Crowded trades and consequences

January 4, 2025

A crowded trade is a position with a high ratio of active institutional investor involvement relative to its liquidity. Crowding is a form of endogenous market risk as it arises not from contracts’ fundamentals but from the market itself. The risk of crowding has increased in past decades due to the growing share of institutional investors in the market, particularly the activity of hedge funds. Liquidations of crowded positions can trigger price distortions and, in cases of self-reinforcing deleveraging, even systemic pressure.
Crowdedness can be measured by the total value of active institutional positioning in an asset relative to its trading volume. It indicates how long it would take institutions to exit their trades under normal market conditions. For U.S. stocks, these ratios can be calculated based on reported data. Crowding typically skews risk to the downside. This point has been proven empirically for the U.S. equity market. However, crowdedness should also command excess premia. Historically, crowded stocks have outperformed non-crowded stocks materially and with high statistical significance.

(more…)

FX trading signals: Common sense and machine learning

December 21, 2024

Jupyter Notebook

Two valid methods to combine macro trading factors into a single signal are “conceptual parity” and machine learning. Conceptual parity takes a set of conceptually separate normalized factors and gives them equal weights. Machine learning optimizes models and derives weights sequentially, potentially with theoretical restrictions. Both methods support realistic backtests. Conceptual parity works best in the presence of strong theoretical priors. Machine learning works best with large homogenous data sets.
We apply conceptual parity, and two machine learning methods to combine 11 macro-quantamental trading factors for developed and emerging market FX forwards in 16 currencies since 2000. The signals derived by all methods have been highly significant predictors and produced material and uncorrelated risk-adjusted trading returns. Machine learning methods have failed to outperform conceptual parity, probably reflecting that theoretical priors in the FX space are abundant while data are limited and heterogeneous.

(more…)

U.S. Treasuries and macro-enhanced trend following

December 7, 2024

Jupyter Notebook

Trend-following strategies rely on the persistence of market trends. Such persistence can arise from the gradual dissemination of information or behavioural biases. In light of these inefficiencies, trends that coincide with supporting economic information (macro tailwinds) are more likely to persist than those accompanied by opposing macro information (macro headwinds). As a result, a macro-enhancement of standard trend-following signals should produce better investment returns.
This post supports this proposition for the U.S. Treasury market over the past 32 years. It tests simple macro enhancement types for directional return trends and curve-flattening return trends. In all cases, macro enhancement would have materially improved predictive power and backtested trading profits. This echoes previous research for other asset classes that illustrated the complementarity of price and economic information in systematic trading.

(more…)

How random forests can improve macro trading signals

November 23, 2024

Jupyter Notebook

Random forest regression combines the discovery of complex predictive relations with efficient management of the “bias-variance trade-off” of machine learning. The method is suitable for constructing macro trading signals with statistical learning, particularly when relations between macro factors and market returns are multi-faceted or non-monotonic and do not have clear theoretical priors to go on. This post shows how random forest regression can be used in a statistical learning pipeline for macro trading signals that chooses and optimizes models sequentially over time. For cross-sector equity allocation using a set of over 50 conceptual macro factors, regression trees have delivered signals with significant predictive power and economic value. Value generation has been higher and less seasonal than for statistical learning with linear regression models.

(more…)

How to build a macro trading strategy (with open-source Python)

November 9, 2024

This post is a condensed guide on best practices for developing systematic macro trading strategies with links to related resources. The focus is on delivering proofs of strategy concepts that use direct information on the macroeconomy. The critical steps of the process are (1) downloading appropriate time series data panels of macro information and target returns, (2) transforming macro information states into panels of factors, (3) combining factors into a single type of signal per traded contract, and (4) evaluating the quality of the signals in various ways.
Best practices include the formulation of theoretical priors, easily auditable code for preprocessing, visual study of data before and after transformations, signal optimisation management with statistical learning, and a protocol for dealing with rejected hypotheses. A quick, standardised and transparent process supports integrity and reduces moral hazard and data mining. Standard Python data science packages and the open-source Macrosynergy package provide all necessary functionality for efficient proofs of concept.

(more…)

Using principal components to construct macro trading signals

October 26, 2024

Jupyter Notebook

Principal Components Analysis (PCA) is a dimensionality reduction technique that condenses the key information from a large dataset into a smaller set of uncorrelated variables called “principal components.” This smaller set often functions better as features for predictive regressions, stabilizing coefficient estimates and reducing the influence of noise. In this way, principal components can improve statistical learning methods that optimize trading signals.

This post shows how principal components can serve as building blocks of trading signals for developed market interest rate swap positions, condensing the information of macro-quantamental indicators on inflation pressure, activity growth, and credit and money expansion. Compared to a simple combination of these categories, PCA-based statistical learning methods have produced materially higher predictive accuracy and backtested trading profits. PCA methods have also outperformed non-PCA-based regression learning. PCA-based statistical learning in backtesting leaves little scope for data mining or hindsight, and the discovery of trading value has high credibility.

(more…)

How macro-quantamental trading signals will transform asset management

October 12, 2024

Macro-quantamental indicators and trading signals are transformative technologies for asset management. That is because they allow plugging point-in-time fundamental economic information into systematic trading, backtesting, and statistical learning pipelines and remove an important barrier to information efficiency. While the predictive power of macro information for asset returns has been evident for decades, its use in systematic trading and research has remained rare. This disconnect reflects the historical difficulties of replicating past data and analyses point-in-time and the need for expert curation of data updates going forward.

Macro-quantamental signals allow systematic trading to become more informed and make prices more “anchored” in economic reality. Value generation is not merely a zero-sum game but also a profit share from a more efficient financial system. The principles of quantamental success are the faster pricing of macroeconomic developments, the correction of implausible risk premia, the adjustment of evident price-value gaps, and an improved pricing of market “setback risks”. Empirical evidence has shown macro-quantamental signals succeeding in many areas, including market timing, enhancement of trend following, improvement of risk premium strategies, equity allocation, and higher-frequency information change-based strategies. Macro quantamental signals also integrate neatly with statistical learning.

(more…)

Macro information changes as systematic trading signals

September 28, 2024

Jupyter Notebook

Macro information state changes are point-in-time updates of recorded economic developments. They can refer to a specific indicator or a broad development, such as growth or inflation. The broader the economic concept, the higher the frequency of changes. Information state changes are valuable trading indicators. They provide daily or weekly signals and naturally thrive in periods of underestimated escalatory economic change, adding a layer of tail risk protection.
This post illustrates the application of information state changes to interest rate swap trading across developed and emerging markets, focusing on six broad macro developments: economic growth, sentiment, labour markets, inflation, and financing conditions. For trading, we introduce the concept of normalized information state changes that are comparable across economic groups and countries and, hence, can be aggregated to local and global signals. The predictive power of aggregate information state changes has been strong, with material and consistent PnL generation over the past 25 years.

(more…)

Inflation and equity markets

September 14, 2024

Jupyter Notebook

Rising inflation is a natural headwind for equity markets in economies with inflation-targeting central banks. As consumer prices accelerate, expected monetary policy rates and discount factors tend to increase more than dividend growth. Over the past three and a half decades, there has been a strong negative correlation between changes in reported inflation and simultaneous global equity futures returns. A similar negative relationship is evident between seasonally adjusted CPI trends and concurrent returns.
Furthermore, inflation dynamics have shown predictive power. Short-term changes and trends in consumer price growth have proven to be valuable early warning signals for serious market downturns and leading indicators of recoveries. Also, inflation-sensitive strategies have performed on par with long-only portfolios during stable periods. Overall, they enhanced risk-adjusted returns by improving the timing of equity risk exposure.

(more…)

Statistical learning for sectoral equity allocation

September 4, 2024

Jupyter Notebook of factor calculation Jupyter Notebook of statistical learning

There is sound reason and evidence for the predictive power of macro indicators for relative sectoral equity returns. However, the relations between economic information and equity sector performance can be complex. Considering the broad range of available point-in-time macro-categories that are now available, statistical learning has become a compelling method for discovering macro predictors and supporting prudent and realistic backtests of related strategies. This post shows a simple five-step method to use statistical learning to select and combine macro predictors from a broad set of categories for the 11 major equity sectors in 12 developed countries. The learning process produces signals based on changing models and factors per the statistical evidence. These signals have been positive predictors for relative returns of all sectors versus a broad basket. Combined into a single strategy, these signals create material and uncorrelated investor value through sectoral allocation alone.

(more…)

Tags

Tags
asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation carry China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
ABOUT US

Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

FOLLOW US
Linkedin
LEGAL
Research
Quantamental Academy
About Us