The window for a soft landing is closing…. Deep Dive into the US LABOUR MARKET, INFLATION,  and Bear Markets

At Beowulf’s Treasury we focus on Leading Indicators and market sentiment to help supplement our trend-following portfolio strategy, along with fundamental research.

The Labour Market is strongest right before a recession (Unemployment rate tends to be lowest/Wage growth highest). The payroll report in early August surprised to the upside with 2x jobs added versus consensus expectations. We note however, Inflation and unemployment are lagging indicators. The loosening of Financial conditions (lower interest rates, lower US dollar, low energy prices) in 2020/2021 (from 18 months before) gave rise to increased aggregate demand (goods/services), along with significant changes in consumptions patterns (goods favoured to services when we were sheltering in place/working from home during COVID-19) that created supply disruptions and in turn increased the demand for labor.

Following leading indicators such as the credit cycle/peaking in home prices may provide keys to when the unemployment rate (follows a home price top historically 12-15 mths) may start moving up. We note that Home Prices for most jurisdictions peaked around January/February 2022 and continued to decrease as Financial conditions have tightened. We have noticed that Job Openings have declined by 1.2 million roles since April 2022, though the ratio to Unemployed workers remains elevated. Wage growth has decelerated by about 50 bps since March 2022. We note its more important to follow the Rate of Change in the Job Openings-to-Unemployment ratio against Wage growth, rather than focusing on the absolute number of the ratio. Initial jobless claims have increased since March 2022 and a number of companies have recently announced layoffs which are potentially not counted in the statistics yet. Generally speaking work hours, job openings will start to decline about 6 months before we see a move in the unemployment rate and we are seeing this now.

On the Inflation front, Global supply chain pressure has eased (partially due to remix of consumption to services from goods), however, higher acceleration in labor and shelter costs, impacting core inflation, have not been considered. As we have noted before, with tightening financial conditions, world trade declines and therefore global supply chain pressure reduces.

Loosening of Financial condition from June-August 2022 (lower real rates based on lower inflation momentum) has resulted in additional risk-taking/high-beta equities up in the last couple of weeks. There has been much discussion of a Central Bank pivot now that inflation momentum has slowed in July. However, it is expected that Central Banks will remain restrictive from a policy perspective going forward until inflation momentum significantly slows (i.e. a number of months of flat or declining inflation prints before pivoting from hawkish stance) and inflation is much closer to the inflation objective of 2%.

Changes in Financial condition (i.e. tightening of rates, higher US dollar, and higher energy prices) take about 18 months to see the results and flow through the economy and impact consumer and corporate behavior. Current tightening is expected to result in a 15-20% S&P 500 EPS decline in 2023 based on historical correlations holding. This is much lower than the current consensus growth of 5% for 2023.  If corporate earnings growth is lower as implied by the relationship with financial conditions, labor markets are expected to continue to weaken into 2023.  Q2/2022 EPS report may mark peak cyclical earnings growth.

Recessions have followed Bear markets ~70% of the time. Bear markets along with recessions tend to be long/drawn out before new equity market highs tend to be associated with tightening of financial conditions in the preceding 18 mths (1981/82 (471 days before new high), 2000-2002 (999 days), 2008-2009 (525 days). There were numerous bear market rallies. A market trough or bottom tends to occur when evidence of loosening financial conditions and significantly lower equity market allocations in investor portfolios (lower by ~10% to 15% from original position). Bear markets with no recession, tend to be associated with loosening financial conditions (weaker dollar, lower rates, etc.)  (1987/1997/1998) in the preceding 18-mths. As a rule of thumb, deviation from the 13612W3 Moving Average of -3.0 Z-Score or Lower has historically provided a margin of Safety and a higher probability of success in the next 12 months. Given the current macro setup and history, investors proceed with caution if capital is deployed into equities in current markets. Please enjoy our research in the attached pdf.

Business Cycle Update: There are only CYCLES and Rate of Change, No Market Prophets

This week’s post discusses where we are at in the business cycle. Inflation expected to peak in H2/2022. As inflation is a lagging indicator, past price changes may be used to help forecast inflation (we have included our model in the attachment below). Changes in monetary policy has tended to take some time (between 12-24 months) to reflect its impact in inflation.

World Trade Volume appears to be slowing = Deflationary impulse (Expect lower USD Liquidity/higher US dollar(DXY) ahead). We started to see economic sensitive commodity price momentum slowing (i.e. copper) as it appears to have peaked in May and rolling over as liquidity (Central Bank and Private Liquidity) continues to decline. China’s central bank liquidity growth has been tightening as well, which tends to be correlated well with growth in commodity prices.  

Historically, Labour (wages) and commodity prices tend to be the last measures to peak and rollover before a recession. The June Jobs report was released late last week. Nonfarm payrolls increased 372,000 in the month, better than the 250,000 Dow Jones estimate. Employment continued to grow in June. However, we are always looking for changes in trend/momentum, and there are some signs that the labour market may be rolling over soon as the marginal cost of labour (Job Openings/Unemployment Level) appears to be peaking for the cycle. Job Openings may have peaked in April 2022 and have declined in May and June. We see wage growth has potentially peaked while unemployment rate is at a trough. Initial Jobless Claims have also been rising since April 2022 month-over-month and average hours per week and overtime hours per week, declined slightly from May.  

Despite lower growth impulse, Central Banks are expected to maintain hawkish stance until inflation pressure subsides and rate hikes are expected to chase inflation prints (which are lagging indicators). We do not expect a Central Bank pivot despite lower growth trajectory, until inflation is sustainability under control. In such an environment, the US dollar is expected to strengthen. 

Beowulf ‘s Treasury Tactical Asset Allocation portfolios have been in the Safety portfolio based on BT Global Risk indicator since January 2022, as result of slowing momentum in Global Liquidity (Current Holdings: UUP, TFLO, SHV). See Beowulf’s Armory for Updates to the Model Portfolios for July 2022 positions.

Financial Conditions Update

Financial conditions have tightened significantly resulting in higher costs (higher interest rates/credit spreads/stronger USD) which is meant to reduce aggregate demand and consumption to bring aggregate demand back in line with the aggregate supply of goods and services in the global economy.

This process will reduce inflation back to Central Bank inflation targets. Changes in the Financial Conditions affect the Business Cycle/Liquidity cycle (borrowing/lending). Tightening cycles have tended to last 24 months. It is expected that growth in the economy will slow as well.  

However, it takes a while to see the impact across the entire economy, company earnings, and inflation (which is a lagging indicator) and impact the expectations across all asset markets and borrowing and lending decisions.

We have looked at the last 11 periods in which the ISM PMI survey (proxy of GDP growth and aggregate demand) and impact of financial conditions (from 9 months prior) to determine the impact on equity markets and if we have reached a bottom. As of the closing price on Friday, we are down ~23% on the S&P 500 from the peak.

We note that the ISM PMI has slowed significantly in the past 4 months and try to model out based on past episodes where the path may take us. We examine other leading indicators as well. See below for our research on the topic.

Introducing BT Model Portfolio Tracking!

Over the past 5 months, we have been laying our views on the Economy and Financial Markets. In the process, we have been taking our readers on this journey as we have been building up our investment framework and related methodology.

We plan to review and monitor the Trend Following strategies we have covered in our previous two posts as summarized going forward on this website every month in the Beowulf’s Amory section of the website.

There are 14 tracking portfolios that are based on trend following strategies that we have previously covered in our posts. Trend following has a long history of academic and empirical support.  Evidence suggests that trend following can be an effective means of avoiding large negative returns that coincide with traditional bear markets/drawdowns and we have demonstrated this in our prior post.

In the current environment, our Wave Runner strategy combined with any risk asset strategy would have protected investor capital in the latest drawdown/equity bear market we are currently seeing in risk assets thus far in 2022.

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.

Our global risk indicator which captures both fundamental leading indicators (Global Liquidity and OECD Composite Leading Indicator) and market sentiment, has been in Risk-off mode since January 2022 and invested in our Safety Portfolio. Table 1 below shows YTD 2022 results across all 14 portfolios relative to Benchmarks (AWCI MSCI All-World Index and the S&P 500).

In 2022, global equity markets have been impacted by interest rate normalization by global central banks and higher than expected inflation, which has reduced asset values which some indexes such as NASDAQ 100 (QQQ) down by >20% from the previous peak. This brings to mind a quote attributed to Warren Buffett when considering the current market, “When the [economic/liquidity] tide goes out you get to see who’s swimming naked.” 

The reduction of liquidity/increase in interest rates has particularly hurt businesses that have benefitted from the ongoing liquidity support and cheap money in the recent past, which are financing growth with little to no profit/significant cash flow in the short term, and the potential for turning a profit is very far out into the future. When the price of money changes (interest rates), all asset prices are re-rated.   

This ‘reserve wealth effect’ will potentially result in lower aggregate demand/consumption, and Central Bank hope to bring aggregate demand back in line with supply which has been constrained due to COVID-19 and the Russia/Ukraine war.  The rebalance of demand/supply is thought to cool the highest inflation we have seen in a generation. Our trend following strategy has outperformed the benchmarks as 12 out of 14 trend following portfolios remain in positive territory for year-to-date 2022. 

Our BT Momentum portfolio which reviews all 107 ETFs on a relative momentum basis across all asset classes (Commodities, Country, Sector, Factor Anomalies, Market-Cap, Fixed Income/Currency) and invests in the top 3 based on prior month’s relative momentum, has seen the highest returns at 21% year-to-date given strength of energy-related commodities (oil, natural gas, and gasoline) and restricted supply, which has protected capital on a real-returns basis given the current high inflation regime.

Table 1 – Year-to-Date 2022 Performance

The results are hypothetical and are NOT an indicator of future results and do NOT represent returns that any investor attained.

What we are Watching Going Forward.

As we have explained in prior posts, the rate of change of parameters within the economy is a lot more important to markets, rather than the absolute magnitude or level.  Whether or not the terminal Fed Funds Rate is 2.50% or 3.50% is less important than the overall rate of change in the cost of financing for businesses and households relative to their incomes.

Coming out of the COVID-19 pandemic, aggregate debt levels are larger than pre-pandemic, so it’s important to understand the relative changes in both interest rates, and corporate credit spreads.

These inputs are very important in the cost structure of many companies as they look to refinance maturing debt in a highly financialized economy and may have a significant impact on corporate profit margins, given the rate of change, as well as wage growth.  Historically, corporate profit margins or reduction in demand, have been defended by temporary layoffs of workers.

In Table 2, we note historically (going back to 1965) as interest rates have increased (represented by 10-year yield), investment credit spreads and unemployment have increased with a 6-9 month lag as corporates look to protect profit margins as demand/consumption may reduce overtime in 2022 due to the ‘reverse wealth effect’ in both stock and housing markets. We note that in April 2022, interest rates have hit a 3x z-score, which has historically signaled a peak and is followed by a widening of credit spreads/higher unemployment in the next 6-9 months.  

To date in 2022, labor markets appear strong with job vacancies outpacing unemployed and wage growth robust at ~6%, though running below inflation. The structural deflationary trends that existed before the pandemic still exist, namely, high debt levels, demographics, and innovative technology – are still there. Does high inflation change this calculus? It remains to be seen what the answer to this question is and our investment framework will answer this question over time, as our investment framework takes into account the rate of change and invests based on relative momentum based on the rate of change, we expect to see how higher interest rates may play out over time across asset classes. We do not need to answer this question correctly or have a correct forecast of these dynamics to see our investment portfolio benefit.  

Table 2 – US Investment Grade Credit Spreads, Interest Rates, and Unemployment

We hope that you have found this informative and endeavor to provide updates to the 14 portfolios going forward every month.

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

BT 

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

Note: Levered returns are equity market returns as realized as there is embedded leverage in Berkshire Hathaway’s business model as an insurance company. Unlevered returns are estimated by taking month-over-month market returns and dividing them by the amount of embedded leverage of 1.6x equity. This additional leverage has magnified returns and annual volatility as well. More on this later.

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

1. Excludes Mark-to-Market gains on securities portfolio which would lower Price-to-Earnings Ratio. 2. After-tax equity method earnings include our proportionate share of earnings attributable to our investments in Kraft Heinz, Pilot, Berkadia, Electric Transmission of Texas, and Iroquois Gas Transmission System. 3. Largely from investments above.    

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 FactorsWhat It is?How is it measured?Behavioral Bias
ValueCaptures excess returns to stocks that have low prices relative to their fundamental valueBook to price, earnings to price, book value, sales, earnings, cash earnings, net profit, dividends, cash flowHigher 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 counterpartsMarket capitalization (full or free float)Incorrectly extrapolating the past into the future
MomentumReflects excess returns to stocks with stronger past performanceRelative returns (3-mth, 6-mth, 12-mth, sometimes with last 1 mth excluded), historical alphaOverconfidence, self-attribution, conservatism bias, aversion to realizing losses   Under reaction and overreaction
Low VolatilityCaptures excess returns to stocks with lower than average volatility, beta, and/or idiosyncratic riskStandard 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 YieldCaptures excess returns to stocks that have higher-than-average dividend yieldsDividend yieldErrors-in-expectations
QualityCaptures excess returns to stocks that are characterized by low debt, stable earnings growth, and other “quality” metricsROE, earnings stability, dividend growth stability, strength of balance sheet, financial leverage, accounting policies, strength of management, accruals, cash flowsErrors-in-expectations
Source: MSCI Foundations of Factor Investing

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

Source: Berkshire Annual Reports, BeowulfsTreasury.com

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  

Source: Berkshire Annual Reports, BeowulfsTreasury.com

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 FactorsAQR MeasureBerkshire 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 stocksNegative 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

* Significant at 0.05 p-value

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 3rd 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 3rd 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 

Riding the Waves of Liquidity and Momentum – Introducing the BT Momentum and BT Wave Runner Portfolio Strategies

Our last post got us thinking about how difficult it must be for individual investors to stick to a portfolio strategy and drown out the noise given the recent news about inflation, interest rates, and Russia/Ukraine war and calls for a potential recession or even stagflation. 

So we set out to create a systematic trend following portfolio strategy based on academic and practitioner investment research that would work well in any market or economic regime (based on simulated historical analysis) and would allow investors to potentially make money in any environment by using both fundamental and sentiment indicators to help position portfolios to ride the waves of liquidity and momentum.

These portfolio strategies build upon the posts we have previously published that show how our opinions and views may be executed to provide tangible value over time.    

We will cover the following topics in today’s post:  

Topics Covered:

  1. Introduction to Momentum/Trend-following Investing
  2. Introducing BT Momentum Strategy – Systematic Portfolio Management
  3. Introducing BT Wave Runner Strategy – Systematic Portfolio Management
  4. BT Wave Runner Risk Composite Indicator – the components
    • Fundamental
      • BT Global Liquidity Index
      • OECD Composite Leading Indicator
      • U.S. Housing Starts
    • Market Sentiment  
      • High Yield vs US Treasuries
      • High Beta vs Low Beta Currency
      • Copper-to-Gold Ratio

Summary:

  • We believe that trend-following/tactical asset allocation portfolio strategies provide great risk-return trade-offs and offer a significant improvement in terms of risk-adjusted returns over static asset allocation portfolio strategies. We evaluate 2 portfolios (BT Momentum and BT Wave Runner) based on simulated asset returns. To be included in the investment universe, the asset classes require a separate ETF to exist and are investable by individual investors.  
  • The combination of fundamental and market sentiment data provides risk control when incorporated with momentum that has improved the risk-adjusted returns of portfolio strategy by riding the liquidity and momentum waves.

1) Introduction to Momentum/Trend-following Investing:

What is Momentum?

Momentum is the phenomenon that securities that have performed well relative to peers (winners) on average continue to outperform, and securities that have performed relatively poorly (losers) tend to continue to underperform.

Momentum is an investment style that has been studied extensively by academics for many years. AQR Capital Management has extensively researched momentum as an investment style and shown evidence for momentum is pervasive and supported by almost two decades of academic and practitioner research.

Studies have documented momentum in the U.S. as far back as the Victorian age. The evidence also shows that momentum works broadly across asset classes, including foreign stocks, bonds, commodities, currencies, index futures, and global country index selection. These studies are largely based on price returns and some fundamental considerations (i.e. earnings momentum). 

So Why Does Momentum Work?

Momentum is driven by investor behavior: slow reaction to new information, asymmetric responses to winning losing investments, and the bandwagon effect/overreaction as short-term traders may use recent performance as a signal to buy or sell.

Momentum tends to persist for some time (6-12 months) before leading to reversals as too many investors pile in the same investments as prices become detached from fundamentals, causing volatility and drawdowns in asset prices.   

2) Introducing BT Momentum Strategy – Systematic Portfolio Management:

Based on this excellent research, we wanted to see if we may apply it in a systematic way that may reduce investor behavioral biases (i.e. selling at the bottom of the market cycle or buying at the top). As we have noted in our research, investor risk appetite/behavior is a very significant factor in the results as measured through Household Balance Sheet Allocation to Equities (based on Flow of Funds position Accounts) and Margin Debt.

We have taken this research and applied it to ETF investing on a global basis looking at 64 ETFs/asset classes spanning equities, currencies, bonds, commodities, and Real Estate Investment Trust (REITs).

The idea is if you can maximize the investment universe, an investor may maximize their returns by catching a momentum wave in a particular asset class at a particular time (i.e. when the trend is your friend).   

We have called this the BT Momentum strategy (a long-only strategy), which is a simple strategy of investing in the top 3 ETFs (weighting of 40%/40%/20%) each month based on prior month data using a momentum score called Fast Momentum (13612W) which we will discuss later on, starting with monthly asset class returns in 1985.

Each month the investor would sell the previous month’s investments and buy the 3 strongest asset classes based on the 13612W fast momentum filter for each, based on buying at the beginning of the month based on the prior month’s momentum signals.   

This would lead to systematic buying and selling every month based on momentum signals. We have examined the portfolio statistics over the last ~40 years and split them into 4 different periods (roughly 10 years) to determine if there is persistence in returns as the research states. Given that trading is largely commission-free, implementing this strategy on a go-forward basis, we believe transaction costs should be minimal.        

In Table 1, we show the results of the simulated portfolio starting with $100 (no further contributions) in 1985, which has multiplied to $28 million by end of 2021 based on compound annual returns of 40% per year, far outperforming the Wilshire 5000 by ~30% per year and 60% of the time on a monthly basis – which are very strong results.

The overall correlation of monthly results of the BT Momentum portfolio to US equity markets is low (0.32) over the period, thus providing some diversification to standard long-only heavily weighted US equity portfolios. These results are impressive indeed and appear that results are persistent across time.

Table 1 – Summary Results – BT Momentum Portfolio Strategy vs Wiltshire 5000 – 1985 to 2021 (monthly results)

Note: The results are hypothetical and are NOT an indicator of future results and do NOT represent returns that any investor attained.

However, the annual volatility of 40% of the BT Momentum strategy is far too much for most investors to stick with this strategy. Also, the BT Momentum portfolio strategy provides little drawdown protection relative to a buy-hold strategy of the Wilshire 5000 index (see Table 2). 

Table 2 – Summary Results – BT Momentum Portfolio Strategy vs Wiltshire 5000 – 1985 to 2021 (monthly results)

Note: The results are hypothetical and are not an indicator of future results and do not represent returns that any investor attained.

We set out to try to reduce the annual volatility/max drawdown by using the BT Market Risk Indicator composition which identifies periods of economic and liquidity expansion, which we will cover later on in our Wave Runner Portfolio Strategy section.

Defining the Momentum Score

Typical momentum strategies use a trailing 12-month return or 10-month Simple Moving Average (SMA) to determine which asset classes may exhibit higher relative scores of momentum. 

We do not use the traditional 10-month SMA filter in our analysis, but something slightly faster, the weighted variant of the well-known average return over the last 1, 3, 6, and 12 months, called Fast Momentum (13612W filter) which has been popularized by Wouter J. Keller on his website TrendXplorer

Table 3 below shows how different momentum filters pick up monthly returns at a different paces. We note that the 13612W filter picks up about 70% weighting on the previous 3 months (bottom right corner chart). So the 13612W momentum factor picks up reversals in trend faster than other commonly used methods. We have used the 13612W momentum score within both the BT Momentum and BT Wave Runner portfolio strategies as well as Risk Composite Indicator.

Table 3 – Momentum Filter Comparisons

Investment Universe

For inclusion in the investment universe, all asset classes require a separate ETF to exist and are investable. We have attempted to maximize the investment universe to all global equity markets (including sector ETFs), select commodity markets that currently have ETFs in the market, Bond Markets (largely $US Sovereign Debt), and FX ETFs (based on US dollar). In all, we have selected 64 asset classes – see Table 4 for select asset classes.

As the longest-serving ETFs only go back to 1994, and we wanted to have at least over 30 years of monthly price data in our test, we have simulated historical results based on index returns using information from MSCI, and the US Federal Reserve FRED database. 

We have chosen to not include crypto assets such as Bitcoin and Ether in these back test as we have previously modeled crypto prices and noted that they tend to trade similar to a 5x leveraged NASDAQ index. In future back tests, we may include crypto assets as more data becomes available.

Table 4 – Investment Universe – 1985 to 2021

Source: Tiingo, MSCI, FRED US Federal Reserve

Asset Allocations BT Momentum – 1985 to 2021

As we mentioned earlier, capital is allocated every month to the top 3 ETFs based on momentum scores in a weighting of 40%/40%/20% to the risk assets.

Below in Table 5, we have noted that the top portfolio allocations over time have been largely allocated to commodities and energy (priced in US dollars), supply of US dollar liquidity, and consistent with a growing global economy over time.

The momentum effect is well known in commodity markets and trend-following strategies are used by commodity trading advisor (CTA) firms, so it is not surprising to see many in the Top 10. Also, countries that benefitted from high economic growth and further development during the period showed higher momentum (i.e. Mexico, China, India, and Russia).

We also note that US REITs have a high allocation over the period, but very little has been allocated to US equities. As we have noted in the past, Real Estate is the largest asset class in the world, and US Real Estate through increasing collateral values and increasing credit creation/higher liquidity is particularly important to the “Wealth Effect” and driving consumption in the US, which helps support the global growth cycle through further consumption. Over each period, the BT Momentum Strategy provides returns at least as good as US equity market returns.   

Table 5: BT Momentum Strategy – Average Asset Allocations Over Time

We note that BT Momentum provides some risk diversification to US equity portfolios based on low diversification over the measurement period (below 0.50 for most of the period based on a 3-year rolling correlation) based on Table 6.

Table 6: Rolling Correlation of BT Momentum vs US Equity Markets

3) Introducing BT Wave Runner Strategy – Systematic Portfolio Management:

The BT Wave Runner Strategy builds upon the BT Momentum strategy by adding a Risk Composite Indicator based on Fundamental data and the BT Market Risk Sentiment Indicator as a risk control to reduce the annual volatility, as the indicator identifies periods of economic and liquidity expansion.

The Market Risk Sentiment indicator is the same one introduced in our first post which helps out monitor the global growth cycle daily and has been highly correlated with the OECD Composite Leading Indicator on a monthly basis.

The idea of this portfolio strategy is simple, to avoid investing in risk assets in volatile periods that tend to be more susceptible to drawdowns (i.e. investor risk appetite tends to change quickly and frequently, which tends to create volatility in momentum portfolios), invest when the trend of liquidity in markets and industrial production/general economic outlook (as measured by the OECD Composite Leading Indicator) is better than the 3-year moving average trend. Or in other words, the trend is your friend.

In periods of more volatile markets, the portfolio is invested in a Safety portfolio based on the 13612W momentum filter of the safety investment universe.

In Tables 6 and 7, we show the results of the simulated portfolio starting with $100 in 1985 (with no future contributions), which has multiplied to $265,000 by end of 2021 based on compound annual returns of almost 25% per year, far outperforming the Wilshire 5000 by 2x and outperformed the Wilshire 50% of the time on a monthly basis. The largest drawdown is about 50% of equity markets as well and offers similar drawdown protection as a 50% risk-asset/50% safety asset portfolio.

The overall correlation of monthly results of the BT Wave Runner portfolio to US equity markets is low (0.20) over the period, thus providing some diversification to standard long-only more heavily weighted equity portfolios. These results are impressive as volatility is roughly that of US equity markets. The BT Wave Runner portfolio strategy offers about 50% of return/volatility of the BT Momentum Portfolio. It offers 42 times the cumulative value of a long-only equity market portfolio by sidestepping large drawdowns and riding the combined waves of momentum and liquidity.

Table 6 – Summary Results – BT Wave Runner Portfolio Strategy vs Wiltshire 5000 – 1985 to 2021 (monthly results)

Note: The results are hypothetical and are NOT an indicator of future results and do NOT represent returns that any investor attained.

Table 7 – Summary Results – BT Wave Runner Portfolio Strategy vs Wiltshire 5000 – 1985 to 2021 (monthly results)

Note: The results are hypothetical and are NOT an indicator of future results and do NOT represent returns that any investor attained.

BT Wave Runner Decision Rules:

In Table 8, the flow chart reviews the decision rules in the Wave Runner model.  

If the Risk Composite Indicator is above trend, we expect that global investor risk appetite should be above trend based on the 13612W momentum filter. As a result, the portfolio is invested in the top 3 asset classes as measured by 13612W.  If Risk Composite Indicator is below trend, the portfolio is invested in a safe portfolio allocated to 6 assets that tend to be very safe in periods of extreme market volatility (flight to quality to reserve assets).

Table 8: Decision Rules BT Wave Runner Strategy

The Safety Portfolio:

The safety portfolio is based on the idea that the US dollar and US Treasuries are the world’s reserve assets underpinning the world’s financial system. In our previous post, we have discussed the dominance of the US dollar, US Treasuries, and Gold held by Central Banks reserves as the US dollar holds significant weight in the Financial System. There tends to be a flight to quality (US dollar reserve assets and Gold) when markets are volatile. 

As a result, we have restricted our safety allocation to largely US dollar reserve assets. When the Risk Composite is below trend, the portfolio is invested in the top 3 assets based on the 13612W momentum filter based on a weighting of 40% for 1st and 2nd and 20% for 3rd highest momentum.

  • iShares 20+ Year Treasury Bond ETF (TLT)
  • iShares 7-10 Year Treasury Bond ETF (IEF)
  • iShares 1-3 Year Treasury Bond ETF (SHY)
  • iShares GNMA Bond ETF – Mortgage-backed Securities (GNMA)
  • Invesco DB US Dollar Index Bullish Fund (UUP)
  • SPDR Gold Shares (GLD or PHYS)

4. Risk Composite Indicator

The Risk composite indicator is a Z-Score measure based on the following measures below. The weighting of each component of the composite is based on the inverse of the volatility of the Z-Scores for each component.

52% of the time the portfolio is in the safety portfolio (see Table 9 for historical Risk Composite Indicator) and 48% of the time in the BT Momentum Strategy (Risk-on). We should note that we have not fitted this indicator via regression to equity markets to optimize the weighting between the various indicators (this could significantly improve the backtest metrics).   

Fundamentals – 50% of the Risk Composite: 

  • BT Global Liquidity Index, 6-mths advanced  
  • OECD Composite Leading Indicator, 6-mths advanced
  • US Housing Starts

Market Sentiment – 50% of the Risk Composite:

  • High Yield Credit vs US Treasury
  • High Beta Currency (AUS)  vs. Low Beta (JPY)
  • Copper vs Gold Ratio 

Table 9: Risk Composite Indicator

A. Fundamentals (50% of Risk Composite Index)

  1. Why is Liquidity Important for Asset Returns?  

As we discussed in our previous post, ‘The ‘Wealth Effect’ and Debt – Two Sides of the Same Coin – Part 1’ liquidity and the credit cycle are important to the functioning of our global financial system.  

The credit cycle refers to the self-reinforcing interactions between perceptions of value and risk, risk-taking, and financing constraints. Typically, rapid increases in credit drive up property and asset prices, which in turn increase collateral values and thus the amount of credit the private sector can obtain until, at some point, risk appetite reduces due to change in conditions where the debt can no longer be serviced or bankers are no longer willing to supply credit (i.e. increasing unemployment/bankruptcies, pandemics which reduces the labor supply, natural disasters, interest rates are raised beyond the neutral rate of interest, war/tensions, etc.).  

Each time a bank creates a loan, a corresponding deposit is created through the banking system, which adds to the money supply. Money is used to purchase goods and services and accumulate and save for a property. As the money supply grows, cash is devalued against assets (financial assets, real estate, commodities, art, etc.), causing nominal prices to rise.

Famed investor Stanley Druckenmiller has said before: “Earnings don’t move the overall market; it’s the Federal Reserve Board… focus on the central banks, and focus on the movement of liquidity… most people in the market are looking for earnings and conventional measures. It’s liquidity that moves markets”.

We can see this in action in Table 10 as BT Global Liquidity Index leads S&P 500 growth by about 12 months. We use information from National Statistical Authorities OECD, IMF, Bank of International Settlements, and US Federal Reserve to calculate the BT Global Liquidity Index.   

Table 10: Global Liquidity vs S&P 500 EPS Growth – 1993 to 2022

Financial liquidity explicitly drives investors’ risk appetite and asset allocation. Rising collateral values then positively feedback to underpin new liquidity/credit creation. We have seen that more liquidity has reduced the risk premium on risk assets, as well as cutting defaults, allowing investors to invest in riskier investments/longer duration assets such as technology stocks that may not turn a profit until many years in the future. Higher investment in technology also improves economic productivity, which offsets older demographics that plague most countries globally. We can see below in Table 11, that Global Liquidity, advanced 6-months tends to lead NASDAQ returns (Z-Scores) (a proxy for returns on technology investment).  

Table 11: BT Global Liquidity Index vs NASDAQ Returns – 2000 to 2022

We have used a similar methodology that Cross-Border Capital uses to determine Global Liquidity as defined in Michael Howell’s book Capital Wars – “The Rise of Global Liquidity”.

Howell defines Global Liquidity, “as a source of funding that measures gross flows of credit and international capital feeding through the world’s banking system and collateral-based wholesale funding markets. It determines the balance sheet capacity of all credit providers and the private sector’s access to cash through savings and credit”.

  • Private Sector Liquidity: Includes credit flows from the Banking sector, and Shadow Banking sector (consisting of entities such as asset-backed commercial paper (ABCP) conduits, credit hedge funds, finance companies, government-sponsored enterprises (GSEs), money market mutual funds (MMMFs), securities lenders, insurance/pension  and structured investment vehicles (SIVs))
  • Central Bank Liquidity: Foreign and Gold Reserves and balance sheet allocation to Quantitative Easing programs.     
  • Cross Border Funding: Foreign investors and lenders through cross-border flows. These flows have historically provided a preview of the pending economic crisis and tend to be marginal capital allocated which drives markets.    

BT Global Liquidity Index includes flows from the following countries: China, United States, Europe, UK, Japan, Canada, Australia, South Korea, Russia, India, Brazil Mexico, South Africa, Switzerland, and Singapore. Table 12 shows US Liquidity by sector.

Table 12: US Liquidity by Sector

2) Is the OECD Composite Leading Indicator helpful in forecasting the real economy? 

The OECD system of composite leading indicators (CLI), first developed in the 1980s, is designed to give early signals of turning points in economic activity.

The CLIs aim to anticipate fluctuations in economic activity over the next six to nine months based on a range of forward-looking indicators such as order books, confidence indicators, building permits, long-term interest rates, new car registrations, etc., as well as a monthly index of industrial production (IIP) as a proxy measure for economic activity. OECD CLIs aim to predict turning points in this business cycle estimate (signal a turning point in the business cycle in 6-9 months).

The CLI is timely and published every month (a lag of about 45 days after month-end) and is more frequently updated than GDP which is usually updated every quarter with a 2-month lag. High-frequency monthly production data such as Plastic production and Electricity consumption appear significant in China and Mexico (countries that rely more on production for economic growth on a relative basis). The OECD CLI is a significant input in our country’s Machine Learning models which we have discussed in prior posts.    

The OECD CLI is used within the BT Wave Runner portfolio strategy to aid in assessing the growth/inflation (business) cycle and has been able to identify turning points in advance so that investors may reduce risk exposure in anticipation of drawdowns/market volatility in risk assets.

In Table 13, we observe that GDP growth is strongly correlated with OECD CLI. Based on a Granger causality test, the OECD CLI leads the real GDP growth by about 2-quarters (F=3.7247, statistically significant at 5% level).

We believe the OECD CLI would be useful in measuring the real activity in the economy and is complementary to the BT Global Liquidity Index as we believe the flows of liquidity via the ‘Wealth Effect’ through the financial cycle drive real economic growth.

Table 13: OECD Real GDP growth vs OECD CLI

3. Why Do U.S. Housing Starts Matter?

Housing/Real Estate — and all of the ancillary spending associated with the purchase of a home, including renovation and remodeling costs and utilities — is a huge part of the global economy.

Real Estate is the largest asset class globally. Every recession since 1960 has been preceded by a double-digit decline in housing starts. It can take several months for home builders to construct a new property. And homebuilders are reluctant to break ground on new projects if they fear the economy may slump later in the year.

In our previous post, we have covered the importance of US Real Estate and how it contributes to consumption in the US which drives U.S. GDP growth, and further drives the wealth of the Top 10% within the country. Continuing global growth depends on the consumption of the American consumer and the continuance of the Wealth Effect.

Table 14 – U.S. Housing Starts – Good Recession Indicator?

B. BT Market Sentiment Indicators (50% of the Risk Composite Index):

In our first post, we discussed a daily risk indicator that we have used in our Wave Runner portfolio. To manage and monitor our risk on a more frequent basis, we have attempted to create a customized index that may be tracked daily that aligns well every month with the OECD CLI which tracks the business cycle.  

This custom index which we have called the BT Market Risk Indicator includes the following measures:

  • Copper-to-Gold Ratio
  • High Beta (AUD/USD)-to Low Beta (JPY/USD) Currency Pairing 
  • U.S. Discretionary Consumer Spending-to-Consumer Staples Ratio*
  • U.S. High Yield Corporate Credit to Intermediate Gov’t Bond Ratio
  • U.S. TIPS Bonds to Intermediate Gov’t Bond  Ratio*

* Not included in current 1985 to 2021 backtest given data limitations.

The OECD CLI is a significant indicator based on our research to track going forward, however, there is a lag of about 45 days to report results for a given month. This makes our approach susceptible to quick market drawdowns, such as the one we experienced in February/March 2020. However, looking back during that time the OECD CLI was already in negative territory and would have indicated an extremely cautious risk position in our investment portfolio.

In Table 15, we show that the BT Market Risk Sentiment indicator and OECD CLI tend to move in the same direction over time. We have discussed in our prior posts, the importance of the Copper-to-Gold Ratio and how commodity currencies (Australia dollar, Canadian dollar) tend to follow a cyclical upswing, and generally, during a crisis, there is a flight to safety (U.S. Treasury bonds, gold, and the Japanese Yen).  We have also discussed how credit spreads tend to widen in the late-cycle period as market interest rates start to rise higher than the neutral rate of interest, reducing investor risk appetite.

Table 15 – BT Market Risk Sentiment – A Timely Proxy for OECD CLI?

Asset Allocations – BT Wave Runner

The Wave Runner portfolio shows a similar allocation to BT Momentum, however, was able to side steps large drawdowns, and reduce the annual volatility by about 50% (see Table 16).

Table 16 – BT Wave Runner Average Allocation over time (Risk Assets Only)

The Wave Runner Portfolio is in the Safety Portfolio for 229 months out of 443 months (52% of the time). This has significantly reduced the annual volatility, but also lowered the return as well. The Safety Portfolio (when in Risk-off) provides strong returns with limited volatility and drawdown, providing an annual return of about 7% as noted in Table 17. 

Capital is allocated to the Safety portfolio based on a similar momentum scoring system (13612W) with about 20% allocated to each Intermediate Treasury, Long-term Treasuries, and Gold (Table 18).

Table 17 – BT Wave Runner Safety Portfolio Summary

Table 18 – BT Wave Runner Average Allocation (Safety Portfolio)

Though we use US equities as a benchmark, we note that based on the holdings throughout BT Wave Runner (50% risk assets and 50% safety assets) based on the movement of the BT Risk Composite Indicator, the more appropriate benchmark should be measured on similar allocation (50% risk assets and 50% safety), which the BT Wave Runner provides a significant improvement over as cumulative value throughout 1985 to 2021 is 121 times.

In Table 19, we note similar max drawdowns were exhibited by both Wave Runner and Appropriate Benchmark with static allocations at around 25% of capital (both portfolios provide similar improvements relative to risk asset drawdowns). 

Table 19 – BT Wave Runner vs Risk Appropriate Benchmark

1.    Benchmark based on 50% US Equities/50% Intermediate Treasury Bonds (static allocation).

The overall returns of the Wave Runner portfolio are also significantly non-correlated with US equity markets (see Table 20), more so than the BT Momentum portfolio, given the low-to-negative correlation of the Safety Portfolio to equity markets.

Table 20 – Rolling Correlations – BT Wave Runner vs US Equities

The combination of fundamental and market risk sentiment data as risk control, incorporated with momentum has improved the risk-adjusted returns of the portfolio by riding the liquidity and momentum waves. Who said riding waves isn’t fun? 

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

BT 

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