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July 10, 2024

The critical role of Point-in-Time data in economic forecasting and quant trading

In the investment world, accuracy and reliability are paramount. This is the reason why point-in-time (PIT) data is crucial for economic forecasting and quantitative trading strategies. It ensures historical accuracy by preserving data as it was originally reported, along with any subsequent revisions.
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In-house blogger
Guest blogger
Grégoire Haftman
Global Head of Data Solutions
Macrobond Financial
All opinions expressed in this content are those of the contributor(s) and do not reflect the views of Macrobond Financial AB.
All written and electronic communication from Macrobond Financial AB is for information or marketing purposes and does not qualify as substantive research.

In the investment world, accuracy and reliability are paramount. For economic forecasting and quantitative trading strategies, the integrity of the data used can significantly impact outcomes. One crucial aspect of data integrity is point-in-time (PIT) aspect, which ensures that historical data reflects the information available at specific moments in time. In this blog, we explore the importance of PIT data and how Macrobond is leading the way in this critical area.

Why Point-in-Time data matters

Point-in-time data refers to data that is timestamped and recorded as it was originally reported, without subsequent alterations. This means that any revisions to the data are preserved and displayed alongside the original data. The value of PIT data lies in its ability to provide a clear and accurate historical record, essential for backtesting and forecasting.

Preserving data integrity

One of the key attributes of reliable data is the preservation of its original state. As noted in our recent discussions, the data needs to comply with a few attributes. For example, if the original data is being revised it should be preserved as original data and displayed alongside the revision data. Both sets of data should be timestamped. This ensures that analysts and researchers can understand the context in which the data was originally reported and avoid introducing biases into their models.

Challenges with traditional data

Working with traditional data can be challenging due to its sparsity and the frequent deletions of historical records.

Dr Lasse Simonsen of Macrosynergy highlighted, "Working with traditional, standard economic time series is very challenging, as oftentimes there is little standardization of economic concepts across countries, and the formats can be unwieldy and mismatched. The point in time dimensionality of these series is sparse, largely because so much data out there that has been deleted."

This scarcity shows the important requirement for robust PIT data to maintain the integrity of financial analyses.

Enhancing forecasts with PIT data

Point-in-time data is crucial not only for historical accuracy but also for enhancing forecasts. Using PIT data effectively requires understanding its availability and limitations. By integrating PIT data from the moment it becomes available and applying it consistently, analysts can create forecasts with historical depth and improved accuracy. This approach allows for a more accurate reflection of past conditions, leading to better-informed predictions.

Systematic trading strategies using PIT data

Systematic trading strategies involves backtesting strategies or sets of strategies algorithmically. This systematic approach is crucial for developing robust and reliable trading strategies that can withstand various market conditions.

Macrobond's PIT data coverage

Macrobond has established itself as an industry leader in building up point-in-time dimensionality.

Simonsen stated, "Macrobond is an industry leader in terms of their effort into building up your point in time dimensionality. It has been one of the helpful inputs for us, as we develop novel macro quantamental indicators."

This recognition underscores our commitment to providing high-quality, reliable data that financial professionals can trust.


Point-in-time data is indispensable for accurate economic forecasting and quantitative trading. By preserving the original state of data and ensuring its historical accuracy, PIT data provides the foundation for reliable analyses and robust forecasting and trading strategies. We continually invest in our data to ensure its integrity and to set the standard for the industry.

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