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. 

  • 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 importance of timely macro information for trading is not contentious. It has been a profitable trading principle for decades. However, quantamental information is difficult to find and expensive to produce. Standard economic data archives are not well-suited for the needs of markets, with publication time stamps disregarded, history compromised by revisions, adjustments altered with hindsight, and data records suffering from numerous errors and distortions. JPMaQS aims to clean “dirty” and messy data, and make the information more accessible and beneficial for all market participants.

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

Why do quantamental indicators add investor value?

Quantamental data increase trading profits for two simple reasons. 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.

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.