INVESTING Archives - Page 5 of 5 - Centura Wealth Advisory

Active Management and Risk Adjusted Returns

Active versus passive management is a long-standing debate that tends to divide rooms of investment professionals. At Centura Wealth Advisory, we believe in both – but not universally.

We propose investors opt for a blended approach of active and passive management to benefit from the advantages of each. This strategy can deliver the best risk-adjusted after-tax returns.

Let’s review the advantages of a blended management approach, our philosophy, and the research that supports it.

What are the Advantages of a Blended Active and Passive Management Approach?

Active management offers the potential to outperform passive indexing but has become increasingly difficult to do on a consistent basis. Recent research has called into question the merits of active management, but not all markets (i.e., stocks, bonds) are created equal.

Let’s break down our philosophy at Centura.

Our Philosophy: Why a Blended Approach?

At Centura Wealth Advisory, we utilize a blend of both active and passive portfolio management. However, we believe it is the skill of knowing which tactic to employ on which asset classes that contributes to an improved risk-adjusted return.

For example: When constructing diversified portfolios, we usually take a passive approach to equities unless we are actively managing taxes via index replication and tax harvesting.

However, regarding fixed income, we typically utilize a diversified active and passive approach due to the favorable economic backdrop that fixed income markets provide as related to active management.

But, are these philosophies rooted in sound economics and, perhaps more importantly, does research support them?

Does Current Quantitative Research Support Our Thesis?

Fund Selection Criteria

We believe that actively managed funds (equity or fixed income) must meet the following mandate(s) in order to be selected over an index:

Funds held in a portfolio must add statistically significant alpha versus their respective index*
Funds held in a portfolio must be accretive to risk-adjusted returns (i.e., Sharpe Ratio)

*To determine whether funds outperform their respective index, net of fees, we employ Fama-French Regression Analysis using a variety of factor returns for both equity and fixed income markets.

Then, we analyze the portfolio of funds over varying periods of time. In these analyses, we assess their return/volatility profile as compared to the appropriate index (or blended index).

Economic Backdrop: Equities vs. Fixed Income

Equity and fixed income markets are very different in their structure, policies, and participants. Therefore, a complete understanding of the subtle nuances is paramount to understanding why the opportunity for outperformance may or may not exist.

Equity Markets

Equity markets are fiercely competitive and well-covered by highly skilled analysts, traders, and various media outlets. This level of competition and sophistication creates an environment that has democratized information, access to markets, and technology.

For these reasons, we believe actively managed equity funds underperform their respective indices on a risk-adjusted, net-of-fees basis most of the time.

Given our belief, we typically look to access market beta for equities as cheaply and efficiently as possible through the use of large, liquid, low-cost index ETFs. This passive, low-cost approach to indexing equities ensures that we will participate in market returns but reduces the risk of underperforming on a net basis due to fee drag.

Equities are not typically an area of the market where we look to source alpha; unless we do so through tax management.

Fixed Income Markets

We believe actively managed fixed-income funds offer more opportunities to outperform based on the following considerations (including, but not limited to):

Fixed-income investors have different objectives and may have mandates and/or other incentives when making investment selections
The bond market(s) are dynamic in that thousands of issuers constantly issue new bonds, which provides ample supply of both primary and secondary issues of
Various yields and maturities
Bonds are generally held to maturity and therefore trade infrequently
Trading occurs via over-the-counter (OTC) transactions and not on exchanges
Infrequent, over-the-counter trading, across thousands of different issues can lead to mispriced assets, negotiated trade prices, and opportunities for outperformance (alpha)
Return profiles of individual bonds are far more skewed

For these reasons, we utilize actively managed fixed-income funds in our fixed-income portfolio whereas with equities we generally rely on passive strategies alone.

Additionally, we retain a portion of our fixed-income portfolio in the respective index as we recognize there are periods where indexing will still outperform. This allows us to create a blended portfolio.

Let’s Test It: Qualitative and Quantitative Testing

Now that we have outlined our general philosophy and economic rationale supporting it, we will test whether a sample fixed income portfolio that we utilize at Centura Wealth Advisory meets our specified mandate(s).

Test: Part 1 – SPIVA Results

We will use the 2018 year-end Risk-Adjusted SPIVA scorecard provided by S&P Dow Jones Indices to begin our test.

The Risk-Adjusted SPIVA Scorecard measures the performance of actively managed funds against their benchmarks on a risk-adjusted basis, using net-of-fees and gross-of-fees returns.

Risk-adjusted performance in SPIVA is measured by the Sharpe Ratio (i.e., higher = better) and evaluates results over three distinct time periods: five years, 10 years, and 15 years. For purposes of our study, we will utilize these SPIVA findings to evaluate our philosophy on active vs. passive fund selection.

For detailed results, please reference the SPIVA research report for year-end 2018. Key highlights relevant to our analysis include:

Benchmarks outperformed U.S. Equity Funds 81% to 95% of the time, depending on whether looking at five, ten, or 15-year periods
Unlike their equity counterparts, most fixed-income funds outperformed their respective benchmarks’ gross of fees
However, when using net of fees returns, most actively managed fixed-income funds underperformed across all three investment horizons on a risk-adjusted basis
This gross vs. net performance divergence highlights how the role of fees in fixed income fund performance was especially critical

Do the Results Support our Thesis?

These findings confirm our thesis. This research supports our rationale for taking a passive approach to equities and a diversified active/passive approach to fixed income.

Test: Part 2 – Quantitative Analysis

Next, we will evaluate the actively managed funds (held in the portfolio) that we utilize in our fixed income model(s) at Centura. Our goal is to determine:

If our fixed income portfolio adds statistically significant alpha
To see if our fixed income portfolio has outperformed the bond index on a risk-adjusted, net of fees basis over the recent one, three, and five-year periods

To assess whether our fixed income portfolio produces statistically significant alpha, we run a Fama-French multi-factor regression which includes term and credit.

We run this regression over the longest common period – four years. The result is a statistically significant (p-value = 0.000) model with an adjusted R2 of 73.2% and annualized alpha of 1.22%.

Do the Results Support our Thesis?

These results confirm our first mandate that our fixed income portfolio must add statistically significant alpha.

Table 1 – Regression Results

Turning to risk-adjusted returns in a portfolio backtest, we find diverging results between the actively managed funds we have selected and the index itself.

For example, in the tables below we see that the index has outperformed on a risk-adjusted, net of fees, basis over the one-year period. However, over the three and five-year periods, the actively managed funds are preferred.

These outcomes help support the notion of holding both active and passive funds together in a portfolio.

Table 2 – Risk-Adjusted Returns

Note: Returns are net of expense ratios. However, AUM fees are not included.

Test: Part 3 – Stress Testing

Lastly, we will evaluate our portfolio (versus the index) under simulated stress test scenarios including rising interest rates and inflation; risks paramount to fixed income markets.

We seek to understand how different types of portfolios behave under different types of “stress” conditions. The stress tests conducted include:

Rising Interest Rates
Inflation

Table 3 – Stress Test Results: Potential Downside

The table above displays a marked difference between the potential downside risk of unconstrained actively managed bond funds versus the index alone. Thus, we believe active management decreases portfolio risk in ways that may not be captured through returns and volatility data alone.

Consider Centura

At Centura Wealth Advisory, we believe in active fund management for specific markets at specific periods of time. We acknowledge that there are periods of relative outperformance between one strategy and the other—and we caution readers not to try and time these swings.

Rather, skillful portfolio construction and prudent risk modeling can help build a diversified, actively managed fixed-income portfolio that leverages a strong economic backdrop that favors such an approach.

Our team specializes in portfolio risk management; designing our fixed income portfolios to optimize risk-adjusted returns against the index and to mitigate key fixed income risks over time (e.g., rising interest rates and inflation). We leverage industry and academic research paired with rigorous quantitative analysis to do so.

If you have been indexing your fixed-income investments, chances are you can do better. Contact us for a portfolio evaluation and stress test to see if our fixed income solutions could improve your portfolio’s risk-adjusted returns.

Interested in learning more? Read on to learn how Centura supports goals-based investing.

July 22, 2019

INVESTING, NEWS

Capital Market Projections for Asset Allocation and Portfolio Construction – Part 3 in series

Executive Summary

Under Modern Portfolio Theory, the goal of portfolio construction is to extract as much return as possible for a given level of risk.
Capital Market projections (the forecasting of expected rates of returns on assets, and the variance of those returns) are essential inputs to constructing efficient client portfolios.
In addition to expected returns, and variance of those returns, efficient portfolio construction relies on the co-variance or correlation of each asset’s return with that of the others.
The combination of a suite of portfolios optimized for efficiency and a sophisticated planning process that matches an individual’s cash flow needs with an optimal investment portfolio for their assets is at the core of true wealth management services.

Introduction

In this post, the third in a series of three on capital market projections, we will cover the importance of capital market projections in the process of asset allocation and portfolio construction. When constructing a portfolio, a portfolio manager will typically start with a desired level of risk or a target rate of return. From there the goal would be to maximize the portfolio’s expected return for the particular level of risk, or minimize the portfolio’s risk in pursuit of the targeted rate of return. In order to do either, we need to use capital market assumptions in the calculations. The greater the quality of the capital market forecasting, the greater probability that our targets will be met (i.e., efficient portfolios, successful financial plans, etc.)

The Fundamentals

Nobel Prize winning economist Harry Markowitz wrote the article “Portfolio Selection” in 1952 where he introduced the notion that combining uncorrelated assets reduced a portfolio’s risk, or variation of returns, to a greater extent than would be assumed by taking the average level of risk of each of the underlying holdings. This is incredibly important given that volatility can erode returns over time, and more efficient portfolios are preferred. Consider this example: If you lose 20% on an investment ($10 to $8) you need a 25% return to get back to $10 ($2/$8 = 25%), but if you lose 50% on an investment ($10 to $5) you need a 100% return to get back to $10 ($5/$5 = 100%). Since markets tend to move downward quicker and more violently than they go up, this is of key importance when building wealth over time. Thus, limiting downside risk when constructing portfolios is of paramount importance.

Portfolio Standard Deviation

When calculating the expected rate of return of a portfolio, one merely takes the simple weighted average of the expected return for each asset. But, when calculating the expected risk of a portfolio the calculation is lengthy and far more complex. It uses the expected variance (standard deviation squared) of each asset’s return along with the correlation of each pair of assets in the portfolio to one another. Let’s take a simple example to illustrate how this works in a portfolio of two assets that are perfectly uncorrelated (Correlation=0).

The return of the two-asset portfolio is the weighted average of the returns of the two assets. The risk of the portfolio, as measured by standard deviation, is significantly lower than the weighted average of the risk levels of the two assets. This is the math behind the benefits of diversification. Let’s be even more clear. In this example we will assume the two assets have the same risk and return expectations but are still perfectly uncorrelated.

By combining two uncorrelated assets, the same level of return with a much lower level of risk would be theoretically achievable. This is referred to as having a better “risk adjusted” return, which is the ultimate goal of modern portfolio theory. In building a portfolio to target either risk or return, the forecasting of both is critical to the task.

Portfolio Construction

We can use the math from the previous examples to build a customized portfolio to suit a particular need. The inputs needed could be found in Table 1 (forecasted returns and risk), and Table 2 (asset class correlation matrix), which shows how correlated each of these asset classes are to each other.

^{Sources: Callan Institute, JP Morgan, Blackrock, Bank of New York, MFS, RBC}

^{Source: Silicon Cloud Technologies, LLC 2019.}

In practice, we use data like this to build a suite of portfolios. Each portfolio in the suite is like a tool in the chest. They can be used independently or in combination with one another to accomplish a variety of tasks across a portfolio/estate. The array of potential solutions spans from low risk/low return portfolios to high risk/high return portfolios, with a variety of vehicles employed (e.g., stocks, bonds, ETF’s, mutual funds, SMA’s, LP’s, insurance, other). Regardless of need, the goal is that each portfolio in the suite has the highest expected level of return for its specified level of risk.

See a sample illustration of three different portfolios below in Exhibit 1, each representing a different risk tolerance (i.e, conservative, moderate, aggressive). By design, the expected returns increase as the risk increases and returns are maximized for each unit of risk taken.

When done properly, a well-tailored financial plan includes custom portfolio solutions designed to meet specific client needs/goals. Portfolios like those shown in Exhibit 1 are crafted carefully and their constituents are not random. They are chosen based on investment ethos, capital market risk/return forecasts and the unique interplay between assets in the portfolio. At Centura, we believe that multi-asset portfolios are significantly more complex to model than most realize.

For example, illiquid alternatives do not behave the same way as exchange traded alternatives, and neither behaves the same way in all markets. Thus, sophisticated risk modeling must be employed to get a true understanding of how assets could behave in different scenarios. Ultimately assets selected for inclusion are accretive to risk adjusted returns at the portfolio level and can be combined with tactical shifts to over and under-weight different assets at different times to take advantage of opportunities.

Conclusion

Realizing that capital market projections play a critical role in the Liberated Wealth® management process, we take great care to ensure that we use thoughtful, forward looking capital market projections and that they are updated on a regular basis. These projections are used in our financial planning, asset allocation, and portfolio construction processes. The quality of these inputs’ ties directly to the quality of our process outputs and in striving to drive positive outcomes for our clients we ensure no detail is overlooked; this includes the capital market projections that are used.

At Centura, we use forward looking capital market projections to design and construct bespoke portfolios which seek to maximize risk adjusted returns, take advantage of opportunities presented in the market(s) and integrate with Monte Carlo simulation to forecast cashflows and asset growth over time. We don’t just take generic data prepopulated by software vendors, rather we take great care to analyze available data to ensure the quality of the projections used. If you have not had your portfolio analyzed for risk/return optimization or stress tested proactively to understand exactly what risks you are taking, contact Centura Wealth Advisory to learn how we can help.

Disclosures

Centura Wealth does not make any representations as to the accuracy, timeliness, suitability or completeness of any information prepared by any unaffiliated third party, whether linked to or incorporated herein. All such information is provided solely for convenience purposes and all users thereof should be guided accordingly.

Past performance is no guarantee of future results and may have been impacted by market events and economic conditions that will not prevail in the future. This newsletter contains certain forward‐looking statements (which may be signaled by words such as “believe,” “expect” or “anticipate”) which indicate future possibilities. Due to known and unknown risks, other uncertainties and factors, actual results may differ materially from the expectations portrayed in such forward‐looking statements. As such, there is no guarantee that the views and opinions expressed in this letter will come to pass.

Indices are unmanaged. Any reference to a market index is included for illustrative purposes only as it is not possible to directly invest in an index. The figures for each index reflect the reinvestment of dividends, as applicable, but do not reflect the deduction of any fees or expenses, or the deduction of an investment management fee, the incurrence of which would reduce returns. It should not be assumed that your account performance or the volatility of any securities held in your account will correspond directly to any comparative benchmark index. Bonds and fixed income investing involves interest rate risk. When interest rates rise, bond prices generally fall.

For additional information about Centura, please request our disclosure brochure as set forth on Form ADV using the contact information set forth herein, or refer to the Investment Adviser Public Disclosure web site (www.adviserinfo.sec.gov). Please read the disclosure statement carefully before you engage our firm for advisory services

June 11, 2019

INVESTING, NEWS

Capital Market Projections & Monte Carlo – Part 2 in series

Executive Summary

Capital Market projections (mean & variance) serve as parameters for Monte Carlo simulation
The Monte Carlo Method is used by Centura for Liberated Wealth planning in order to solve complex problems when other methods fail
Relying on historical data for Monte Carlo simulation may produce misleading results with potentially harmful ramifications (e.g., spending too much, retiring too soon, etc.)
Forward looking capital market projections incorporate structural market changes that are present today and/or are expected to continue in the future
Careful consideration of inputs and related assumptions is paramount when crafting long term financial plans and forecasting portfolio returns & risk; garbage in, garbage out

Introduction

Sophisticated projections are critical to crafting a well-designed financial plan and capital market projections are one of many key inputs that play a vital role in doing that. At Centura Wealth Advisory, we pair forward looking capital market projections with the Monte Carlo Method to estimate:

Probability of a client running out of money before their “end of plan” (i.e., death)
Most likely “end of plan” value (e.g., wealth transfer, charitable giving purposes)
Optimal asset allocation strategy for a given plan

Part 1 of this series introduced capital markets, historical returns and forward-looking return/risk projections. In Part 2, we explore the Monte Carlo Method and evaluate the potential pitfalls of using historical results vs. forward looking projections when conducting experiments/simulations. Part 3 of the series will focus on portfolio construction and strategic asset allocation using mean variance analysis. Readers can take our quick assessment survey provided at the end of this blog or found here.

Monte Carlo Method

The Monte Carlo Method is a risk management tool that allows financial professionals to model and predict the future with varying levels of confidence. This tool is particularly valuable when it comes to retirement planning, which tasks advisors with forecasting a wide range of variables including, but not limited to:

Asset returns
Future income from all sources
Asset distributions (withdrawal rate) to support future income shortfalls
Taxes (based on current law and potential for sunset provision)
Varying inflation rates for different types of expenses (e.g., general vs healthcare)
Other volatile, subjective and potentially unknown factors as well

Problems of this nature are too complicated to solve with one formula, so we must employ an alternate approach.

Enter, the Monte Carlo Method. This method uses scenario modeling to predict a range of future possibilities, all with varying levels of probability (or likeliness to occur). At the upper end of the range are the very best scenarios (90^th percentile), and at the lower end of the range lie the worst (10^th percentile). At the midpoint of this range (50^th percentile) lies the median which represents the most likely end of plan value (best estimate). If all scenarios end in assets at the end of plan above $1, the simulation is considered a success. If assets are exhausted prior to end of plan, it is a failure. The percentage of successful simulations represents the plan’s overall probability of success.

Monte Carlo Experiment: Hypothetical example

To illustrate how the Monte Carlo Method works, we will run a simple Monte Carlo simulation on a $1,000,000 portfolio invested as follows:

We will first conduct this simulation using historical returns & risk (Chart/Table 1) and then we will re-run the simulation using forward looking return & risk estimates (Chart/Table 2). Last, we will compare the results (Table 3) and highlight any key insights garnered.

Monte Carlo Experiment: Historical Returns

In Chart 1– Simulated Portfolio Using Historic Returns, we show a $1,000,000 portfolio simulation run 10,000 times based on historical asset class returns & risk. The different color lines indicate different percentiles of returns and summary statistics for each percentile can be found in Table 1.

In Chart 2– Simulated Returns Using Capital Markets Projections, we show a $1,000,000 portfolio simulation run 10,000 times based on forward looking asset class returns & risk. The different color lines indicate different percentiles of returns and summary statistics for each percentile can be found in Table 2.

Monte Carlo Experiment: Comparing Results

To analyze simulations using historical vs forward looking projections we will select the 10^th percentile (worst) returns and 90^th percentile (best) returns to compare the nominal and inflation adjusted ending portfolio values as well as maximum drawdown and the safe withdrawal rate; see Table 3 – Comparative Results of Simulated Historic versus Projected.

Comparing statistics in Table 3 reveals some key insights:

Using historical returns may significantly overstate future portfolio values
The portfolio’s safe withdrawal rate(s) may be overstated when using historical #’s
Tail risk (max drawdown) is approximately equal, confirming that forward looking risk is commensurate with historical levels if not slightly higher (i.e., lower expected risk adjusted returns; see Part 1 of Capital Markets Blog)

These insights highlight some of the primary reasons why forward-looking capital market projections are preferred to historical numbers when modeling the risk that someone may run out of money before their “end of plan” (i.e., death). But why do they diverge? One of the primary reasons forward looking estimates diverge from historical results are due to what are known as “structural changes” or shifts.

Structural shifts are changes in the overall landscape of a market/economy, and if not handled properly, skew data. For example, a future riddled with tariffs and global tension is much different than the coordinated global easing (QE) that took place in the wake of the Great Recession. Similarly, the high interest rate environment of the 1980’s is materially different than the low interest rate environment of today, and not accounting for such structural components can produce misleading results; as evidenced above. Thus, investors and advisors must be careful when crafting plans and modeling long term risk.

Conclusion

Monte Carlo simulation is a complex, but effective risk management tool used by Centura that pairs asset forecasting with cash-flow modeling, over a long period of time and allows investors to evaluate the impact of different decisions on their long-term financial wellness.

Monte Carlo simulation usefulness is predicated upon the accuracy of input data whereby capital market return and risk forecasts represent the input parameters. You can determine the status of your current Retirement Plan analysis by taking our quick survey here.

In Part 3 of this series, we explore how these same capital market projections are used to construct portfolios and form strategic long-term asset allocation plans using mean variance analysis and optimization.

Disclosures

Centura Wealth Advisory (“Centura”) is an SEC registered investment adviser located in San Diego, California. This brochure is limited to the dissemination of general information pertaining to Centura’s investment advisory services. The statistical projections contained herein are provided only as an example to illustrate how the choice of methodology impacts those projections. Historical performance is no guarantee of future results and may have been impacted by market events and economic conditions that will not prevail in the future. Investing involves risk, including risk of loss.

May 31, 2019

INVESTING, NEWS

Capital Market Projections

Executive Summary

Capital market projections (risk & return) allow Centura Wealth Advisory (CWA) to help clients with long-term strategic financial planning
Estimates are applicable to financial planning, portfolio construction and risk management
Represent the best thinking regarding forward looking markets and a longer-term outlook
Issued by many sources and different methodologies are employed
Financial planning risk models (e.g., Monte Carlo simulation) require such assumptions as inputs
Portfolio construction utilizes these projections to evaluate markets and make informed decisions around asset allocation and investments
Careful planning is recommended given current views on forward looking markets and the uncertainty represented therein

Introduction

Capital market return (and risk) projections are at the heart of wealth management. These projections are a critical input to financial planning and portfolio management applications where the opportunity cost of mis-estimation is material: project too high and one may get a false sense of security out of their retirement plan and/or portfolio estimates; project too low and one may not provide a realistic estimate of the future, thereby making naïve decisions with potentially harmful results (e.g., working too long, saving too much, taking too much portfolio risk, etc.).

In part 1 of a 3-part series, we discuss capital market projections, provide a framework for creating current estimates and compare those estimates to historical results. In parts 2 and 3 of this series, we discuss how capital market projections are used in financial planning and portfolio construction (i.e., Monte Carlo simulation and mean/variance optimization) applications.

Capital Markets

Capital markets are venues where buyers and sellers engage in trade of financial securities. Examples include stock and bond markets where savings and investments are exchanged between the suppliers of capital and those who demand it. Suppliers of capital include retail and institutional investors whereas those in need of capital are businesses, governments and people. ^1,2,3

Capital markets consist of various types and sub-types. For example, stock markets can be broken down into large, mid and small company stocks as well as growth, value or blend.⁴ While there are many ways to slice and dice capital markets, below is a list of asset classes that are common among many of the providers and are also utilized in both financial planning and portfolio management applications at Centura:

Table 1 – Capital Market Asset Classes
Equities	Fixed Income	Alternatives
Large Cap Growth	Government	Real Estate
Large Cap Value	Municipal	Hedge Funds
Mid Cap	Corporate	Private Equity
Small Cap	High Yield	Commodities
International Equities	International
Emerging Markets	Cash

Each asset class has its own drivers of both risk and returns and must be evaluated differently when measuring and predicting both risk and returns. In addition, different firms and analysts within those firms may have different methods of evaluating each asset class and that means a wide variety of methodologies are employed.

To illustrate how firms may vary, here is an example of how Invesco estimates asset class returns which differs slightly from the approach used at the Callan Institute. We won’t dive into the specifics of different methods employed, but one should understand that differences exist between firms and careful consideration should be paid as to which estimates are utilized, when and why.

Capital Market Returns: Historical Results

Now that we understand different capital markets and their related asset classes, we can evaluate historical data to see how various asset classes have performed over time:

Table 2- Asset Class Historical Results
Asset Class	Index	*Annualized Return (10yr)**	*Annualized Return (25yr)**
Large Cap	S&P 500	13.12%	9.07%
Mid/Small Cap	Russell 2500	13.15%	9.62%
International Equities	MSCI World ex USA	6.24%	4.76%
Emerging Markets	MSCI Emerging Mkts	8.02%	7.9%¹
US Fixed Income	Barclays Aggregate	3.48%	5.09%
Non-US Fixed Income	Barclays Global Agg ex-USA	1.73%	4.39%
Cash	90-day T-Bill	0.37%	2.55%
Hedge Funds	Callan Hedge FOF	5.26%	6.06%
Commodities	Bloomberg Commodity	-3.78%	2.03%
Private Equity	Cambridge PE	11.62%	15.46%
Real Estate	NFI-ODCE	6.01%	8.05%
Annualized returns for periods ended 12/31/2018. ¹Denotes 15 yr annualized return as 25 yr data is not availableSource: Callan Institute

Historical returns are the baseline for which forward looking projections can be evaluated against and contextualized upon. While historical returns are insightful and provide context for both planning and portfolio management applications, they may have little or nothing to do with what is expected to take place in the near, intermediate and/or long term. To highlight this point, we note the well-known industry disclaimer which states, “past performance is not indicative of future results”. So, to cover our bases and provide a more robust view, we will now look at forward looking return projections followed by a comparison between the past and present.

Capital Market Return & Risk: Forward looking projections

To predict the future, however futile that may be, many institutions provide capital market projections that provide practitioners (and interested readers) with their firms best thinking regarding forward looking markets and long-term outlooks. These estimates serve as inputs for a variety of applications including Monte Carlo simulation and portfolio construction using mean variance optimization, both of which are key considerations to a healthy and sustainable long-term financial plan.

A partial but influential list of firms that provide capital market forecasts include:

Callan Institute
JP Morgan
Blackrock
Bank of New York
MFS
RBC
PIMCO
Goldman Sachs

While any single provider can be utilized, each brings a different methodology to the table and an average of several providers can be a good way to obtain exposure to many firms’ best ideas and to reduce risk associated with any one firms’ method being off in any given year.

The Table 3 below shows different asset classes and the 10 year forward looking estimates, averaged amongst several providers included in the list above. Table 3 also shows the estimated Sharpe ratio (i.e., risk adjusted return) which allows for an apples-to-apples comparison of asset classes, controlling for risk. Furthermore, we also include the real return, which is gross return less inflation (estimated to be 2.14% over the same period). In Chart 1 we represent these same results visually.

Table 3 – Forward Looking Estimates
Asset Class	Return	Risk	Sharpe Ratio	Real Return
Cash Equivalents	2.20%	0.48%	0.00	0.06%
US Fixed	3.49%	3.45%	0.37	1.35%
Non-US Fixed	1.95%	6.57%	-0.04	-0.19%
Hedge Funds	5.25%	6.91%	0.44	3.11%
Emerging Market Debt	5.37%	9.08%	0.35	3.23%
High Yield	5.47%	9.35%	0.35	3.33%
Real Estate	5.52%	11.34%	0.29	3.38%
US Large Cap Equity	6.33%	15.58%	0.27	4.19%
Commodities	3.04%	16.40%	0.05	0.90%
Dev International Equity	7.03%	17.55%	0.27	4.89%
US Mid/Small Cap Equity	7.03%	19.31%	0.25	4.89%
Private Equity	7.41%	21.45%	0.24	5.28%
Emerging Market Equity	8.61%	22.75%	0.28	6.47%
Note: inflation estimate is 2.14% annualized. Sources: Callan Institute, JP Morgan, Blackrock, Bank of New York, MFS, RBC

Chart 1

What is notable about the returns in Table 3, is that while asset class trends may be the same (e.g., stocks > bonds > cash) domestic equity returns are significantly lower than the returns in Table 2 as are private equity and real estate (over both time periods). This means that firms expect future returns in these asset classes to be less than historical results, which is in line with the big picture takeaways garnered from analysis of the current Shiller P/E ratio (as well as the current Buffett Indicator), both of which seek to estimate forward returns by incorporating capital market and economic data such as stock prices, GDP and earnings cyclicality. However, volatility in these asset classes is expected to stay in line with historical levels (if not slightly higher) which implies that investors should expect lower returns for the same level of risk on a go forward basis.

These lower forward-looking return projections are due to the cyclical aspect of business, credit and the economy. In the United States, we are late in the economic expansion cycle(s) and most expect some negative years (i.e., economic slowdown) in the coming decade which would materially impact return figures as compared to a decade prior when economic expansion was predominate.

This does not necessarily mean doom and gloom ahead but does imply that caution should be heeded in terms of where risk assets are allocated. Perhaps a greater allocation to cash and other stable investments is warranted given the relatively low level of anticipated inflation. However, if real returns on cash go markedly below zero, investors will be incentivized to purchase risk assets (e.g., stocks & bonds) at even more elevated prices than today, meaning the opportunity cost of sitting in cash is high. With such a dichotomy, careful planning is certainly required.

Conclusion

Individuals looking to retire (i.e., access investment assets for income) in the next 15 years would be well served to review their investment allocations and future income/cash-flow plans in the wake of a decade worth of gains in most risk assets. For these investors, locking in gains and preserving capital is of paramount importance, but in markets such as these professional guidance will certainly help navigate choppy waters.

Additionally, for investors already in retirement drawing down investment assets, extreme caution must be paid to asset distribution plans and how those assets are invested. Sequence risk can exacerbate financial plan failures and in order to protect against running out of money in adverse scenarios, sophisticated planning software and risk models must be employed to develop a robust cash-flow and integrated portfolio plan that is well suited to defend wealth in any and all markets.

In part 2 of this 3-part series, we will explore risk modeling in financial planning (e.g., Monte Carlo simulation). Last, part 3 will explore how capital market projections are used to construct portfolios and develop strategic asset allocations.

About the Author

Sean Clark holds a Master of Science in Risk Management from New York University and a Bachelor of Arts in Economics from Clemson University. Areas of practice include financial planning and portfolio management, specializing in applied mathematics and risk.

References

Disclosures

Centura Wealth Advisory (“Centura”) is an SEC registered investment adviser located in San Diego, California. This brochure is limited to the dissemination of general information pertaining to Centura’s investment advisory services. Investing involves risk, including risk of loss.

Centura Wealth does not make any representations as to the accuracy, timeliness, suitability or completeness of any information prepared by any unaffiliated third party, whether linked to or incorporated herein. All such information is provided solely for convenience purposes and all users thereof should be guided accordingly.

Past performance is no guarantee of future results and may have been impacted by market events and economic conditions that will not prevail in the future. This newsletter contains certain forward‐looking statements (which may be signaled by words such as “believe,” “expect” or “anticipate”) which indicate future possibilities. Due to known and unknown risks, other uncertainties and factors, actual results may differ materially from the expectations portrayed in such forward‐looking statements. As such, there is no guarantee that the views and opinions expressed in this letter will come to pass.

Indices are unmanaged. Any reference to a market index is included for illustrative purposes only as it is not possible to directly invest in an index. The figures for each index reflect the reinvestment of dividends, as applicable, but do not reflect the deduction of any fees or expenses, or the deduction of an investment management fee, the incurrence of which would reduce returns. It should not be assumed that your account performance or the volatility of any securities held in your account will correspond directly to any comparative benchmark index. Bonds and fixed income investing involves interest rate risk. When interest rates rise, bond prices generally fall.

For additional information about Centura, please request our disclosure brochure as set forth on Form ADV using the contact information set forth herein, or refer to the Investment Adviser Public Disclosure web site (www.adviserinfo.sec.gov). Please read the disclosure statement carefully before you engage our firm for advisory services.

May 8, 2019