问一道题：NO.PZ2015120204000037

问题如下：

Paul suggests the following step which would be repeated every quarter.

Step 4 Our five experienced securities analysts are each assigned four of the groups, and then each analyst selects their one best stock pick from each of their assigned groups. These 20 “high-conviction” stocks will be added to our portfolio (in replacement of the 20 relatively underperforming stocks to be sold in Step 1).

A couple of additional comments related to the above:

Comment 1 The ML algorithms will require large amounts of data. We would first need to explore using free or inexpensive historical datasets and then evaluate their usefulness for the ML-based stock selection processes before deciding on using data that requires subscription.

Comment 2 As time passes, we expect to find additional ways to apply ML techniques to refine Alef’s investment processes.

Regarding Comment #2, Moresanu has been thinking about the applications of neural networks (NNs) and deep learning (DL) to investment management. Which statement(s) best describe(s) the tasks for which NNs and DL are well-suited?

Statement I NNs and DL are well-suited for image and speech recognition, and natural language processing.

Statement II NNs and DL are well-suited for developing single variable ordinary least squares regression models.

Statement III NNs and DL are well-suited for modelling non-linearities and complex interactions among many features.

选项：

Statement II only

Statements I and III.

Statements I, II and III.

解释：

B is correct. NNs and DL are well-suited for addressing highly complex machine learning tasks, such as image classification, face recognition, speech recognition and natural language processing. These complicated tasks are characterized by non-linearities and complex interactions between large numbers of feature inputs.

A is incorrect, because NNs and DL are well-suited for addressing highly complex machine learning tasks, not simple single variable OLS regression models.

C is incorrect, because NNs and DL are well-suited for addressing highly complex machine learning tasks, not simple single variable OLS regression models.