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Filippa Bång
Intraday price prediction of Nordic stocks with limit order book data
Abstract
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Predicting the direction of mid price changes could facilitate the decision of
when in time an order should be placed on the market. The purpose of this the-
sis is to evaluate modelling approaches used to classify the direction of mid
price changes in the limit order book on short term. Multi-layer perceptron
and long short-term memory neural networks are evaluated with two sets of
features derived from Nordic limit order book data. Moreover, we are taking
order flow imbalance into account for the mid price modelling. Linear re-
gression is used to model the possible linear relation between the order flow
imbalance and price change in the limit order book.
In the results we can see that an average accuracy score of 0.5 are achieved
for multiple experiments. However, a majority of the models are prone to
consequently classify the price change to be stationary. The LSTM neural
network model achieves the highest precision score due to more variation in
the predicted classes.
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