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What is the output of the following Python code snippet? >>> import numpy as np >>> np.random.seed(5) >>> x = np.random.uniform(0, 4) >>> np.random.seed(5) >>> y = np.random.uniform(0, 4) >>> z = np.random.uniform(0, 4) >>> print(z == y, y == x)

Suppose you are using one of the minimizing optimizers from Scipy. You are using it to optimize your portfolio for MINIMUM volatility, and port_vals are the daily total values of the portfolio for a particular allocation. Which of the following would be the best way to compute the objective function for the optimizer?

Consider the following valuation factors of a company: It owns 1000 cars valued at $50,000 each It holds real estate worth $7,000,000 It owes $10,000,000 in loans It pays $1.00 per year per share in dividends starting in one year The stock price is $60.00 per share There are 1,000,000 shares outstanding The discount rate is 5% The risk free rate is 0% What is the book value of the company?

Consider the following order book. Bid size Price Ask size 210.10 490 210.05 500 210.00 1000 2000 209.95 2000 209.90 1500 209.85 How would you expect the price to change over the next few minutes?

Consider the following valuation factors of a company: It owns 1000 cars valued at $50,000 each It holds real estate $10,000,000 It owes $5,000,000 in loans It pays $1.00 per year per share in dividends starting in one year The stock price is $55.00 per share There are 1,000,000 shares outstanding The discount rate is 2% The risk free rate is 0% What is the intrinsic value of the company?

What is the output of the following Python session? >>> q = 4 >>> r = q >>> q = 8 >>> print(r * q)

Consider the following order book. Bid size Price Ask size 200.80 300 200.75 200 200.50 100 200.25 200 100 199.95 400 199.90 100 199.85 At what average price would a limit order to BUY 500 shares at $200.75 be executed?

Comparing a decision tree model using information gain to a decision tree model using randomized feature selection and splits with the same leaf size, the decision tree using information gain will likely require:

Consider the following code snippet.   >>> import numpy as np>>> a = np.random.uniform(size=(3, 3)) >>> a array([[0.81622475, 0.27407375, 0.43170418] [0.94002982, 0.81764938, 0.33611195] [0.17541045, 0.37283205, 0.00568851]]) >>> b = np.random.uniform(size=(3, 3)) >>> b array([[0.57509333, 0.89132195, 0.20920212] [0.18532822, 0.10837689, 0.21969749] [0.97862378, 0.81168315, 0.17194101]]) >>> XXXX >>> a array([[0.81622475, 0.27407375, 1. ] [1. , 1. , 1. ] [0.17541045, 0.37283205, 1. ]]) What code could you replace with XXXX to cause the following output?

Consider the following code snippet.   >>> import numpy as np>>> a = np.random.uniform(size=(3, 3)) >>> a array([[0.80228309 0.05297495 0.30860601] [0.48194334 0.90905674 0.32110407] [0.28256525 0.23255754 0.08857034]]) >>> b = np.random.uniform(size=(3, 3)) >>> b array([[0.08248317 0.23192471 0.08286411] [0.86504732 0.28871177 0.51897854] [0.87400201 0.21622928 0.44841032]]) >>> XXXX >>> a array([[0.80228309 0.05297495 0.30860601] [1. 0.90905674 1. ] [1. 0.23255754 1. ]]) What code could you replace with XXXX to cause the following output?