One property of High-Frequency data is it's subject to bid-ask bounce.

Description : Unlike traditional data based on just closing prices, tick data carry additional supply-and-demand information in the form of bid and ask prices and offering sizes.

As a researcher, it can be an advantage because bid and ask quotes can carry valuable information about impending market moves.

However, bid and ask quotes are separated by a spread. Continuous movement from bid to ask and back introduces a jump process, difficult to deal with through many conventional models.

I am actually working on high-frequency trading project. At the beginning, I wanted to predict the price movement, but because the price is non-stationary and other reasons, it made the problem harder than I was expected. Now, instead of dealing with the price, I deal with the log-returns on the midprice (similar to the standard price) and the features.

Why am I talking of features?

For autoregressive problem, it is well known that RNN-LSTM perform pretty well on a time-series forecasting problem. As the midprices as well as the features are not stationary, then I applied the log-returns on all of them. I think it will be easier to approach the solution of my problem.

However, now, I think I face the problem of bid-ask bounce. How can I get over that setback? Does the log on the midprice is sufficient to face the bid-ask bounce problem?

  • 3
    $\begingroup$ bid ask bounce is due to the fact that the trade can switch from being a bid trade to an ask trade so it can look like the price moved when it really didn't. So taking the log of the mid-price is supposed to help to get rid of that problem. Of course, there can be some left after doing that but, if you're talking about NN's and non-intraday trading, the bid-ask bounce effect on your trading profit is probably negligible. others hopefully can say more. $\endgroup$ – mark leeds Nov 25 '18 at 17:30

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