Investing Lessons Learned over the past 60 years from Warren Buffett and Berkshire Hathaway

Berkshire Hathaway’s returns since 1965 have been phenomenal, beating the market handily and most of the other investment alternatives from an active management perspective (i.e. mutual funds and hedge funds).  Berkshire Hathaway is a multinational holding company and diversified conglomerate run by the investor, chairman, and CEO Warren Buffett (since 1965). Buffett is a famed value investor. Berkshire Hathaway has created a significant legacy and impacted many investors over the last 60 years.

If an investor started in 1983 by investing $100 in Berkshire Hathaway stock, they would have compounded growth by 18% annually, significantly outperforming the market by roughly 900 bps per year.  

Berkshire Hathaway has a higher Sharpe ratio than any stock or mutual fund with a history of more than 40 years – Truly remarkable!  

As you will note in Table 1, outperformance has narrowed over time as Berkshire Hathaway has significantly grown in size, and this size reduces its pool of investable companies.

Table 1 – Berkshire Hathaway vs the U.S. Market Returns – 1983 to 2022

While we sat through the Berkshire Hathaway Annual Shareholders Meeting recently we began to think, about the two leaders – Warren Buffett (age 91) and Charlie Munger (age 98) as they are towards the end of their life’s journey have bestowed so many lessons over time.

Buffett/Munger are both still sharp as tacks, yet no one knows when the last Buffett/Munger-led Berkshire meeting will be. When asked about his succession, Buffett didn’t focus on the inner workings of the board of directors, the voting power of the shareholders, or even the strategy of the investment team. Rather, he talked extensively about the Berkshire culture almost as an embodiment of his values that will carry on even after he and Munger are gone.

This got us thinking, about how we could try to understand their methods overtime to try to systemize their approaches that survive well beyond their natural lives and try to clone their collective actions over time through quantitative models that capture their actions over a long period.

What we have found is that we believe that Buffett/Munger have captured many factor anomalies (Quality, Low Volatility, and Value) before anyone had “discovered them”, sector tilts, and through the use of leverage/liquidity and thoughtful risk-taking and has been compensated for taking on risk, outperforming market cap indexes and many mutual funds/institutional investor over time. These factor anomalies are connected to behavioral biases/psychology that is exhibited by investors. “Behavioral finance” literature is the study of how human behavior interacts with markets and suggests that investors exhibit behavioral biases due to cognitive or emotional weaknesses.

Examples include chasing winners, over-reacting, overconfidence, preferring “familiar” investments such as securities of the companies they work for or the country they live in (“home bias”), and myopic loss aversion.  Markets at times are driven by emotions including these behavioral biases.

We have tried to decompose Berkshire Hathaway’s investment results between factor anomalies, sector tilts, and leverage, so that we understand the types of risks taken to drive the returns so that our clone funds may emulate the principles in a largely passive portfolio of ETFs via systematic risk associated with factor anomalies and sector tilts, as human behavior tends to repeat over history and did not want to rely on individual stock picking behavior.

As we have mentioned in our prior posts, markets are influenced by human behavior and we aim to create quantitative strategies that model this behavior before it occurs, so that we are able to profit from it.

We will review the following sections in today’s post:

1. Background on Berkshire Hathaway – Private vs Public Companies
2. Key Lessons from Buffett/Munger  
3. Buffett’s Alpha  – A review of AQR’s factor model
4. BT BRK Clone – Cloning through ETFs
5. Conclusions

1) Background on Berkshire Hathaway – Private vs Public Companies:

Berkshire Hathaway was originally a textile manufacturer, but now owns or holds controlling interests in dozens of big companies including Fruit of the Loom, Kraft Heinz, Benjamin Moore, Geico, Dairy Queen, and more.

Berkshire Hathaway originally (and still today) holds a lot of insurance companies, including National Indemnity Company and National Fire & Marine Insurance Company (now a part of National Indemnity), acquired in 1967, as well as Geico, acquired in 1996. Berkshire Hathaway owns or has a large stake in dozens of big companies, including public and private companies.

There are two components when it comes to analyzing Berkshire as a company:

A) 100% owned private operating companies where their financial statements are fully consolidated into BRK’s statements and thus their earnings are fully reflected in Berkshire’s results.

Main private businesses disclosed by Berkshire include:

• Insurance and Reinsurance Businesses
• Railroad (Burlington Northern Santa Fe)
• Utilities and Energy (Berkshire Hathaway Energy)
• Manufacturing and retailing (largely consumer staples)

B) Public Company equity holdings where only the dividends are reflected in Berkshire’s financial statements within the insurance income which included about $5B in 2021. We note that only ~30% of total earnings are paid out via dividends and the total ownership stake associated with earnings is not included in Berkshire’s financial statements. Mark-to-market changes in securities prices from quarter to quarter are included in Berkshire’s financial statements. 

Table 2 – Total Earnings Available to Berkshire Hathaway – Public Securities vs Private Business Units

In Table 2, we note that as Berkshire has become larger, the securities portfolio has become a smaller proportion of total earnings, representing about 20% currently when looking at dividends provided by portfolio companies. Berkshire runs a concentrated portfolio as the Top 5 positions in the public securities portfolio, representing about 80% of the dividends and the total securities portfolio beta is consistent with the market at 1. Generally, there are no targeted allocations by investment type or attempts to match investment asset and insurance liability durations. However, investment portfolios have historically included a much greater proportion of equity securities than is customary in the insurance industry.

As Berkshire has increased in size, there has been a larger contribution coming from full consolidated private businesses ~80% of the total earnings as more excess cash provided by “float” or existing business operations is redeployed to full acquisition of businesses (i.e. taking public companies private). 

We note that total potential earnings inclusive of total stake ownership of earnings within each company in the securities portfolio would represent about $44B, putting Berkshire’s Price-to-earnings ratio at 17.5 times (which is lower than the S&P 500). To put Berkshire’s earnings size and diversification into context, the largest company in the world (Apple) earns about $100B annually and Berkshire’s ownership stake is about 6% of Apple.  

There are high returns on invested capital of almost 8% when considering consolidated private companies and earnings rather than dividends of the public company companies of the securities portfolio.

Berkshire has focused on companies in defensive industry sectors with high-free cash flow, high quality as they have a large moats, and low volatility including:

• Consumer staples (Manufacturing, Service, and Retailing),
• Utilities, Financials, Health Care, and Transportation (Railroads).

2. Key Lessons from Buffett/Munger

There have been numerous books and articles about lessons from Buffett/Munger over the 60 years of Berkshire that recount the numerous lessons. Below we have offered a summary to provide context around the decomposition of Berkshire’s returns.

We believe investors can take the following away which have led to Buffett’s success over time: 

1. Investing in Factor Anomalies that persist over time:

• An unconstrained investment style (able to go long/short and use leverage including derivatives)

• Act Greedy when others are Fearful (buy a company at a value below intrinsic value – the Value Factor Anomaly)

• “It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price” (Buy high-quality companies for a fair price moving away from the deep value/cigar butt investing style of his mentor Benjamin Graham – the Quality Anomaly)

• Invest in companies with wide moats (extremely high returns on assets and capital) Buffett has detailed the many common attributes of these companies, and a few of them are as follows:
  • They see their profits in cash.
  • They are not natural targets of competition.
  • They have the freedom to price their products.
  • They are understandable.
  • They do not take a genius to run.
  • They earn very high returns on capital and assets.

These companies tend to be low volatility equities as their performance is consistent and boring.

2) Leveraging Permanent Sources of Capital by cultivating long-term high-quality shareholders and superior capital allocation skills:  

• Permanent capital is required for the buy-hold long-term strategy of investing in factor anomalies based on low volatility, high quality, and value, based on a levered strategy. These factor premiums-to-market could not be earned if the capital was transitory. 
• Buffett courted quality shareholders by providing informal education, mainly through an acclaimed annual letter and legendary annual meetings. By these means, Buffett taught the fundamentals of business and investing, such as moats and circles of competence.
• He also conveyed the special Berkshire’s special features to this group, especially the concepts of partnership and permanence.
• This enabled shareholders’ equity to be permanent and Buffett would not have to liquidate his portfolio strategy at the most inopportune time, say a significant market drawdown or significant underperformance of a benchmark.
• By educating investors to focus on buy-hold superior long-term capital growth rather than demanding Berkshire to return capital in the form of short-term dividend income, and short-term capital gains, has allowed for capital within the group of businesses to compound uninterrupted and redeployment into further high cash flow/high-quality businesses, growing the size and diversification of Berkshire over time. Historically, Berkshire has not returned capital to its shareholders through dividends or buybacks consistently, therefore not creating an ongoing capital obligation. From time to time Berkshire has repurchased or bought back its shares, but this is usually when Buffett considers that the market price of shares is lower than the “intrinsic value” of Berkshire, therefore benefitting shareholders. This is the same framework he’s used for acquiring a business and public company shares over his career.

• Establishing the business around the insurance float business as a means to compound capital over time by using other people’s money or leverage to grow the size of the permanent capital base for investment, rather than solely relying on the free cash flows of existing businesses to acquire new businesses.  Buffett has continued to use leverage to magnify the returns on lower-risk equities and maintained his operating principles over time and continues operating at high risk even after experiencing some ups and downs that have caused many other investors to rethink and retreat from their original strategies.
• Float is the money that a business receives today but doesn’t have to pay out until sometime in the future. Float is most commonly seen in insurance companies with customers paying premiums upfront to insure themselves against bad things happening sometime in the future.  A dollar you receive today is worth more than a dollar you get a week from now because you can invest it at some rate to receive more money in the future. Buffet realized that the more float he had, the more float he could invest to buy other businesses. 
• The final step was increasing the duration of time between when the premiums are received and when they are finally paid out. So, for Buffett, this meant buying up longer-tailed insurance companies like an insurance for catastrophes, and recently, Reinsurance companies – which may be low a probability but high impact events. Insurance companies also will maintain high-credit quality and high credit ratings, which enables them to raise debt at low costs relative to lower credit quality issuers.

Also, Buffett has discussed while the business model may be levered, having loads of liquidity (cash and US treasury bills), though is a small cost to pay bear though, lets them sleep well. Moreover, during the episodes of financial chaos that occasionally erupt in the economy, they will be equipped both financially and emotionally to play offense while others scramble for survival. We cover this concept later on.   

3) Decentralized Management Structure

• While many public corporations implemented strict controls and oversight mechanisms to ensure management performance and regulatory compliance, Berkshire Hathaway moved in the opposite direction. The company had only two main requirements for operating managers: submit financial statement information every month and send free cash flow generated by operations to headquarters.

• Management was not required to meet with executives from corporate headquarters or participate in investor relations meetings; nor was it required to develop strategic plans, long-term operating targets, or financial projections. Instead, local managers were left to operate their businesses largely without supervision or corporate control.

3) Buffett’s Alpha – A Review of AQR Capital’s factor model

AQR Capital’s model and analysis have been the most complete analysis of sources’ of Berkshire Hathaway’s return over time via many well-known factor anomalies that have persisted over time.

Before we jump to AQR’s model and analysis, let’s spend a few moments discussing factor investing and the sources of these returns which have been covered in academic studies over time. A number of these factor anomalies are covered in Capital Asset Pricing Model covered by Fama and French and other studies have expanded the original CAPM over time.  

Factor Investing

The CAPM (Capital Asset Pricing Model) explains that an asset’s expected return should be comprised of a risk-free rate and a return associated with a market premium.  In the CAPM, securities have only two main drivers: systematic risk and idiosyncratic risk. Systematic risk in the CAPM is the risk that arises from exposure to the market and is captured by beta, the sensitivity of a security’s return to the market.

Since systematic risk cannot be diversified away, investors are compensated with returns for bearing this risk. In other words, the expected return to any stock could be viewed as a function of its beta to the market and/or the risk factor premium.

The market premium represents the risk associated with movements in the overall market (something that cannot be diversified away, as it affects all assets in the universe).

The CAPM is defined as:

In general, a factor can be thought of as any characteristic relating to a group of securities that is important in explaining their returns and risk. As noted in the early CAPM-related literature, the market can be viewed as the first and most important equity factor. Beyond the market factor, researchers generally look for factors that are persistent over time and have strong explanatory power over a broad range of stocks.

Risks associated with factor anomalies such as small size, value, low volatility, momentum, and quality cannot be diversified away either. Assets that move closely in unison with this excess market risk will achieve higher returns than assets that do not.

Table 3 summarizes six of the most widely studied factor anomalies. More recently, Low Volatility, Yield, and Quality factors have become increasingly well-accepted in the academic literature.

Table 3 – Systematic Factor Anomalies from the Academic Research

Systematic Factors	What It is?	How is it measured?	Behavioral Bias
Value	Captures excess returns to stocks that have low prices relative to their fundamental value	Book to price, earnings to price, book value, sales, earnings, cash earnings, net profit, dividends, cash flow	Higher real or perceived risk (business cycle risk)   Loss aversion and mental accounting biases
Low Size (Small Cap)	Captures excess returns of smaller firms (by market capitalization) relative to their larger counterparts	Market capitalization (full or free float)	Incorrectly extrapolating the past into the future
Momentum	Reflects excess returns to stocks with stronger past performance	Relative returns (3-mth, 6-mth, 12-mth, sometimes with last 1 mth excluded), historical alpha	Overconfidence, self-attribution, conservatism bias, aversion to realizing losses   Under reaction and overreaction
Low Volatility	Captures excess returns to stocks with lower than average volatility, beta, and/or idiosyncratic risk	Standard deviation (1-yr, 2-yrs, 3-yrs), Downside standard deviation, standard deviation of idiosyncratic returns, Beta	“Lottery effect” overpay for high volatility stocks and underpay for low volatility stocks due to the “irrational” preference for volatile stocks.   Leverage aversion
Dividend Yield	Captures excess returns to stocks that have higher-than-average dividend yields	Dividend yield	Errors-in-expectations
Quality	Captures excess returns to stocks that are characterized by low debt, stable earnings growth, and other “quality” metrics	ROE, earnings stability, dividend growth stability, strength of balance sheet, financial leverage, accounting policies, strength of management, accruals, cash flows	Errors-in-expectations

In addition to historically exhibiting excess returns above the market, an equally important rationale for factor investing is the wealth of evidence that they can account for a significant portion of fund returns and institutional active fund returns.

In the next section, we note that AQR Capital’s paper has tried to determine how much of Berkshire’s active returns relative to the benchmark have been a result of investing in these factor anomalies versus stock-specific outperformance via superior securities selection.   

Summary of AQR’s Buffett’s Alpha Paper

This paper by AQR Capital (https://www.aqr.com/Insights/Research/Journal-Article/Buffetts-Alpha) suggests that Buffett’s success is a reward for the successful implementation of exposures to factors that have historically produced high returns.

Buffett’s portfolio and performance can be understood using these factors:

• Has unique access to leverage
• A disciplined approach to high quality, low-risk stocks may generate strong risk-adjusted and absolute-returns
• Short sells options
• Buffett utilizes debt in a very judicious and strategic fashion. 
• Buffett understands the inherent value in his investments, so he does not allow market fluctuations to control his emotions.
• Buffett creates wealth and takes on leverage because he maintains high levels of liquidity.

Warren Buffett’s large returns come from both his high Sharpe ratio and his ability to leverage his performance to achieve large returns at high risk. While Buffett is known as the ultimate value investor, we find that his focus on safe quality stocks may be at least as important to his performance. Leverage has appeared to magnify returns of these safe quality stocks and enable strong growth in capital over time.

So Buffett uses leverage to magnify returns, but how much leverage does he use? 

Berkshire Hathaway’s insurance float represents about 50% of its liabilities. Collecting insurance premiums upfront and later paying a diversified set of claims is like taking a “loan.”

This is very similar to how a bank makes money from demand deposits (borrowing money in the short-term that can be redeemed at any time) as well, by making long-term loans on properties backed by collateral (“low risk”), which is known as running a maturity mismatch risk. Runs on banks may occur from time to time, but Buffett appears to be a strong balance sheet manager holding ample excess liquidity which may be deployed in times of crisis for lower valuations for high-quality companies as compared to intrinsic value.  

Over the period of 1994 to 2022, Berkshire’s insurance float grew at a compound annual rate of 14% per year, which compares to annual growth in Berkshire Hathaway’s stock price of ~13% and investment portfolio of ~12% over the same period.

Other businesses that produce float include:

• Insurance and Pension Funds – funds collected upfront and paid out later
• Retail Banks & Brokerages (Wells Fargo, Bank of America)
• Loyalty Programs/Gift Cards (Blue Chips Stamps, Starbucks)
• Subscription and Pre-paid Contracts/Negative Working Capital (Netflix, Coca-Cola)   

You will notice that Berkshire has historically invested in these types of businesses as well given the high returns on capital.  

Berkshire sells derivatives, which serve as both a source of financing and a source of revenue.

In Table 4, we observe that Berkshire’s cost of liabilities has been very low over time, even relative to US Government yields as debt has been raised at a negative spread (assuming an average duration of 10-years for the liabilities).

We note that over the past 40 years, we have also enjoyed a bull market in bonds as interest rates have fallen significantly and the focus on the wealth effect, has significantly benefited levered players in the process, including Berkshire Hathaway, as assets repriced higher due to lower costs of capital.  

Table 4 – Cost of Leverage and Amount of Leverage in Berkshire Hathaway Business Model

Being able to repeat this method in today’s relatively low-interest-rate environment may be very difficult. He has stuck to a good strategy—buying cheap, safe, quality stocks—for a long period, surviving rough periods where others might have been forced into a fire sale or a career shift, and he boosted his returns by using leverage and maintaining liquidity for buying opportunities when the equity market dives.

We estimated that Buffett applies a leverage of about 1.6 to 1, boosting both his risk and excess return in that proportion. As a result of the leverage, he has also managed to hold a significant amount of liquidity as well over time which is very important to levered businesses in large drawdowns, as well as deploying it when stock valuations of high-quality businesses are beaten down.

The focus on maintaining a stable level of liquidity is critical as many have been caught by seeing asset prices rise and thinking of the added cost of maintaining a liquidity buffer is taking away from returns and reducing this buffer at the most inopportune time– see Table 5. You can see that Berkshire positioned itself ahead of the Financial Crisis of 2008-2009 to have significant liquidity to deploy into value opportunities that were trading below their “intrinsic value”, despite the added cost of maintaining a higher liquidity buffer.   

Table 5 – Liquidity in Berkshire Hathaway Business Model  

In its study of Buffett’s performance, AQR reviewed several factor anomalies and regressed the performance of Berkshire stock and its equity portfolio performance (via 13F reports).

This is a common way to measure the performance of a portfolio manager to determine the Alpha (positive return relative to a benchmark) due to security selection, rather than beta due to factor anomalies/tilts in the portfolio.

AQR publishes its factors every month. Based on these factors, we re-performed the regression study using more current data (as the paper was based on data from 1976 to 2017) on both Berkshire Hathaway’s shares outstanding and Berkshire’s Public U.S. stocks (from 13F filings) to determine which of the following factors had the greatest impact. AQR’s Regression to determine Buffett’s exposures:

BRK-Return𝑡 = 𝛼(alpha) + 𝛽1MKT𝑡 + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3HML𝑡 + 𝛽4UMD𝑡 + 𝛽5BAB𝑡 + 𝛽6𝑄𝑀𝐽𝑡 + 𝜀𝑡

In Table 6, we summarize these factors and the Berkshire Regression Factor Loadings. 

Table 6 – AQR Regression Factors Anomalies

Systematic Factors	AQR Measure	Berkshire Factor Loading
Value (HML)	Buying stocks of high book value to market value while shorting stocks of low book value to market value.	Positive loading thus reflects a tendency of buying cheap stocks
Low Size (Small Cap) (SMB)	Long small-capitalization stocks and short large-cap stocks	Negative loading reflects BRK’s tendency to buy large-cap stocks
Momentum (UMD)	Buying stocks that have performed well relative to peers over the past year (winners) while shorting the stocks that are relative underperformers (losers).	Insignificant loading on UMD means that Buffett is not chasing trends
Low Volatility/Bet-against-Beta (BAB)	Bet-against-Beta (BAB) factor is simply a long-short portfolio that buys stocks with low market beta (lowest 10%) and sells stocks with high market beta (highest 10%).	Exposure to the BAB factor and Buffett’s unique access to leverage is consistent with the idea that the BAB factor represents the reward for the use of leverage
Quality (QMJ)	Quality minus Junk (QMJ) factor buys companies that are profitable, safe, and have high payout (top 30%), and sells companies on the other end of this quality spectrum (lowest 30%)	BRK’s tendency to buy high-quality companies—that is, companies that are profitable, growing, and safe and have high payout

Looking at each of the factors in Table 7, we note that Quality and Low Volatility (Beta Against Beta – BAB) have provided significant premiums to market returns, relative to other well-known styles such as Value, Momentum, and Size. We note that despite many investors investing in these styles, these anomalies have persisted over time calling into question the Efficient Market Hypothesis.

You can also see that factors such as Value and Momentum tend to be negatively correlated as the Value style will be a buy as the Momentum style breaks trend, typically when global liquidity is reducing.

Quality and Size are negatively correlated as smaller companies may be more levered to the business cycle and not all will survive to become large-cap high-quality companies.

Berkshire’s investing philosophy has focused on long-term investing in cheap/quality/low-volatile stocks through the business cycle which has allowed it to generate significant premiums relative to the market.     

Table 7 – AQR Factors – 1972 to 2022 – Factor Return Premium to Market

This may suggest that human emotion/behavior biases are inherent within institutional investors’ investment policy statements (i.e. many investors are long-only and not able to use leverage). Also, Bet-against-Beta’s high return may suggest that institutional investors that are not able to take on leverage to magnify returns of low volatility securities to beat their benchmarks, may bid up the price of high beta stocks as they require high unlevered returns, which eventually reduces the return premium of high beta assets. When a majority of market participants face leverage constraints, they may acquire more high-beta stocks to achieve a target exposure to market risk or a target return volatility.

Beowulf Treasury’s Roll-Forward of AQR’s Study to 2022

Below in Table 8, we have used a rolling regression over 36 months to try to quantify Berkshire’s and Berkshire’s equity portfolio exposure to the various factors of systematic risk as we have reviewed above. As Berkshire Hathaway is levered 1.6-to-1, we have reduced the stock price changes impact associated with leverage, to make returns comparable to unleveraged factors.     

In Table 9, we show the factor tilts on Berkshire’s stock and notice that the largest exposures over time are Value, BAB, and Quality. Value and Quality are expected, given Buffet’s noted style. We also note that very little alpha due to security selection appears to have been generated and much of Berkshire’s outsized returns are largely due to factor-betas and leverage. This is consistent with the original paper.

We have noted that some practitioners have raised a few issues with the Beta against Beta construction. In their study “Betting against Betting against Beta” (BABAB), published in the January 2022 issue of the Journal of Financial Economics, Robert Novy-Marx, and Mikhail Velikov re-examined the performance of the betting against beta factor. They began by noting that the authors used unconventional procedures to construct their factor, which Jack Vogel also covered in his review of BAB here.

Their main critique of the betting against beta paper was that its construction methodology results in an equal-weighting strategy instead of the conventional market-cap weighting (and thus ends up with large positions in very small-cap stocks with high transaction costs).

There have also been discussions around BAB being regime dependent on either a value or growth regime. In our research, we note that generally higher levels of growth of global liquidity tend to be associated with growth regimes that benefit High Beta stocks vs. value regimes that tend to be lower growth of global liquidity which benefits Low Volatility stocks.

Despite this, we believe the discovery of the long low beta/short high beta anomaly is something we could look to implement. 

Table 8 – Berkshire Hathaway’s shares outstanding and Berkshire’s Public U.S. stocks (from 13F filings)

Table 9 – Average Factors Weights – 1981 to 2022

So it appears that Berkshire Hathaway’s secret to success is the early identification of these factor anomalies and strategic use of leverage to magnify returns, especially in a period of declining interest rates/lower cost of funding over the past 40 years.

In our review, to be able to mimic the BAB factor to get the same factor-beta as Buffet/Berkshire would create significant implementation issues for most individual investors. Buffett has had unique access to leverage and remained liquid during downturns to potentially take advantage of situations by buying up undervalued stocks that his competitors potentially would not have been able to take advantage of as they had been previously liquidated via investors redeeming due to poor relative performance. His courting of long-term shareholders to provide permanent capital and partnership and related education has supported his strategy.  

We wonder, is there another way to take advantage of the behavioral bias that Beta-Against-Beta has uncovered?

As not everyone would have the unique access to leverage or significant multiples of permanent capital relative to personal capital, what could an individual investor do?

What if we could tactically invest in times when High Beta and Momentum get bid up by institutional investors that are incapable of following Buffett’s buy-hold leveraged low volatility strategy to beat the benchmark due to leverage constraints?   

In the next section, we cover how we may be able to leverage some of the learnings such as:

1. Investing in Low Volatility/Quality stocks via ETFs 
2. Strategic use of Leverage and Liquidity
3. Does the Long-Short portfolio strategy outperform Trend Following for individual investors?
4. Could we use Tactical Trend Following to take advantage of the Bet-Against-Beta anomaly?

Does Trend Following Factor Momentum Persist over time and potentially outperform buy-hold or long-short portfolio?

We have tested our Trend Following Wave Runner Framework from the previous post on global assets on the AQR Capital factors to see if trend following Factor momentum persists over time. See the prior post for further details on the methodology of our Risk Indicator.

The underlying economic justification for trend following rules lies in behavioral finance tenets such as those relating to herding, disposition, confirmation effects, and representativeness biases. At times information travels slowly, especially if assets are illiquid and/or if there is high information uncertainty; this leads to investor under-reaction. If investors are reluctant to realize small losses then momentum is enhanced via the disposition effect.

Trend Following – Factor Anomalies (AQR factors) x Wave Runner Portfolio

We have constructed the trend following portfolio using AQR’s factors combined with BT’s Risk Indicator. What this means if say the Value, Quality, and Low Volatility factor has outperformed on a relative basis the other factors based on weighted 1-mth, 3-mth, 6-mth, and 12-mth (13612W) and the Global Risk Indicator are positive (i.e. above-trend growth of business/liquidity cycle), invest 40% in the top 2 and 20% in the 3^rd ranking factor.

In Table 10, the Trend Following strategy paired with the BT Risk indicator would have outperformed the US Market by 240 bps and unlevered BRK-A by 150 bps per year. By using the same level of leverage ($1.6 of debt to $1 of equity), the BT Factor Wave runner would have outperformed BRK-A over the period.

However, we note that AQR factors are not investable at this time so we will cover how we may implement this in the next section.  

Table 10 – Trend Following on AQR Factors vs Berkshire Hathaway

4) BT BRK Clones – Cloning Berkshire Hathaway Exposures through ETFs

So how could we turn this research into a sustainable portfolio strategy to implement for individual investors? Rather than trying to pick the right stocks that may be industry leaders as Buffet has done over time, we may be able to ride factor and sector betas that mimic Buffet’s allocation over time.    

We are going to review the following factor ETFs as we have reviewed them relative to the AQR factors we covered in Section 3 and run similar rolling regressions to determine our monthly allocation to each factor to mimic Berkshire/Warren Buffet’s position. 

In Table 11, we have selected ETFs based on single MSCI factor indexes and backtested against the AQR factors to ensure that each ETF largely represented the factor that is it supposed to. You will note that each ETF has the strongest loading to the AQR factor, however, there is some crossover into other factors as High Dividend Yield and Min Vol have some cross-over to the Value factor loading, though VLUE ETF has the strongest Value component.  The R-squares for each are all greater than 0.75, which suggests that the factor returns generally explain a lot of the variation in returns over time.  

Table 11 – ETFs Factor Loadings based on AQR Factors:

Table 12: Summary of Backtests – BRK Clones and Trend Following Portfolios

In Table 12, we note that Rolling Regression models over the period have largely kept up with Berkshire Hathaway on an unlevered basis and have a higher Sharpe ratio.  The trend-following model (BT Factor Anomalies x Wave Runner) has outperformed all portfolios and has had lower drawdowns. This is significant as we have not used leverage to magnify returns of the factor anomalies. We will review this model later on. We have for comparative purposes included the BT Momentum and BT Wave Runner which are trend following portfolios on global assets, and have outperformed all portfolios as Sharpe Ratios are largely above 1.  

Rolling Regression Models:

We have used a rolling regression over a 36-month period to try to quantify Berkshire’s stock to the various factors of systematic risk as we have reviewed the above Factors (Low Volatility, High Dividend Yield, Value, Momentum, Quality, and Equal-weighted) as well as Industry Sectors (Consumer Staples, Technology, etc.) against Berkshire’s Stock and Berkshire’s Equity Portfolio to determine the factor/sector betas of Berkshire Returns.

With this methodology, we are essentially mimicking Buffett’s stock picks on a systematic factor basis on a rolling 36-mth basis, without taking a specific bet on a single stock, as we have seen in section 2, much of Buffett’s market outperformance has been his superior selection of factor anomalies. 

A) BRK Clone–Factor Anomalies Regression Model

In Table 13, we have reviewed the Factor model. This Factor Model is based on regressing factor anomalies ETFs against Berkshire stock, to determine factor loadings to each of the factor anomalies. This model was not constrained to the long-only portfolio, allowing it to go long and short ETFs similar to Berkshire exposures over time.

Table 13 – BT BRK-Clone Factor Anomalies Model vs Unlevered BRK and US Market

B) BRK Clone – Industry Sector Model

In Table 14, we have reviewed the Industry model. This Industry sector Model is based on regressing factor anomalies against Berkshire stock, to determine factor loadings to each of the sectors. This model was not constrained to a long-only portfolio, allowing it to go long and short ETFs similar to Berkshire exposures. We note, that this portfolio is quite volatile.   

Table 14 – BT BRK-Clone Industry Sector Model vs Unlevered BRK and US Market

Trend Following Model – BT Factor Anomalies Model x Wave Runner:

So we mentioned earlier that we potentially could we leverage the research done on the Bet-against-Beta research on a tactical basis by capitalizing on the behavioral anomaly noted that institutional investors that are leveraged constrained tend to bid up high beta/momentum stocks to outperform their benchmarks. We suggest that the BT Factor Anomalies Model does exactly this – capitalize on this behavioral bias by participating in business/liquidity cycle upswings in these types of stocks, and tactically moving to safe assets as the growth rate of the business/liquidity cycle slows.   

We have used the same methodology using the Trend following methodology, paired with the Global Risk Indicator similar to what we would have done in Section 3 with AQR Capital’s Factor anomalies. AQR Capital’s Factors are based on customized portfolios which may be difficult to replicate for individual investors. With this model, we have utilized investable ETFs available for all individual investors – see Table 15 for more information on the ETFs.  

For example, this model would consider if the Value, Quality, and Low Volatility factor has outperformed on a relative basis the other factors based on weighted 1-mth, 3-mth, 6-mth, and 12-mth (13612W) and the Global Risk Indicator are positive (i.e. above-trend growth of business/liquidity cycle), invest 40% in the top 2 and 20% in the 3^rd ranking factor in risk-assets which are the Factor ETFs. If the Global Risk Indicator is negative, the portfolio will invest in the Safety Asset portfolio based on relative momentum based on weighted average returns of weighted 1-mth, 3-mth, 6-mth, and 12-mth (13612W) – See Table 17 for more information on the Safety portfolio and Global Risk Indicator. 

The BT Global Risk Indicator is a proprietary index based on several factors both Market Sentiment and Fundamental that we equally weight: 1) High Yield Credit Spreads 2) VIX term spreads 3) OECD composite leading indicator, 4) Global Liquidity Index 5) US Housing Starts 6) High Beta Currencies vs Low Beta Currencies 7) Copper and Gold Ratio. 

Table 15 – Factor ETF and Inclusion Criteria

In Table 16, we summarize the simulation of BT Factor Anomalies x Wave Runner Portfolio (unlevered) since 1993 has been on par with Berkshire Hathaway Returns as stated (based on 1.6x leverage) and significantly outperformed the market over the period and separate sub-periods.  

We note that higher risk assets/higher return assets (momentum, high beta, small size) tend to have large outperformance to market when the global risk indicator is signaling risk-on – see Table 18.

The portfolio is allocated to a greater extent to these higher-risk assets during the risk-on period which tends to benefit from higher than trend liquidity conditions, resulting in higher returns. These high-risk assets are particularly susceptible when liquidity conditions are tightening or below trend, as we note in Table 18.

However, as the portfolio tactically moves into the safety portfolio (safe assets) when the Global Risk Indicator is risk-off protecting our capital base as the large drawdowns associated with these high-risk assets do not impact the portfolio. We have summarized our Global Risk Indicator and average allocations to each of our Safety Assets in Table 17.

Table 16 – BT Factor Anomalies Model x Wave Runner vs Unlevered BRK and US Market

We note that in risk-off periods when liquidity conditions are tightening or below trend which represents about 46% of the 1993 to 2022 period when the portfolio is in safe assets, Quality and Low Beta ETFs outperform the market-cap-weighted index by 4.4% and 2.4% respectively.  As a result, we expect Berkshire to outperform during these periods and tend to be when they deploy more excess liquidity to market opportunities in the Value spectrum (value is below intrinsic value). 

Table 17 – BT Global Risk Indicator and Safety Assets used in Risk-off Periods

In Table 18, we note that on a relative basis Min Vol, Quality, and Low Beta provide relative protection versus other equity factor strategies and market-cap indexes. We did calculate the inclusion of Low Beta, Min Vol, and Quality in the Safety Portfolio when the Global Risk Indicator was in a Risk-off position but did not consider the impact on the overall portfolio returns and volatility to be significant enough for inclusion in the Safety Portfolio.   

The Trend Following Factor Anomalies strategy optimizes return vs risk, in this backtest period relative to Berkshire without taking on single stock risk and remaining very diversified over the period, and does not take leverage on and achieves a better Sharpe ratio at 0.91 versus Berkshire Hathaway at 0.67. Returns are comparable to Berkshire’s returns of 12.5% which relies on leverage of 1.6x-to-1. 

We believe, that the Trend Following Factor Anomalies portfolio capitalizes on the following investment behavioral anomalies we have discussed previously:

• As we noted earlier the Bet-Against-Beta (BAB) factor, institutional investors that are not able to take on leverage to magnify returns of low volatility securities to beat their benchmarks, may bid up the price of high beta stocks as they require high unlevered returns, which eventually reduces the return premium of high beta assets. When a majority of market participants face leverage constraints, they may acquire more high-beta stocks to achieve a target exposure to market risk or a target return volatility. This portfolio rides the momentum wave as the business/liquidity cycle is on the upswing. As we mentioned earlier, the “Lottery effect” forces investors to overpay for high volatility stocks and underpay for low volatility stocks due to the “irrational” preference for volatile stocks.

• There have also been discussions around BAB being regime dependent on either a value or growth regime. In our research, we note that generally higher levels of growth of global liquidity tend to be associated with growth regimes vs. value regimes which tend to be lower growth of global liquidity.

• By splitting the portfolio’s investment allocation between Risk-on (Growth-regime) and Risk-off (Value-regime) on a tactical basis based on our Global Risk Indicator, the portfolio takes advantage of trends on a relative basis as the extrapolation of the recent past into the future by investors is significant across many studies. The Global Risk Indicator reduces the drawdown risk of the portfolio before the correction of investor expectations occurs which reduces the value of higher return/risk factors anomalies (high beta, small size, momentum). 

Given that volatility of this BT Factor Anomalies x Wave Runner strategy of 14% is lower than Berkshire Hathaway’s of ~19%, we could potentially use leverage strategically during risk-on periods, to lever up to say 1.34x-to-1 so that we could match Berkshire’s volatility of 19%, which potentially magnifies returns to above 17% (higher than Berkshire’s returns of 12.5% over the period), though could increase drawdown to 32% (still below Berkshire’s 44%). So by adding leverage to the BT Factor Anomalies portfolio, annual returns would be roughly 400 bps more than Berkshire Hathaway over the simulation period.  

Brokerage firms can establish their own rules for how much leverage they allow to be placed when their clients’ trade and how much collateral must be on hand. However, the Federal Reserve Board established Regulation T which requires at least half of the purchase price of a stock position to be on deposit (leverage ratio of $2 of assets per $1 of equity), so this strategy may be practicably implemented by individual investors.

Table 18 – Average Allocation Risk-on Periods and Factor Returns Premium to Market

Table 19 – Drawdowns by Factor during Risk-on and Risk-off Periods (1991 to 2022)

5) Conclusions: So what can we conclude from this analysis?

Table 20: Summary of Backtest Period (1993 to 2022)

• Berkshire Hathaway has created a significant legacy and impacted many investors over the last 60 years. There have been many lessons over the years. Strategic use of leverage to magnify returns of cheap, high-quality, and low beta equities, along with ample liquidity in a buy-hold strategy over the long term has been a centerpiece of Berkshire’s strategy. Buffett/Munger appears to have “discovered” these factor anomalies/risk premium earlier than most investors and stuck to this strategy while cultivating permanent capital through the informal education of investors that did not demand short-term capital returns through dividends and buybacks. This allowed for capital to be compounded at a significant rate in an economy that relies on the ‘wealth effect’ to drive forward consumption to drive economic growth.  Factor anomalies have a basis in investor behavior/psychology, even in algorithmic trading strategies which may persist going forward despite trading/investment decisions being outsourced to computers.  

• Trend Following based on trailing momentum of 13612W and BT Wave Runner strategy when combined with Factor Anomalies exposure may provide an optimal risk vs return portfolio when compared to Berkshire Hathaway’s portfolio returns on an unlevered basis, as the Sharpe ratio of Factor Anomalies x Wave Runner portfolio of 0.91 is greater than 0.67 of Berkshire Hathaway.

• We note that BT Factor Anomalies x Wave Runner portfolio uses tactical allocation to higher return/risk factor anomalies (high beta, momentum, small size) which capitalize on institutional investor constraints against using leverage and over-indexing on high beta to beat the market-cap-weighted indexes.

• We note that the tactical asset allocation we have described may not be available to all individual investors, and may be too concentrated and too time-consuming in implementation.

• We note this is a very different strategy as compared to Berkshire’s buy-and-hold of low volatility, high-quality and cheap equities on a 1.6x levered basis strategy which may not be available to all individual investors, and have therefore outsourced capital allocation, and leverage decisions to Berkshire, by buying their stock. However, we do note that the strategies take advantage of investor behavioral biases which have historically persisted over time. We believe that these strategies are two sides of the same coin so to speak.  

• Institutional Investor Policies based on a largely fully invested strategic asset allocation bands, lack of leverage use, and constraints against short positions may not allow for the flexibility required to implement either strategy we have discussed in this post.         

• We plan to monitor the 3 Trends following strategies we have covered in our previous two posts as summarized in Table 20, going forward on this website every month.

• We also plan to determine if the inclusion of factor anomaly ETFs from other jurisdictions may reduce the risk or/and enhance the return of the US-only-Factor Anomalies portfolio.

If you are interested in this portfolio strategy or had any questions, feel free to contact us at beowulftreasury@gmail.com.

BT