It is no secret that the vast majority of investors, including hedge funds managers, have underperformed market indices over time. However, some managers have distinguished themselves generating very strong risk adjusted returns. Outperforming an index requires an information advantage over what is broadly available in market. It also requires a way to process that information to more accurately price securities and select those that demonstrate divergence versus the market. It is becoming increasingly more challenging to achieve this, resulting in an arms race for alpha. Investment processes must be constantly enhanced to stay relevant.
We recently spoke to four leading hedge fund industry experts who will be presenting on the “Arms Race for Alpha” panel at our Gaining the Edge – 2018 Hedge Fund Conference this fall, including David Gilmore, Managing Director-Investments, The Harry and Jeanette Weinberg Foundation, Inc., Alifia Doriwala, Managing Director and Partner, Rock Creek, Robert Kiernan CEO, Advanced Portfolio Management and Karen Inal, Senior Portfolio Manager, The Andrew Mellon Foundation.
“The goal is to turn data into information, and information into insight that can be expressed in portfolios to deliver Alpha. Active management has always been about identifying investment opportunities before they are priced into the broader market,” stated Gilmore. “We are at the intersection of finance and technology, and being able to effectively use new quantitative tools will increasingly determine better outcomes,” said Doriwala.
Four ways investment processes have evolved over time include: achieving an information advantage, processing information, trading securities, and risk management.
Information advantage
Many hedge funds devote significant resources in an effort to gain an information advantage. This includes private surveys, hiring industry consultants, and/or acquiring third party information. Private polling by hedge funds recently made headlines in the UK press because some hedge funds accurately forecasted that Brexit would pass, and supposedly traded on that information. Hedge funds are acquiring an endless supply of information about industries, individual companies, and attempts to predict economic activity in advance of official government reports.
Ten years ago, hedge funds invested time visiting retail stores – counting customers to see how volume was changing over time. They attended industry conferences, purchased industry trade journals, and met with various companies within an industry. In addition to analyzing company competitors funds performed ‘food chain’ analysis on the company’s suppliers and customers. Early adaptors of these research techniques had the potential to outperform the market by a wide margin. However, as more competitors replicated these strategies, the information advantage declined over time.
Today’s firms can buy satellite imagery and quickly count the number of cars in the parking lot. They can purchase information from credit card companies for frequent, granular updates on the purchasing behavior of consumers and businesses. Data is available that tracks the movement of different demographic groups based on their cell phones records and linked zip code attached to it. Key word or phrase data in Google and social media can identify an event impacting a company and highlight changes in sentiment for a product or service relative to its competition. Paul Zummo, CEO/CIO, JP Morgan Alternative Asset Management, another speaker at the conference, stated “The increase in data, especially unstructured data like social media and satellite imaging has been exponential. 90% of the data that exists in the world was created in the last two years.”
Processing information
Information advantages in the investment industry are very short lived. Sifting through huge volumes of information is only useful if it can be processed quickly enough to develop securities valuations that are more accurate than the market consensus. Quantitative hedge funds like Renaissance Technologies, Two Sigma, and Bridgewater have been utilizing sophisticated analytics to process information and execute trades almost instantaneously. “Looking at the investment landscape today there are clear leaders in the race for smart ways to build a technology and quantitative platform that provides an edge in the research process,” observed Doriwala.
Many long/short equity managers have highly granular earnings models that can break down company revenue and expenses in minute detail. This allows them to quickly calculate how sales revenue will be impacted by a change in demand, or how costs will be impacted by changes in exchange rates, tariffs, or commodity prices. “Incorporating and correctly analyzing data in a timely manner is crucial to gaining a competitive information advantage,” notes Gilmore.
Proprietary analytics have been key drivers of performance across many different hedge fund strategies. A few examples include:
- Commodity Trading Advisors who are continuously tracking and processing price changes across the global markets in futures markets relative to stock indices, commodities, currencies and interest rates.
- Structured credit managers can quickly arrive at a valuation by processing cash flows from millions of mortgages, home prices, servicer foreclosure rates, loan modification algorithms, and unique structures. .
- Reinsurance, where millions of insurance policies, home values, and property locations can be evaluated to create a loss probability curve in order to determine an appropriate valuation.
Trading
Advancements in trading execution have been focused on eliminating slippage and transaction costs by increasing the speed of trade and properly managing trading based on market liquidity. For example, annual trading costs for some CTAs in the early 2000s were approximately 5% of NAV. Today these costs have been brought reduced to less than 1%. High frequency traders have reduced execution time to milliseconds by locating their offices closer to exchanges and switching from fiber optics to microwave technology.
Risk management
Historically portfolio risk management meant being equally weighted in positions diversified across a number of different securities, sectors and markets. Over time many investors began to weight securities based on expected return.
Today many managers have adopted Risk Parity (or risk premia parity) an approach to investment portfolio management which focuses on allocation of risk, usually defined as volatility, rather than allocation of capital. By focusing on the volatility contribution of a security to a portfolio, a security that has twice the price volatility of another security would have half the dollar weighting. Risk managers have also adopted a preference for correlation analysis over attempts to diversify based upon the sector or market of a security. This analysis focuses on both current and historical security volatilities and correlations and includes stress testing and scenario analysis across the portfolio which shows how the portfolio will perform during a market sell-off or rising interest rates scenario.
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