Forecasting Price-Level on Trades of Financial Instruments using Orderbook Activity.
Abstract: In today’s highly competitive trading climate it is getting more important to understand the fundamentals of the orderbook and how it works in practice in order to stand out in the competition. Better knowledge of the orderbook statistics can be applied in areas such as high frequencyand execution trading. Therefore the focus of this study is to determine the probabilities of the settlement prices of financial instruments traded on a public order-driven market exchange i.e to predict the probabilities regarding whether the next executed trade will happen on the bid or on the ask side. The mathematical methods used in this study are logistic regression and Maximum Likelyhood (ML)-estimation for optimization of parameters. First the parameter matrix was optimized using orderbook data on the OMXS30 future and then use the estimated model on an out-ofsample test. Keywords: Logistic Regression, Orderbook, level-2 data
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