According to the CAPM, which statement is true regarding Alpha and Beta for a typical stock drawn at random from the market portfolio in an downward tranding market?

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

Consider two decision trees trained on the exact same data. DT was trained using correlation for splitting, RT was trained using splits determined randomly. Both trees were trained with leaf_size = 1. Which option below correctly describes (in order): The slowest to train, the slowest to query, the highest accuracy on in-sample data?

Suppose you have historical stock price data with data missing on some days in history (the values are NaN). You still want to use the data in backtesting and calculation of technical factors. Which of the following options are recommended (in the book)?

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

Consider two decision trees trained on the exact same data. DT was trained using correlation for splitting, RT was trained using splits determined randomly. Both trees were trained with leaf_size = 1. Which option below correctly describes (in order): The fastest to train, the fastest to query, the highest accuracy on in-sample data?

Suppose you have historical stock price data with data missing on some days in history (the values are NaN). You still want to use the data in backtesting and calculation of technical factors. Which of the following options are recommended (in the book)?

Consider a data set composed of 1500 samples where X is drawn randomly uniformly from -2*PI to +2*PI, and Y = 2*X^3 + 4*X^2 + 5. Consider linear regression and how it relates to kNN (k=1), decision trees (leaf_size=1), and random trees (leaf_size=1). Which statement is false regarding in-sample RMSE?

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)

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?