Highlights

• Over 50% of U.S. Large-Cap active managers have underperformed the S&P 500 in 18 of the past 21 years.
• The percentage of funds underperforming expands as the investment horizon lengthens, ranging from around 55% over a 1-year horizon to 94% over a 20-year horizon for U.S. funds.
• The IKF AI algorithms can assist in identifying the latest investment opportunities and enhancing the performance of active investment strategies.

The Pros and Cons of Active and Passive Investment Strategies

Investment Strategies: For quite some time, investors have deliberated over choosing between earning returns that match the market index or striving to surpass them by investing in actively managed mutual funds. Active investment refers to an investment strategy that involves actively selecting and managing individual securities in an effort to outperform a market index or benchmark. Unlike passive investing, which aims to replicate the performance of a specific index, active investment involves making active decisions regarding the buying, selling, and holding of securities with the goal of generating higher returns than the overall market.

Let’s begin by examining the active investing side. Active investing offers the potential for higher returns compared to passive strategies, such as index funds. Skilled fund managers can leverage their expertise and research to identify undervalued securities or market inefficiencies, with the aim of outperforming the market. Active management allows flexibility in portfolio construction, enabling managers to adjust holdings based on changing market conditions or investment opportunities. However, active investment also comes with certain drawbacks. One of the main concerns is the higher fees associated with actively managed funds. These fees are typically higher than those of passive funds and can erode overall investment returns over the long term. Moreover, consistently outperforming the market is challenging, and many active managers struggle to consistently beat their benchmark indices. Market timing and stock selection can be difficult, and even skilled managers can make mistakes or be subject to unpredictable market events.

At the same time, passive investment refers to an investment strategy that aims to replicate the performance of a specific market index or benchmark, rather than actively selecting and managing individual securities. In passive investing, investors typically allocate their funds to passive vehicles such as index funds or exchange-traded funds (ETFs) designed to mirror the composition and performance of a particular index. Passive investment strategies operate on the principle of market efficiency, assuming that stock prices already reflect all available information. As such, it promotes investing in a broad market portfolio instead of attempting to time the market or pick individual stocks based on perceived opportunities or undervaluation. Passive investing offers several advantages, including lower costs compared to actively managed funds, broad market exposure, transparency, and simplicity. It particularly appeals to investors who prefer a more hands-off approach, as it involves less active decision-making and relies on the long-term growth of the market.

However, passive investing does come with certain drawbacks. One limitation is that passive strategies are inherently designed to match the performance of the underlying index rather than to outperform it. As a result, passive investors may miss potential opportunities for higher returns that active managers could identify through skillful stock selection or market timing. Furthermore, passive investing exposes investors to the full extent of market downturns or underperforming sectors without the ability to actively adjust their holdings. This lack of flexibility can be perceived as a disadvantage for those aiming to mitigate risk or capitalize on short-term market trends.

Investment Strategies: Who is on the Top?

SPIVA (S&P Indices Versus Active) measures the performance of actively managed funds in comparison to their respective benchmark indices. SPIVA reports are produced and published by S&P Dow Jones Indices. The chart below shows that over 50% of U.S. Large-Cap active managers underperformed the S&P 500 in 18 of the past 21 years. Furthermore, during a 20-year period ending on June 30, 2022, 95% of U.S. Large-Cap funds underperformed the S&P 500.

Moreover, the situation becomes clearer when examining various investment horizons. The percentage of underperforming funds increases with the investment horizon, ranging from around 55% over a 1-year period to 94% over a 20-year span for US funds. Additionally, a similar pattern emerges for funds from other geographic regions.

Based on the figures presented above, active investment does not appear to be a preferred method for enhancing investors’ well-being and effectively increasing funds. This does not imply that there are no successful investment managers who can be entrusted with your money to outperform the market. Nevertheless, it does indicate that when seeking an active investment fund, you should recognize that you may be starting from a position of potentially lower final returns compared to implementing a passive investment strategy.

Beat the Market with AI?

Modern progress in machine learning methods enables us to uncover patterns that remain beyond human understanding. Complex investment models allow for the partial unveiling of the asymmetry within the capital market, and not all experts possess a clear comprehension of them. AI can encompass and test numerous potential investment scenarios, utilizing its deep machine-learning capabilities to provide a comprehensive view of the investment landscape and reveal risks and rewards in manners that the human mind cannot achieve. The AI endeavors to identify factors and trends that may delve deeper than traditional stock-picking methods. Given that many portfolios already incorporate conventional factors, discovering alpha through these traditional means could prove more challenging. As more people adopt the same strategy, it often becomes embedded in the price, eroding price discovery.

Previously, we discussed the Conceptual Framework of Applying ML and AI Models to Analyze and Forecast Financial Assets. The I Know First predictive algorithm is a successful attempt to discover the rules of the market that enable us to make accurate stock market forecasts. Taking advantage of artificial intelligence and machine learning and using insights of chaos theory and self-similarity (the fractals), the algorithmic system is able to predict the behavior of over 13,500 markets. The key principle of the algorithm lays in the fact that a stock’s price is a function of many factors interacting non-linearly. Therefore, it is advantageous to use elements of artificial neural networks and genetic algorithms. How does it work? At first, an analysis of inputs is performed, ranking them according to their significance in predicting the target stock price. Then multiple models are created and tested utilizing 15 years of historical data. Only the best-performing models are kept while the rest are rejected. Models are refined every day, as new data becomes available. As the algorithm is purely empirical and self-learning, there is no human bias in the models and the market forecast system adapts to the new reality every day while still following general historical rules.

Conclusion

There is always a trade-off between pursuing an active investment strategy to capitalize on one’s skills and knowledge in order to outperform the market or entrusting funds to a professional who can manage this, versus investing in a passive strategy to achieve returns in line with the stock market. It’s worth noting that over 50% of U.S. Large-Cap active managers underperformed the S&P 500 in 18 of the past 21 years. Furthermore, this underperformance escalates significantly with an extended investment horizon. 

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