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When we use AI (machine learning/deep learning) in trading does that assume efficient market hypothesis?
I know quantitative finance assumes price moves are random (efficient market hypothesis). Does quantitative analysis for trading also assume the same?

Even if we assume price moves in random we can predict price of stock options.(Black-Scholes equation).

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    $\begingroup$ I'd say trying to use ML in trading assumes that the markets are inefficient. $\endgroup$
    – MWB
    Apr 17 at 17:04
  • $\begingroup$ Doesn't all trading assume some level of market inefficiency? If you assume the market is completely efficient, trading is pointless. $\endgroup$
    – JimmyJames
    Apr 17 at 19:17
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    $\begingroup$ Why does this question depend on whether AI is used or not? $\endgroup$ Apr 17 at 21:53

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Most traders that I know have a complex relationship with the Efficient Markets Hypothesis, difficult to summarize.

You could say that they accept a "soft version of EMH" but not the original Chicago version. They believe financial markets are indeed very efficient and therefore EMH is useful and good to know. But they believe that there may be exceptions to EMH that perhaps have not been discovered or become widely known.

They tend to be very confident (you could even say a little arrogant) and believe they will be the ones to discover these inefficencies if they do sufficient research and testing. They laugh at the people who have not heard of EMH and think that just by applying a simple machine learning tool to some data from Yahoo Finance they will be able to make a fortune with an afternoon of work. They have tried that kind of thing and know it is not that easy. But they are still interested in looking for money making opportunities despite the difficulty (it is an attitude, perhaps a personality trait).

If they do find something, they are very secretive about it because they know (the EMH again) that once it is widely known it will stop working.

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Using machine learning models (or other quantitative models) in trading does not necessarily assume the efficient market hypothesis. While some quantitative finance strategies may be based on the assumption that prices move randomly and follow efficient market dynamics, AI algorithms mostly are designed to identify and exploit patterns, trends, and anomalies in market data that go against the efficient market hypothesis (identifying inefficiencies in the market).

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  • $\begingroup$ As I understand it, a fair amount of algorithmic trading is designed around taking advantage of the predictability of other traders including algorithmic traders. I see that as basically the opposite of an efficient market assumption. $\endgroup$
    – JimmyJames
    Apr 17 at 19:13
  • $\begingroup$ JimmyJames: What you said is interesting and I agree in that, as you go deeper and deeper along the chain ( length of your chain was n = 2), some group of traders has to believe it's not efficient. Otherwise, you'd have everyone just mimicing efficiency which wouldn't provide any inefficiencies. $\endgroup$
    – mark leeds
    Apr 19 at 6:52
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A common consensus among many academicians and even professional traders is that the markets are quite efficient over the long run.

But there are mispricings, arbitrage opportunities, and other inefficiencies that present themselves for short time periods.

The premise of using ML is essentially to recognize these edges, and capitalize on them, while trying to be on the side of the winning trades.

So simply put, ML, just like any other approach to trading, or like trading itself, work on the assumption of the markets not being efficient (strongly or weakly, temporarily or permanently, would depend on the belief of the people harnessing these tools).

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