 9)plt.plot(history.history[‘loss’]) 10)plt.xlabel(‘Number of Epochs’) 11)plt.ylabel(‘Loss’);In which of line does the training of the neural network takes place?

Consider the pseudo code below to obtain the effient portfol…

Consider the pseudo code below to obtain the effient portfolios:from scipy.optimize import minimize f = lambda w: TO BE FILLED mu = np.linspace(15, 30, 31) sd_optimal = np.zeros_like(mu) w_optimal = np.zeros([31, 5]) for i in range(len(mu)): # Optimization Constraints cons = ({‘type’:’eq’, ‘fun’: lambda w: np.sum(w) – 1}, {‘type’:’eq’, ‘fun’: lambda w: w @ ER * 252 * 100 – mu[i]}) result = minimize(f, np.zeros(5), constraints=cons) w_optimal[i, :] = result.x sd_optimal[i] = np.sqrt(result.fun)Assuming that ER are Cov given, what should we substitute TO BE FILLED for in order to get the desired result?

Consider the same pseudo code from the previous question to…

Consider the same pseudo code from the previous question to compute the efficient portfolios:from scipy.optimize import minimize f = lambda w: TO BE FILLED mu = np.linspace(15, 30, 31) sd_optimal = np.zeros_like(mu) w_optimal = np.zeros([31, 5]) for i in range(len(mu)): # Optimization Constraints cons = ({‘type’:’eq’, ‘fun’: lambda w: np.sum(w) – 1}, {‘type’:’eq’, ‘fun’: lambda w: w @ ER * 252 * 100 – mu[i]}) result = minimize(f, np.zeros(5), constraints=cons) w_optimal[i, :] = result.x sd_optimal[i] = np.sqrt(result.fun)For any given iteration i, what is the shape of the array w_optimal[i, :]?