MMs, Dealers, Institutions and Predictability of Retail
Updated
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This activity is proven in a number of markets.
This activity is proven in a number of markets.
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s3.amazonaws.com/tradingview/snapshots/i/i5fPIiyU.png Notice the 11th-13th price action. You will find many more examples in FX and commodities. It depends on the MMs strategy.
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It’s also important to know that FX brokers who advertise small spreads will probably have a “liquidity provider”, which is a dealer or MM. The fine print will often reveal that the advertised spread is only available in certain market conditions, I.e, low volatility. These liquidity providers are 3rd parties and have the capacity to widen the spread at will. FX brokers will reveal in the fine print that they must mover the spread onto their clients (retail traders). Retail traders pay the difference in the spread, and the MMs, Dealers and brokers will make an “OTC gain” from taking the other side of the retail traders positions as less than half of these positions are profitable.Note
Automated Liquidity sensitive DPM - Could be tested nicely in crypto if you want a bot to capture the bid ask spread. eecs.harvard.edu/cs286r/courses/fall12/papers/OPRS10.pdfNote
This isn't a joke. Christie and Schultz (1994) document an absence of odd-eighth quotes for Nasdaq stocks. Christie et al. (1994) find a significant drop in spreads following the disclosure of the findings of Christie and Schultz (1994). Dutta and Madhavan (1997) provide a theoretical model of a dealer market and demonstrate conditions under which implicit collusion is sustainable. Barclay (1996) finds that stocks that trade with spreads of a quarter point on Nasdaq subsequently trade with spreads of an eighth of a point after they list on exchanges. Huang and Stoll (1996) document significantly higher bid-ask spreads for Nasdaq stocks than for comparable exchange stocks. However, Chan and Lakonishok (1997) find little difference in trade costs between Nasdaq and exchange stocks for a sample of institutional traders. See also Blume and Goldstein (1992), Fama et al. (1993), Shapiro (1993), Christie and Huang (1994), and Chan et al. (1995) for additional evidence on the returns and costs of trading Nasdaq stocks.
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1. The market for ABC is $25.53 bid / offered at $25.54.2. Due to Latency Arbitrage, an HFT computer knows that there is an order that in a moment will move the NBBO quote higher, to $25.54 bid /offered at $25.56.
3. The HFT speeds ahead, scraping dark and visible pools, buying all available ABC shares at $25.54 and
cheaper.
4. The institutional algo gets nothing done at $25.54 (as there is no stock available at this price) and the market moves up to $25.54 bid / offered at $25.56 (as anticipated by the HFT).
5. The HFT turns around and offers ABC at $25.55 or $25.56.
6. Because it is following a volume driven formula, the institutional algo is forced to buy available shares from the HFT at $25.55 or $25.56.
7. The HFT makes $0.01-$0.02 per share at the expense of the institution.
Guess who has clearing and execution about 30 meters outside the NYSE?
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