What Are Macro Quantamental Indicators?

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What are macro quantamental indicators?

Macro quantamental indicators are time series of macroeconomic information states designed for the development and backtesting of financial markets trading strategies. They are the building blocks for the macro-quantamental technology, i.e., investment strategies based on the systematic use of macroeconomic information.

The term “macro quantamental” generally refers to data that directly inform on the activity, balance sheets, and sentiment of various parts of an economy. This distinguishes them from market data, which dominates algorithmic trading, because they are timely and easily accessible. Market data may also relate to fundamentals. However, market data provide this information only indirectly and are influenced by other factors.
Values are always public information states of the latest instance of a measure, such as GDP growth, earnings ratios, or real interest rates, as observed in real-time at the end of a reference day. This sets quantamental data apart from standard economic data. It means that quantamental data are based on data vintages, i.e., time sequences of full data histories. They are principally designed to replicate what the market knew at a point-in-time in the past and to prevent any look-ahead bias in the development and evaluation of trading strategies. Moreover, quantamental data include other helpful particulars, such as publication lags and the accuracy of replicating the historical information status.

Example: Standard economic time series of production trends versus quantamental series

Example: Explanatory power of standard economic versus quantamental series

The key source of macro quantamental information for institutional investors is the J.P. Morgan Macrosynergy Quantamental System or JPMaQS. It is a service that makes it easy to use quantitative-fundamental (“quantamental”) information for financial market trading. With JPMaQS, users can access a wide range of relevant macro quantamental data that are designed for algorithmic strategies, as well as for backtesting macro trading principles in general.

The official documentation site of JPMaQS on J.P. Morgan Markets can be found here.

Basics of investment management with quantamental indicators

How macro-quantamental trading signals will transform asset management

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.

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

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.

Quantamental economic surprise indicators: a primer

Quantamental economic surprises are point-in-time measures of deviations of economic indicators from expected values. There are two types of surprises: first-print events and pure revisions. First-print events feature new observation periods, and the surprise element depends on market expectations of the indicator. Market surveys can approximate such expectations, but only for a limited number of indicators. Quantamental surprises use econometric prediction models and can be calculated for all indicators and transformations, principally using the whole information state.

How do quantamental indicators add investor value?

Quantamental data increase trading profits in two simple ways. First, they greatly enhance the feature space of macro trading factors. Second, they drastically reduce costs and development time of proprietary trading strategies with fundamental macro content.

Quantamental data broaden the scope of easily backtestable and tradable macro factors for investment strategies. They capture critical aspects of the economic environment, such as growth, inflation, profitability, or financial risks. The quantamental format of daily information states means that now this information can be used systematically. JPMaQS allows institutional investors to trade on many relevant fundamental macro trends across multiple countries and to check if such information efficiency has historically added value. Moreover, the quantamental data format makes it easy to combine different indicators into customized composite factors tailored to the purposes and know-how of the investment manager. Finally, JPMaQS enhances the feature space that can be applied to standard algorithmic strategies and machine learning pipelines. At present, the vast majority of algorithmic strategies focus on market data, such as prices, returns, carry, flows, and maybe some alternative real-time data. JPMaQS not only adds new features, but by presenting them as information states, makes it very easy to combine them with standard algorithmic factors.
JPMaQS reduces quantamental information costs through scale effects. It spreads the investment of low-level data wrangling and codifying fundamental domain know-how across a range of institutions. For individual managers, the development of trading strategies that use fundamentals becomes much more economical. Access to the system removes expenses for data preparation and reduces development time. It also centralizes curation and common-sense oversight. This allows investment managers to focus on their core strengths: the development of investment strategies or trading ideas and capital allocation. JPMaQS also reduces moral hazard. Normally, if the production of indicators is a lengthy and expensive proprietary project, there is a strong incentive to salvage a failed proposition through flexible interpretation and effective data mining.

The economic value of enhancing portfolio management through macro-quantamental trading factors at low cost is significant. Macrosynergy has demonstrated the predictive power and stylized PnL value of a range of plausible signals (see detailed analysis here). Notably, the correlation between conceptually distinct macro-quantamental strategies has remained low over the past decades, owing to the differences in signals and the broad range of asset classes and contracts that can be traded with such signals.

The correlation heatmap below illustrates this point for all 19 macro-quantamental strategies available on the Macrosynergy Academy as of the end of August 2024, covering fixed income, equity, FX, credit, and commodities (excluding cross-asset strategies). The average Pearson correlation has been 4%, with only one strategy pair exceeding a 50% correlation. Due to this diversification, a simple equally weighted average of these strategies, weighted by past volatility and rebalanced monthly, would have generated a high-Sharpe PnL with minimal seasonality and modest correlation to market benchmarks.

What are the basic principles of quantamental indicator construction?

Macro quantamental indicators simply align measurements of economic events with their lifespan as the latest available information of its type. For instance, measurements of economic flows for a given month are associated with the time span from their release date up to but not including the date they become obsolete, due maybe to a revision or newly released month. This means that quantamental indicators always represent the knowledge of a fully-informed investor with respect to the concept, recorded on a timeline of real-time dates, although not everyone may use the information at the real-time date.

The real-time date principle implies that quantamental indicators are principally based on a two-dimensional data set.

The first dimension is the timeline of real-time dates. It marks the progression of the market’s information state.
The second dimension is the timeline of observation dates. It describes the history of an indicator for a specific information state.

For any given real-time date, an indicator is calculated based on the full information state, typically a time series that may be based on other time series and estimates that would be available at or before the real-time date. This information state-contingent time series is called a data vintage.

The two-dimensional structure of the data means that unlike regular time series quantamental indicators convey information on two types of changes: changes in reported values and reported changes in values. The time series of the quantamental indicator itself shows changes in reports arising from updates in the market’s information state. By contrast, quantamental indicators of changes are reported dynamics based on the latest information state alone.
This implies that a transformation (such as % change) of a quantamental indicator is not the same as a quantamental indicator of a transformation. The former operates on the first dimension (real-time dates), while the latter operates on the second dimension (observation dates).

A data vintage is an instance of a complete available time series associated with a real-time period. Conceptually, vintages are complete past states of information or “time series of time series”. They come about through data revision, data extension, and re-estimation of the parameters of the underlying model. Vintages allow replicating what markets knew at any day in recent history, which is critical for backtesting algorithmic strategies. Disregarding vintages leads to survivorship and look-ahead biases in evaluating trading ideas.

The vintages-of-vintages paradox: Why historic point-in-time series change

An inconvenient truth

Conceptually, all JPMaQS indicators are based on vintages, i.e., estimates of time series as they were available on the date of their timestamp. If all information was pristine, generated by use of a “time-machine” that allowed to go back to each day and check what market participants found on their databases, the history of indicators should never change.

However, in practice, indicators and underlying vintages do change, sometimes even market-related data. This is inconvenient insofar as analyses based on these indicators change as well. Typically, these changes are tiny. However, changing backtests are principally disconcerting, and actual live trading decisions may need to be reviewed if changes are material.

Why point-in-time data evolve

Changes in past data vintages occur for three basic reasons:

Convergence: As JPMaQS continuously ingests new information, its history gradually converges towards a closer replication of the past. Standard parts of this evolution include the retrieval of older original vintages that supersede estimated ones, the discovery of a better vintage archive, or the switch to a better process to estimate older vintages. Also, sometimes release date information. Vintage changes due to convergence are desirable as they improve the quality of indicators. Convergence is a process that will go on for a long time, particularly when older vintages are added that are not available in electronic format.
Corrections: JPMaQS relies on vintage archives from any sources. Unfortunately, few people peruse the archives, and hence, curation is minimal. Data errors are regularly discovered (not least by Macrosynergy or JPMaQS clients) and trigger changes in the electronic archives underlying the JPMaQS data warehouse. Mostly, these changes are desirable, but it is possible that sometimes “new and improved” data sources have unexpected faults.
Fixes: JPMaQS code continuously evolves with a focus on optimization and improvement in data quality. If errors are discovered, they will be fixed as soon as possible. Related changes in history reveal inaccuracies in the system, but the JPMaQS team is committed to always providing the best quality at each point in time. Mostly, bug fixes are desirable, but the bugs that existed before are naturally disconcerting.

How JPMaQS manages vintage evolution

Transparency

JPMaQS reports changes as soon as possible, desirably before they are implemented.
The ‘JPMaQS daily indicator updating report’ provides clients with a list of indicators for which historical values were restated due to the overnight calculations, along with the real date range and count of values restated. We also explain the reason behind the indicator value changes.
A weekly post-production release notification summarizes the changes released by JPMaQS team, whether it is a methodology change, injection of new data or bug fix. It will also show which indicators would see an impact on their historical values. The JPMaQS team also follows up with clients that have deeper questions.

Preparation

A system tool helps trace the impact of changes in calculation specifications or methodology at the (original) series level onto the final indicators that would be impacted. This helps generate the list of impacted indicators for client notification.

Checks

The JPMaQS team operates dedicated checks in the UK morning to go through the list of indicators with historical values. To help with the process, the system has a few tools:
- Generation of six reports including one which determines what the reasoning behind the changes in historical values is (data change, new release, series change, etc).
- Tools have been developed for a quick analysis during the investigation of a particular series or indicator. This provides a chronologically arranged series of events that happened and their effect on the release/observation (whether it was new data inserted or an update to existing data).

Records

The JPMaQS data warehouse contains three capabilities to maintain data/metadata about changes in values. These trace the changes from the very first value for an observation to the latest value with timestamps of all the updates.

Data values history maintains any changes observed while collecting data from sources.
Series values history maintains any changes observed in the outputs of series calculated using source data.
Indicators values history maintains any changes observed in the outputs of calculated indicators. It has the capability to record changes happening in any of the four attributes of a JPMaQS indicator: value, eop_lag, mop_lag and grading.