Behavior based learning in identifying high frequency trading strategies. The Volume Clock: Insights into the High-Frequency Paradigm (Digest Summary)
In order to protect themselves from predatory HFTs, many institutional investors employ algorithmic tactics operating at high speeds as countermeasures. This is a reasonable conclusion, if the focus is narrowed to that level though incomplete given the Kirilenko Flash Crash study. Sometimes the same bank and its affiliates originated the mortgages, assembled and packaged them, underwrote the bonds backed by the mortgages, made markets in the bonds and transacted the derivatives, profiting at each layer. The point is made eloquently by Eric Hunsader, the founder of Nanex, a high speed market date feed service. Intermediaries, that is to say Market Makers, tended to moderate the price moves because they did not instantaneously exit the markets, dumping inventories on the way out.
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- Behavior based learning in identifying High Frequency Trading strategies - Semantic Scholar
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- Deep Reinforcement Learning in High Frequency Trading
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Mark Paddrik - Google Scholar Citations
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Andrei Kirilenko - Google Scholar Citations
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Deep Reinforcement Learning in High Frequency Trading
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