Sirca DataConsults

Sirca members are able to access the Sirca DataConsult team for assistance in accessing complex, non-standard or unwieldy data to support their research objectives. Examples include gaining access to the ASX CHESS database (see this post for more details on CHESS), accessing forms of data which are not downloadable from our online resources, and mashing data across multiple sources.

Our DataConsult team is expert in curating data for academic research and as a result tends to suffer from having a large number of requests to complete. We try hard to fulfill these requests as rapidly and efficiently as possible, however due to their nature and the economics of the academic segment it is not always possible to turn these jobs around quickly, particularly at peak times of the academic year when research projects are beginning.

At present we are also having to recreate our software request libraries for ASX data due to third party coypright issues which have emerged recently, as well as modernising some of the infrastructure which underpins data retrieval.

As a result there is currently quite a big backlog being managed by the team. In order to try and tackle the workload, we are seconding additional resource to the DataConsult function, however we do ask for understanding from members whilst we get to grips with things.

As a reminder, the current process to submit a DataConsult job is to submit a formal request using the DataConsult form. The form can be accesses from this page, by clicking on the “Data Consult Form” link at the bottom of the page. Requestees will note that this prompts you for a password. This password is held by the designated Sirca liaison point within your university; this steps act as validation for your research request by your university. Get in touch with us if you need to know who to contact for your university password.

Access to ASX CHESS for Sirca members

Sirca member universities can access the ASX’s Clearing House Electronic Sub-register System, better known as CHESS, to support eligible research projects. Since 1995, CHESS has been performing two core functions for the ASX: (i) it facilitates the settlement and clearing of trades in shares, and (ii) it provides an electronic sub-register for shares in ASX listed companies.

Sirca is able to facilitate access to CHESS, for reasonable research requests, from qualified academics, within member universities. The first step is to contact Sirca in order to begin the process of describing the research goals and how these can be achieved via access to the CHESS database.

Members will appreciate that CHESS contains sensitive data where the privacy of market participants needs to be protected; any proposed access to the CHESS database must be designed therefore in such a way that the privacy of market participants is maintained, in line with ASX standards and Australian legislation. Both Sirca and ASX staff and processes will be vigilant in ensuring that the data extraction goals from CHESS are designed with this in mind.

Given this, we have agreed on a process with the ASX whereby any data extracted from CHESS is subjected to the following tests before it can be released to the researcher:

(i) “Threshold Test”

The “Threshold Test” checks that there are a minimum of 10 observations within each point for each data category. For example, if a researcher is seeking to identify the number of Super Funds who are invested in a particular stock, we will only be able to release data in the event that at least ten Super Funds are present on the sub-register for that company.

(ii) “Dominance Test”

The “Dominance Test” checks that no single observation accounts for more than 50% of the value for each data point for each data category; that no two observations combined account for more than 75% of the value of each data point for each data category; and that no three observations combined account for more than 90% of the value of each data point for a data category.

(iii) “Activity Test”

Where time series data is provided, the “Activity Test” checks that no single observation accounts for more than 50% of the absolute value of aggregate changes for the data category compared to the previous period, and that no two observations combined account for more than 75% of the absolute value of aggregate changes for the data category compared to the previous period.
Below are the categories for which holdings data can be extracted. Please also note that these are further subdivided into domestic vs foreign branches making 18 categories in total.

1. Bank.
2. Other Deposit Taking.
3. Nominees.
4. Insurance.
5. Super Funds.
6. Trusts.
7. Government.
8. Inc. Companies.
9. Individuals.
It is also worth noting the following frequently asked questions about CHESS data:

(i) Why don’t the categories sum to 100%?

Since CHESS only accounts for shares being registered on the CHESS sub-register, if a shareholder decides not to register his/her shares with the CHESS sub-register of a company after its IPO for whatever reasons, then a portion of the shares issued won’t be registered on CHESS and as a result the sum of categories may not account for 100% of shares on issue.

(ii) Why does the sum of the two categories, individual, and institutional, on some occasions exceed 100%?

A cross-listed (dual-listed) company will have shares issued in Australia as well as in at least one overseas jurisdiction. In the event that this company’s foreign issued shares are traded in Australia, they may be registered in CHESS, in addition to the domestically issued shares. In this case, working out the ownership percentage using the Australian issued shares as a denominator, may result in a percentage greater than 100% given that we wouldn’t have accounted for the foreign issued shares in arriving at the denominator value.

(iii)  Why can’t I get a complete breakdown of ownership holdings for a company from CHESS?

This source will only show ownership holdings for individuals/entities who trade. This means the holdings of entities that have not traded over the history of the CHESS data set, are not known.  A complete breakdown of ownership holdings is therefore not usually possible for larger companies. CHESS records for Woolworths, for example, would not record a key shareholder’s holdings if this entity had never traded them since the inception of CHESS.  Of course, as soon as a trade does occur, the holdings in that trading entity would be reported.  Holdings that this entity may control in other entities would remain unknown until some of these were also traded.