Analytics Series - Article 2

Foundation Evaluation: Discovering Your Current Trajectory, Part One

In our first publication, we proved how the literacy required to accurately capture a financial trajectory precludes most consumers of financial advice from setting reasonable expectations of their advisors. Without a frame of reference for progress or the ability to vet the intellectual appeal of the ideas presented to them, most clients have no choice but take a delegative, blind trust approach to one of the most important aspects of their lives. This segment is designed to elaborate further on the drivers of success outside of the context of investment outperformance and, in turn, enable you to appreciate the strategies that best integrate them. 

But before we get too caught up in the aesthetics of a financial plan, we must first make sure that it has a reliable foundation. Regardless of how quantitatively sound a comprehensive strategy might be, it will have minimal impact if it lacks practical perspective. As such, this first installment of Discovering Your Current Trajectory not only introduces several influential planning parameters, but also outlines the administrative infrastructure they rely on. 

Assets and Liabilities

Assets such as investment and personal property, as well as liabilities such as student loans and mortgages, can have a profound impact on both short and long-term outcomes. In the short-term, a lack of asset liquidity can pose cash flow challenges especially if there are liabilities that need to be serviced or heavy expenses to keep up with. In the long-run, the relationship between these parameters takes on a more dynamic role as the returns earned on assets compete with the interest paid on debt. The following example illustrates this concept in the context of a very common planning dilemma.

For many individuals, deciding on how much to put down on a home and how best to structure the associated mortgage is not a particularly analytical exercise. To the extent that one is constrained by liquid assets or cash flow, the only thing to think about is how to scrape together enough for the minimum down payment and qualify for the lowest rate possible on a 30-year loan. Ironically, even those with more than enough assets and cash flow to support various combinations of loan amounts and structures often take a similar approach — though for very different reasons. By maximizing the size of the loan and its term, the excess cash kept on hand both up front and through the slower pace of repayment can be invested in assets expected to yield higher rates of return than the rate of interest accruing on the mortgage. Given that the capital deployed to generate this delta would effectively be financed by the bank, the borrower’s return — measured as the income generated divided by the amount of their “own” assets that were tied up in the process — would be infinite. 

Beyond quantifying the additional risk that comes along with using leverage such as the extent to which higher payments could put strain on future cash flow and/or that returns will actually underperform the rate of interest on the loan, there is a very important administrative issue that is often overlooked in a home purchase or refinance analysis — budgeting. Without a strict budget derived from a specified savings target, who is to say that the excess cash from the lower down payment or longer amortization period will actually be invested? To the extent that an investor operates with a very loose budget, there is a strong possibility that their back might start acting up due to an uncomfortable vehicle or that their emotional well-being will suddenly no longer tolerate antiquated kitchen appliances. Even a very disciplined investor who happens to derive discretionary spending from disposable income rather than a fixed savings target is unlikely to discern their checking account liquidity attributable to a longer mortgage term from that of any other item of income or expense. This same issue can arise with respect to other liquidity-generating strategies such as 401(k) or Traditional IRA contributions, which would also be futile if the up-front tax reductions they facilitate are not invested. Thus, before using analytics to inform asset and liability decisions, a thorough savings-driven budget must be put into place. 


Cash inflows can help to either augment portfolio accumulation or mitigate its decumulation. While capturing basic inflows such as annual salaries and bonuses has long been a feature of financial planning software, there are more complex parameters that the average model fails to take into consideration.

Though they may not be infinite, the effective “returns” offered by employer matching and other retirement plan contributions are among the most efficient ones around. The challenge of projecting these contributions lies not in their arithmetic nature — account inflows of up to $67,500 annually based on a specified percentage of salary and/or bonus — but their regulatory guidelines. Specifically, qualified retirement plans like 401(k)s contain provisions relating to vesting, liquidity, and even socioeconomic discrimination that can impact a participant’s ability to actually realize its benefits. 

The Employee Retirement Income Security Act, or ERISA, has remained the most influential piece of legislation affecting retirement planning since its enactment in 1974. Administrators of plans subject to ERISA face steep punitive measures for failing to draft and enforce guidelines that strike a delicate balance between company and participant interests. One of the more well-known plan provisions relates to its vesting schedule — or the pace at which a participant takes actual ownership of the employer contributions that have been made to their account along with the investment earnings thereon, such that they can take these balances with them upon their subsequent departure. By law, these vesting rates can be as slow as either 20% annually from Years 2 through 6 of employment or 100% upon completion of Year 3. Given the magnitude of potentially forfeiting several years’ worth of contributions and earnings, an investor’s career dynamics and employer plan provisions must be factored into a comprehensive model of their income. 

While the portion of a plan account attributable to an employee’s own contributions is never subject to forfeiture upon severance from employment, it is effectively off-limits until that time. By law, these amounts may not be distributed during an employee’s tenure until they turn at least 59½ unless the funds are needed for very specific expenses relating to medical, housing, and other emergencies. Many plans choose to go beyond the letter of the law, by prohibiting “in-service” distributions even with respect to employer contribution amounts —emergency or not. As such, an investor that plans to rely on these inflows as a source of pre-retirement spending might be in for a surprise down the road depending on the language of their Summary Plan Description. 

Finally, certain “highly-compensated employees” (HCEs) — defined as those that either own more than 5% of the sponsoring company or receive compensation in excess of $130,000 — may not receive 401(k) matching contributions at all. Due to a complex ERISA convention known as the ACP test, the average percentage of earnings that this segment of employees can receive is constrained by the average percentage awarded to the non-HCE segment of the payroll. Given that these non-HCEs often have a limited capacity to defer the salary needed to earn a match, HCEs may not actually be awarded their employer’s target contribution. It would thus be prudent to consider recent company trends when projecting future matching contributions, rather than relying exclusively on the percentage stated in a plan document or brochure. 

Portfolio Returns

In our most recent publication, Financial Planner vs. Investment Advisor: The Economics of Paying For Advice, we distinguished between the concepts of alpha and beta — the former relating to returns generated via the zero-sum game of buying high or selling low before other market participants catch on to a particular narrative i.e. outperforming the index, and the latter providing for the risk-adjusted return of the index itself. We further introduced Monte Carlo analysis — a simulation engine that can help determine an investor’s optimal beta exposure given their time horizon and other factors. While using sophisticated software to inform portfolio allocation decisions might be the most powerful equilibrium planning technique, it is an investor’s behavior during periods of market turmoil that will ultimately make or break their realized returns. 

As noted in the comments section of our most recent article, not all market downturns are created equal. While the enactment of antitrust legislation to curb the dominance of the FAANG (Meta, Amazon, Apple, Netflix, and Alphabet) stocks would certainly harm their respective stock prices, this event would not necessarily test an investor’s risk tolerance. To the extent that this news would strictly reduce expectations of future cash flows without increasing the risk associated with them, there would be no fundamental reason to believe that the market would return any more going forward than it would have had this theoretical crash never occurred. This is not to suggest that a fierce comeback from certain economic calamity cannot not be made, but rather that it would rely heavily on the resolve of its constituents rather than on market fundamentals. Although it would very possibly be in an investor’s best interest to maintain a substantial degree of market exposure in the wake of such an event to benefit from “normal” returns, a temporary divestiture would not be expected to impact their probability of financial success. 

What can truly undermine the intellectual integrity of a Monte Carlo analysis is if its subject might divest during periods of macroeconomic uncertainty such as the COVID-19 outbreak or the current crisis in Ukraine. Had someone pulled out of the S&P on February 28, 2022 — the date our last article was published — they would have missed out on what has been a very sharp, 5% recovery as of this release on March 31. As Russia’s intentions have become more and more clear, the additional E(r) that investment advisors were using to discount prices just a few weeks ago has all but dissipated. While a recent Monte Carlo analysis may have accounted for portfolio declines driven by adverse market conditions — such as the Ukraine conflict ending in less favorable economic fashion — in its estimate of standard deviation, it would not have projected an entire year’s worth of gains being wiped out every few months by poor market timing. Thus, unless a portfolio is tailored to an investor’s unique behavioral tendencies regarding economic loss, asset allocation analysis and its associated return assumptions will remain purely theoretical. 


As these examples demonstrate, comprehensive financial planning requires a robust qualitative infrastructure. Without the foundational knowledge and processes in place to support the drivers of financial success, even the most brilliantly designed plan will eventually collapse under the pressure of practicality.

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