According to the CAPM, which statement is true regarding Alpha and Beta for a typical stock drawn from the market portfolio? Assume the market has gone up 1% in the last day.

Consider overfitting when using kNN and a polynomial parametric model. When overfitting occurs with these two methods, in which “direction” does it occur?

Consider kNN, linear regression (LR), Decision tree (DT) (using correlation for splitting), and Random tree. Which model has the slowest training time?

Consider the following code snippet.   >>> import numpy as np>>> a = np.random.uniform(size=(3, 3)) >>> a array([[0.51639863, 0.57066759, 0.02847423] [0.17152166, 0.68527698, 0.83389686] [0.30696622, 0.89361308, 0.72154386]]) >>> b = np.random.uniform(size=(3, 3)) >>> b array([[0.18993895, 0.55422759, 0.35213195] [0.1818924 , 0.78560176, 0.96548322] [0.23235366, 0.08356143, 0.60354842]]) >>> XXXX >>> a array([[1. , 1. , 1. ] [1. , 0.68527698, 0.83389686] [1. , 1. , 0.72154386]]) What code could you replace with XXXX to cause the following output?

According to the 60 minutes video “Is the stock market rigged?” how did Katsuyama and RBC cut out the high frequency trader out of the equation?

Consider kNN, linear regression (LR), and Decision tree (DT) learning (using correlation for splitting). Which option correctly lists the methods from fastest to slowest in training time?

Consider two hedge funds.  Both of them have provided equivalent positive cumulative returns.  Which of the following might be a valid reason for choosing HF1 over HF2?

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(x == y, y == z)

Consider the following data: COL_1 COL_2 COL_3 100.00 0.00 0.00 101.00 0.01 0.01 102.00 0.02 0.01 100.00 0.00 -0.02 102.00 0.02 0.02 What might the data in each of the columns represent (from left to right)?

Which code snippet below would correctly calculate the Sharpe ratio for time series daily_rets that represents 224 days of daily returns? The risk free rate is represented by rfr