AI Asset Management Report (Part 1)
A three-part series that detail how AI can be a viable innovation driver for the asset management industry.
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Investors are generally more interested in the brand and performance of asset managers, but they are relatively indifferent when it comes to how their assets are managed in practice. Because of the zero-sum characteristics of the asset management industry, asset managers prefer to hide their hard-won innovation from competitors for as long as possible — especially when the R&D results are fruitful and successful. Thus, innovation in the asset management industry is difficult to detect from the outside.
However, numerous innovations have quietly driven the asset management industry, and the hunt for the next innovation is still in progress. Before assessing if and how AI can innovate the industry, let us take a look at what kinds of innovations have been taken place in the asset management industry. By doing this, we will be able to get a clue about whether AI technology can be a viable innovation driver in the asset management industry.
Daily Contrarian Trading Strategy: STRATEGY C
Let us consider a simple investment strategy called the “contrarian strategy” — ‘strategy C’ for convenience and see how it works. Before the market opens, find the stocks that rose (“winners’) and fell (“losers”) on the previous day. Long losers, with a higher portfolio weight assigned to the stocks with a greater decrease in performance (i.e. buy more stocks that had fallen more), and Short winners, with a bigger portfolio weight assigned to the higher outperforming stocks. Assemble a market-neutral portfolio with the same amount of long and short. Rebalance this portfolio every day.
Strategy C was first published in 1990 — even though hedge fund managers have adopted the strategy a little earlier. Early quant funds, such as D.E. Shaw & Co., and Renaissance Technologies, made a huge fortune with Strategy C and were able to level up their houses at the early stages of its history. Famous quant funds such as PDT Partners (which used to be Morgan Stanley’s proprietary trading group), had also made a lot of profit with Strategy C.
The return of the same daily contrarian trading strategy had diminished from 1995 to 2007, while many managers scrambled to deploy the same strategy. However, many quant funds are still actively using a revised version of this strategy (i.e. neutralizing the portfolio by industry to reduce risk, or to find a better universe where the strategy works better).
The research paper, “What Happened to the Quants in August 2007” (Khandani and Lo), shows that in August 2007, many high-profile quant funds that used a daily contrarian strategy experienced unprecedented losses which were triggered by the rapid unwinding of one or more sizable quant portfolio — despite little movement in the equity market. (Andrew Lo, a Harvard graduate and the co-author of this paper, is a Chinese professor at MIT Sloan School known for covering the first paper on contrarian strategy). James Simons of Renaissance Technologies came up with the excuse that “the funds sudden underperformance appears to be the result of rapid unwinding from other quant funds with similar strategy.”
Two questions can arise here:
1. Why was this simple and powerful strategy only discovered in 1990?
2. Why has the strategy worked for so long after it was first announced in 1990?
The answers to both questions are actually the same. And it’s deeply related to the innovation in asset management through the use of AI technology.
From Individual Stock to Portfolio: The Beginning of Statistical Arbitrage
Strategy C was simple. Yet, it was able to powerfully eliminate the market(beta) impact by buying and selling the same number of winners and losers every day. Most people never researched this process, and it wasn’t until the late 1980s that few people noticed and acted. What could have been the reason?
In fact, to implement STRATEGY C, all you need is a daily price data for all stocks and a simple computer for programming. These two conditions were met even in the 1950s. However, the necessary conditions are different from sufficient conditions, and there is a big difference between what was available and what is actually achievable. In the 1980s, the person who has access to all the past stock price data probably was not able to test Strategy C with a computer programming.
Back in the 1980s, star managers like Peter Lynch and hostile M&A raiders (the background of ‘Pretty Woman’) were at their peak, and most investors were just focused on how to pick better stocks and obtain high-profile (or sometimes insider) information. (It’s not much different from these days). At the time, computer and price data were only used importantly at IT departments dealing with backend systems. Individual stock analysis functions and chart functions were enough for most of the investment departments. The number of Bloomberg terminals broke through 5,000 only in 1986, and the first MS Excel for Windows 2.05 (it was not for 3.1) was released in 1987. In other words, people were focused on analyzing individual stocks, not portfolio strategies.
Even after Andrew Lo published a paper on Strategy C in 1990, there were still only a handful of teams with the will and capacity to backtest and execute Strategy C on the entire stock universe. These teams that moved first and fast, while keeping the secrets, made a lot of money.
The first movers were computer scientists and mathematicians like D.E. Shaw, James Simons, and Edward Thorp. They were Wall Street outsiders who wanted to make money through a scientific approach. And for the first time in history, people who were well-versed in computer science and skilled in technology approached the financial sector. And as this team discovered innovative strategies like Strategy C, the quiet revolution in asset management slowly gained traction. Most quant funds today know this strategy as statistical arbitrage.
“Innovation is difficult to start, but once it does, it progresses rapidly.”
The Era of Quant Fund: The Birth of Quant Fund and a New Balance
As the first-generation quant hedge funds continued to show outstanding returns, quant hedge funds became bigger and bigger.
Morgan Stanley’s proprietary trading group is known as one of the first teams to start statistical arbitrage, as well as Renaissance Technologies by James Simons (a mathematics professor), D.E. Shaw & Co. (originated from Morgan Stanley’s proprietary trading group) by David Shaw (a computer science professor), and Englander’s Millennium Management are 1st generation quant funds who started computer algorithm trading and statistical arbitrage trading in the mid-80s. Those who were from the first-generation company independently established second-generation quant funds to achieve better performance.
Second generation quant funds include: WorldQuant, which was founded by a game programmer from Millennium Management. PDT Partners, led by Peter Muller, a mathematician from Morgan Stanley’s proprietary trading group (with the introduction of Volcker Rule after the financial crisis, Morgan Stanley’s prop trading group was spun off as PDT Partners). And lastly, Two Sigma, which was founded by an International Mathematical Olympiad silver medalist from D.E. Shaw & Co.
As the number of quant funds increased and their AUM increased, problems began to arise. In a market with zero-sum attributes, most strategies have had short lives with a sharp drop in returns as large-scale quant funds quickly found similar strategies and roll out huge amounts of money at the same time. The return erosion from the increased competition outpaced the speed of quant funds coming up with new strategies. The name of the game has changed to race for faster research with more and more quant analysts on board than other competitors in a labor-intensive way. As a result, the number of employees hired by quant funds has increased, and for the top quant funds, average AUM per person has fallen to around $30 million.
According to our research, the combined remuneration and overhead costs of each employee at quant hedge funds can sometimes exceed $500K. If $30 million is managed by one person, the fund will likely have to charge at least 2% to keep the fund running smoothly. As a result, most quant hedge funds are operated based on a 2–20 structure — charge 2% fixed fee and 15–20% in performance pay. Finding alpha became no longer a cheap process.
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Long — Refers to the purchase of an asset with the expectation it will increase in value.
Short — Refers to a trading technique in which an investor anticipates the price of a security to fall in the short term.
Arbitrage — Type of trade in which a security, currency, or commodity is nearly simultaneously bought and sold, in different markets.
Beta — Used to measure the volatility or systematic risk of a security portfolio compared to the market as a whole.
Volcker Rule — This rule prohibits banks from using customer deposits for their own profit. It also won’t let them own, invest in, or sponsor hedge funds, private equity funds, or other trading operations for their own use.
Alpha — Describes a strategy’s ability to beat the market. Also referred to as “excess return” or “abnormal rate of return.”
To continue reading to part 2, click here.
