photo of nimbus clouds


We recently had a chance to find an article on asset allocation, Chapter 40 of The Financial Analysts Handbook second edition, which says asset allocation is universal across time horizons, and this paper is cited as its basis. We developed our review on it as follows:

BALANSTONE: Investment time horizon remains a hotly debated topic in financial circles. Some suggest the length of time one intends to hold assets has minimal impact on optimal portfolio design. Yet, I firmly believe the time horizon is critical to financial planning and portfolio management. It shapes risk profiles, influences return potential, and even impacts investors’ psychological approaches. A paper [NBER WORKING PAPER SERIES, SERIAL CORRELATION OF ASSET RETURNS AND OPTIMAL PORTFOLIOS FOR THE LONG AND SHORT TERM, Stanley Fischer and George Pennacchi, Working Paper No.1625] supports the former view, but several aspects warrant critical analysis.

BALANSTONE: A fundamental premise in your model seems to be static asset return serial correlation. However, extensive research shows asset returns are dynamic – evolving in response to the economy, regulations, technology, and global events. Ignoring these shifts presents an overly simplistic view of financial markets.

Authors: We did incorporate potential correlations to address variations in risk/return across timeframes. We felt this adequately reflects realistic conditions.

BALANSTONE: While accounting for correlations is necessary, assuming covariances don’t evolve over time might be problematic. There are studies illustrating that significant events can drastically alter asset class relationships, potentially invalidating portfolio risk assumptions. This risk miscalculation becomes more pronounced as the investment horizon extends.

Authors: Perhaps some refinement is needed, but static covariance is a simplifying factor to isolate core principles.

BALANSTONE: Let’s shift our focus. The power of compounding returns plays a vital role over long investment horizons. Even slight incremental return improvements, consistently realized over time, can generate exponential growth. Also, studies illustrate declining serial correlations in returns over extended periods. Combined with diversification, this implies less inherent volatility for the long-term investor. Do these features hold weight in your framework?

Authors: Compounding is undeniably a mathematical reality. But what about the short-term volatility risks a long-term investor takes on while this slow growth effect occurs?

BALANSTONE: The very definition of ‘risk’ needs adjustment for the long-term investor. Short-term drawdowns shouldn’t equate to increased risk when backed by sound fundamentals. Risk models of multiple years, 5,10, and even 20 years, and goal-based investing frameworks have gained prominence as they take the unique crucial advantage of time horizon into account. Especially extreme low probability events such as the recent market turmoil due to inflation showed how periodical returns are not serially correlated. Thus, keeping the time horizon long-term significantly generated positive outcomes. At the same time, as you proposed, all investors should have the same allocations regardless of time, and horizons assume equal serial static correlations of high-correlated assets, which actually could suffer disproportionately if the risk wasn’t adjusted in anticipation of changing conditions. Can an “optimal” portfolio ever truly exist if it excludes such a crucial context?

BALANSTONE: While investment risk cannot be eradicated, adopting a long-term perspective can significantly improve overall risk/return profiles, thanks to compounding effects and decreased serial correlation. This doesn’t negate the importance of reassessing portfolios to mitigate unexpected significant disruptions. Yet, your paper may underestimate the unique significance of time and evolution in investment practice. This opens the door to valuable research refining models to be more adaptable for both theoretical and real-world applications.

BALANSTONE: Let’s examine, for instance, the healthcare sector as a case study. Regulatory changes, like drug approval processes, patent expirations, and even shifts in healthcare insurance policies, profoundly impact how the companies within this space generate returns. Innovation cycles within pharmaceuticals create winners and losers that cannot be predicted using historical correlation data alone. Additionally, longer-term demographic shifts related to aging populations will alter which areas within healthcare see more demand. Doesn’t a static covariance model lack the nuance necessary to address this changing landscape?

Authors: You paint a convincing picture of the healthcare sector’s complexities. Perhaps a broad ‘one-size-fits-all’ model does fall short in such cases. Augmenting static covariance approaches with qualitative assessments focusing on trends within specific sectors could be helpful. Yet, this introduces a degree of subjectivity, doesn’t it?

BALANSTONE: This very dilemma confronts real-world investors. When theory encounters messy practice, how can one create actionable strategies? It could be prudent for an investor to incorporate the best aspects of portfolio optimization theory – namely diversification and risk analysis – while integrating their understanding of sectors where correlation isn’t merely a historical statistic but a constantly evolving story.

Authors: There’s an inherent tension here. Theory provides rigor, while practice necessitates adaptation. Perhaps instead of searching for the optimal formula, one can develop adaptable frameworks that balance fundamental investment principles with an in-depth understanding of chosen sectors and evolving macroeconomic climates.

BALANSTONE: It appears finding a perfectly harmonized intersection of the idealized, static model and the ever-changing landscape investors operate within is challenging. This conversation has raised valuable questions on how the theory might evolve to provide more granular tools for adapting portfolio construction to both industry-specific and long-term economic trends.

Authors: Perhaps the goal isn’t one static “solution” but rather a constant and informed refinement of how theory and the lived reality of investing can best inform one another. This leaves room for future research and further discussion; I welcome a continued dialogue on this topic.