I'm a bit confused on these two terms. Could someone explain a bit on these strategies? I'm aware of L/S, macro, quant/quantamental, activist, distressed/loan to own, and event-driven. But more confused about convergence trading / pair trading. Is it similar to event-driven or merger arb? Any prominent examples/funds? Search function wasn't too helpful. Thanks!
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Often hedge fund strategies have market exposure or beta. A market-neutral strategy has no long bias or beta. This term can be applied to all types of strategies L/S equity, bond, macro, etc.
Pairs trading, or more often called statistical arbitrage, is usually market neutral. Therefore, these terms are together, but they don't have to be. This method of trading is usually short term, <1 week, and focuses on exploiting breaks in relationships. For example, if the S&P500 opens up 1% and Nasdaq opens down 1%, sell S&P500 and buy Nasdaq to create a market-neutral bet. The view here is that these indexes are correlated and the relationship will return to normal.
I disagree with the above. Long Nasdaq short SPX is effectively a sector bet (long tech, short everything else), not a pair trade.
When people speak about pair trading, they generally refer to stuff like long American Airlines and simultaneously short United airlines, and such. A portfolio made up of pair trades would be market neutral and even sector neutral.
The example I see when I search online is long TWTR and short FB. But isn't this bullish towards one company while bearish towards the other? What's the correlation you speak of? Are you saying in this example TWTR and FB are correlated positively because they are both tech social media platforms? And SPY500 and NASDAQ are correlated because they both represent multi-sector companies? Are we ignoring the fact that SPY500 is more tech-weighted? In your example, isn't AAL more concentrated on short-haul flights?
You're thinking about this as a fundamental guy. To a quant, they're not looking at the underlying business as much as they are the price action. To them, Twitter and Facebook (or American and United) are correlated tickers, with historical correlations driven by similar sectors driving their price action, and similar capital flows given similar underlying holders. If there is a slightly off move in one relative to the other, and it doesn't seem to be exogenous (i.e. one is up 1.3%, the other should be up 1.8%, but is only up 1.5%) they try and capture the difference between the two, which will generally correct themselves eventually. You don't do this once, you look at all liquid stocks, with all interdependent relationships, and end up with a huge portfolio of stock. Generally is run market neutral, but sometimes with beta = 1.
This was developed in the 80's at Morgan Stanley under Tartaglia, and is the basis for DE Shaw and Ren Tech. The simple stat arb that worked in the 80's likely doesn't work today, so I wouldn't go into an interview just talking about looking at short term inefficiency in correlations, as the deeper engines that now probably look at earnings dates, index fund capital flows, dividend-driven capital flows etc. and have a strong risk management program laid on top of it.
Well yes, the whole point of the exercise is that the long and the short leg are similar securities, but not exactly the same.
Breaking news: if you go long something and short the exact same something, your net profit is zero.
its actually negative because of the spread on financing rates and transaction costs
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