Abraaj Group was the poster child of Emerging Markets private equity investment. Founded by Arif Naqvi in 2002, the firm grew to $14 billion and counted among its investors such prominent entities as Teachers Retirement System of Texas, Bill & Melinda Gates Foundation, Washington State Investment Board, World Bank, and dozens of other highly sophisticated investors. At the height of its success in 2015, the firm raised almost $1.5 billion to invest in Africa, in just under 12 months.
Then things spiraled out of control quickly.
Several limited partners began asking questions about the use of funds. A 2017 independent audit showed that the management company was siphoning investors’ money to fund its own operations. A spectacular collapse followed. The founder, Arif Naqvi was arrested on fraud charges, along with other two top executives. What took 16 years to build unraveled in less than 4 months.
Many investors who made an investment in Abraaj followed advice of a single large consultant, Hamilton Lane, who encouraged investors to commit as much as $900 million to Abraaj in 2017, just a few months before things began unraveling.
Yet, others avoided investment. TorreyCove Capital, another consulting firm, wrote: “we met with Abraaj several times over the course of months and had concerns… we were fortunate enough to be able to speak with former employees who described problematic management practices”, according to a Pension & Investments article.
To paraphrase Tolstoy, every debacle in the world of private funds is different in its own way. What unites most of them is that sophisticated investors who suffered the loss always realize in hindsight how easily the problem could have been avoided had the right questions been asked early enough.
The fundamental problem lies in how due diligence is performed. Many institutional investors pay hundreds of thousands (or millions) of dollars to consulting firms to generate due diligence reports on all their current and prospective investments. Many others, including smaller investors, perform some level of due diligence themselves, or worse yet, not at all.
In every case where due diligence is performed, each investor obtains it from a single source – either an external consultant or an internal team. In case of Abraaj, the most prominent external source of due diligence missed important warning signals, resulting in many clients making a bad decision.
A large component of the risk of investing in private funds comes from non-investment activities. It is known as operational risk. Because it’s so difficult to quantify, it is used as a binary filter. If a problem is noticed, the operational risk filters out the investment, otherwise it is allowed. As with all binary decisions, it is critically important to minimize the likelihood that something is missed. As readers of our previous blog posts know, we are fervent believers that a consensus opinion of experts can help increase the effectiveness of the operational risk filter.
Imagine the world where due diligence performed by many experts is recorded on a private blockchain and shared in a secure, trusted manner. How many problems would be avoided if the dissenting voices were heard along with the majority opinion? This, by the way, is the reason why public companies are eager to obtain analyst coverage from multiple sources: broader coverage = better decisions = wider investor participation.
In the world we envision, access to multiple expert opinions about private funds would make the entire ecosystem more transparent, less prone to manipulation, and improve transactional flow, something that all participants are ultimately hoping for.
DDX Technologies is developing a comprehensive protocol for exchange of due diligence on a distributed ledger. To learn about our project or apply to join the group of large financial institutions helping us with the development of the blockchain due diligence protocol, visit our website, https://ddx.exchange
A 2008 paper “How Unlucky is 25-Sigma”, describes an interview with Goldman Sach’s then-CFO David Viniar who referred to their flagship fund’s 27% loss in August 2007 as a “25 Sigma event”. The paper goes on to analyze this statement and quickly shows that a 25-Sigma event would take place once in 1.309e+135 years, a number so large that the paper accurately describes this time frame as “being on par with Hell freezing over”.
Mr. Viniar’s estimate was off by more than the age of the universe. Clearly, no measure of market risk (a.k.a. Sigma, or standard deviation of returns) could provide a clue into what happened. What was missing?
The vast majority of investors measure and compare the quality of their investments by calculating risk-adjusted returns provided by any number of readily available ratios such as Sharpe, Sortino, and others. The denominator in these ratios is always risk. Risk, conveniently, is measured by calculating standard deviation of returns, an observable metric. Even if Viniar exaggerated and the event that occurred was “only” a 15-Sigma event (27% monthly loss divided by the fund’s monthly standard deviation of ~1.8%) this would imply a one-in-1.308E+49 chance of such a loss, an equivalent of winning the Powerball lottery 5 times in a row.
The only logical conclusion is that the models that Goldman (and most other investors in 2007-2008) were using were inadequate. The true risk could not be explained by volatility of historical returns. Another source of risk creeped into their portfolio, resulting in a massive overallocation of risk capital.
Let’s reverse engineer the true risk that was embedded in Goldman’s portfolio at that time. If we assume that a loss like this might occur once every 1,000 years (=12,000 months), this would have been an approximately 5-sigma event, implying the monthly standard deviation of 5.4%, triple the market risk implied by volatility of returns. Assuming the frequency of such loss to be once every 100 years, the implied total risk would be 6.8%, almost quadruple the market risk.
Clearly, traditional measures of risk failed to predict the true size of the problem. Proper due diligence could have uncovered the fat tail not visible through the lens of market analysis.
What was the source of that additional risk? In Goldman’s case, it was something called “model risk”, or the likelihood that internally-developed investment models did not properly account for all aspects of complex investments. In other cases, infrastructural risks such as a cyber-security breach, unauthorized cash movements, trade errors, or plain fraud, could decimate an investment that may otherwise seem relatively safe.
Estimating non-market risk must be a critical component of any investment analysis. The challenge is that these risks are incredibly difficult to quantify ex ante. Just like in the above example, we can only do this ex post, by which time it’s always too late.
One of the problems in quantifying non-market risk is that it does not have a clear scale or a standard measure. Most investors who perform a thorough due diligence do so using their own proprietary metrics, and thus no standard measure is available for investors to use in estimating their true risk.
DDX blockchain protocol aims to change this. With multiple participants sharing their findings in a trusted, secure distributed network, a quantifiable measure can potentially be established. It will not be as clear cut or formulaic as standard deviation of returns. But in a world where no measure exists at all, consensus-based risk metrics can help make risk management and capital allocation decisions a lot more efficient.
To learn about our project or apply to join the group of
large financial institutions helping us with the development of the blockchain
due diligence protocol, visit our website, https://ddx.exchange
 Probability of winning a single Powerball lottery is 1 in 292 million according to an Allstate report. Winning it 5 times in a row is (1/292 million)^5 or 4.7197E+43, slightly better odds than 15 sigma.
In Part 1, we made the case that blockchain technology can be profitably deployed only if the solution can be defined in terms a specific economic arbitrage opportunity. For those taking exception to the word “economic”, bear in mind that any addressable problem can be expressed in economic terms, and thus be potentially described in terms of economic arbitrage.
In Part 2, we introduce the concept of Cooperation Arbitrage, a unique form of arbitrage which may exist in certain service-based ecosystems, and discuss how a blockchain solution can help take advantage of this phenomenon.
As an example, I’m going to take an area with which we, as active investors, are intimately familiar — due diligence of private assets.
For those of us who make investment decisions on a regular basis, the process of due diligence can be frustrating, inefficient, and costly. Even at its most basic, a process of diligencing a private asset costs thousands (sometimes tens or hundreds of thousands) of dollars and takes weeks or months to complete. Even when finished, much of the output is subjective, based on the opinion of an analyst or consultant carrying out the work.
Let’s consider a simple numeric example of a unique arbitrage phenomenon which exists in this ecosystem. Here’s how it works:
Assume that two investors are interested in analyzing the same private investment opportunity. Each hires a well-respected consultant to perform necessary due diligence analysis, and provide a report.
We are going to further assume that each consultant charges the same price for their work product, $A, and spends the same resources, $B, to produce this work product.
The economic model of this small ecosystem looks as follows:
- Each customer pays $A
- Each consultant earns $A – $B
- Total Gross Revenue = 2 x $A
- Total Gross Expense = 2 x $B
Now imagine that all four participants know and trust each other, and decide to cooperate. Each investor hires both consultants who split the work equally. In that case, the cost of work product becomes ½ x $B for each consultant.
Let’s say that with doing only half the work the consultant will happily charge 75% of the full price.
Magically, the economics of the ecosystem transforms:
- Each customer pays ¾ x $A
- Each consultant earns ¾ x $A – ½ x $B
- Total Gross Revenue = 1.5 x $A
- Total Gross Expense = $B
Here’s what just happened: we reduced the cumulative revenue by 25% yet the expense of the ecosystem dropped by 50%. Each customer received the same completed work product for 25% less, while the consultants substantially improved their margins by reducing the cost of their work by 50%.
There are other ways in which this arbitrage may present itself. Say, each of the two consultants produces a complete report and offers it to both clients. In theory, a client should value two opinions more than one, especially if they provide different perspectives. In that case, wouldn’t the client be willing to pay slightly more for better quality information? Assuming the client pays just 20% more for the opportunity to access the opinions of two experts, we are in a win-win scenario again. The customer gets a premium product, and each consultant gets 20% more revenue for doing the same work.
This simple arithmetic exercise points to the existence of a huge arbitrage opportunity. Assuming a fully cooperating ecosystem of many participants, the same two consultants would have access to all customers looking for the same work product. The resultant economics would continue to improve for all participants, until a new equilibrium is found. The difference between that equilibrium and the original pricing structure is arbitrage. It is only possible to extract these arbitrage profits when there is a cooperation among participants. Hence, we coin the term “Cooperation Arbitrage”.
There are many benefits to solving the Cooperation Arbitrage conundrum: paying less for the same product, paying the same price for a much better product, increasing speed of work product delivery, lowering the cost of work product, etc.
Unfortunately, in the real world it is highly unlikely that the ecosystem participants trust each other enough to cooperate in this manner. As a result, everyone suffers.
Enter the distributed ledger solution, aka blockchain. With proper implementation of the blockchain technology, it is quite possible to achieve the necessary level of cooperation and trust among anonymous participants. No centralized system can assure such cooperation; blockchain in essence changes participants’ behavior by offering substantial economic incentives to cooperate, without imposing an offsetting cost. In the process, the large inefficiency we call Cooperation Arbitrage will be eliminated.
At DDX Technologies, we are hard at work mapping the future of due diligence. We believe that some problems have not been addressed only because a technological solution has not been found. That is, until now. The blockchain applications in various consulting ecosystems can potentially disrupt these large, multi-billion dollar industries. We are focusing on one specific use case that we know intimately — due diligence of thousands of private assets. To learn more about our efforts, sign up for updates on our website.
I was introduced to the concept of arbitrage at my first job on a convertible arb trading desk, 32 years ago. Back then, arbitrage meant exactly what it was supposed to – a riskless opportunity to extract profits from a relative mispricing of multiple securities. In plain English, when information is scarce, buyers and sellers tend to price similar assets inefficiently. That results in an opportunity where one could simultaneously buy and sell the same thing at different prices, generating a guaranteed profit.
This was free lunch.
The advent of trading technology, speed and transparency has virtually eliminated every arbitrage opportunity in the public securities markets. Many fund managers retained the moniker “arbitrage” in describing their strategy. The nomenclature became more of a marketing tool to gather assets from gullible investors rather than a repeatable, robust investment strategy Proper arbitrage was then in turn labeled, “bona fide arbitrage”, as if to separate truth from fiction. Being old-fashioned, I will drop the “bona fide” from the name – it’s either arbitrage, or it’s not.
Arbitrage breeds well when a number of conditions are present. First, there has to be lots of participants in a marketplace. Second, the information available to these participants should be difficult to obtain and hard to analyze. And third, the product being priced should not be homogenous. Arbitrage opportunities go back thousands of years, with coins-by-weight arbitrage in ancient Babylonia as an early example, and continue to appear regularly in different markets: bills of exchange arbitrage in 17th century Europe, geographical stock exchange arbitrage in the late 19th century in London and New York, and more recently, convertible arbitrage in the 1970’s and 80’s, and ETF and index arbitrage in the 90’s. By and large, these opportunities for riskless profits are gone.
One thing we know for sure: technology is a great enemy of arbitrage. Technology creates efficiency, equal access to information, and enough transparency to eliminate mispricing. It democratizes inefficient markets, whether it’s paper money printing in Tang Dynasty China in 700 AD, advent of telegraph in late 1800’s, or the ubiquitous Bloomberg terminal in late 20th century.
Over the past decade or so Internet 1.0 provided an amazing opportunity to address certain arbitrage opportunities by means of centralized marketplaces. Amazon, Airbnb, Uber, OpenTable, Yelp – every one of these tech businesses seized on pricing and informational inefficiencies existing in specific markets, and as a result became multi-billion dollar behemoths.
Oddly, as some arbitrage opportunities are eliminated by certain technology applications, others continue to exist.
Similarly to many previous opportunities, arbitrage can still be observed in ecosystems with scarce information, multiple participants, mutable and opaque pricing structures, and unequal access to the underlying asset. Examples are numerous – money transfers for the unbanked, pricing of water resources in emerging economies, identity theft, voter fraud, and medical records systems, to name a few. Like it or not, these are all arbitrage opportunities that exist today (another question is whether capitalizing on some of these is entirely risk-free as they may actually carry a stiff prison sentence).
In the past couple years, we have seen the emergence of a new technological revolution: blockchain. For too many people this is a cool buzzword, usually preceded by words “I don’t understand the first thing about… “. I’d imagine that most of us could easily replace the word “blockchain” in that quote with “cell phone technology”, “global positioning systems”, “telegraph”, “electricity”, or even “the printing press”. Yet, all of the above acted as great arbitrage busters and provided massive rewards for those with the foresight to use these technologies toward elimination of specific arbitrage phenomena.
Much like revolutionary technologies of the past, blockchain’s main reason to exist and its main benefit is the elimination of specific economic arbitrage cases created by various human activities. Nothing more, nothing less. There are now millions of ideas of how to identify various arbitrage use cases and eliminate them, and thousands of applications attempting to experiment with this new technology. Most of them will prove futile, as is often the case when mankind gets a shiny new toy. Just like the telegraph and Internet 1.0, blockchain is not an answer to all problems of humanity. It can, however, address certain specific arbitrage use cases very elegantly and efficiently. Those who identify real-world, practical cases where meaningfully large arbitrage can be eliminated by blockchain applications, will reap massive rewards in the process.
In the follow up to this article, I will provide a specific numerical example of how one arbitrage opportunity which, shockingly, continues to exist in the investment world, expresses itself. Stand by.