This insight draws inspiration from the key topics discussed during an interview between Frank Fabozzi, CFA (John Hopkins University), and Jason Hsu (Raylian Global Advisors), published on the CFA Institute – Research and Policy Center page, accessible via the following link.
This article highlights the most relevant aspects for retail investors, commenting on their importance for portfolio construction purposes.
Be Cautious About Trading in Individual Stocks
When trading individual stocks, consistently finding the optimal entry and exit points is a challenging task, especially in the short term, often resulting in value destruction. Even when extensive information about markets and stocks is available, markets can remain inefficient and irrational for extended periods, nullifying any “bets.” As always, a diversified and consistent approach over time is what one should expect from a fund manager.
It is also worth noting that “indiscriminate” trading activity can unintentionally alter a portfolio’s sector or geographic exposure, deviating from the target strategic asset allocation (in more technical terms, this could lead to trading on a portfolio’s beta rather than alpha, with distorting effects).
Active vs. Passive Approach in Stock Selection
Financial markets are heterogeneous and require different approaches. For example, the U.S. market is currently the most efficient in terms of transaction transparency, quickly incorporating new information and expectations into prices. This has led many investors to hold the market index (passive approach), such as the S&P 500, rather than trying to outperform it (active approach). Unsurprisingly, most transactions in the U.S. market are carried out by institutional investors capable of disciplined investment approaches and access to volumes of information often unavailable to retail investors.
Conversely, emerging markets (e.g., China, Taiwan) still exhibit many inefficiencies and are currently dominated by retail investors, causing prices to deviate significantly from their fundamentals. In this context, a more active approach may make sense, perhaps employing investment styles that wouldn’t work in other markets.
For instance, in the Chinese market, listed companies are penalized and sanctioned (up to being delisted) if they report losses for several consecutive periods. For this reason, healthier companies tend (paradoxically) to underreport their earnings over time, revealing them during crises. With this information, additional returns over the market index can be achieved by overweighting companies that underreport earnings (this approach would make no sense in the U.S. market).
Applying Machine Learning to Investments
Artificial intelligence, driven by machine learning, is a hot topic generating revolutionary expectations in various sectors. In finance, machine learning may disappoint those who believe it will radically change how managers approach markets.
Models developed through machine learning are evaluated based on the quality of their backtesting (i.e., how they would have performed historically if investments were made based on the model). Considering the U.S. market, which is highly efficient, producing a model capable of replicating its performance often results in a complex model mimicking a simple “random walk.” Such a model would be highly counterproductive if adopted by fund managers to make investment decisions.
Machine learning is a valuable tool for analyzing vast amounts of data quickly, an impossible task just a few years ago. It can be used to discover relationships not easily identifiable, but qualitative judgment will always be needed to determine whether it makes sense as an investment strategy.
A quantitative approach to investments must be transparent, interpretable, and explainable—it is unacceptable to execute trades solely “because the model says so.”
Current and Future Investment Themes
The ESG (Environmental, Social, Governance) theme is particularly prominent today, especially in Europe and Taiwan. However, caution is needed regarding greenwashing, where investments appear aligned with ESG principles but are not. A rigorous approach based on reliable data is necessary for consistency.
It’s also important to remember that sustainable (or ESG compliant) does not always mean profitable. From this perspective, an ESG approach should be seen as a type of investment that impacts beta (and therefore strategic asset allocation), not the alpha of a portfolio.
Another slow-moving yet impactful theme is demographics. In the U.S., around 12,000–14,000 people retire daily. This mass of consumers exiting the workforce and needing to “spend” will significantly impact inflation risks. Simultaneously, human capital will become scarcer and more critical than financial capital.
This is good news for younger generations and their future, provided they acquire skills and competencies relevant to the job market. Returning to artificial intelligence, many skills will be needed to manage the software aspect of machine learning. From this perspective, robots will create jobs rather than take them away from the new generation.
