The Magical Power of the Copper/Gold Ratio. Is Copper the New Oil as the World transitions to Clean Energy?

Estimated Reading Time – 14 minutes

In our last post, we covered the BT Market Risk Indicator to help track the OECD Composite Leading Indicator (CLI), which is a significant input in our BT G20 Models from a cyclical perspective.

The Copper/Gold Ratio plays a significant role within the BT Market Risk Indicator and gives us a read on the day-by-day cyclical economic activity implied by markets.  

We will cover the importance of copper to the physical economy and the role of gold as a safe-haven financial investment in a fiat currency system. We have split the discussion into a two-part series, we will cover Part I in this post and Part II will be provided in a few days.

We will cover the following sections:  

Part I:

  1. How Does the Copper/Gold Ratio work?
  2. What is the ratio currently telling us?
  3. Demand and Supply of Copper
  4. Overview of BT Copper Model

Part II:

  1. History of Gold Usage in the Financial System
  2. Demand and Supply of Gold
  3. The Golden Rule!
  4. Have Cryptocurrencies replaced Gold as a Store of Value?
  5. Overview of BT Gold Model
  1. How Does the Copper-to-Gold Ratio work?

1. How Does the Copper/Gold Ratio work?

The Copper/Gold ratio measures the risk appetite of risk assets relative to sovereign bonds (US Treasuries). The absolute number is not as important as the direction/momentum of the ratio.  

In Chart 1, we review the uses of copper and gold and how each metal reacts to different parts of the economic cycle. An increasing copper/gold ratio typically is correlated with an upswing in cyclical economic activity and a reduction in the copper/gold ratio usually signals a deceleration of cyclical economic activity. Jeffrey Gundlach, CEO of DoubleLine Capital LP has noted that the copper/gold ratio was a “fantastic” indicator of interest rates – specifically the yield on the benchmark U.S. 10-year U.S. Treasury. We believe the ratio is helpful across both bond and equity markets.

Chart 1 – Summary of Copper/Gold Ratio

UsesUsed in manufacturing and construction and used as a proxy for economic growth  

Known as “Dr. Copper” given the ability to assess overall economic conditions through the price of copper because of the metal’s wide-ranging application (industrial production/electrical equipment/building materials).  
Perceived Safe Haven in response to Fiat Currency System (debasement through increasing money supply) and Geopolitical Risk as the anti-currency.  

Gold is universally accepted and therefore serves as valuable collateral during times of crisis (trust and confidence) as it carries no credit or counterparty risk.  

Historical Store of Wealth and maintaining purchasing power and tracked M2 money supply over time   Negative or lack of price correlation to the US dollar, commodities, and many financial assets
Cyclical Economic ActivityRisk-on, Higher GDP on higher demand /growth accelerating/higher inflation – copper prices higher    

Risk-off, growth is decelerating, GDP is declining/lower inflation – copper prices decline  
Risk-on, Higher GDP on higher demand for manufacturing, construction, and consumer goods,  growth accelerating/higher inflation – risk assets offer better return relative to gold

Risk-off, growth is decelerating, GDP is declining/lower inflation – capital preservation, gold should outperform risk assets
Interest RatesU.S. Treasury 10 year (UST10Y) yield up, Central Bank overnight rate up when copper prices accelerate/going upUST 10Y yield down, Central Banks reduce rates when copper prices are decelerating/going down
Copper-to-Gold RatioRises as the economy grows, the ratio falls as economic demand slows. Tracks UST10Y yield well.  Ratio declines as capital preservation bid up the gold price in recession  
Source: DoubleLine,

2. What is the Copper/Gold ratio currently telling us now?

The current Copper/Gold ratio has largely been range-bound since the middle of 2021 and has not shown strong momentum to the upside, despite the market narrative of a cyclical upswing emerging from COVID-19. The Copper/Gold ratio also tracks the OECD CLI well at almost a 0.80 correlation since 2002 (see Chart 2 below).

As a reminder we highlighted in our last post, the OECD Composite Leading Indicator (CLI) is a key factor in all country models and appears to pick up turning points in stock markets well in advance of major drawdowns, as well as, economic turning points.

Chart 2: Copper/Gold Ratio, US Treasury (UST) 10Y, China Government Bond (CGB) 10Y Yield, OECD CLI  

Source: Federal Reserve, PBOC, OECD,
  1. Custom China index based on all freight (20%), bank loans (20%), electricity consumption (20%), yield curve spread (10%), M2 Money Supply(10%), Purchasing Manager Index(10%), Copper(5%), and China OECD CLI(5%)

A few other interesting observations from Chart 2 when looking at the Copper/Gold ratio and various 10-year yields: 

  • Despite very supportive monetary policy since the Great Financial Crisis through quantitative easing (QE) which was intended to reduce yields at the long end of the yield curve, cyclical changes as measured by the copper/gold ratio have been strongly correlated with changes in 10-year U.S. Treasury yields.  When quantitative easing is implemented, long-term interest rates tend to decline. When the Fed enters the market as a major buyer, supply and demand should push interest rates lower. This encourages individuals and businesses increase their risk appetite and take on loans at new, lower rates. Additionally, when interest rates are low, investors turn to riskier investments, like equities, to bolster their returns. As additional capital enters equity markets and the incentives for businesses to borrow funds for expansion or share buybacks , quantitative easing can lead to larger stock market gains. There is a psychological effect of quantitative easing for investors as there is the perception is that the Fed is taking an active role in bolstering the economy. This perception can lead to more confidence in the stability of the underlying securities and stimulated economic performance.  A future post will dive deeper into the yield curve and attempt to bifurcate between cyclical and structural impacts.
  • Despite strong copper demand from China, one may expect that CGB 10-year bond yields would rise with strong copper demand, and CGB yields would fall with lower copper demand over the economic cycle. However, this is not the case as changes in 10-year CGB yields are not significantly correlated with changes in the copper/gold ratio and are very close to 0.50 over multiple periods (see Chart 2), which may suggest that China is managing its bond market against the gold market (more on this in the next post).  
  • Famed macro researcher, Louis-Vincent Gave (GaveKal) has proposed that if China is executing this strategy, the objective may be aligned with a long-term policy strengthening of the Renminbi as a creditable store of value to be used in trade agreements/potential path of de-dollarization. We go into this idea further in Key Trade Flows and Supporting Agreements section (link). In Part II, we will review the gold holdings of China and test this idea further.  Further internationalization of the Renminbi, would further diversify China’s economy from production/manufacturing of goods to a focus on domestic markets consumption. This has been a typical path as economies have matured over time.     
  • One factor that we are watching which we do not believe has been fully integrated into market pricing/economic data yet is the current monetary policy divergence between central banks (U.S. and China) with the recent loosening of monetary policy by China, while the Federal Reserve and other countries that provided significant fiscal and monetary stimulus in response to COVID-19, look poised to tighten in 2022. The combination of these moves may extend the global growth cycle further and reduce tail risk in China’s property sector. We note that the current OECD CLI and OECD Diffusion Index as of December 2021 (subject of a future post), showed a decelerated growth, even before considering any hiking of monetary policy rates, so the recent moves by China may further extend the global growth cycle. This remains to be seen.

To further understand the direction/momentum in the Copper/Gold ratio, we took a deeper dive into the Demand, Supply, and Reserves Global before attempting to model the price of copper.

3. Demand, Supply, and Reserves of Copper

Chart 3: Summary Facts on Copper and Supply Chain

Source: International Copper Group Study, IEA

Before we started to build our model from our BT G20 dataset we reviewed in the last post, we reviewed the demand and supply of the underlying copper market to determine the feature sets that may be used in estimating the drivers of copper prices currently and potentially in the future as well. Given thermal and electrical conductivity, copper is used mainly in electronic and industrial applications.

Copper plays an important role in modern human life (powering and protecting our homes, cars, cellphones, and computers). Currently, China plays a major role as a producer, processor, consumer, and trade flow of copper (see Chart 3) – consuming 50% of the world’s copper. China is expected to continue to strengthen its hold in the market through the clean energy transition.

Chart 4: Identified Copper Resources and Top 20 Copper Mine Production

Source: International Copper Group Study

Roughly 40% of global output is mined in South America (Chile and Peru) – see Chart 4. China, the Democratic Republic of Congo (DRC), the United States, and Australia are the other major producing countries.

According to the International Energy Association (IEA), in a scenario that meets the Paris Agreement goals, clean energy technologies’ share of total demand rises significantly over the next two decades to over 40% for copper and rare earth elements, 60- 70% for nickel and cobalt, and almost 90% for lithium.

EVs and battery storage have already displaced consumer electronics to become the largest consumer of lithium and are set to take over from stainless steel as the largest end-user of nickel by 2040. With the move towards clean energy technologies, transition minerals are more geographically concentrated than oil or natural gas, with China playing a major role in both extraction, processing, and consumption (see Chart 5).

Chart 5: Mining and Processing of Fossil Fuels and Minerals

Source: International Energy Agency (IEA)

Copper has long played an important role in China’s economy. Currently, China is the world’s biggest importer of copper by some margin, accounting for 43% of global copper ore imports – more than three times the level of Japan in second place.

While copper usage in China is primarily driven by the fact that it is an important component in manufacturing and construction several ongoing government initiatives are expected to increase demand further – See Energy Transition and Key Challenges section below.

While there is expected higher demand for copper due to the clean energy transition, do we have enough to meet the increased demand? According to the United States Geological Survey (USGS), since 1960 there has been roughly 38 years of production in reserve –see Chart 6.  Copper can be recycled and used again, so primary copper used today may be reused at a later date without a loss in performance. If the IEA’s estimates of 40% more copper is required annually are proven correct, this would reduce reserves to ~31 years of production.   

Chart 6: Reserves and Annual Production

Source: USGS

In 1960, predominately most consumption of copper came from North America and Europe at 87% of total global consumption which is consistent with post World War II redevelopment. Since 1980 most of the growth in global copper has come from Asia (74% of global consumption), most notably China (represents 50% of global consumption) given the vast amount of industrialization and construction (see Chart 7).

Copper’s use is pervasive across all aspects of life from transforming energy and minerals to power our homes, computers, iPhones, and cars/boats. So increases in consumption during economic upswings would be expected to raise copper prices and decreases in consumption during an economic recession would lead to fewer homes being built/renovations occurring and cars/computers bought.

Hence, this is why the global growth cycle may be inferred from tracking copper prices given the wide-ranging applications across modern human life. Major uses by the major application are provided in Chart 8.

Chart 7: Refined Copper Consumption

Source: International Copper Group Study, World Bureau of Metal Statistics

Chart 8: Major Uses and Applications of Copper

UsesMajor Applications
Electrical Safest Residential and Commercial Building Wiring 
Electronics and Communications Local area networks, Transformer, Switches  Mobile phone, computers, semiconductors/circuitry in silicon chips/remove heat from transistors to drive Data and Artificial Intelligence  
Construction Plumbing and construction materials for its durability, machinability, corrosion resistance, and ability to be cast with high precision (does not burn or release toxic fumes in case of fire and resistance to extreme weather events)
Industrial Machinery and Equipment Gears, Turbine Blades, Pipes exposed to seawater for oil platforms and coast power stations depend on copper given corrosion resistance for protection 
Consumer Electrical appliances, cookware, brassware
Transportation Cars, Trains, boats including Electric Vehicles 
Source: International Copper Study Group

China continues to be the world’s largest consumer and importer of Copper. The price of copper has historically been influenced by economic activity/cycles in China. It has long been rumored that official GDP statistics of China are man-made and to get a sense of the level of activity one can follow the Li Keqiang Index1 or other custom index made up of high-frequency indicators – see Chart 9 below.

Chart 9: China’s Demand for Copper Driven by Strong Economic Activity

Source: National Bureau of Statistics,
  1. Li Keqiang Index based on growth in electricity consumption (20%), rail freight (40%), and bank loans (40%).
  2. BT Custom China index based on all freight (20%), bank loans (20%), electricity consumption (20%), yield curve spread (10%), M2 Money Supply(10%), Purchasing Manager Index(10%), Copper(5%), and China OECD CLI(5%).

Energy Transition to Clean Energy and Key Challenges

The current electricity generation mix (see Chart 10 below) continues to be heavily weighted towards fossil fuels (Coal, Oil, Natural gas) in some countries (China, Australia, Indonesia) and the switch to renewable energy systems is likely to be a key demand driver for copper moving forward. Carbon Dioxide or CO2 emissions are expected to decline as electricity generation from coal switches over to renewable energy over the next 10-15 years.

Chart 10: Current Electricity Mix

Source: IEA

Current Key Challenges – Copper

  • Performance: Challenging to substitute due to its superior performance in electrical applications
  • Ore Quality: Mines currently in operation are nearing their peak due to declining ore quality and reserves exhaustion. Declining ore quality exerts upward pressure on production costs, emissions, and waste volumes
  • Climate Change Impacts: Mines in South America and Australia are exposed to high levels of climate and water stress

Source: IEA

Over the coming decades, the demand for copper is expected to be robust through the development of clean energy technologies – see Chart 11 below comparing the current state with the future state.

Chart 11: Clean Technologies Expected to increase demand for Copper

Source: IEA

While the West (European Union, United States, UK, and Canada) have begun the transition with a greater proportion of renewables driving electricity production, China’s switch to renewable energy systems is likely to be a key demand driver for copper going forward. Copper is a key material in energy-efficient generators and renewable energy systems, with solar and wind energy installations using larger volumes of copper as compared with thermal power generators.

Electric vehicle production is also expected to drive China’s copper consumption. Copper is an important component in electric vehicles, used in the batteries, windings, and copper rotors of electric motors, as well as in the wiring and charging infrastructure. At an average of 83kg of copper, the typical electric vehicle uses nearly four times as much of the metal as a conventional car (see Chart 11).

China has ambitions to be a leading electric vehicle manufacturing center by 2025, as part of its Made in China 2025 initiative.

Chart 12: New Energy Sources Introduce New Trade Patterns and Geopolitical Concerns

Source: IEA

China has emerged as a major force in global supply chains for critical minerals and clean energy technologies over recent decades. Despite its current reliance on coal to power electricity, the country’s rise to becoming the leader of clean energy supply chains has largely been underpinned by its long-term industrial policies, such as five-year plans for economic development, the Made in China initiative, and the Belt and Road Initiatives. This has not been an accident, but part of a 25-year plan to secure strategic minerals and natural resources…more on this later.

The shift to a clean energy system is set to drive a huge increase in the requirements for these minerals, meaning that the energy sector is emerging as a major force in mineral markets. Until the mid-2010s, the energy sector represented a small part of total demand for most minerals. However, many of the minerals are controlled the by 3 largest category producers (>75% of the market) geographically concentrated, suffering from the reduced resource, quality, and water-stressed stressed areas. This may introduce new geopolitical concerns and new trade patterns – see Chart 12 above.

With the new energy sources and shift away from oil, there could be a threat to the petrodollar system that the current U.S. dollar financial system has been built upon given that much of the global production and consumption of copper and other minerals required for clean energy transition may reside outside of the U.S. going forward – see Trade Flows section below. For background, Investopedia provides a good summary of the U.S. petrodollar system:

  • Petrodollars are U.S. dollars paid to an oil-exporting country for the sale of oil, or simply, an exchange of oil for U.S. dollars.
  • Petrodollars are the primary source of revenue for many OPEC members and other oil exporters.
  • Because they are denominated in U.S. dollars, the purchasing power of petrodollars relies on the value of the U.S. dollar. When the greenback falls, petrodollars do, too.
  • The Petrodollar came to prominence during the oil crisis of the 1970s and regained traction in the early part of the 2000s.
  • The petrodollar system creates surpluses, which lead to large U.S. dollar reserves for oil exporters, which need to be recycled, meaning they can be channeled into domestic consumption and investment, used to lend to other countries, or be invested back in the United States.

Many of the conflicts in the Middle East since the 1970s have been related to protecting the petrodollar system and maintaining status quo given that many of the countries with significant oil reserves are in the Middle East (Iran/Iraq/Saudia Arabia/Qatar). However, with the world moving towards renewable energy, trade flows, supply chains and geopolitical focus may move away from the Middle East towards Latin America and Africa.

Key Trade Flows and Supporting Agreements – Copper

China’s expansive, multi-billion-dollar Belt and Road Initiative (BRI) traverses several continents – Southeast Asia to Eastern Europe, Latin America, and Africa – and encompasses major projects in 71 countries – see Chart 13 below. The BRI focuses on infrastructure investment, education, construction materials, railway and highway, automobile, real estate, power grid, and iron and steel and improves trade across the infrastructure built. Linking up road and rail connections with global ports is essential for the functioning of the maritime road aspects of BRI. China’s motivations for food and energy security and regional development intersect and are mutually beneficial for trade partners. In response to COVID-19, China has also seized the opportunity to improve relations further via vaccine diplomacy, in particular in hard hit countries in Latin America.

The BRI is expected to drive copper demand higher.  The BRI includes Peru and Chile (40% of global annual copper production) and China has signed Free Trade Agreements with both countries, as they have been keen to secure and diversify their supply of copper, as the globe enters the new era of de-carbonization and electrification moving ahead. The BRI also fulfills a secondary objective of de-dollarization by settling trade in the Renminbi (RMB) instead of U.S. dollars. 

Chart 13 – Copper Trade Flows and China’s Belt and Road Initiative

Source: International Copper Study Group

Given that the world’s two consumers (China and the U.S.) are net importers and with much of the current reserves and production capacity in Latin America (Chile and Peru), and the importance of copper to the energy transition, does the focus of energy resource security shift from the Middle East (Oil and Gas) to Latin America (Minerals for Clean Energy)?

With decarbonization Is Copper the New Oil? How Do Central Bank Bilateral Swap Lines Play into Trade?  

In Chart 14, we have attempted to map relevant bilateral central bank liquidity swap arrangements with the U.S. Federal Reserve and People’s Bank of China (PBOC) and Copper/Oil production. These swap agreements stabilize markets when markets become stressed so that cross-border trade can continue. Swap lines keep plenty of currency available during times of stress so that countries can continue to trade and secure important raw materials for manufacturing and production. In a fiat currency system, liquidity is necessary to keep financial markets functioning smoothly during crises.

Currently, China’s net import reliance is at ~60% of domestic consumption compared with ~40% net import reliance in the US (See Chart 14).

We observe that while these swap arrangements are typically reserved for trading partners that are exporting key goods/services into either US or China, we note, that the China-only swap line countries skew heavily towards having a larger proportion of copper production (with generally larger trading relationships) on an annual scale which may help bolster domestic production in periods of liquidity stress going forward.

We note that the existing split between the US and China is much more balanced when looking at Oil Production and Bilateral Liquidity Swap Arrangements (BLSWAPs) (29%/31%) relative to Copper Production/Reserves (40%/23%) in Chart 14. China and BLSWAPs have a significant foothold in the world’s refining annual capacity at ~60% (mostly within China). The European Union also has a high annual net import reliance at 82% with little mine capacity. Allocation of energy resources has typically been the source of geopolitical conflicts in the past given they are limited and mining/extraction has been energy-intensive.  

Note that Chart 14 does not include significant production from African nations such as Zambia, the Democratic Republic of Congo, or Peru, which on a combined basis have copper reserves of 28 years’ worth of annual production and represent ~24% of annual world production or 15% of the world’s copper reserves. However, despite not having a bilateral swap arrangement with the PBOC, all three countries are members of China’s Belt and Road Initiative and Free Trade Agreements exist (Peru) which may secure the supply of copper from these countries for China in exchange for infrastructure/further economic development. As an recent example, Shanghai-based China Cosco Shipping is building a new $3 billion port at Chancay in Peru, while there are ambitious proposals for a transcontinental railway linking South America’s Atlantic and Pacific coasts from Brazil to Chile.

However, despite not having Free Trade Agreements in place with Chile and Peru, the U.S. has had strong diplomatic and economic ties with Latin America for many years and currently imports copper from the region. Latin America has historically been the part of the world with the highest approval rating for the U.S., rooted in foreign assistance, law-enforcement cooperation, education, and cultural ties.

The question remains if existing agreements and supply chains will be enough to meet the increased demand for copper and other relevant minerals as the global transition to clean energy moves ahead, without severe geopolitical conflicts over resources.

Chart 14: Bilateral Swap Lines (US Fed /PBOC), Copper/Oil Production, Copper Net Import Reliance US and China

*Based on the relative value of bilateral swaps in place. Note alliances subject to change over time.  
Source: Federal Reserve, PBOC, EIA, Copper Alliance, USGS,
Proceedings of the National Academic of Sciences of the United States of America,

4. Overview of BT Copper Model – Another Shield for the Armory

Now that we have reviewed the supply and demand dynamics of copper, we will start to integrate the BT G20 dataset, along with further data gathered based on our review of the global copper market.  

As Chile plays a significant role in the global copper market, we have included monthly statistics including the Chilean Peso/USD exchange rate, Chilean electricity production, and total copper production/exports.

As copper plays heavily into international trade and the a significant proportion of trade is moved via maritime transport. Therefore, we have also included the Baltic Dry Index as a proxy for shipping costs that may factor into copper market pricing.    

Each country’s dataset was tested against the 12-month change in the price of copper and the top 10 features at each country level were selected and combined with the Chilean data set. See Chart 15 for the feature set used.

The training monthly dataset period began in October 2002 until January 2018. The out-of-sample testing period ran from February 2018 to November 2021.

Chart 15: BT Select G20 Model Feature Set and Features with High Model Value (in Green)

The rate of 12-month change in Copper prices appears to be strongly correlated with the rate of change with an R-squared of 0.89 (goodness-of-fit) in the out-of-sample period 2018-2021 (Chart 16):

  • Canadian dollar/US dollar exchange rate
  • Mexico’s OECD CLI/Mexico exports to the U.S.
  • U.S. dollar liquidity (Money  Supply)
  • U.S. Treasury 10-Year Yield
  • Oil Price
  • Chilean Peso/US dollar exchange rate
  • New Orders in European Union
  • Motor Vehicle Sales in China
  • China Economic Activity represented by BT Custom Index

Chart 16: Summary BT Copper Model an Example Feature Set (Extra Trees Regressor) Sensitivity


Summary Observations on the BT Copper Model:

  • Despite being a top 5 producer, the U.S. is a net importer of copper and relies on trade partners such as Canada, Chile, and Mexico to supply copper and other value-added processing/smelting. As a result, cyclical changes in demand/supply may have a significant impact on the world price of copper. Canada also attracts major foreign investment flows from aboard as well (see Chart 17 below) given unparalleled access to capital markets (TSX and TSX Venture Exchange), innovation, and expertise across the clean technology and mining services and supply chains.
  • Changes in Maritime Shipping rates were not significantly correlated with changes in copper prices
  • South Korea’s Industrial Production and exchange rate relative to USD were significantly correlated. South Korea is a net exporter of copper (11th largest exporter) and 55% of exports go to China. South Korea produces copper for cars, vehicle parts, and marine applications.  Also, Samsung and SK Hynix are South Korea’s biggest chipmakers and two of the world’s biggest producers of memory chips (which include copper) used in everything from smartphones to automobiles and aerospace equipment. All of the microchips used in Apple iPhones are made by Samsung.
  • The price of Copper and U.S. Trade-weighted dollar appear to be negatively correlated – see Chart 18. This is common across most commodities priced/settled in USD. The Trade-weighted dollar index strengthens as the U.S. Fed raises short-term rates slowing the pace of copper price growth (and the economy including slowing the consumption of imported goods and building homes which rely on copper), and tends to decline with large U.S. budget/trade deficits/lower Fed Funds rate, supporting higher copper prices (improving the growth in the economy including higher consumption of imported goods and housing starts which rely on copper) – see Chart 19.
  • China’s EV car market share is only at about 6% of total sales, yet changes in copper prices, are strongly correlated with changes in Motor Vehicle sales. Given the relative size of the Chines economy on a global scale, change in demand for cars (conventional and EV) may impact copper prices.
  • Commodity Driven Economies: Countries that have significant activity related to mining resources have a significant correlation to changes in industrial metals such as Copper (Canada, Russia, Australia, Brazil, Chile). The Copper/Gold Ratio also tends to track the G20 OECD CLI well over time and could be a significant cyclical indicator that you can monitor daily.
  • China’s role as a top consumer and net importer of copper appears to have an impact on the price of copper via changes in industrial activity as measured by the BT China Custom Index.

Chart 17 – Global Mineral Investments in Exploration

Source: S&P Global

Chart 18 – Copper Price vs U.S. trade-weighted Exchange Rate

Source: U.S. Federal Reserve,

Chart 19 – U.S. Twin-Deficits, U.S Trade-weighted Exchange Rate, Fed Funds Rate

Source: U.S. Federal Reserve,
  1. Includes Fiscal Balance and Trade Balance as % of GDP.

Join us for Part II in which we review our Gold model and Gold market dynamics (we plan to post in a few days). Thanks for reading. Appreciate any questions or feedback you have or other research topics you may suggest.

Please contact us at


What Drives G20 Equity Markets over time?  A First Pass at our review of Global Equity Markets through Data Science…

We routinely are inundated with news on minute-by-minute moves on stock markets, interest rates, commodities, the latest Central Bank announcement that has moved the market, what about inflation vs. deflation, etc.  Markets are a weighing machine as new information comes into play – but how does one measure the weight of each piece of information and does this weighing machine change over time? Markets are a very complex puzzle.

This is our inaugural post to review our research and models we have developed to date. We do not expect that future posts will be as detailed or as long.  

At Beowulf’s Treasury, we endeavor to provide fundamental research, insights, and education to help investors to slay their market dragon (emotional selling during unexpected drawdowns) and secure their treasure with proper risk management and quantitative tools to help an investor manage their emotions and behavioral biases.  

The Problem:

  • The difficultly for most investors (both retail and institutional) is weighing the impact on market prices of new information or a change in expectations based on the new information is in constant flux which makes the puzzle very difficult to complete as the market is based on human behavior. Human emotions take over in highly stressful situations and are not always rational. Narratives or political agenda or bounded rationality all may skew an investor’s judgment as well.
  • Due to the high level of indebtedness of the modern economy, financial markets have a significant impact on the business cycle. There appears to be global synchronization of the business cycle via the credit creation process given the interconnectivity of countries concerning the allocation of production and consumption activities. With a fiat currency system, the credit creation process has increased the level of indebtedness across households, businesses, and governments over time. At the end of each business cycle, debt service levels overwhelm the levels of cash flow as the wealth effect (“assets increasing in value enhance customer optimism increasing further consumption”) which leads to higher optimism leading to further credit expansion until the level of debt outstrips cash flow growth and debt serviceability. The only response to ensure the game continues to be played has been to reduce the debt service levels via interest rates and continue the flow of liquidity through all markets. This has benefited holders of real assets (stocks, real estate, etc.) over time. The short-term business cycle has historically lasted between 6-8 years.
  • The current fiat currency/highly levered financial system is susceptible to periods of low volatility over extended periods, only to be interrupted by large unexpected volatility spikes and volatility tends to cluster which may result in large drawdowns (and market dragon if this results in emotional selling). Investors may follow the crowd and sell out of fear during these drawdowns as there is innate human behavior to avoid losses which are valued at nearly twice as great as the hope of earning a reward (known as loss aversion). Many investors may simply track price dynamics and high-level fundamental data without measuring the weights over time as they may not have the time or interest in tracking this data. Tracking the business/liquidity cycle and many of the other inputs in our BT model over time will help investors sidestep these costly drawdowns (known to us market dragons) by reducing equity market risk in decelerating conditions (so that you can secure your treasure), as there appears to be a repetitive pattern over time.  

A Potential Solution – Introducing the Beowulf’s Treasury (BT) Select G20 Machine Learning Model:

  • Quantitative models may help identify these patterns, and based on our research these patterns tend to repeat over time. Through our process of building models, we understand cause-and-effect relationships to the best of our ability based on data rather than a narrative or emotion. This is critical in an ambiguous/uncertain environment and makes decision-making repeatable over time. The COVID-19 experience has been one of the most uncertain situations that many of us have faced during our lifetimes. It also highlighted the importance of quantitative modeling via projected infection rates as compared to hospitalization capacity, which led to informed policy decision-making and reduced uncertainty for the overall population. However, we do not blindly trust our models.
  • We started this blog to provide our research, insights, and education on global markets based on free publicly available data. Within our research, we empirically test our hypothesis with quantitative models to determine how the market may be weighing new information and how factors may behave historically and in the future as well. This research may help us improve the probability of success when it comes to slaying the market dragon by attempting to sidestep large drawdowns.

Our Approach:

  • We have a data-driven approach and built separate machine learning models for select G20 countries’ Equity Markets (as measured by national indices listed below in Table 4) and a non-correlated Safe Haven asset (the price of Gold in U.S. dollars). Table 1 below shows summary data on each of the countries captured in the study.  
  • We were inspired to perform this study, after reading Ray Dalio’s recent book “Principles for Dealing with the Changing World Order – Why Nations Succeed and Fail” and previous book “Principles for Navigating Big Debt Crisis”. 
  • We wanted to empirically test many of the theories and other hypotheses that other recent authors/pundits have proposed in the past to drive/lead equity markets. Through this testing and research, we believe our work may help investors position themselves for market turbulence and protect from large drawdowns through prudent risk management.
  • For those readers that have not read Dailo’s most recent book, the central theme is that there is a certain shelf life for the world’s main reserve currency based on the past 500 years of recorded financial history and follows a similar arc over time, eventually leading to over-indebtedness and may result in a conflict between the declining existing power (reserve country) and a country that is an emerging power and/or an internal conflict (civil war). These conflicts are generally based on disagreements about how to allocate and divide the world’s resources (inclusive of wealth inequality). The perils of greed (accumulation of wealth over lifetime consumption, without redistribution) and overconfidence are common themes within the epic poem Beowulf which inspired us – more on this in the About Us section of the website.    
  • The reserve currency countries have historically lasted between 75-100 years and these conflicts tend to disrupt the existing world order. Since WWII we have largely been able to keep large-scale world conflict to a minimum as a result of mutually assured destruction. Trade, innovation, and economic prosperity have improved the lives of many globally. As a consequence, debt and asset prices have grown over time as there is a confidence that the future may be better than what we see today through enhanced productivity and innovation. Historically, these worldwide conflicts tend to redistribute the world’s resources including normal households that may be saving for future consumption or investment.   
  • This pattern has played out via the Dutch Guilder, British Pound, and now potentially the U.S. dollar and potential conflict with an emerging power in China. A longer-term objective of this blog will be to monitor the central theme of the book, is the time of the U.S. dollar as a world’s reserve currency coming to an end? What currency may replace it?
  • In our first pass BT model, to help us answer this question over time, we integrated monthly uncertainty measures such as the Economic Policy Uncertainty (EPU) and Geopolitical Risk Indices (GPR) provided by and Partisan Conflict Index published by the Philadelphia Federal Reserve which tracks the degree of political disagreement among U.S. politicians at the federal level.  These indices are based on news-based measures of policy uncertainty, adverse geopolitical events, and internal political division. We have observed the increasing risk of a U.S. internal conflict through January 6th insurrection and protests during the last few years and these types of events would be reflected in these measures.  
  • Market participants and central bank officials view economic policy uncertainty and geopolitical risks (inclusive of internal political division) as key determinants of investment decisions and stock market dynamics. So the rate of change over time will give us a data point in our model to track the potential for conflict.  
  • Before jumping into the models, you may ask why cover the G20? The G20 was founded in 1999 in response to several world economic crises. These countries have collaborated further to construct the financial infrastructure of the global financial system today (i.e. Basel III bank regulations) which underpin the creation of credit via capital and liquidity rules. These countries account for a significant amount of the global equity market cap, private debt, and total reserves held by central banks. These countries have also attempted to coordinate their commitments on Climate change. By following these countries we get a better sense of the global liquidity cycle influencing the business cycle, and stock prices globally.     
  • The BT Machine Learning models attempt to capture underlying fundamentals of the economy, market sentiment/investor positioning, short-term debt cycle also known as the economic cycle which is determined by liquidity and credit from the banking system and actions of the government (fiscal and monetary policies), as well as the structural themes which Dailo’s book covered in detail.

The Model Details:

  • We compared 12-month log changes against several features across 7 categories across Investor Sentiment, the Banking/Credit Environment, Growth/Inflation Cycle, Macroeconomic/Geopolitical (Structural), Household Finance/Consumption, Market Sentiment, and Other/High-Frequency data which are largely consistent across each of the countries reviewed. For each country model, we have gathered 50 to 60 features/indicators across the above 7 categories (see Table 3 below) to train and fit our models. The information has been either disclosed by the respective country’s Central Bank, National Statistical Organization, Securities Exchanges, or global organizations such as the Organization for Economic Co-operation and Development (OECD) or other academic organizations. To the extent possible, we attempted to automate the data collection process so that we may quickly update our findings at a later date. Table 2 below illustrates the model infrastructure and training and testing splits that were followed for each of the country models.
  • The features used in the models are mainly leading and coincident indicators. These feature sets are used as training data to fit each model. Training a model involves using an algorithm to determine model parameters (e.g., weights) to map inputs (independent variables) to a target (dependent variable – rolling log 12-month change in Share Prices).
  • A horserace of 12 models was trained with the objective of minimizing an error function which was the mean squared error (MSE). On average, 18 years of data (on a monthly basis) were used to train the models (using Python Jupyter Notebook).

Table 1 – Selected Features – Select G20 Countries

The models were tested out-of-sample for an average of 5 years. We averaged the results across the top 5 performing models which are both linear and non-linear regression models (decision tree and random forest models). 80% of the data was used to train the models and the remaining 20% of the data is used as out-of-sample testing.  Test data was not used to build the models. The duration of the training set of data is typically limited by the availability of data. For example, Margin Debt reporting only began in Canada, Korea, and Australia in the early 2000s. An earlier version of these models started in 1991 and results were similar to the output below.

Table 2 – BT Select G20 Model Infrastructure and Training/Testing Split


So what did we find? Are these models predictive?

The short answer, the first pass results we believe are promising…however this is the first pass. Table 4 below summarizes the summary model output.    

Table 3 – BT Select G20 Model Feature Set and Features with High Model Value (in Green)

Table 4 – Summary of BT Select G20 Model Machine Learning Models

Table 5 – Summary Economic Cycle (Share Prices, Liquidity, Credit Impulse, and Select G20 OECD CLI)

Source: OECD, IMF,

Table 6 – Correlation of Equity Markets based on rolling 12-mth Returns (Daily Prices) – 2013 to 2021

Source: Tiingo,

BT Model Observations:

The models have held up well in the out-of-sample testing period of the previous 5 years (2017-2021) with an average of R-squared of 0.76 (goodness of fit of actual returns versus predictive modeled returns based on test data) and Root Mean Squared Error of 0.10 in the out-of-sample period.  

  • Models with longer data sets across multiple business cycles have performed relatively well across time (the U.S. model – S&P 500). Given the depth and maturity of the U.S. capital markets, high-quality information on a more frequent basis on a monthly or quarterly basis (more on this model later)…     
  • Growth/Inflation Cycle: OECD Composite Leading Indicator (CLI) is a key factor in all country models and appears to pick up turning points in stock markets well in advance of major drawdowns, as well as, economic turning points. The composite leading indicator (CLI) is designed to provide early signals of turning points in business cycles showing fluctuation of the economic activity around its long term potential level and appears to be a good proxy for GDP given the lag in reporting of quarterly GDP (typically we have to wait 2 months after quarter-end for these numbers). 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).    
  • Banking/Credit – The rate of change in Global Money Supply/Liquidity and Credit Impulse appears to have an impact on the economy, as well as, returns in equity markets over time. This is significant to note as currently as the rate of change in global liquidity is decelerating and appears will continue to do so through balance sheet management and interest rate normalization, which is a major risk to equity markets in the near term. Table 5 shows there appears to be a certain amount of synchronization of the global liquidity cycle. European equities appear significantly sensitive to the overall global liquidity cycle based on the BT models.      
  • Investor Behavior – Household Balance Sheet Allocation to Equity Markets: The rate of change in Household allocation to equity markets relative to fixed income implied by Financial Account in the Flow of Funds and Margin Debt features tends to perform well across all markets in which the information is available.  Though it is hard to directly observe in the data, there appears to be some home bias (domestic investors holding a high proportion of their portfolio in domestic securities) given the strong correlation between household balance sheet equity allocations and home markets.
  • Investor Behavior – Trading Margin Debt Balances: With adopted of quantitative easing, investors have increased their risk appetite as evidenced by higher allocation to equity markets and higher margin debt. As markets have grown over time, margin balances (where disclosed) have expanded. Changes in margin balances are correlated with changes in market values

Table 7 – Z-Scores: Share Price, Household Equity Allocation and Margin Debt – Rolling 12-mth change

Source: Federal Reserve Database, Statistics Canada, RBA,

12-mth change in Share prices, Household Allocation, and Margin Debt have generally stayed within 2 standard deviations over time supported by the credit/liquidity cycle. Current conditions across most markets show a deceleration 12-month share price change, Household Equity Allocation, and Margin Debt (see Table 7).  

  • Commodity Driven Economies: Countries that have significant activity related to mining resources have a significant correlation to changes in industrial metals such as Copper (Canada, Russia, Australia, Brazil). The Copper/Gold Ratio also tends to track the OECD CLI well over time and this could be a significant cyclical indicator that you can monitor daily (more on this later)….  
  • Growth/Inflation Cycle: Corporate Profits and Liquidity: Earnings-per-share at the index level and Corporate Profits within GDP are significantly correlated and importance to GDP over time has become more significant as policy frameworks since the 1980s have generally favored capital relative labor across most G20 countries. Also, the rate of change in the Money Supply/liquidity and Corporate Profits/EPS appears to be strongly correlated. We note that companies have also grown reliant on the ongoing flow of ample liquidity since the Great Financial Crisis (GFC): a growing cohort is borrowing to deploy buybacks and support earnings-per-share through financial engineering. With central banks poised to normalize rates and reduce liquidity, the second cohort of companies that are cash flow negative and relying on ongoing new investments to finance their business is at risk. With less money flowing into the system, that funding will be harder to come by. We may be able to observe the initial smoke related to such a situation via widening of lower quality credit spread (a feature in the models). This may be a topic for further research in due course.

Macroeconomic/Geopolitical – Outperformance in Economic Growth has not Translated to Share Price Outperformance:

  • China’s entry into the World Trade Organization in 2001, has become the world’s largest exporter via investment in production capacity, utilization of natural/labor resources becoming a net creditor, dethroning the U.S. as the world’s largest trade partner. The rest of the world has benefitted from this increase in the global labor force (benefitting from a demographic dividend) which has been a significant deflationary impulse across all economies over the last 15-20 years. The People’s Bank of China (PBOC) has become the largest central bank reserve assets holder with almost 40% of total central bank reserve assets globally. However, despite the strong economic performance, this has not led to outperformance in equity markets on a relative basis.
  • Domestic allocation to equity markets appears low on a relative basis as roughly 10% are allocated to financial markets and 70% of Household Assets are allocated to Real Estate. By comparison, U.S. households hold 40% of assets in equities and 30% allocated to Real Estate.  Also, foreign portfolio investment positions continue to be low in China relative to the size of the economy and household assets, as well as, more mature capital markets such as the U.S., EU, and the UK with a higher foreign investor base (foreign portfolio investment is >50% of the money supply). China has gradually opened its capital markets over time to foreign capital has improved and globally held ETFs have expanded their allocation to China over time. However, China’s vast yet protected home market has allowed some of its domestic firms to acquire a scale that provides them with significant advantages when they compete in other global markets. China has been particularly protective of strategic industries such as technology (including last year’s crackdown on domestic technology firms and their founders), given the importance to future economic growth and maintaining existing governance structures. Both the United States and China are taking measures to reduce the leakage of sensitive technologies, and we expect scrutiny of foreign investment to continue given the strategic competition between the U.S. and China. This is a theme we will continue to monitor.  

  • Prioritization of real estate relative to financial assets by Chinese citizens, coupled with strong credit creation since 1999, could be one of the reasons that Chinese Real Estate has become one of the largest asset classes in the world. However, since 1999 financial markets in China have been slowly liberalized, encouraging capital inflows from foreign investors, while keeping domestic social stability in check and encouraging domestic financial literacy. 
  • However, for the yuan or any other currency including crypto to displace the U.S. dollar as the world’s leading reserve currency, the yuan must take on a larger role for capital raising and investment and trade/payment system. China has been taking steps to reduce its reliance on the U.S. dollar payment system by introducing a Central Bank Digital Currency (CBDC) which may eliminate its dependence on the U.S. dollar payment system. Also as China attempts to move from a production-led export economy to focus on the development of domestic market/consumption, we will be watching the performance of the model over time. Table 8 shows the evolution of Global Central Bank Foreign Exchange Reserves.  At this point, though the U.S. has lost ground since 1999, central banks hold roughly 60% of foreign exchange reserves in U.S. dollars.

Table 8 – Global Central Bank FX Reserve – the U.S. Remains on Top for Now

Source: Currency Composition of Official Foreign Exchange Reserves (COFER), International Financial Statistics (IFS),
  • Despite increasing fiscal deficits/debt, trade deficits, and lower GDP growth,  the U.S. dollar continues to be the world’s reserve currency in which goods are quoted and traded, and with which payments are settled (particularly in global commodity markets). This has provided the U.S. the exorbitant privilege of borrowing trillions of dollars from the global capital markets in U.S. dollars without facing a significant balance of payments crisis/depreciation of the dollar, at minimal interest rates and with virtually zero default risk. Also, other net creditors may have excess dollars over above their fixed income holding requirements at the current rate which drives these excess dollars into U.S. financial assets including equity markets.
  • China’s foreign currency assets composition is not disclosed but has been suggested that it approximates the overall global composition of ~60% of USD, ~20% Euro, GBP ~5%, and JPY ~5% given the relative size of its overall holding of central bank reserve assets at 40% of the total.  Both Russia and China have been trying to reduce their reliance on U.S. dollar trade (including a higher allocation to gold as part of central bank reserves) as they currently rely on trade in U.S. dollars and sanctions by the U.S. government may be placed which may disrupt the flow of trade. China is poised to continue to move from producing/export-led economy to consuming/service economy, in which stronger currency and continuing broadening of access to capital markets becomes important, coupled with enhanced domestic financial literacy and a higher allocation of household savings to financial assets.
  • Historically when the devaluations (via increases of money relative to real assets such as stocks, real estate, gold) have become extreme that the financial system breaks down. In response, governments are forced to back the money with a physical form of hard currency (typically a limited resource such as gold) to rebuild the trust and faith in the currency within the financial system so that the value of the underlying money is not volatile and maintains its store hold of wealth.  Since 1999 gold reserves have represented ~10% of central bank reserve assets of the select G20 countries and have increased relative to the broad money supply from 0.2% to 1.0% in 2021 (see Table 9).       

Table 9 – Gold as % of Broad Money Supply of Select G20 Countries

Sources: IMF, Federal Reserve Economic Database,
  • Demographic trends in China are expected to put pressure on economic growth over the next decade based on UN projections (without significant immigration), the population will shrink significantly from current levels. We have seen similar trends play out in Japan before during the late 1980the /the early 1990s as well.
  • We plan to dig into this demographic theme in a future report given that over the next 10-15 years there is an expectation that Brazil, India, Mexico, China, and Indonesia are expected to make up a larger proportion of the global middle class. As a result, these economies may focus more on the development of consumer-led consumption and more reliant on the ‘Wealth Effect’ to grow the economy which may substantially change how production and consumption are allocated globally, and potentially further development of the domestic capital markets. As an example, recent ‘Common Prosperity’ measures in China focus on reducing wealth inequality over time to spur domestic demand, increase self-reliance, reduce the reliance on real estate/credit growth, and create more balanced economic growth going forward. These measures may enable greater wealth to be invested by the middle class in equity markets and spurn further innovation over time. This is expected to reduce internal conflicts over resource allocation as well reducing economic policy uncertainty.
  • High levels of Foreign Portfolio Investment (openness to foreign capital and trade, which has historically driven innovation and growth), coupled with High Domestic Household Allocation to the domestic Equity Market appear to be the winning combination for the U.S. for now. This may be supported by strong earnings growth and lower volatility in earnings on a relative basis. This may also be the fact that the U.S. dollar is the world’s reserve currency which has run twin deficits (trade and fiscal) and net creditors such as China, Germany, and Japan must recycle US dollars back to buy financial instruments (bonds and equities).   
  • Household Finance/Consumption: In highly financialized economies, the ‘Wealth Effect’ in which higher asset prices (Real Estate Wealth and Stock Prices) cause households to be more confident in the economy and spend more appears to be more evident in China, the U.S ., EU, UK, Canada, South Korea, and Australia based modeled outcomes. Real Estate prices have tended to trend up along with Private sector Credit-to-GDP ratios. Banking regulations support the ‘Wealth Effect’ through housing prices given that residential mortgages which are secured by collateral generally receive a relatively low-risk weight given that probability of default and loss given default tend to be the lowest given high-quality collateral, which in turn may provide a bank a higher return on equity relative to relatively risker loan such as a business loan. Also, capital regulations treat sovereign bonds on a preferential basis at a 0% risk weight. Liquidity regulations require large cushions of High-Quality Liquid Assets (HQLA) to be held by banks, which are mainly sovereign exposures. Most banks tend to have the bulk of their sovereign portfolios in debt of their home country given the preferential regulatory treatment and banks are significant investors in sovereign debt. We witnessed this type of behavior during 2020 as increased client deposits via payments sent from the government are backed by sovereign bonds held in the bank’s liquidity portfolio. For the ‘wealth effect’ to continue, new liquidity/credit must enter the economy through credit creation or payments from the government to mitigate disinflation which is why the credit impulse and liquidity growth is an important metric to track going forward.
  • It should be pointed out that since the GFC, the countries at the center of the banking crisis driven by the collapse of a real estate bubble (U.S., EU, and the  U.K.) have been able to slow the credit growth to the private sector and improve the overall balance sheet partially through higher fiscal spending via debt monetization. Credit to the private sector in China, Canada, Korea has significantly expanded over this time.  Going forward, the composition and evolution over time of private versus public debt are worth watching, as historically the public sector which can create money tends to assume private sector debt during a crisis or recession to avoid a collapse of the financial system.   

BT Model Review – What surprised us?

  • Leading indicators for GDP such as the OECD Composite Leading Indicator performed extremely well across all jurisdictions. Careful monitoring and positioning (exiting the equity market when 12-mth change falls below 0%) may help protect investor portfolios from significant downturns.
  • Earnings per Share/Corporate Profits, Yield curve/interest rates were not as significant as expected. The short-end of the curve (changes in the 3mth rate) appear to have the most impact on model output.  
  • Risk Appetite/Investor Behavior is a very significant factor in the results as measured through Household Balance Sheet Allocation to Equities (Flow of Funds position Accounts) and Margin Debt were significant across developed nations where interest rates/economic growth is low.
  • Despite a relatively long time series for model training, the South Korea BT model did not perform as well out-of-sample period (low R-squared, high RMSE) as other countries. The model performed well in the out-of-sample period between August 2017 to June 2021. From June 2021 to October 2021, actual returns were higher by at least 10% on a rolling 12-mth basis than predicted by the BT Model.
  • Geopolitical Risk Index did not have a significant impact on BT Gold model in training and out-of-sample period, however, the GPR has been relatively range-bound throughout 1999 to 2021 (ex-Sept 11/2001 Terror Attacks).  Generally, during a crisis, there is a flight to safety (U.S. Treasury bonds, gold, and the Japanese Yen).

What are some of the limitations of the BT Machine Learning model?

  • We may not have chosen the right independent variables/predictor variables of the overall stock markets of the select G20 economies. Significant pivots in the economic sectors of the economy and stock market indices and/or energy transition from fossil fuels to green sources over a short period may not be fully incorporated into the model without further expansion of the data set.
  • Statistical relationships of certain predictor variables may change over time given that markets are highly influenced by human behavior and production and consumption allocations may change over time.
  • The fact that an independent variable turns out to be significant says nothing about causality. Certain factors may be significantly intertwined and difficult to separate from each other. Also by their very nature, financial or economic outcomes/data may not be normally distributed and may exhibit a non-normal distribution such as fat-tails (high kurtosis, low probability of high impact events).  This is an especially relevant point concerning observational studies.
  • We have tried to the best of our ability to gather comparable data from each jurisdiction. However, data availability is not consistent across the countries (e.g. Climate Policy Uncertainty Index and Partisan Conflict Index only exist for the U.S. at this point) within this study which limits the amount of data that can be used to train the machine learning models. We noted above those models with a longer time series of data generally performed better in out-of-sample testing.

A Deeper Dive into the U.S. Model (S&P 500): 

  • The rate of 12-month change in U.S. markets appears to be strongly correlated with the rate of change with an R-squared of 0.90 (goodness-of-fit) in the out-of-sample period 2013-2022:
    • OECD Composite Leading Indicator for the U.S. (a proxy for GDP)
    • U.S. Short-term interest rate (3-mth) – a proxy for Fed Funds – 3 rate hikes in 2022;
    • U.S. dollar liquidity (Money  Supply);
    • U.S. Margin Debt/Household Allocation of Net Wealth to Equities (Risk Appetite);
    • Domestic Consumer Confidence;
    • Domestic Credit Impulse;
    • High Yield Corporate Credit Spreads;
    • ISM/ISM New Orders;
    • Earnings-per-share;
    • Personal Income/Retail Sales; and
    • Housing Starts/Real Estate Prices.
  • These factors are consistent with an economy that is driven by consumption (retail sales, housing, vehicle sales, etc.) which has the world’s leading reserve currency and is as a result of twin deficits (trade and fiscal) within a mature financialized economy with a population of high financial literacy on a relative basis.  
  • Based on 2022 consensus estimates for Corporate Earnings/EPS of the S&P500 (expected up 9% YoY) and the economy from Survey of Professional Forecasts Philadelphia Federal Reserve, based on the BT model it is expected the S&P 500 will end the year up 5% from 2021. This is consistent with the decelerating trend we are experiencing in liquidity, credit impulse, equities allocation, and margin debt.

Table 10 – Out-of-Sample Testing U.S Model (S&P 500) – Model Prediction vs Actual


Table 11 – Other Equity Market Out-of-Sample Results – Model Prediction vs Actual


A Deeper Dive on the Gold Model:

  • The rate of 12-month change in the gold price (in $US dollars) appears to be strongly correlated with the rate of change with an R-squared of 0.88 in the out-of-sample period 2018-2021 along with the following features:
  • Central Bank Gold to Total Reserve Asset Ratio;
  • Select G20 Total Reserve Assets;
  • The U.S. Household Balance Sheet Allocation to Gold relative to Stocks and Bonds;
  • U.S. Real 10Y/30Y interest rates;
  • Price of Oil and U.S.CPI;
  • Select G20 BT Credit Impulse/Commercial & Industrial Lending in the U.S.;
  • Safe Haven Asset (Japanese Yen/weakness in USD(DXY)); and
  • U.S. Economic Policy Uncertainty measured by the EPU Index.

Table 13 – BT Gold Model – Out-of-Sample

Source: See Table 4 above for all data sources.

Introducing the Daily BT Risk Indicator: 

  • 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.
  • 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 to the select G20 OECD CLI (see Table 14 below). This custom index which we have called the BT Market Risk Indicator which includes the following measures:
    • Copper-to-Gold Ratio
    • High Beta-to-Low Beta 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

Table 14– BT Market Risk Indicator

Source:, OECD, Tiingo

Potential Further Research Topics:

  • The first pass at the model introduces many further research questions that we hope to provide updates over time: 
    • Inclusion of Forecast for 2022 and beyond based on consensus estimates (where available) to determine expected returns for each index and potential portfolio allocation for informational purposes.  
  • Determine if we may be able to further calibrate models and remain predictive out-of-sample. Review Feature sets for completeness. Based on reader interest/feedback, we may update our analysis from time to time.  
  • Deeper dive on each equity market composition by sector and understanding how this may tie into the factor analysis. Also extending the models to determine if there are any insights on a cross-sectional basis (i.e. the impact of U.S. consumption on large exporting nations such as China).
  • We have scratched the surface on the global liquidity cycle in this post, a deeper dive on underlying sensitivity to liquidity conditions by index may be warranted. 
  • Additional models developed for other equity indices (NASDAQ, Russell 2000), other G20 countries (South Africa and Indonesia), Commodities models (i.e. Copper price and further discussion on Copper-to-Gold ratio), and potentially other asset classes including interest rates and cryptocurrency. Review the potential for inclusion of the Taiwan Stock Exchange and other related data given the strategic importance of semiconductors related to the U.S./China relationship going forward.     
  • Daily tracking of BT Risk Indicator for real-time Global OECD CLI

If you have gotten this far, we appreciate your focus and thank you for reading our inaugural post. We do not expect that future posts will be as detailed or as long. Appreciate any questions or feedback you have or other research topics you may suggest.

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