The Search for Alpha: What Drives Strong Performance In Illiquid Investments
All asset allocators suffer from a similar conundrum: Do I sacrifice liquidity to try to achieve greater returns?” The fact remains that managers should match their assets and liabilities. The so-called liquidity mismatch was greatly exacerbated during Q3 and Q4 of 2008 and Q1 and Q2 of 2009, when managers were hit with billions in redemptions and caught unprepared, as their positions became illiquid.
The key is to understand the underlying strategy and positions a manager holds, and how those positions match their redemption terms and liquidity profile. Lax redemption terms mean absolutely nothing, and we simply need to go back to the financial crisis as a clear example. Transparency is key, but transparency also kills alpha.
As an allocator, why would you decide to move down the liquidity spectrum? And by doing so, what is necessary to make sure the correct due diligence is being made?
As an investor evaluates managers in liquid strategies, the difference between top and bottom quartile performers is quite small.
Sometimes 100/200bps can make or break manager performance – especially since the market is dominated by gatekeepers, like consultants, who have taken the incentive away from managers to diverge from their strategies underlying indices. It is better to hug the index than to risk short term underperformance. This mentality has practically killed the alpha generation in liquid strategies, and with the institutionalization of hedge funds, similar phenomena has occurred.
Twenty years ago, or even pre-financial crisis, hedge funds were still unknown to most institutional allocators. Most of the capital allocated into hedge funds came from sophisticated family offices and were used in prop desk places like Goldman Sachs, which managed its proprietary capital to generate excess return. Post crisis, we saw institutional players like pension funds and endowments, move into hedge funds. Due to all the redemption restrictions, side pockets and Madoff, the family offices were gone – and since the crisis, prop desks have practically disappeared, as investment banks were no more.
This move into hedge funds brought billions into an industry that had suffered heavily. And in most cases, the funds that out-performed post 2009, were the ones that had suspended redemptions and locked investor capital during the crisis in the first place. We saw that with the Fed and other central banks pumping liquidity via the Troubled Asset Relief Program (TARP) and quantitative easing (QE), low quality assets rallied even more. Excess liquidity “lifts all boats.”
Institutional investors moved into hedge funds, and the billions that followed, coincided with the advent of ultra-fast trading algorithms that would canvass the market for any pricing discrepancies. These so-called systematic strategies became a perfect fit for allocators as they offered large capacity to take on their investment money and provided liquidity.
With a roaring bull market, fundamentally-driven strategies were no match – primarily due to the distortions created by QE and fundamental analysis in public markets, which is a thing of the past. The jury is still out on how the so-called systematic trading strategies will perform on a downturn. I always had an issue with computerized trading strategies – I felt uncomfortable deploying capital where I would not be able to explain to investors how I made money – and more importantly, why I lost it.
With that said, the search for top/down, fundamentally-driven strategies went into overdrive. Public markets are no longer the place to be if you seek strong alpha. Since 2009, it has been next to impossible for managers to beat the S&P 500 – the market went into overdrive due to excess liquidity and the Fed Put, and investors had to turn to private markets in their search for alpha.
In private loan markets, pricing was – and still is – quite subjective. Long-term capital management (LTCM) still casts a large shadow in the industry. Those strategies carry significant default risk due to low credit ratings and limited upside in comparison to equity bull market. Also, as spreads have tightened, potential for duration exposure (as rates eventually rise) is a real risk. In many cases, these strategies perform well in short volatility scenarios, and as the graph below shows, can get crushed when volatility spikes, like when the DJIA dropped over 1,000 points.
Private equity and venture have faced similar experiences with those in hedge funds. The PE and VC markets are now dominated by large institutional allocators and billions are flowing into the industry. This has created a problem where ticket sizes are becoming much larger and companies are staying private longer, as massive funds have taken the place of traditional IPOs.
In my previous update, I discussed the recession happening in early stage deal flow. Early stage venture has experienced a 50 percent drop in number of deals since 2014, while investment dollars have stayed stable – fewer deals with bigger checks. Less than 20 percent of VC deals are now done on pre-revenue companies and around one percent at concept stage. The traditional venture model, where VCs would fund companies on the founders’ garages and university dorms, is no more.
After much analysis, understanding that focusing on longer term, less liquid strategies means that the dispersion of return between top and bottom performers is extremely wide. It means that skill matters!
For a strong alpha generation, you must:
- Identify the right opportunity set.
- Build solid investment and due diligence processes.
- Apply rigorous vetting. To do so, look for strong top/down macro and industry trends, coupled with bottom-up vetting of underlying technologies and opportunities.
- Then, identify processes that are proprietary, diversified and de-risked once the investments are backed by substantial research capital that has been – and will continue to be – invested by top research institutions.
- Compile a world class team.
The graph below comes from the Kaufman Foundation and analyzes a large sample of angel investments over a 10-year time frame. While the mean return for angels was 2.6 times during a three-and-a-half year holding period, the impact of due diligence made a significant difference on return profile. Angels that performed low due diligence had 1.1 times return over a 3.4-year time frame, while those that performed heavy due diligence had returns of 5.9 times their capital. Also, heavy due diligence decreased the chances for negative returns with 40 percent of deals losing capital versus 60 percent for those that performed low due diligence.
By performing more due diligence, measured by the amount of time, investors went from simply getting their money back to making 5.9 times their money. Comparing this scenario with liquid markets means that the extra time spent on understanding the investment has a significant payoff.
We believe that investing in technology commercialization will lead to these significant payoffs. It offers an abundance of opportunities, with over $60 billion invested in basic R&D via US universities. As untapped as the universities themselves are, they invest less than one percent in the commercialization of their technology. Add to that a geographical focus on the Midwest where 25 percent of all basic US R&D is performed, but only four percent of VC investments are made. This results in commercialization on steroids.
At Ikove we see ourselves as venture developers. We focus on identifying and vetting disruptive technologies – and launching the ones that we feel have significant potential into successful startup companies.
We apply button-up analysis on the selection of the technologies coupled with fundamental top-down macro and economic research to evaluate the best industries to go into. This strong involvement and multiple points of contacts creates a strategy that is unique in a space ready to be disrupted. In short, Ikove’s venture development approach to technology commercialization in the Midwest is massively profitable and proven to generate uncorrelated alpha.