scholarly journals Sequence Classification of the Limit Order Book Using Recurrent Neural Networks

2017 ◽  
Author(s):  
Matthew Francis Dixon
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Limit Order Book ◽  
Order Book ◽  
Sequence Classification ◽  
Limit Order

Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks

2017 ◽  
Author(s):  
Avraam Tsantekidis ◽  
Nikolaos Passalis ◽  
Anastasios Tefas ◽  
Juho Kanniainen ◽  
Moncef Gabbouj ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Stock Prices ◽  
Limit Order Book ◽  
Order Book ◽  
Limit Order

Predicting Jump Arrivals in Stock Prices Using Neural Networks with Limit Order Book Data

2018 ◽  
Author(s):  
Milla MMkinen ◽  
Alexandros Iosifidis ◽  
Moncef Gabbouj ◽  
Juho Kanniainen
Keyword(s):  
Neural Networks ◽  
Stock Prices ◽  
Limit Order Book ◽  
Order Book ◽  
Limit Order

scholarly journals Queue Imbalance as a One-Tick-Ahead Price Predictor in a Limit Order Book

2016 ◽  
Vol 02 (02) ◽  
pp. 1650006 ◽  
Author(s):  
Martin D. Gould ◽  
Julius Bonart
Keyword(s):  
Logistic Regression ◽  
Null Model ◽  
Limit Order Book ◽  
Order Book ◽  
Limit Order ◽  
Price Movement ◽  
Probabilistic Classification ◽  
Price Movements ◽  
Computationally Intensive

We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. For each of 10 liquid stocks on Nasdaq, we fit logistic regressions between the queue imbalance and the direction of the subsequent mid-price movement, and we find a strongly statistically significant relationship in each case. Compared to a simple null model, we find that our logistic regression fits provide a considerable improvement in both binary and probabilistic classification of mid-price movements for large-tick stocks and a moderate improvement in both binary and probabilistic classification of mid-price movements for small-tick stocks. We also perform local logistic regression fits on the same data, and find that this semi-parametric approach slightly outperforms our logistic regression fits, at the expense of being more computationally intensive to implement.

scholarly journals Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data

2020 ◽  
Vol 136 ◽  
pp. 183-189 ◽  
Author(s):  
Nikolaos Passalis ◽  
Anastasios Tefas ◽  
Juho Kanniainen ◽  
Moncef Gabbouj ◽  
Alexandros Iosifidis
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Limit Order Book ◽  
Time Series Forecasting ◽  
Order Book ◽  
Limit Order ◽  
Bag Of Features ◽  
Financial Time
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