It's well known that professional money managers and hedge funds are having a hard time beating the stock market. While everyone talks about generating alpha -- the holy grail for hedge funds -- the reality is that only a fraction of the open-ended mutual funds are achieving it.
Alpha is the additional return that everyone is looking for, which is usually above a standard benchmark such as the S&P 500. "There are various means of achieving it. Some people talk about trading alpha or investing alpha. To me it's the icing on the cake," says Sang Lee, managing partner and analyst at financial research firm Aite Group.
Active mutual fund managers have struggled to beat the stock market and justify higher fees. In 2012, a little more than 39%, or 2,710, of the 6,925 open-ended mutual funds that Morningstar tracked beat the S&P 500 index, according to the Chicago research firm. While the average open-ended mutual fund return was 14.83%, most funds failed to beat the S&P 500's 16% return.
One reason that alpha is so hard to find is that with rapid access to information and computerized trading, stock prices immediately reflect changes in earning estimates and news announcements. It has become next to impossible to gain an information edge.
Citing the availability of data feeds and financial statements that are easily found on a Bloomberg terminal, where a young MBA graduate student can pull up information on a company's debt, shareholders, competitors and peers, a September story in the Financial Times suggested there may be no more "alpha" left for fund managers.
However, Michael Herbst, director of active fund research at Morningstar, cautions against leaping to the conclusion that active management cannot beat the market. "There are active managers that do consistently and repeatedly beat the market," Herbst says. Some of the characteristics of those firms are low expenses and trading efficiencies. "One of the ways to add alpha is by keeping trading costs to a minimum or by adding value -- executing trades that are so successful that they can actually add returns to a fund," he says.
Hunting for alpha nowadays can require firms to dig through gigabytes and even petabytes of data. Finding a unique idea is not something that a hedge fund or asset manager will pick up in a news article or a brokerage report. It usually means that firms need to sift through historical data to find patterns that can be applied to the current market environment.
To find ideas with alpha, in some cases, buy-side firms are turning to their own proprietary data as well as social media sources such as Twitter on the Internet. Sitting on huge troves of transactional data, asset managers are discovering they can use analytics and big data technologies to identify patterns in historical data that can help them choose an algorithmic strategy. Also, the trading desk can add to returns by reducing trading costs and not incurring market impact.
"From the data, we look at optimal times to trade," says Ryan Larson, head of U.S. equity trading at RBC Global Asset Management in Chicago. A key focus is on "the timing of our orders and using that data to correspond to trading strategies to determine optimal aggression levels," says Larson, who explains that the main source of alpha in traditional long-only firms is coming from stock selection by the portfolio manager. "Our main job is to preserve the alpha that the portfolio managers are identifying in the market when they send buy and sell orders over," he says.
Buy-side traders like Larson utilize transaction cost analysis, or TCA, to look at the trading numbers, which is one way they might increase alpha. "Not only are we analyzing our trading performance against various measures, such as VWAP [volume-weighted average price], IS [implementation shortfall] and the close, we're looking at the timing of the orders that the portfolio manager sent to the desk," says Larson.
For instance, Larson will look at whether there is momentum leading into the decision to trade the stock, and then any momentum two days later. He will graph a five-day momentum window of buy and sell orders. "We use this data to identify what kind of environment we're trading in and to alter our trading strategies to be more or less aggressive based on the momentum in our orders," he says.
To crunch the data, traders are pulling information from their OMS and EMS systems and from TCA vendors, and they may grab other data to analyze their trading decisions from an order-timing standpoint. "When you go back and look at a year's worth of data, you can identify certain patterns specific to each investment style," Larson says. For example, if the buy occurs at $10, and two days later, the stock is at $8.50, that's a substantial move, he says. "That tells me there's a lot of momentum, but two days later there is meaningful reversion. If a trader saw that, one might think you should take your time because that buy, with a lot of momentun built up, will eventually return to normal levels," Larson says.
Since there is so much data to analyze, and the number of algorithmic strategies offered by brokers has increased dramatically, the buy side is inundated with information. Yet choosing the right algorithm could help generate alpha in a stock. "In a market environment that is so fast and electronic and so fragmented, it's really beyond the capacity of a human being to make sure that every order is executed with the right strategy at the right time," says Harrell Smith, head of product strategy at Portware, which offers Alpha Vision.