Logo
RiskAPI
Time to Manage Your Risk

19 September 2014

Analyzing the Risk of the Alibaba (BABA) IPO

As with other big IPO's, the Alibaba deal has drawn a lot of attention from Wall Street - it is the single largest U.S. initial public offering ever - and with that, a likely large amount of institutional investors. As such, chances are high that there are now more than several chief risk officers and portfolio managers grappling with how, exactly, to measure the exposure of this new issue.

Ownership Structure

To begin with, it's worth spending a moment talking about what specifically shares of the NYSE-listed BABA represent. Unlike shares of common stock in a typical public corporation, which resolve to one unit of direct equity in a corporation, shares of BABA are actually units in an offshore, Cayman Islands-based trust, which has a contract to share in the profits of the local Chinese Alibaba corporate entity. This is due to legal restrictions in place by the Chinese government which prohibit direct foreign ownership in Chinese Internet service companies. To get around this restriction, something called a Variable Interest Entity (VIE) was created to allow a foreign-owned investment vehicle to experience correlated returns vs. the onshore Chinese stock. In short, everybody involved agrees that one share in the VIE will track the performance of the onshore-Chinese equity. To be clear, this agreement is only as good as all of the players decide it will be, of these most notably are Alibaba's CEO Jack Ma and the Chinese government. If the on-shore owners or the authorities decide to change or invalidate elements of this agreement, it's not clear what legal recourse foreign investors could have.

Needless to say, there are a great deal of structural risk factors in the very nature of the shares of BABA that need to be carefully considered as part of the systemic risk inherent in owning shares of BABA.

Market Risk of an IPO

Analyzing the market risk of an IPO has always been difficult. By definition, shares in an IPO do not have any historical pricing data, something ex post facto risk calculations rely on heavily to generate exposure analytics. If a stock only has one day's worth of pricing history (as is currently the case with BABA) how does one calculate return observations? How can predictions based on historical data even be made if no historical data exists? So what's a risk manager to do?

Enter data proxying. Data proxying is a mechanism whereby the original market-driven historical pricing used to analyze a financial instrument is replaced with a single or combination of adjusted or derived historical time series. There are several reasons to proxy market data. Chief among them is data scarcity- there simply isn't enough market data available. Another is a belief that the market is mis-pricing a security and that proxied data better reflects the "real" historical value and risk.

The source of proxied data can be a geographically relevant index, a sector-relevant stock, or a basket of financial instruments chosen for macro-economic or fundamental reasons. In the case of a proxy for an IPO, the idea is to come up with a replacement for the non-existent historical pricing data and provide instead a suitable time series that approximates the return behavior of the stock, pre-IPO. With the proxied data in-hand, useful exposure analytics can be derived and utilized as is the case with instruments that have abundant pricing information.

Proxying in RiskAPI

The RiskAPI system provides a built-in proxying mechanism that enables the return history of an existing data set to be mathematically joined to any available IPO data. Proxying is done via a dedicated symbol format which contains the information necessary to construct the proxy. In the case of BABA, this can be done as follows:

SPX;BABA;09-18-2014.PRX

The result of the proxy symbol above is a data set that combines the return history of the S&P 500 Index prior to 9/19/2014 (the IPO date) and any available data for BABA since the IPO. In contrast to a wholesale replacement of BABA with a position in the S&P 500, no quantity adjustment needs to be made in order to match the position size of the proxy. The market value of 10,000 shares of BABA and 10,000 shares of SPX;BABA;09-18-2014.PRX will be equivalent.

The proxy above represents a crude approximation of pre-IPO behavior using the returns of a US equity index. This proxy would only be appropriate if one holds the view that the S&P 500 is a reasonable substitute for the behavior of BABA pre-IPO. In order to more closely approximate the economic risk of BABA shares, a more fitting proxy could use the China-based Shanghai Composite Index:

180167;BABA;09-18-2014;USD.PRX

Note that even though the Shangahi Index is a China-based equity index and as such would represent a CNY-denominated asset, the RiskAPI system allows users to denominate the proxy in US Dollars (for example) allowing the proxy to sample the market returns only of the index, eliminating any currency exposure, which would not be present for holders of BABA, a stock listed in the US and denominated in dollars. Below are sample results run using the RiskAPI Add-In with both forms of proxying:

The output above shows the results of three different forms of proxying: outright substitution (note the different quantity change made to match the market value), simple US-based index proxying, and local-market index proxying. Note the significant difference in VaR as a result of the application of the Shanghai Composite index vs. the S&P 500.

The results above were calculated using The RiskAPI Add-In, our unique software client which allows risk practitioners, portfolio managers, and traders to access a whole spectrum of on-demand portfolio risk analysis calculations.

17 September 2014

PortfolioScience to Sponsor the EzeSoft Client Conference

We are pleased to announce the PortfolioScience will be the Silver Sponsor in this year's Eze Software Group's Client Conference.

From the conference website:

Eze Software Group is excited to host the 2014 EzeSoft Client Conference on October 15-17, 2014 in Boston, Massachusetts.

The conference offers several tracks of sessions for attendees to learn about Eze Software products and industry trends. Over the course of 1.5 days, client attendees and sponsors can attend a number of discussions and presentations.

2014 CONFERENCE HIGHLIGHTS

  • 2014 Guest Speaker: Mel Robbins, Founder of Inspire 52, CNN Contributor, Motivational Speaker & Syndicated Talk Radio Host
  • Multiple tracks with targeted sessions led by Eze’s product experts in trading, portfolio management, compliance, data management, middle/back office, and risk
  • Network with other EzeSoft clients
  • Case studies and panels involving clients and industry analysts
  • Interact with key vendor partners in the Partner Pavilion
  • Discuss best practices and more efficient workflows

The conference agenda can be found here: http://ezesoftconference.com/schedule/

04 September 2014

Integrated Custom Data Upload

The latest version of the The RiskAPI Add-In now includes the ability to import custom time series directly via the RiskAPI Add-In from within Excel. Prior to this, importing custom data was only available via uploaded text files on the support website. This new feature makes access to custom data rapid and trouble-free.

Custom data time series are segregated and secured on a per-account basis. The import process (pictured above using the Market Macro keywords mechanism) allows users to assign a custom symbol to any historical data they choose to import. Once the import process takes place, the custom data can be accessed as if it were a listed financial instrument:

Pictured above is the same custom data in the first image being accessed via the custom symbol "CUSTOM.CST". In this example a realized volatility calculation is executed on the imported time series using the Market Macro tool. Custom data can be used to proxy portfolio components, to replace standard indexes for exposure analysis, to analyze historical NAV's, and more.

The results above were calculated using The RiskAPI Add-In, our unique software client which allows fund managers to access a whole spectrum of on-demand portfolio risk analysis calculations.

<<  1 2 [34 5 6 7  >>