Strategic and Tactical Asset Allocation: A Recipe for Underperformance
Strategic Asset Allocation (SAA) and Tactical Asset Allocation (TAA) are common approaches in modern investment management, guiding how portfolios are structured and adjusted. SAA involves setting fixed allocations to different asset classes based on an investor’s risk tolerance, time horizon, and investment goals. TAA, on the other hand, involves making short-term adjustments to these allocations in response to perceived market opportunities. While both approaches are widely adopted, they are inherently sub-optimal when compared to maintaining a market-cap-weighted portfolio. Any deviation from market capitalisation that is not driven by systematic factor risk tilts is, in my opinion, naïve and fundamentally misguided. This blog post will explore the limitations of SAA and TAA and discuss why more adaptive, evidence-based approaches are preferable.
The Flawed Logic of Deviating from Market Capitalisation
At its core, both SAA and TAA often result in portfolios that deviate from market capitalisation. This practice implicitly assumes that an investor has superior insight compared to the collective market wisdom, which is fundamentally a form of discretionary active management. Such biases, whether favouring value over growth, small-cap over large-cap, or overweighting particular sectors or regions, reflect subjective forecasts rather than evidence-based investment principles (Swedroe 2016).
Tactical vs. Strategic Asset Allocation: A Misguided Approach
Both tactical and strategic asset allocation strategies often involve deliberately deviating from the market portfolio. This is naïve and, arguably, misguided unless such deviations are driven by systematic factor tilts supported by robust empirical evidence. Simply choosing to favour certain asset classes, sectors or regions based on intuition or short-term market sentiment does not constitute a rational investment strategy. In contrast, systematic factor-based approaches, such as tilting towards value, size, or momentum, are underpinned by decades of academic research and have a clear rationale (Fama and French 1993).
The Issue with Active In-House Investment Management
A particularly problematic application of SAA and TAA arises when active in-house investment managers at financial advisory firms take on the role of portfolio management. Often, these managers attempt to predict market movements or adjust allocations based on subjective analyses. This approach is not only inefficient but also lacks a robust theoretical foundation. Financial advisers typically do not possess the same quantitative resources or empirical rigour as dedicated asset management firms, leading to decisions based more on opinion than data (Ilmanen 2011). In practice, this leads to portfolios that are poorly diversified, prone to human error, and misaligned with evidence-based principles. I would go as far as to say that adjusting the SAA and TAA is not just naïve but often driven by malevolent intentions. This is because some advisory firms charge fees for fund switches, thereby creating an incentive for in-house investment managers to make unnecessary adjustments. The informed investor must be aware of this practice.
Figure 1. The chart compares the cumulative returns of four portfolio strategies—Market Cap Weighted 100% stock portfolio, Factor Investing (100% stocks), Strategic Asset Allocation (SAA - 100% stocks), and Tactical Asset Allocation (TAA - 100% stocks)—over a 50-year period. To ensure a realistic representation, the results are averaged across 100 simulations to reduce random fluctuations that can distort outcomes.
One of the key takeaways here is that Factor Investing shows the highest cumulative returns over the long term. This aligns with academic evidence suggesting that systematic factor tilts (like value, size, or momentum) tend to deliver superior performance over extended periods despite higher short-term volatility. In contrast, both SAA and TAA—despite being fully allocated to equities—tend to underperform due to their inherent lack of systematic exposure to proven risk premia.
Market Cap Weighted portfolios also perform consistently well, as they are broadly diversified and free from the pitfalls of discretionary adjustments typical of SAA and TAA. This chart reinforces the idea that deviating from a market-cap approach without systematic, evidence-backed rationale often results in lower returns, particularly when strategies like SAA and TAA are based on predicting market movements rather than capturing persistent factor risk premia.
Please see the ‘Methodology’ and ‘Chart References’ section at the bottom of this blog post for further information.
You Cannot Predict Markets
One of the core arguments against SAA and TAA is that they rest on the flawed assumption that market movements can be predicted with consistency. In reality, financial markets are inherently unpredictable, driven by countless variables and evolving economic conditions. Attempting to 'strategically' position portfolios to capture excess returns is naïve. Most investors, including professional managers, are more likely to make errors than accurately anticipate market movements. Empirical research shows that even the most experienced active managers struggle to consistently outperform passive benchmarks (Swedroe 2023). As a result, attempting to predict which asset classes, sectors or regions will outperform typically results in underperformance over the long term.
Instead of trying to second-guess the market, a more rational approach is to build diversified, evidence-based portfolios that minimise the risk of severe underperformance at the initial asset allocation stage. A suitable investment portfolio is not one that can be de-railed at the slightest whiff of economic turbulence. It is one that should be designed with economic turbulence in mind. Investors like Larry Swedroe and Bill Bernstein advocate for maintaining diversified portfolios that do not rely on speculative predictions. Their focus on long-term stability and risk management highlights the importance of acknowledging uncertainty rather than attempting to outsmart it (Bernstein 2022; Swedroe 2023).
Conclusion
Whilst Strategic Asset Allocation and Tactical Asset Allocation have been mainstays in portfolio management for decades, their inherent rigidity, reliance on static assumptions, and tendency to introduce discretionary biases render them sub-optimal in today’s volatile and complex market environment. Both strategic and tactical deviations from market capitalisation are naïve unless driven by systematic, evidence-backed factor tilts. The practice becomes particularly questionable when executed by active in-house managers at financial advisory firms, where resource constraints and lack of empirical grounding can lead to misguided portfolio decisions.
References
Ang, A., Goetzmann, W. N., and Schaefer, S. 2012. ‘The Efficient Market Theory and Evidence’. Annual Review of Financial Economics 4: 401-432.
Asness, C. S., Krail, R. J., and Liew, J. M. 2000. ‘Do Hedge Funds Hedge?’. The Journal of Portfolio Management 28 (1): 6–19.
Bernstein, W. 2022. ‘The Four Pillars of Investing: Lessons for Building a Winning Portfolio’. McGraw-Hill.
Fama, E. F., and French, K. R. 1993. ‘Common Risk Factors in the Returns on Stocks and Bonds’. Journal of Financial Economics 33 (1): 3-56.
Ilmanen, A. 2011. ‘Expected Returns: An Investor’s Guide to Harvesting Market Rewards’. Chichester: Wiley.
Swedroe, L. 2016. ‘The Quest for Alpha: The Holy Grail of Investing’. New York: McGraw-Hill.
Swedroe, L. 2023. ‘Risk vs. Uncertainty: The Investor’s Blind Spot’. My Worst Investment Ever.
Methodology
Assumptions and Data Generation:
Initial Value: All strategies start at a base value of 100%.
Simulation Period: 50 years to better capture long-term performance.
Number of Simulations: 100, to average out random variability and reflect more consistent outcomes.
Return Distributions:
Market Cap Weighted: Average annual return of 7% with a standard deviation of 15%.
Factor Investing: Average annual return of 8% with a standard deviation of 20%.
Strategic Asset Allocation (SAA - 100% stocks): Average annual return of 5% with a standard deviation of 20%.
Tactical Asset Allocation (TAA - 100% stocks): Average annual return of 4% with a standard deviation of 20%.
Cumulative Return Calculation:
Returns for each year were simulated using a normal distribution based on the specified mean and standard deviation.
The cumulative product of these returns over the 50-year period was calculated to generate the final values.
Results were averaged across 100 simulations to reduce the impact of short-term variability and better represent long-term trends.
Randomness:
Random values were drawn using a normal distribution for each year in each simulation, ensuring that each run captures different performance paths.
The average of 100 simulation runs was used to smooth out anomalies and provide a clearer comparative picture.
Rationale for Assumptions:
Market Cap Weighted (100% stocks):
Assumed to have relatively stable, moderate returns due to broad diversification and low tracking error relative to the market.
Factor Investing (100% stocks):
Assumed higher returns than market-cap weighting due to exposure to systematic risk premia (e.g., value, size, momentum).
Higher volatility reflects the riskier nature of factor tilts.
Strategic Asset Allocation (SAA - 100% stocks):
Assumed similar volatility to factor investing (20%) but with lower returns, reflecting the lack of targeted risk premium exposure.
Represents a fixed, static allocation without the benefit of dynamic factor tilts.
Tactical Asset Allocation (TAA - 100% stocks):
Assumed even lower returns (4%) and high volatility (20%), reflecting the inherent risk of attempting to time the market.
Emphasises the difficulty of making accurate tactical adjustments, leading to potential underperformance.
Chart References
Market Cap Weighted (7% average annual return, 15% standard deviation):
Dimson, E., Marsh, P., and Staunton, M. 2021. ‘Global Investment Returns Yearbook’. Credit Suisse Research Institute.
Fama, E. F., and French, K. R. 2004. ‘The Capital Asset Pricing Model: Theory and Evidence’. Journal of Economic Perspectives 18 (3): 25–46.
Ilmanen, A. 2011. ‘Expected Returns: An Investor’s Guide to Harvesting Market Rewards’. Chichester: Wiley.
Factor Investing (8% average annual return, 20% standard deviation):
Asness, C. S., Ilmanen, A., Israel, R., and Moskowitz, T. 2015. ‘Investing with Style’. Journal of Investment Management 13 (1): 27–63.
Bender, J., Briand, R., Melas, D., Subramanian, R. A., and Subramanian, L. 2013. ‘Foundations of Factor Investing’. MSCI Research Paper.
Ilmanen, A. 2011. ‘Expected Returns: An Investor’s Guide to Harvesting Market Rewards’. Chichester: Wiley.
Strategic Asset Allocation (SAA) (5% average annual return, 20% standard deviation):
Ilmanen, A. 2011. ‘Expected Returns: An Investor’s Guide to Harvesting Market Rewards’. Chichester: Wiley.
Swedroe, L. 2016. ‘The Quest for Alpha: The Holy Grail of Investing’. New York: McGraw-Hill.
Tactical Asset Allocation (TAA) (4% average annual return, 20% standard deviation):
Alpha Architect. n.d. "Tactical Asset Allocation: Does Day or Month Matter?" Accessed May 17, 2025. https://alphaarchitect.com/tactical-asset-allocation-day-month-matter/
Faber, Mebane T. 2006. "A Quantitative Approach to Tactical Asset Allocation." Accessed May 17, 2025. https://www.trendfollowing.com/whitepaper/CMT-Simple.pdf
Morningstar. n.d. "Why Tactical Allocation Funds Failed - Again." Accessed May 17, 2025. https://www.morningstar.com/funds/why-tactical-allocation-funds-failedagain