Alef Associates is a company that manages a fund specializing in global small-cap equities. Since its founding a decade ago, Alef maintains a portfolio of 100 stocks (out of eligible universe of about 10,000 stocks). Paul Moresanu, the chief investment officer of Alef Associates has been thinking about modernizing the way Alef selects stock investments. Paul writes the following email to Alef's CEO: Subject: Investment process reorganization I propose that we continue managing a portfolio of 100 global small-cap stocks but restructure our process to benefit from machine learning (ML). Importantly, the new process will still allow a role for human insight, for example, in providing domain knowledge. In addition, I think we should make a special effort to identify companies that are likely to be acquired. Specifically, I suggest following the four steps which would be repeated every quarter. Step1. We apply ML techniques to a model including fundamental and technical variables (features) to predict next quarter's return for each of the 100 stocks currently in our portfolio. Then, the 20 stocks with the lowest estimated return are identified for replacement. Step2. We utilize ML techniques to divide our investable universe of about 10,000 stocks into 20 different groups, based on a wide variety of the most relevant financial and non-financial characteristics. The idea is to prevent unintended portfolio a concentration by selecting stocks from each of these distinct groups. Step 3. For each of the 20 different groups, we use labeled data to train a model that will predict the five stocks (in any given group) that are most likely to become acquisition targets in the next one year. Step4. 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 Step1). A couple o 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. Comment2: As time passes, we expect to find additional ways to apply ML techniques to refine Alef's investment processes. What do you think? Paul Moresanu. Comparing two ML models that could be used to accomplish Step3, which statement(s) best describe(s) the advantages of using Classification and Regression Trees (CART) instead of K-Nearest Neighbor (KNN)? Statement I. For CART there is no requirement to specify an initial hyperparameter (like K) Statement II. For CART there is no requirement to specify a similarity (or distance) measure Statement III. For CART the output provides a visual explanation for the prediction Statement I only O Statement Ill only Statements I, II, and III Statement I and II only
