There are three key topics for today’s blog post:
- Efficient and precise inputs are foundational for modern trading strategies.
- Fundamental data is more complex than ever before.
- XBRL is the global digital reporting standard; a sophisticated and efficient solution when handled properly.
Reduced Friction Creates Efficient Trading
The modern trading firm in 2022 requires a degree of speed in every aspect of operations. From the signal generation to the risk allocation, to the execution, each process needs to be conducted with as little lag time as possible. Being correct about the future movement of security decreases in value when friction enters into the process in volatile market conditions.
Every piece of news is now consumable in near real-time for hedge funds and other advanced trading firms. There is a saturation of signals. Alternative data conferences will sell anything from satellite feeds to highly particular energy stock and usage information.
The core ability of markets to efficiently price tradable securities rests on data about the financial realities of the businesses themselves. No matter what someone may be tweeting, when a 10-Q or 10-K comes out, the entire market and trading ecosystem gain access to an immense amount of information AT THE SAME TIME.
The Main Question is Speed Parity
How long does it take for that information to make it into a trading model, and what are the paths?
1. The hard financial numbers that come from company financials made available by the Securities and Exchange Commission power this trading economy.
2. News sources and feeds will report on the topline financial numbers quickly after they are published, and alternative data providers will scrape that info from the web, and structure it for consumption.
3. Large legacy vendors can also be a pathway for this fundamental data. They employ teams of highly trained people to scrape financial reports and structure and deliver this data to clients.
How long in practice does it take for data to make it through this process and into a trading model?
Core Data, Faster
idaciti deals in this core, fundamental financial data. We take an XBRL-first approach, where 98% of our data is curated by machine learning. In a world awash in data, the quality and precision of our dataset deliver speed and granularity.
How long does it take idaciti to normalize a 10-K or 10-Q, etc. for immediate structured consumption by trading models?
About 1.5 minutes. 90 seconds mean processing time, from when the report is downloaded from EDGAR to when the normalized data has made it into your trading process.
Metadata Relationships and Authoritative Definitions
How significant is that? First of all, no other firm can match that speed on a large scale. Our technology relies on XBRL as described here:
XBRL allows the creation of reusable, authoritative definitions, called taxonomies, that capture the meaning contained in all of the reporting terms used in a business report, as well as the relationships between all of the terms. XBRL International
At idaciti, our technology process has already absorbed and structured these taxonomies, making deep use of the relational and dimensional data. When a new filing comes in, we are able to draw on every reported instance of a similar fact to power machine-enhanced data normalization.
Basic vs Advanced XBRL
Many firms have basic XBRL practices in place. Your firm itself may ingest some XBRL data, and use it for trading and/or research processes.
Do you feel confident that every piece of information is being internalized properly? Let us see if we can shake that confidence a little bit.
Untangling XBRL before it can be used for trading purposes.
The following passage from an internal IFRS meeting is dense but gives you a sense of the depth of nuance that goes into “untangling” the XBRL before it can be used for trading purposes:
16. To illustrate this point, consider property, plant, and equipment (PP&E). The IFRS taxonomy specifies, among other things, elements for total cost and total accumulated depreciation for PP&E. The taxonomy also specifies some children of these elements, by identifying some specific classes of PP&E (each with the same element structure as for total PP&E).4 In creating the taxonomy the classes were limited to classes mentioned in IFRSs. Even then, the taxonomy identifies these as common practice elements. There is, for example, no requirement to have motor vehicles as a class of PP&E, but it is referred to as a possible class by IAS 16. The purpose of designing the taxonomy in this way was to ensure that an element in the taxonomy could be traced to a reference in the Bound Volume. This ensures the integrity of the taxonomy and reduces the risk of the taxonomy defining IFRS requirements. Instead, IFRSs define the taxonomy.
17. The US GAAP taxonomy was designed using a different approach. Financial statements and other references were used to identify common practice. As a consequence, the core US GAAP taxonomy has many more elements than the IFRS taxonomy. An example is shipping containers, for which there are elements as a class of PP&E in US GAAP. There is no requirement in US GAAP to disclose or define shipping containers as a class of PP&E, but it is included because it was identified in the documents used to create the US GAAP taxonomy. The IFRS taxonomy does not have equivalent elements.
18. The most recent experience with XBRL filings indicates that filers had to generate entity-specific extensions (ie elements created by the companies filing) whether they were using the IFRS or US GAAP taxonomies. In other words, neither taxonomy provides a complete set of elements. It also seems that there were just as many extensions using the US GAAP taxonomy as there are with the IFRS taxonomy. IFRS 2010
Did the above passage put you to sleep? :-)
TLDR: There are multiple taxonomic methods of reporting similar data, and these can overlap or diverge to produce challenging outcomes for a financial analysis model. GAAP, IFRS, and the custom tags that companies produce for things like “iPhone Sales” all contribute to the complexity.
Our machine intelligence process answers the questions posed by the above analysis. Encoded within the 13 years of historical XBRL tagging behavior, our team has seen it all. This allows us to efficiently parse the latest 10-Q into its component parts, associate them with the appropriate metadata, and expose it to your firm in under 90 seconds flat.
Speed is a Handy Tool in Volatile Markets
By the time an alternative data model is incorporating the headlines about a revenue number, idaciti customers already have that data and can be establishing positions with confidence. Before the markets move, in whichever direction.
Traditional traders' lack of speed places them on the wrong side of the market.
- Professor Johan Hombert, The Role of Speed in Today's Financial Markets | HEC Paris
The rise of high-frequency trading presents a constant challenge to the efficiency of any modern hedge fund trading practice. The exceptional advances in processing time offered by idaciti can give your firm an edge in quality, speed, and precision when it comes to fundamental financial data.
If you are curious to know more about the types of data you can receive from idaciti - schedule a call today and hear about our unique trial sets covering the Russell 3k.
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