Bear Bull Wolf Eagle Markets

In this 2025 update to our 2018 published whitepaper on the same topic, the author both qualitatively and quantitatively identifies four types of financial market environment that may be experienced by the investor, including “Wolf” and “Eagle” markets (in addition to traditional “Bull” and “Bear” markets), effectively redefining financial market history since 1950.

Evidence is provided to link these four market environments to specific investment methods that tend to outperform or underperform during each environmental time period. Actionable approaches for investors to capitalize on this information are posited with the creation of Multi-Method Investing ®, a new form of investment management methodology, and a sub-category thereof, called Multi-Method Adaptive^TM investing. Evidence is provided to support the use of these approaches over the widely accepted single-method approaches currently in use in the investment management industry today. Important updates in this 2025 version of our original 2018 article include the use of intraday data (as opposed to closing price data); a longer historical data set; the inclusion of proprietary separately managed account strategy performance data to accompany index data; and the addition of information related to Multi-Method Adaptive investing (information which was not ready for publication in the 2018 edition).

INTRODUCTION

Financial market history has traditionally been defined as an alternating progression of “Bull” and “Bear” markets, with Bull markets loosely representing rising asset prices, and Bear markets representing declining asset prices. This is a simple and convenient way to describe the investing experience, and is at least partially responsible (along with the bulk of the literature on Modern Portfolio Theory) for the widely held belief that the best way to diversify an investment portfolio is to use an allocation split between stocks and bonds (as stocks do well in a stock Bull market, and bonds may do comparatively better in a stock Bear market - due to their low or negative expected returns correlations). But labelling markets as either Bull or Bear is probably a gross oversimplification of financial market realities. The author does not presume to have found a perfect replacement to the traditional Bull/Bear approach that fully explains all financial market environments, but does posit that the simple addition of two new categories of market environment to the mix – “Wolf” markets and “Eagle” markets - dramatically reshapes the perspective from which one views financial market history and may have profound implications for the way that an investment portfolio should be managed.

"Labelling markets as either Bull or Bear is probably a gross oversimplification of financial market realities."

Our initial research began in 2007-2008 during the depths of the global financial crisis as an attempt to find or confirm the “best” way to manage investment portfolios, given that our firm’s then-current and widely accepted approach to asset management (“Strategic Asset Allocation” derived from “Modern Portfolio Theory”) was being called into question by clients, academics, the financial media, industry professionals, and just about every other potential critic. As a matter of due diligence, self-confidence, and in an effort to confirm that we were still applying the most practical and effective method of investing for our clientele, we embarked on an internal research project - to start from scratch and re-examine financial market history on our own terms, leaving our presuppositions derived from 50+ years of the aggregated academic research of others aside. What we found reshaped our entire understanding of financial markets and investing, and ultimately led to our development of a new methodology of investment management – “Multi-Method Investing” (MMI).

The information in this article represents a portion of our own internal research conducted over 17-18 years, presented here in a format intended to be more concise and intelligible for the reader.

We started by looking at mutual fund performance data during historical time periods of market turmoil. Mutual fund data was chosen because this is the one appropriate investment vehicle for which extensive and reliable historical records exist. By sorting for the best performance across all mutual funds available (not just within individual categories/styles) during predetermined particularly difficult market periods and excluding strategies whose mandates were too narrow or asset-class-specific, we began to identify individual mutual fund managers/strategies that appeared to be particularly well-adapted to such market environments. Studying the management approach of these time-period-specific top performing managers revealed that they tended to share a similar approach to investing. We then extended our search to historical time periods with benign or favorable environments for stocks. Repeating the process, we found a totally different set of managers and mandates that appeared particularly well-adapted to these environments. We then began to categorize the investing approaches used by the top performing managers (and all other managers) into different philosophies or “methods” (which is the term we’ll use here). We ultimately found that there are four basic categories of investment method that can be utilized by an investor to attempt to achieve his or her objectives:

1. Liability-Driven Investing (LDI): LDI is often used by large institutions such as banks, insurance companies, and pension funds. It is based on the concept of directly matching investor assets with the entity’s known, quantifiable risks and liabilities in an attempt to transfer these (and possibly market-related risks) to another party. It often involves the heavy use of fixed income (bond), derivative instruments, and insurance products.

2. Strategic Asset Allocation (SAA): Strategic Asset Allocation is the most widely utilized and accepted method of investment, based on the original work of Harry Markowitz and his researcher contemporaries in developing Modern Portfolio Theory. In common practice it involves setting a predetermined percentage split between stocks, bonds, and other asset types (an asset allocation); diversifying by asset type and number of securities as much as possible within these categories; and rebalancing back to the original splits as market values shift (making target allocation changes only as necessary based on changes in the investor’s objectives, risk tolerance, and life cycle).

3. Opportunistic Investing (OPP): Opportunistic Investing is a very broad category that encompasses a vast number of sub-categories, all of them sharing the goals of beating the market (either on the upside, the downside, or both) or providing low correlation returns with the more traditional stock/bond portions of an investor portfolio. The strategies used in many hedge funds fit in this category, as do tactical asset allocation approaches and absolute return strategies. Based on our definition, opportunistic strategies are mainly focused on taking advantage of asset type market timing opportunities, and not necessarily on opportunities related to individual companies or stocks.

4. Selective/Concentrated Investing (SEL): Selective or Concentrated Investing is the oldest form of investing, and involves taking positions in one or more individual companies/securities in an attempt to take advantage of some inefficiency related to the price of that security, to capitalize on an associated idiosyncratic risk of the stock, or in expectation of future growth in the company. Many private equity funds apply this method, as do many of the more selective “Value” and “Growth” styles of stock investing. The basic premise is that by knowing as much as possible about the very few positions one is invested in and acting on information related to them, it may become possible to beat the market or a more diversified portfolio over the long term.

During our mutual fund analysis process, we began to notice and accumulate more detailed anecdotal evidence on which investment method categories were most successful (relative to the other categories) in each market environment. We found the following:

Liability-Driven Investing strategies seemed to perform well relative to the others in environments of declining stock prices.
Strategic Asset Allocation strategies seemed to perform well relative to the others in environments of gradually and broadly rising stock prices.
Opportunistic Investing strategies seemed to perform well relative to the others in environments of volatile stock prices without a continuing rising or declining trend.
Selective/Concentrated strategies seemed to perform well relative to the others in environments of rapidly rising stock prices with low volatility.

This realization, that specific investment methods (which come from completely divergent origins and philosophies) may have respective market environments to which they are best suited, struck us as a thesis that required further testing (due to the potentially useful information that might be garnered for portfolio management purposes), so we decided to pursue it further.

Bear Markets (Declining stock prices):
Quantitatively defined as a period of cumulative price decrease of 20% or greater from the most recent record peak
intraday price.

Bull Markets (Rising stock prices):

Quantitatively defined as any period that does not meet the criteria for a Bear Market (Anything other than a period of price decrease of 20% or greater from the most recent record peak intraday price. Bull and Bear markets are mutually exclusive by definition).

Wolf Markets (Volatile/sideways stock prices): Quantitatively defined as any period of 10%+ correction (but less than 20%) starting with the day of the initial peak intraday price and ending after the recovery when the initial peak intraday price is reached again. Also included are corrections of 20% or greater (and their recoveries) that occur after a bear market conclusion but before confirmation of a new bull market (they occur prior to full recovery from the bear). It should be noted that in an August 2010 Wall Street Journal article (by Kristina Peterson), Michael Purves, who was then chief market strategist at BGC Financial, used the term “wolf market” to describe an environment “characterized by a tight trading range, increased volatility, high stock correlations, and quick reversals.”1 While the definition of a wolf market used in this article is somewhat different from Purves’, the basic concept of increased volatility is the same.

Eagle Markets (Rapidly rising stock prices with low volatility): Quantitatively defined as any period exhibiting trailing 1-year returns of +30% or greater (as measured on an intraday basis) without an intervening 10% or greater downward price correction.

It is important to note that based on these quantitative definitions of the Bear, Bull, Wolf, and Eagle environments, markets can overlap with each other. With the exception of Bear markets, the other three market types can occur simultaneously. In effect, based on this “overlapping” set of definitions (which have been used to preserve the traditional mutually exclusive definitions of Bull and Bear markets), our newly identified Wolf and Eagle markets can be viewed as subsets or components of the traditional Bull market.

Bear Markets Measured

There were 15 Bear Markets in the S&P 500 from 1/1/1950 to 4/7/2025 (in 75.27 years). These accounted for 13.15 years in total (17.47% of the history of the S&P 500 Index). The average length of a Bear Market was 0.88 years (10.56 months). The shortest was 0.09 years (1.08 months), and the longest was 2.55 years (30.60 months). The average cumulative drawdown (loss) per Bear Market was -32.03%, with the largest drawdown at -57.69%, and the smallest drawdown at -20.21%. The average rate of loss during a Bear Market was -49.75%/year, with the steepest rate at -99.21%/year, and the shallowest at -17.56%/year.

Bull Markets Measured

There were 15 Bull Markets in the S&P 500 from 1/1/1950 to 4/7/2025 (in 75.27 years). These accounted for 62.12 years in total (approximately 82.53% of the history of the S&P 500 Index). Note that 1 of these has incomplete data (the first), so the following data excludes this first incomplete Bull Market. The average length of a Bull Market was 3.97 years (47.64 months). The shortest was 1.15 years (13.80 months), and the longest was 9.55 years (114.60 months). The average cumulative gain per Bull Market was +128.07%, with the largest gain at +341.05%, and the smallest gain at +44.62%. The average rate of gain during a Bull Market was +25.59%/year, with the steepest rate at +55.42%/year, and the shallowest at +14.73%/year.

Wolf Markets Measured

There were 33 Wolf Markets in the S&P 500 from 1/1/1950 to 4/7/2025 (in 75.27 years). These accounted for 17.68 years in total (approximately 24.49% of the history of the S&P 500 Index). The average length of a Wolf Market was 0.55 years (6.60 months). The shortest was 0.14 years (1.68 months), and the longest was 2.90 years (34.80 months). The average cumulative return and annualized return per Wolf Market was 0%, by definition. The average maximum drawdown (at the trough) during a Wolf Market was -13.71%, with the largest at -21.62% and the smallest at -10.29%.

Eagle Markets Measured

There were 28 confirmed Eagle Markets in the S&P 500 from 1/1/1950 to 4/7/2025 (in 75.27 years). These accounted for 30.36 years in total (approximately 40.33% of the history of the S&P 500 Index). The average length of an Eagle Market was 1.08 years (12.96 months). The shortest was 0.44 years (5.28 months), and the longest was 2.09 years (25.08 months). The average cumulative gain per Eagle Market was +47.13%, with the largest gain at +100.92%, and the smallest gain at +19.14%. The average rate of gain during an Eagle Market was +47.94%/year, with the steepest rate at +98.49%/year, and the shallowest at +24.71%/year.

4 Market Environment Aggregate Analysis

For purposes of comparison of the Exhibit 1 results, first consider that the traditional 2-environment approach to market history analysis results in Bear markets accounting for 17.47% of the history of the S&P 500 Index, and Bull markets accounting for the entirety of the remainder (82.53% of market history). But if we now separate out Wolf and Eagle markets as subcomponents of the Bull market using our 4-environment approach, we find that Bear markets account for 17.47% (the same as before), Bull markets account for 82.53% (the same as before), Wolf markets account for 24.49% (a new subcomponent), and Eagle markets account for 40.33% (a new subcomponent). The total percentage of market history when adding these four proportions together exceeds 100% because we are double-counting the portion of the broader Bull markets that overlap with Eagle and Wolf markets. Again, this is based on the use of overlapping quantitative definitions that preserve the conventional Bull and Bear definitions, and view Wolf and Eagle markets as subcomponents of the Bull market. So, it is important to note that if an alternative “mutually exclusive” definition of the 4 market environments is used (in which we assume only one of the 4 environments can occur at any one time) instead of the “overlapping” definitions used here, then the time periods identified as “Bull Only” (in which none of the other 3 environments is indicated) account for only 18.16 years or 24.13% of the 75.27 years from 1/1/1950 to 4/7/2025. This is in stark contrast to the traditional definition of the Bull market used here, in which the Bull market accounts for 82.53% of market history during this time period. Under a mutually exclusive definition of the 4 market environments, Bear Markets account for 13.15 years (17.47%) of market history, Bull Markets 18.16 years (24.13%), Wolf Markets 17.68 years (24.49%), and Eagle Markets 26.34 years (34.99%). The additional 1.08% when adding them all together is accounted for by recognizing that in a single day one market type will end and another will begin (and each of these transitional days is thus double-counted). These are astonishing results. This means that Wolf and Eagle markets account for a very large proportion of market history, regardless of whether the “mutually exclusive” or “overlapping” definitions are used (even more so with the “mutually exclusive” approach). This is evidence in favor of the use of larger allocations to investment methods that can take advantage of Wolf and Eagle markets. It follows that if investment assets, strategies, or methods can be found that can perform well in these two newly identified market environments, then perhaps they should be utilized as substantial components within an investor portfolio (possibly in very large proportions, depending on the capital allocation approach).

How to read EXHIBIT 2: The table presented in EXHIBIT 2 is a chronological history showing the start date, end date, and duration (in days) of each of the historical Bear, Bull, Wolf, and Eagle markets. Periods with white space are negative indicators for the presence of the market environment shown in that column, whereas positive indicators are shown with repetition of the name of the market type (ie. WOLF). Periods with consecutive positive indicators in the column for a particular market environment (such as WOLF, WOLF) indicate one continuous market environment, with the start date being the first positive indicator’s start date, and the end date being the last consecutive positive indicator’s end date. The discrete historical dates shown in each row are indicative of the exact time periods during which particular combinations of the 4 market environments occurred. For example, 1/5/1953 – 9/15/1953 exhibited both Bull and Wolf market characteristics, while the period from 9/15/1953 – 3/11/1954 exhibited Bull, Wolf, and Eagle market characteristics. The term BULL ONLY is included in order to show time periods exhibiting Bull market characteristics, but not Wolf or Eagle market characteristics (since Wolf and Eagle markets can be viewed as subcomponents of a larger Bull market).

Historical Bear, Bull, Wolf, and Eagle Market Sequences Analysis EXHIBIT 2

The sequence of the traditional 2-market definition of market cycles (Bear and Bull markets) is always the same based on the two environments’ mutual exclusivity (they always alternate Bull, Bear, Bull, Bear, Bull, Bear, etc.). Despite this seeming predictability, investment management practitioners’ ability to time the market cycles has historically been less than ideal. This is because while the alternating sequence is always the same, the length of each Bull and Bear market is always different. This makes effective market timing extremely difficult. However, things become even more complex if one uses our 4-market redefinition, as shown in the table. The sequence of Bear, Bull, Wolf, and Eagle markets has not always followed the same pattern historically. Although a common historically occurring transition sequence is BEAR, BULL, WOLF, EAGLE, the markets can be sequenced totally differently, and they can overlap in a variety of combinations. Also, in an attempt at market timing we still have the problem of different lengths of each occurrence of the market types from one cycle to the next (just as with the easily sequenced traditional Bull/Bear market definitions). This new perspective reveals substantially greater complexity to market history than a traditional Bull/Bear perspective, and adds another layer of unpredictability to timing market cycles (and investment methods). Furthermore, it is important to note that one never knows exactly which type of market environment (or combination of them) one is presently experiencing at any given time. Instead, one must wait until after the quantitative definition has been met (in arrears) before it becomes clear what type of environment they have just been in (one that may have already passed entirely). One generally cannot confirm what type of market environment one is in until after the fact, so based simply on what we’ve explored so far, it is unclear whether knowledge of the most recent market environment type has any predictive power for determining continuation or future experience. Therefore, one should temper their expectations as to whether it is possible to accurately and consistently time a shift from one market environment to another over time.

Our research led us to next analyze data linking the performance of the four broad investment method categories (Liability-Driven Investing, Strategic Asset Allocation, Opportunistic Investing, and Selective/Concentrated Investing) to each of the four market environments (Bear, Bull, Wolf, and Eagle). As mentioned earlier, this was initially prompted by noticing anecdotal evidence within historical mutual fund data (amongst top managers). We noticed that during specific market environments, there was often a contingent of professionally managed mutual funds using a similar approach or method of investing (not specific to an asset class or other narrow mandate) that significantly (sometimes dramatically) outperformed all of the other categories of funds. Different market environments seemed to coincide with the outperformance of different sets of mutual funds and categories of investment management methods. Importantly, we also found an intuitive logic for each pairing of investment method to market environment type:

Liability-Driven Investing and Bear Markets

Liability-Driven Investing may have the potential to outperform the other three methods during a Bear market. If one has transferred his/her market risk to another party through the purchase of fixed income securities, derivatives, or insurance contracts, then a significant decline in stock prices is not likely to be as detrimental to the portfolio as it would be under any of the other three methods. This is because the other three methods by their very nature are likely to have some exposure (or at least potential exposure) to stocks at all times. Strategic Asset Allocation will always have a permanent allocation to stocks. Selective/Concentrated Investing also will always have a permanent (if not revolving) allocation to stocks (assuming it is an equity portfolio). Opportunistic Investing always has the potential for exposure to stocks at the wrong time, even if the portfolio has the flexibility to temporarily allocate to bonds, cash, or other asset classes. In Liability-Driven Investing, the investor has the potential to transfer his/her risks to another party in advance in order to hedge/insure against a Bear market potentiality.

Strategic Asset Allocation and Bull Markets

Strategic Asset Allocation may have the potential to outperform the other three methods during a Bull market. If stock prices are rising on average, then a thoroughly diversified portfolio of stocks will rise on average as well. If stock prices are rising on average, then a thoroughly diversified portfolio of stocks will rise on average as well. It will do this with greater reliability than any alternative approach that would require either a market timing or narrow security/sector bet (some of which will be winners, and some of which will be losers), or an investment in bonds or derivatives contracts (which are historically likely to either lag stocks, hold value, or decline in value in a rising market). Liability-Driven Investing requires the transfer of risk to another party, which will result in an opportunity cost (if fixed income is used) while being out of the stock market or the payment of an insurance premium (through either a derivative contract or traditional insurance contract). Opportunistic Investing typically involves a directional bet on asset classes, the broad stock market, or specific securities, which involves the potentiality (and likelihood) of being out of the stock market for at least a portion of the time during which it is rising – leading to inferior performance relative to Strategic Asset Allocation. Selective/Concentrated Investing also involves the necessity of being more concentrated in one or more securities – and thus a departure from the stock market indexes. But if the Bull market is broad based and most asset prices are rising, then selecting specific securities may only pose an unnecessary risk (of selecting the few securities that aren’t rising as fast) and is also likely to generate transactional friction as the portfolio is shifted – potentially leading to underperformance. Strategic Asset Allocation, however, will have a similar broad exposure to the stock index, and will thus receive the most reliable benefit from the rising market on average.

Opportunistic Investing and Wolf Markets

Opportunistic Investing may have the potential to outperform the other three methods during a Wolf market. In an environment of heightened volatility, strategies that are specifically designed to capture/attack the risk premium associated with this volatility at least have the potential (if not a higher probability) to outperform strategies that resign themselves to simply moving with the market and make no attempt to capitalize on it at all. Strategic Asset Allocation is likely to produce a return of roughly zero during such an environment since it will by definition remain invested in an index-like portfolio without tactical manipulation during a time in which the broad market index is also (by definition) producing zero returns. Selective/Concentrated Investing (in stocks) is also likely to underperform for the same reasons as Strategic Asset Allocation, except that results may further depend on the sub-method of investing used (ie. Fundamental/Value stock investing may perform relatively well, while Momentum/Growth may perform very poorly). While Liability-Driven Investing may also be expected to perform reasonably well relative to Strategic and Selective (because fixed income will continue climbing at its slow and steady pace, and derivatives/insurance contracts could be used to directly hedge against volatility such as with VIX futures), LDI would in theory only hedge out the volatility risk, providing a zero excess return over the hedging objective. Opportunistic Investing on the other hand, has the theoretical potential to provide positive absolute/excess returns in such an environment – but only if the correct tactical moves are made (which is much easier to theorize on than to practice effectively).

Selective/Concentrated Investing and Eagle Markets

Selective/Concentrated Investing may have the potential to outperform the other three methods during an Eagle market. In an environment of soaring asset (stock) prices and low volatility, one of the only ways to beat the broader market indexes is to be different than the broader index but still remain in stocks, which by definition requires being more concentrated than the index (either in those securities that are dragging up the indexes’ average return or in securities that are not in the index at all and are outperforming it). Opportunistic Investing strategies designed to capitalize on volatility will do poorly here both because there is no volatility, and because they have the potential to hold assets other than stocks while stock prices are rising rapidly (creating a drag on relative returns). Strategic Asset Allocation strategies designed for diversity may keep pace with the market, but they are unlikely to significantly outpace it (because by definition they will be tracking it – at least roughly). Liability Driven Investing strategies designed for risk transfer are likely to be missing out on the stock market rally because they had already transferred their stock market risk to another party. They are likely to be earning a lower return on fixed income or even losing money by paying an insurance premium for a derivative contract or traditional insurance contract. Selective/Concentrated Investing on the other hand, has the theoretical potential to provide positive excess returns above the broader stock markets in such an environment – but only if the correct securities are selected (which is much easier to theorize on than to practice effectively).

Mutual fund based analysis: The initial realization of the potential link between market environment and investing method was derived from examining mutual fund data related to top managers only. But the problem with conducting a more comprehensive analysis of the relationship between market environment and investment method using all available mutual funds (not just the top managers) is that they are not categorized by the four investment methods (as we’ve defined them) by any of the major research databases, so we would have to manually individually analyze the management approaches of each of the 21,000+ open-end mutual funds available in the US marketplace in order to categorize them. This is a prohibitive approach. A more expedient but less comprehensive approach, which we’ve used here in EXHIBIT 3, is to select proxies for each of the 4 investment methods in the form of market indexes.

Index based analysis: Index proxies have been used for the 4 broad investment method categories in Exhibit 3. There are significant shortcomings to using a set of indexes as proxies for investment methods, mainly because there are no indexes in existence that specifically track the 4 investment methods as we’ve defined them. Instead, we’ve substituted in pre-existing indexes that come as close as possible to matching the characteristics of a portfolio manager applying the respective investment method - but this approach is far from perfect. The problem is especially true with Opportunistic Investing as we’ve defined it. For Opportunistic investing, the index-only approach is particularly insufficient because hedge fund indexes contain a wide array of strategies, many of which are not specifically designed to capitalize on volatility, so their use would lead to an inaccurate assessment of the performance of Opportunistic Investing during the various market environments - especially during Wolf Markets. An additional and important shortcoming is the lack of available long term historical data on many indexes. The above analysis charts only go back to the earliest common start date of the proxies utilized, which is December 31st, 2002. Therefore this analysis provides only a glimpse at how the 4 investment methods may have corresponded to the 4 market environments in a broader historical context.

* All returns shown have been annualized, including for periods of less than 1 year.

** The returns shown for Strategic Asset Allocation during the Wolf Market from 12/2/2002 to 5/28/2003 and for the Bull Market period from 10/10/2002 to 10/11/2007 are calculated with the start date of 12/31/02, which is the earliest date available for the proxy index.

The “Arithmetic Avg Since Earliest Common Date” shown above is calculated by dividing the sum of each of the annualized returns for each market instance by the number of market type instances identified. It is a measure of the average annualized return experienced during each market type instance (regardless of how long that instance lasted).

The “Time Wtd Avg Since Earliest Common Date” shown above is calculated by weighting the sum of each of the annualized returns for each market instance with its corresponding time duration. It is a measure of the earlier average annualized return experienced during the aggregate time period of all instances (it gives greater weighting to market instances that lasted longer).

While the results for Bear Markets and Eagle Markets were dead-on with our expectations for the outperformance of Liability-Driven Investing and Selective/Concentrated Investing respectively, the results for Bull Markets and Wolf Markets using the index proxies as the measuring tool partially contradict two separate analyses that we’ve conducted internally as a firm using 1. Partial data on manually categorized actual mutual fund performance and 2. Short term data on our own proprietary separately managed account strategies. In both of these separate analyses (the second of which will be presented here), Wolf Markets were characterized by the outperformance of Opportunistic Investing and Bull Markets were characterized by the outperformance of Strategic Asset Allocation. We expect that the difference in results for Wolf Markets is mainly attributable to the fact that the index used for measurement is an imperfect proxy that contains many heterogeneous hedge fund strategies, and not just those that are designed to capitalize on volatility. This likely has delivered skewed results for the Wolf Market outcome.

In addition, this is a returns-only analysis. If maximum draw-downs or a measure of risk-adjusted returns were utilized, this may lead to differing results. We further expect that the discrepancy in results for Bull Markets is at least partially attributable to the fact that our definition of a Bull Market includes Eagle Markets as a subset, and because Selective/Concentrated Investing outperforms during Eagle Markets, this skews the overall results for Bull Markets in favor of Selective/Concentrated Investing over Strategic Asset Allocation. Additionally, in an internal (unpublished) analysis of indexes using a mutually exclusive definition of the 4 market types that measures “Bull Only” time periods (excluding Eagle Markets and Wolf Markets), Strategic Asset Allocation is shown to be the winner using this more limited definition.

Additional published research may be necessary in this area. Furthermore, an analysis of the four investment methods in each of the four market environments using one or more forms of risk-adjusted performance would likely provide further enlightenment on this subject. This is an area for future research and publishing.

Based on the relative inconclusiveness of the prior index-based analysis with regards to Wolf and Bull Markets, we felt compelled to provide further data to look at this from a different angle. Proprietary 4Thought Financial Group Inc. Separately Managed Account proxies have been used for the 4 broad investment method categories in Exhibit 4, illustrating actual historical net of fees returns for a set of our SMA strategy composites. It should be noted that an important shortcoming in this analysis is the lack of available long term historical data on all the strategies referenced. The inception date of the referenced SMAs is 6/1/2012 (with the exception of 4Thought Traditional Strategic Allocation, which has a start date of 8/1/2013), providing us with approximately 13 years of data, which is shorter than the previous index-based analysis, which included over 22 years of data. Therefore this analysis provides a limited picture at how the 4 investment methods may have corresponded to the 4 market environments over longer historical time periods. Future publishing on this topic will likely help to further confirm the relationships once more historical data is available.

*4Thought Financial Group claims compliance with the Global Investment Performance Standards (GIPS®). GIPS® is a registered trademark of CFA Institute. CFA Institute does not endorse or promote this organization, nor does it warrant the accuracy or quality of the content contained herein. GIPS-compliant performance information for all of 4Thought’s strategies may be obtained by email at info@4tfg.com; on the 4Thought website at https://www.4tfg.com/performance-requests; or by phone at (516) 300-1617. The GIPS Reports for the time period ended 6/30/2025 for the specific composites referenced here have been included as addendums to this document. Please refer to these report addendums for additional information and disclosures. The GIPS firm definition excludes any third party asset management programs over which 4Thought maintains oversight advisory agreements on behalf of its clients, any arrangements under which 4Thought provides recommendations for client self-implementation, and any assets under advisory but not under direct management (in which 4Thought provides allocation changes or trading signals to third party firms but does not take discretion over the trading of client accounts).

** Time periods of less than 1 year have not been annualized. This differs from the prior index-based analysis. The net returns illustrated are reduced by all fees and transaction costs incurred (actual fees are used).

*** The data illustrated for SAA during this time period reflects the performance of the 4Thought Global Strategic Allocation (Proprietary SMA strategy) because the 4Thought Traditional Strategic Allocation SMA did not exist until 8/1/2013.

The “Arithmetic Avg Since Earliest Common Date” shown above is calculated by dividing the sum of each of the annualized returns for each market instance by the number of market type instances identified. It is a measure of the average annualized return experienced during each market type instance (regardless of how long the instance lasted).

The “Time Wtd Avg Since Earliest Common Date” shown above is calculated by weighting the sum of each of the annualized returns for each market instance with its corresponding time duration. It is a measure of the average annualized return experienced during the aggregate time period of all instances (it gives greater weighting to market instances that lasted longer).

In this version of the study, which uses proprietary strategies that are specifically designed to outperform in their respective market environments (as opposed to using the best fitting indexes we can find for each method), the results become much clearer and directly in line with our hypotheses. Liability-Driven Investing is the best performer during Bear Markets; Strategic Asset Allocation is the best performer during Bull Markets; Opportunistic Investing is the best performer during Wolf Markets; and Selective/Concentrated Investing is the best performer during Eagle Markets. This makes it abundantly clear that specifically designing strategies to take advantage of a specific market environment type has great benefits for predicting portfolio outcomes, as opposed to just searching for a best-fitting index or actual investment vehicle that is not specifically designed for that purpose.

Based on the results for the separately managed account proxies used here, the author believes there is mounting statistical evidence to support the hypothesis that Opportunistic Investing has the capacity for moderate to significant returns-based outperformance during Wolf Markets (although some may require further convincing evidence with regards to the use of index proxies). But an even more convincing argument for the use of Opportunistic Investing as part of the overall portfolio may be made if risk-adjusted returns and maximum draw-downs are also taken into consideration during these time periods (when compared to the other methods). Although we have conducted our own internal studies on this topic, they are not yet ready for publication and may be included in future published research or in an update to this article. If we can accept that Opportunistic Investing has the ability to outperform in Wolf Markets, and when we recognize (without question) that Wolf Markets account for 24.49% of market history (regardless of whether an overlapping or mutually exclusive definition of the market types is used), it becomes clear that Opportunistic Investing may need to play a larger role than it currently plays in most investors’ overall portfolios.

Based on the results for both indexes and SMA proxies, the author believes there is now significant and convincing statistical evidence to support the hypothesis that Selective/Concentrated Investing significantly outperforms during Eagle Markets, and that Eagle Markets are a highly pervasive element of market history, accounting for 40.33% of the history of the S&P 500 Index since 1950 when using an overlapping definition of market environments, and accounting for 34.99% when using a mutually exclusive definition.

In this published analysis and other internal unpublished analyses conducted by the author using alternative measures of Selective/Concentrated Investing (instead of value and growth stocks), as heterogeneous as private equity, thematically selected stocks and ETFs, multi-factor individual security selection algorithms, mutual fund categories, and combinations thereof, there is a persistent and clear outperformance of Selective/Concentrated Investing during Eagle Markets. It is further important to note that there is no one subcategory of Selective/Concentrated Investing that is responsible for this outperformance. For example, private Equity, growth stocks, and value stocks all outperform during Eagle Markets (interestingly, even 50%/50% allocations to value/growth stocks beat 100% “core” or “blend” allocations in this environment). This lends credence to the idea that the traditional definitions of stock types such as value/growth are not as useful as the broader definition of an investment strategy as Selective/Concentrated. It appears that it does not much matter what type/style of stocks are used in the Selective/Concentrated investment strategy in order to outperform during an Eagle Market, and that it is more important only that the investment strategy is simply concentrated, regardless of style or selection criteria. This last statement appears to be true over long time periods, but it should be noted that during any one instance of an Eagle Market (over a short time period), a concentration in the wrong area of the stock market will clearly produce underperformance of a more diversified stock portfolio, at least for that specific period.

An important point to note about Strategic Asset Allocation is that it is the method of investing that tends to perform best during the market environment that accounts for the largest part of market history (at least based on the traditional definition of the market cycle) - the Bull Market. In addition, although it may show up as the worst performer during Bear Markets in our SMA-based analysis here, for other study methods or time periods Selective/Concentrated Investing often takes last place during Bears. Strategic Asset Allocation is usually not the worst performer during Wolf or Eagle markets either. It tends to have a greater central tendency in returns than the other 3 investment methods. It is for this reason that Strategic Asset Allocation sees such popularity in the world of investment management, because if you had to pick only one investment method to use, this one would make the most sense. But the good news is that you don’t have to choose only one method. You can use them all - and you should.

The historical pervasiveness of Wolf and Eagle markets coupled with an assumption of the unpredictability of market type sequences argues for the use of an investment approach that may be allocated and diversified across 4 types of investment method (each one capable of capitalizing on one of the 4 market environments). If one assumes the total unpredictability of market type sequences (which may or may not be true) one should not expect to be able to accurately and consistently time a shift from one method to another over time. However, if one assumes that the historical data provides at least some predictive information, then the expectation of the ability to capitalize on market changes by shifting from one method to another (in whole or in part) should prove productive over long enough time periods. We refer to the overall method-diversified approach as Multi-Method Investing® (MMI), while we refer to the sub-category approach of shifting in whole or in part between the 4 method types as Multi-Method Adaptive™ (MMA) Investing.

Multi-Method Investing Index-Based Analysis (EXHIBIT 5):

This chart compares the performance of the 4 individual methods of investing to a series of Multi-Method Investing portfolios (all based on indexes) over a 15 year period. The fact that the 4 investment method categories have specific market environments in which they tend to over or underperform the others means that they have returns correlations that are less than 1.0. Generally speaking, the lower or more negative the correlation an asset’s returns with those of the rest of the assets in a portfolio, the better a portfolio diversifier the asset is. Given that the 4 investment methods have correlations of less than 1.0, this means that a portfolio mix that includes multiple methods should theoretically create a better risk/return profile than one that does not. One very simple measure of risk-adjusted return is the Sharpe Ratio, which is shown in the table provided for each of the 4 individual investment methods, as well as for two examples of multi-method portfolios. The higher the Sharpe Ratio, the better the risk-adjusted return. The Sharpe Ratio shown assumes a risk free rate of 0%.

MMI 4 Method Static Mix (EXHIBIT 5):

The first multi-method portfolio, labeled “MMI 4 Method Static Mix”, is composed of 17% Liability-Driven Investing, 24% Strategic Asset Allocation, 24% Opportunistic Investing, and 35% Selective/Concentrated Investing (rebalanced monthly), an allocation that is based on the percentage of time since 1950 that the S&P 500 Index displayed characteristics of each of the 4 market types (when using a Mutually Exclusive definition of the environments). When comparing the Sharpe Ratio of the MMI 4 Method Static Mix and the strategic asset allocation portfolio (the most widely used and accepted investment method), we find the 4 Method Mix has a lower average annualized return (8.36% vs. 11.02%) and a much lower volatility (risk) level (9.44% vs. 14.46%), leading to a much higher Sharpe Ratio (0.89 vs. 0.76). This means that using the MMI 4 Method Static Mix portfolio results in giving up some potential returns, but significantly reduces the portfolio volatility level relative to a traditional globally diversified stock portfolio allocation (Strategic Asset Allocation). However, comparing a 4 method portfolio to a 100% stock portfolio is not necessarily a fair comparison for either party. This is because one of the 4 components, Liability-Driven Investing, is heavily bond-oriented and tends to have a much lower risk/return profile than the other three methods (lower volatility, lower long term returns).

LDI provides the only potential “fixed” return:

In the context of Multi-Method Investing, LDI should be viewed separately from the other 3 methods since it is the only potential “fixed” component in which returns/risks can be “locked-in” (for example, by using a laddered or bulletted bond portfolio held to maturity), whereas the other 3 methods are completely variable in their potential returns/risks. LDI may be better viewed as the prerequisite “base of the pyramid” and as an overall portfolio risk reduction tool when creating multi-method allocations rather than being viewed as component to be mixed with the remaining 3 “variable” methods. In this respect, strategic asset allocation, opportunistic investing, and selective/concentrated investing have more similarities with each other than any of them have with liability-driven investing. For these reasons, we’ve included a second multi-method portfolio in the chart, which serves as a fairer comparison between an all-stock strategic asset allocation (as is used here in “SAA”) and Multi-Method Investing.

MMI 3 Method Static Mix (EXHIBIT 5):

The second Multi-Method portfolio labeled “MMI 3 Method Static Mix” is composed of 29% Strategic Asset Allocation, 29% Opportunistic Investing, and 42% Selective/Concentrated Investing (rebalanced monthly), an allocation that is based on the percentage of time since 1950 that the S&P 500 Index displayed characteristics of each of these 3 market types (when using a Mutually Exclusive definition of the environments and Liability-Driven Investing is excluded from the calculation). When comparing this 3-method portfolio to the strategic asset allocation (SAA) portfolio, we find only slightly lower average returns (11.01% vs. 11.02%), much lower volatility (risk) (10.81% vs. 14.46%), and thus a much higher Sharpe Ratio (1.02 vs. 0.76). Surprisingly (given its widespread popularity), strategic asset allocation showed the lowest Sharpe Ratio of all 4 of the individual methods for the time period referenced. These results point clearly to the potential risk reduction benefits of opening up the portfolio toolkit to include more than just the most popular approach to investing, and specifically to the benefits of the Multi-Method Investing approach over the more widely currently utilized Strategic Asset Allocation approach.

Multi-Method Investing 4Thought SMA-Based Analysis EXHIBIT 6

This chart compares the performance of the 4 individual methods of investing to a series of Multi-Method Investing portfolios (all based on proprietary 4Thought Financial Group Separately Managed Accounts) over a 5 year period. Proprietary 4Thought Financial Group Inc. Separately Managed Account proxies have been used for the 4 broad investment method categories in Exhibit 6, illustrating both gross and net of fees returns**. It should be noted that an important shortcoming in this analysis is the lack of available longer term historical data on all the strategies referenced. While five of the referenced strategies have a historical track record of greater than ten years, one of the most important for the discussion (4Thought Multi-Method Unconstrained) has an inception date of 3/1/2018, reflecting a track record of 7+ years. However, due to technical limitations of the software used to generate the returns data provided (which provides data in only 5 and 10 year increments, but not 7), the above chart data only goes back to the earliest common start date of the proxies utilized (further limited by a five year technical threshold), which results in a data start date of 6/30/2020 (five years of data). Therefore this analysis provides only a glimpse at how the referenced portfolios performed over longer historical time periods.

** Pure gross returns of 4Thought’s Separately Managed Accounts are shown as supplemental information and are stated gross of all fees and transaction costs; net returns are reduced by all fees and transaction costs incurred (actual fees are used).

*** The Sharpe Ratio data provided uses gross of fees returns and gross of fees standard deviation as inputs for the calculation.

MMI 4 Method Static Mix (EXHIBIT 6):

Just as in the prior index-based analysis, the first multi-method portfolio in Exhibit 6, labeled “MMI 4 Method Static Mix”, is composed of 17% Liability-Driven Investing, 24% Strategic Asset Allocation, 24% Opportunistic Investing, and 35% Selective/Concentrated Investing (rebalanced monthly), an allocation that is based on the percentage of time since 1950 that the S&P 500 Index displayed characteristics of each of the 4 market types (when using a Mutually Exclusive definition of the environments). When comparing the Sharpe Ratio of the MMI 4 Method Static Mix and the strategic asset allocation portfolio (the most widely used and accepted investment method), we find the 4 Method Mix has a lower average annualized return (11.46% vs. 14.40%) and a lower volatility (risk) level (12.77% vs. 15.64%), leading to a comparable but slightly lower Sharpe Ratio (0.90 vs. 0.92) for this particular time period. This particular time period does not illustrate any marked improvement in either mean returns or risk-adjusted returns over Strategic Asset Allocation for the MMI 4-Method Mix. But just as in the prior index-based analysis, the inclusion of Liability-Driven Investing in the mix does not allow for a truly fair comparison to a 100% stock portfolio (which is what composes the Strategic Asset Allocation portfolio illustrated). Therefore, we have also included a 3 method mix that excludes LDI as well.

MMI 3 Method Static Mix (EXHIBIT 6):

Just as in the prior index-based analysis, the second multi-method portfolio in Exhibit 6, labeled “MMI 3 Method Static Mix” is composed of 29% Strategic Asset Allocation, 29% Opportunistic Investing, and 42% Selective/Concentrated Investing (rebalanced monthly), an allocation that is based on the percentage of time since 1950 that the S&P 500 Index displayed characteristics of each of these 3 market types (when using a Mutually Exclusive definition of the environments and Liability-Driven Investing is excluded from the calculation). When comparing the 3 Method Static Mix to the strategic asset allocation (SAA) portfolio, we find slightly lower average returns (13.47% vs. 14.40%), but a more significant reduction in volatility (risk) (14.39% vs. 15.64%), and thus a higher Sharpe Ratio (0.94 vs. 0.92). This illustrates the utility (at least for this time period) of a simple static Multi-Method Investing portfolio, which is one way to apply the overall Multi-Method Investing concept. It indicates through real world application that diversifying at the level of investment method, and not just at the level of asset type (as in Strategic Asset Allocation), can provide measurable benefits to the investor in improving risk-adjusted performance. But in fact there is much more to this story.

MMA (Multi-Method Adaptive^TM) Investing (EXHIBIT 6):

So far in this analysis and in the prior index-based analysis we have looked only at the performance of static or fixed allocations to the multiple methods of investing in comparison to individual methods (especially to Strategic Asset Allocation). But this is not the only way to apply Multi-Method Investing. It can also be applied dynamically via an alternative approach or subcategory of Multi-Method Investing that we call “Multi-Method Adaptive ^TM” (MMA) Investing. Such a portfolio is illustrated in Exhibit 6 as “MMA”. Here MMA is represented by 4Thought Multi-Method Unconstrained, which is a proprietary separately managed account strategy. It is a 4-method unconstrained allocation portfolio that features proprietary algorithms that were developed based on historical market type data analysis. The portfolio shifts its allocation to the 4 methods of investing actively using predictive analytics and decision rules applied systematically over time. The algorithms are based on in-depth assessments of the history of the financial markets through the lens of the Bear, Bull, Wolf, and Eagle Market redefinition of market cycles. When comparing the MMA results to the strategic asset allocation (SAA) portfolio, we find only slightly lower average returns (13.94% vs. 14.40%), but a more significant reduction in volatility (risk) (14.18% vs. 15.64%), and thus a higher Sharpe Ratio (0.98 vs. 0.92). Furthermore, when comparing the MMA results to the MMI 3 Method Static Mix, we find higher average returns (13.94% vs. 13.47%), and a slight reduction in volatility (risk) (14.18% vs. 14.39%), and thus a higher Sharpe Ratio (0.98 vs. 0.94). This illustrates the potential improvement of risk-adjusted returns (at least for this time period) that can result from using MMA, not just over Strategic Asset Allocation, but also over a more static allocation application of Multi-Method Investing.

Behavioral Risks of MMI:

There is a significant potential drawback to the use of Multi-Method Investing versus strategic asset allocation - one which was not immediately apparent when we conducted our preliminary internal and theoretical research, but that became apparent once the methodology was put into practice - and it has to do with the emotions and expectations of investors. By nature (and/or by design), some of the investment methods used in Multi-Method Investing have low correlation returns with the broader stock markets and widely publicized indexes such as the S&P 500. This is particularly true of Opportunistic Investing, some forms of Selective/Concentrated Investing, and Liability-Driven Investing, whereas Strategic Asset Allocation tends to correlate more highly with broad stock market indexes. Some investors become skittish when they see parts of their portfolio move in a different direction from broad market indexes. This is particularly true when it occurs for an extended period of time while the broad market indexes are on the rise, and is at least partially related to the fact that media outlets tend to report only on a very limited set of indexes that may have little or nothing to do with what is happening with regards to the management of the investor account(s). As a result, the practical application of Multi-Method Investing typically requires greater investor education relative to the more widely utilized strategic asset allocation approach - the concepts of which are more widely known. In addition, portfolio managers must pay particular attention to ensuring that the single-method sub-portfolios that make up the overall Multi-Method portfolio are built to specialize in their own respective market environment to provide outperformance during these time periods, but also must be made resilient against losses during market environments in which they are out of favor. Thus, any decision rule or algorithm development in single-method strategies must be done with the intent to provide versatility across all market environments while still maintaining focus on the core single environment outperformance objective.

A Potentially Improved Probability of Achieving Objectives:

An approach that uses Multi-Method Investing, whether applied via a static allocation or via a Multi-Method Adaptive approach, provides an additional layer of diversification that is not available through a traditional strategic asset allocation portfolio (which is only diversified by asset type, and not necessarily by investment method), or through any other single-method portfolio. Conceptually, Multi-Method Investing allows one to attempt to achieve one’s investment objectives from multiple angles simultaneously, providing some protection if any one or more of the methods underperforms during a specific time period, and thereby potentially increases the probability of achieving one’s investment objectives in the long term.

Our hypotheses and findings appear to be (at least in part) corroborated by related previous data studies conducted by Modern Portfolio Theorists. For example, a study was published in 1986 by Gary P. Brinson, CFA, Randolph Hood, and Gilbert L. Beebower called “Determinants of Portfolio Performance” (published in the Financial Analysts Journal). It attempted to explain the effects of asset allocation policy on pension plan returns, and asserted that the variability in a portfolio’s returns could be attributed to 3 main determinants: Asset allocation, market timing, and security selection. While their published finding that 93.6% of a portfolio’s return variability could be explained by asset allocation alone is not directly consistent with our own findings, the underlying expression of the three main drivers of portfolio performance as asset allocation, market timing, and security selection is similar to our assertions. If we simply replace these three drivers with three investment methods and add a fourth driver/method that is conceptually similar to the widely utilized “risk-free rate” (associated with Liability-Driven Investing), then we suddenly have a very similar theoretical basis to work upon. Whereas the authors of “Determinants of Portfolio Performance” simply described these three elements as drivers of portfolio performance, we’ve taken the next step in associating each of these “drivers” with the historical method of investing actually used to implement or take advantage of the drivers. Strategic Asset Allocation is the method of investing largely associated with “asset allocation”; Opportunistic Investing is the method largely associated with “market timing”; Selective/Concentrated Investing is the method largely associated with “security selection”. The fourth element not accounted for by the Brinson/Hood/Beebower study that we’ve added is Liability-Driven Investing – which had earlier been used by theorist James Tobin in 1958 in a loosely similar form he called the “risk-free rate” (Tobin had included the risk-free rate as an adjustment to Harry Markowitz’s original “efficient frontier” posited in 1952 in his doctoral dissertation “Portfolio Selection”).

Many other studies, which provide the theoretical origins of the current “Factor Investing” movement in the portfolio management industry, have sought to identify various risk premiums or “factors” that are further determinants of portfolio returns. Starting in 1934 with the Graham and Dodd description of the “Value” premium, later with the famous Fama and French 1992/1993 study that developed a multi-factor model, and in many other studies, researchers have sought to identify specific “factors”, “drivers”, and “risk premiums” and decompose portfolio returns into their basic elements. Based on our own research, the author believes that as opposed to describing the broader overall structure of the determinants of portfolio performance, these studies became preoccupied with identifying only minor sub-sleeves of only one of 4 major risk premiums. Specifically, the “factors” described in these studies and the “Smart Beta” investment products associated with them are actually doing nothing more than tracking sub-premiums associated with the Selective/Concentrated Investing category and its associated major risk premium. Such studies may be missing the forest due to the trees, and are perhaps capturing only a quarter of the total conceptual investment picture.

Many of these prior researchers were describing only partial theoretical elements of broader real-world phenomena. These phenomena are more aptly described through the lens of a Risk Premium Capital Allocation framework derived from the study of the four investment methods and the four associated Bear, Bull, Wolf, and Eagle market environments.

We have identified 4 major risk premiums that may be capitalized on by investors:

Credit Default Risk Premium (CDRP): The “Credit Default Risk Premium” can reap rewards from accepting the risk of a potential default by a guarantor, such as a bond or derivative contract issuer. Liability Driven Investing is the investment method that most effectively captures this risk premium – which is theoretically most widely available during Bear markets. This risk premium is closely aligned with the concept of the “risk-free rate” as a basic determinant of portfolio variability and returns, but with the significant caveat that instead of assuming the availability of a return free of risk (which is just a theoretical concept that does not exist in the real world), it assumes that a fixed rate of return (or a return directly related to a hedged asset) can be achieved by accepting a certain amount of credit default risk. The CDRP is most closely conceptually associated with the “risk-free rate” of traditional Modern Portfolio Theory, but may serve as a more accurate and practical replacement.

Systemic Efficiency Risk Premium (SERP): The “Systemic Efficiency Risk Premium” reaps rewards from accepting the risk of fluctuation of the global financial markets as a whole, and is best captured by Strategic Asset Allocation. It is theoretically most widely available during Bull markets. The SERP is most closely conceptually associated with the “asset allocation” of traditional Modern Portfolio Theory, but may serve as a more accurate and practical replacement.

Cyclical Inefficiency Risk Premium (CIRP): The “Cyclical Inefficiency Risk Premium” accepts manager-specific unsystematic (tactical decision making) risk and can be captured by Opportunistic Investing. It is theoretically most widely available during Wolf markets. The CIRP is most closely conceptually associated with the “market timing” of traditional Modern Portfolio Theory, but may serve as a more accurate and practical replacement.

Secular/Structural Inefficiency Risk Premium (SIRP): The “Secular/Structural Inefficiency Risk Premium” accepts security-specific unsystematic (concentrated security selection) risk and may be captured by Selective/Concentrated Investing. It is theoretically most widely available during Eagle markets. The SIRP is most closely conceptually associated with the “security selection” of traditional Modern Portfolio Theory, but may serve as a more accurate and practical replacement.

Risk Premium Capital Allocation (RPCA)

As mentioned earlier, we suggest that one should build a portfolio that may use multiple investment methods at the same time (whether using a static allocation or a more dynamic/adaptively shifted allocation) so that the overall portfolio is prepared for whatever contingency it may face. But the question is: What methods should one use, when should one use them, and how much of each? The theoretical framework needed to answer this question is something we call “Risk Premium Capital Allocation (RPCA)”, which describes the optimal percentage allocation of an investor’s capital to the major risk premiums. These 4 risk premiums can be plotted on a chart to create an “efficient frontier” similar to the one used in Modern Portfolio Theory, which tells us the most optimal combination of risk premiums (and thus investment methods) for a range of investor objectives, and thus may provide us with an appropriate Risk Premium Capital Allocation for a given investor (see Exhibit 7).

The intention behind using this multi-premium, multi-method approach is to build and manage an aggregate investor portfolio that is better equipped to handle a much wider variety of market environments and contingencies. Making practical investment decisions using the Risk Premium Capital Allocation theoretical framework (possibly in combination with applications of lessons learned from historical market type data analysis) may help to increase the probability that an investor will achieve their financial objectives.

The following are the most critical findings from this study:

Different historical market environments are often characterized by a different respective set of top performing investment managers/funds/strategies. Each discrete historical time period includes top managers that share a similar method of investing.
Investment management approaches can most often be categorized into one of four primary methods of investing:
- Liability-Driven Investing
- Strategic Asset Allocation
- Opportunistic Investing
- Selective/Concentrated Investing
Financial market history (as told by the S&P 500 Price Return Index from January 1st, 1950 to April 7th, 2025) can be defined as a string of 4 different recurring market environments:s:
- Bear Markets account for 17.47% of S&P 500 history.
- Bull Markets account for 82.53% of S&P 500 history under a traditional or overlapping definition of market environments, and only 24.13% under a mutually exclusive definition.
- Wolf Markets account for 24.49% of S&P 500 history.
- Eagle Markets account for 40.33% of S&P 500 history under an overlapping definition of market environments, and 34.99% under a mutually exclusive definition.
The sequence of Bear, Bull, Wolf, and Eagle markets has not always followed the same pattern historically. The market types can be sequenced totally differently, and they can overlap in a variety of combinations. Also, based on the quantitative definitions of the 4 market environments, one cannot say which type of market environment one is in until after the fact. This illustrates the great difficulty in predicting future market environments to take advantage of them, but it does not preclude the possibility. Therefore, although it does not appear that one should expect to be able to accurately and consistently time a shift from one market environment to another over time with certainty, thorough studies of historical data and market types can potentially provide information that may lead to incremental performance gains by using a more dynamic/adapative approach over a more static approach to investing.
While there is a strong logical link between the 4 market environments and 4 respective investment methods, the statistical link is still not as clear, and requires further future research using a larger and more comprehensive data set:
- Bear markets and Liability-Driven Investing: Liability-Driven Investing can be both logically and historically clearly linked to outperformance of other methods during Bear markets.
- Bull markets and Strategic Asset Allocation: While Strategic Asset Allocation can be logically linked to outperformance of other methods during Bull markets, various studies may produce results that show the outperformance of either Strategic Asset Allocation or Selective/Concentrated Investing (depending on a variety of factors) relative to other methods.
- Wolf markets and Opportunistic Investing: While Opportunistic Investing can be logically linked to outperformance of other methods during Wolf markets, various studies may produce results that show the outperformance of either Opportunistic Investing or one of the other 3 methods (depending on a variety of factors).
- Eagle markets and Selective/Concentrated Investing: Selective/Concentrated Investing can be both logically and historically clearly linked to outperformance of other methods during Eagle markets.
With regards to Opportunistic Investing, there is mounting statistical evidence to support the hypothesis that it has the capacity for moderate to significant returns-based outperformance during Wolf Markets. While further study is needed in this area, if we can accept that Opportunistic Investing has sometimes (if not always) displayed the ability to outperform in Wolf Markets, and when we recognize (without question) that Wolf Markets account for 24.49% of market history, it becomes clear that Opportunistic Investing may need to play a larger role than it currently plays in most investors’ overall portfolios.
Selective/Concentrated Investing significantly outperforms during Eagle Markets, and Eagle Markets are a highly pervasive element of market history, accounting for 40.33% of the history of the S&P 500 Index since 1950 when using an overlapping definition of market environments, and accounting for 34.99% when using a mutually exclusive definition. There is no one subcategory of Selective/Concentrated Investing that is responsible for this outperformance. Private equity, growth stocks, and value stocks all outperform during Eagle Markets. It appears that it does not much matter which type/style of stocks or “factors” are used and that it is more important that the investment strategy is simply concentrated. It is clear that Selective/Concentrated Investing may need to play a larger role than it currently plays in most investors’ overall portfolios.
Measurements confirm differentiated risk and return performance characteristics between the 4 investment methods that are reasonably consistent during the 4 different market environments over time. The methods could therefore provide diversification benefits if combined in the same investor portfolio.
The historical relative inconsistency of market type sequences argues for the use of an investment approach that may be diversified across multiple investment methods at certain times, or at all times. We call this method-diversified approach “Multi-Method Investing” (MMI).
The tested results for several versions of static allocation mixes of Multi-Method Investing portfolios revealed a trend towards outperformance on a risk-adjusted returns basis relative to “Strategic Asset Allocation” portfolios, the most widely used single-method approach to investing.
The tested results for a Multi-Method Adaptive^TM portfolio that utilizes data derived from historical market type data analysis to algorithmically shift between investment methods revealed incremental to significant enhancements in risk-adjusted returns relative to both Strategic Asset Allocation portfolios and static allocation Multi-Method Investing portfolios.
Multi-Method Investing potentially increases the probability of achieving one’s investment objectives in the long term, because it provides an additional layer of diversification that is not available through a traditional Strategic Asset Allocation portfolio or through any other single-method portfolio. However, this may only be effective if investor behavioral risks associated with a potential lack of education can be overcome, and if the single-method strategies that make up a Multi-Method Investing portfolio are designed with the intent to provide versatility across all market environments while still maintaining focus on their respective core single environment outperformance objective.
A mathematical approach to determining the appropriate percentage of investor capital to be allocated to each of the 4 primary methods of investing is required in order to effectively implement an MMI portfolio. Risk Premium Capital Allocation is one potential approach, which assumes the existence of 4 major risk premiums that can be capitalized on by each of the 4 investment methods in each of the 4 market environments. Each of these can be plotted on a graph similar to the original “Efficient Frontier” of Modern Portfolio Theory for expected risk/return optimization purposes. When used in combination with historical market type data analysis, this approach can potentially be made even more effective.

Credit Default Risk Premium; Liability-Driven Investing; Bear markets
Systemic Efficiency Risk Premium; Strategic Asset Allocation; Bull markets
Cyclical Inefficiency Risk Premium; Opportunistic Investing; Wolf markets
Secular/Structural Inefficiency Risk Premium; Selective/Concentrated Investing; Eagle markets

Conclusion

The mathematical exercise of dividing market history into the 4 component Bear, Bull, Wolf, and Eagle market environments is somewhat arbitrary in terms of the thresholds set for defining the markets (20%+ decline for Bear; 10%+ correction and recovery for Wolf; 30%+ for Eagle, etc.), but this is no different than the traditional Bull/Bear definitions, and actually proves quite useful as a small step towards revealing the enormous complexity of the markets as a whole. This approach has proven a practical means for identifying and describing methods of investing that have the potential to outperform during time periods with particular market characteristics. Most importantly, although further research and evidence must still be produced, this study has revealed the potential for Multi-Method Investing to produce improved risk-adjusted long-term performance and thus potentially greater probability of achieving investor objectives relative to Strategic Asset Allocation, the current most widely utilized single-method approach to investment management.

AUTHORSHIP

Jesse Mackey is the CEO/Chief Investment Officer and a founding shareholder of 4Thought Financial Group Inc., a Registered Investment Adviser and money manager that utilizes Multi-Method Investing ® as the theoretical basis of client portfolio management. © Copyright 2025 Jesse B. Mackey. All rights reserved.

DATA SOURCES

1. See, “Not Bull, Not Bear: Meet the Wolf Market,” posted August 23, 2010. Available at wsj.com.
2. Index data source for Bloomberg Barclays US Aggregate 1-5 Year Bond TR USD, Credit Suisse Hedge Fund USD, FTSE Global All Cap TR USD, Russell 1000 Value TR USD, and Russell 1000 Growth TR USD: Morningstar Inc.
3. S&P 500 Price Return Index historical daily price data source: Yahoo Finance.

DISCLOSURES

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Bear, Bull, Wolf, Eagle Marketsand Multi-Method Investing®

By Jesse B. Mackey

INTRODUCTION

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Four Alternative Categories Of Investment Method

Markets Redefined As Bear, Bull, Wolf, Eagle

The measured results for each of the 4 market types (analyzed in isolation from each other) are as follows:

Bear Markets Measured

Bull Markets Measured

Wolf Markets Measured

Eagle Markets Measured

Market History Re-Examined

4 Market Environment Aggregate Analysis

Market Sequences And Unpredictability

Historical Bear, Bull, Wolf, and Eagle Market Sequences Analysis EXHIBIT 2

The Logical Link Between Market Environment And Investment Method

Liability-Driven Investing and Bear Markets

Strategic Asset Allocation and Bull Markets

Opportunistic Investing and Wolf Markets

Selective/Concentrated Investing and Eagle Markets

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The Statistical Link Between Market Environment And Investment Method

Statistical Results for Investment Method Returns During Bear, Bull, Wolf, and Eagle Markets EXHIBIT 3

There were five instances of Bear Markets from 12/31/2002 to 4/7/2025.

There were nine instances of Wolf Markets from 12/31/2002 to 4/7/2025.

There were five instances of Bull Markets from 12/31/2002 to 4/7/2025 (using an overlapping definition of the 4 market types).

There were seven instances of Eagle Markets from 12/31/2002 to 4/7/2025.

Comparative performance characteristics of investment methods across all 4 market environments EXHIBIT 3

Proprietary 4Thought Separately Managed Account (SMA) Based Analysis EXHIBIT 4

Statistical Results for Investment Method Returns During Bear, Bull, Wolf, and Eagle Markets EXHIBIT 4

There were four instances of Bear Markets from 6/1/2012 to 4/7/2025.

There were five instances of Wolf Markets from 6/1/2012 to 4/7/2025.

There were four instances of Bull Markets from 6/1/2012 to 4/7/2025 (using an overlapping definition of the 4 market types).

There were four instances of Eagle Markets from 6/1/2012 to 4/7/2025.

Comparative performance characteristics of investment methods across all 4 market environments EXHIBIT 4

Multi-Method Investing®

Multi-Method Investing Index-Based Analysis (EXHIBIT 5):

MMI 4 Method Static Mix (EXHIBIT 5):

LDI provides the only potential “fixed” return:

MMI 3 Method Static Mix (EXHIBIT 5):

MMI 4 Method Static Mix (EXHIBIT 6):

MMI 3 Method Static Mix (EXHIBIT 6):

MMA (Multi-Method AdaptiveTM) Investing (EXHIBIT 6):

Behavioral Risks of MMI:

A Potentially Improved Probability of Achieving Objectives:

Parallels With Other Research

4 Major Risk Premiums And Risk Premium Capital Allocation

Risk Premium Capital Allocation (RPCA)

Summary of Key Findings and Concepts

Conclusion

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AUTHORSHIP

DATA SOURCES

DISCLOSURES

ADDENDUMS

Bear, Bull, Wolf, Eagle Markets
and Multi-Method Investing^®

Multi-Method Investing^®

MMA (Multi-Method Adaptive^TM) Investing (EXHIBIT 6):