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 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. 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 Statement Il only O Statement I and III O Statements I, II, and III Statement II and
