Examen 1. Etapa 3 Quantitative Methods

An analyst estimates the following joint outcomes for Stock A and Stock B: Up (probability 0.30), A = 42, B = 16; Middle (probability 0.50), A = 34, B = 12; Down (probability 0.20), A = 28, B = 6. The covariance of returns between Stock A and Stock B is closest to:

Two corporate bonds have return standard deviations of 0.62 and 0.30. If the correlation of their returns is 0.72, the covariance of returns is closest to:

An analyst builds the following covariance matrix of returns: the variance of a hedge fund is 289, the variance of a market index is 100, and their covariance is 132. The correlation of returns between the hedge fund and the market index is closest to:

The covariance of returns between Asset A and Asset B is 0.12. The standard deviation of returns is 0.35 for Asset A and 0.45 for Asset B. The correlation of returns between the two assets is closest to:

A two-stock portfolio holds Stock 1 (expected return 6%, standard deviation 14%) and Stock 2 (expected return 9%, standard deviation 22%), with weights of 0.30 and 0.70. The correlation between the two stocks is 0.25. The standard deviation of the portfolio’s returns is closest to:

A manager forms a three-asset portfolio with weights of 0.25, 0.35, and 0.40 and expected returns of 4%, 6%, and 8%. The variance-covariance matrix (in percent squared) has diagonal variances 169, 196, and 324, with covariances of 90 (Assets 1 and 2), 120 (Assets 1 and 3), and 130 (Assets 2 and 3). The portfolio standard deviation is closest to:

An analyst wants to estimate the distribution of one-year returns for a bond portfolio and decides to use historical simulation. Which of the following best describes a key limitation of this approach?

Historical simulation and Monte Carlo simulation differ primarily in how the input data are generated. Which statement most accurately distinguishes the two?

A risk manager notes that a major market regime shift occurred in the middle of her historical sample. Relative to a period with no regime change, historical simulation using this full sample is most likely to:

Bootstrap resampling is most accurately described as drawing:

An analyst has only 40 monthly return observations and wants to estimate the sampling distribution of the mean return without assuming normality. The most appropriate technique is:

An analyst regresses the monthly return of Torvane Materials common stock (dependent variable) on the monthly return of a broad commodity index (independent variable) using 42 months of data. The estimated intercept is 0.0182 and the estimated slope is 0.3120. If the analyst expects the commodity index to return -0.02 (i.e., -2.0%) next month, the predicted return on Torvane stock is closest to:

A research analyst studies the relationship between the trailing dividend yield and the one-year forward payout ratio for a sample of 45 utility companies. The sum of the cross-products of the deviations of the two variables from their respective means is -8.316. Based on this information, the sample covariance between the two variables is closest to:

An analyst estimates a simple linear regression of annual equity index returns on annual GDP growth for a cross-section of 8 economies. The sum of the squared residuals from the regression is 0.014208. The standard error of the estimate for this model is closest to:

An analyst models the relationship between the operating margin (in percent) of a group of software firms and their asset turnover (times). After testing several functional forms, she fits a model in which the natural log of the operating margin is regressed on asset turnover. The estimated intercept is 0.6420 and the estimated slope is 0.3185. For a firm with an asset turnover of 3 times, the predicted operating margin is closest to:

An analyst estimates a simple regression of a company’s short interest ratio on its debt ratio for a sample of 48 firms. The estimated intercept is 6.2140 and the estimated slope coefficient on the debt ratio is -3.8850. For Kessington Corp., which has a debt ratio of 0.55, the predicted short interest ratio is closest to:

An investment firm’s machine learning model performs extremely well on the data used to fit it but produces poor predictions when applied to a new, previously unseen dataset. This outcome is most consistent with a model that is:

An analyst is categorizing several data sources by structure. XML files used to tag and organize web content are most accurately classified as:

When implementing a supervised machine learning project, a data science team typically partitions the available data. The dataset is most accurately described as being divided into:

Big Data has traditionally been characterized by three attributes: volume, velocity, and variety. As large datasets are increasingly used for prediction and inference, the attribute most often cited as the important fourth characteristic is: