Identifying Expensive Trades by Monitoring the Limit Order Book

By Benoît Detollenaere, C. D’Hondt. 2017

5 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Dat Thanh Tran
2. Juho Kanniainen
3. Alexandros Iosifidis
4. Martin Magris
5. Moncef Gabbouj
6. Weiwei Jiang

In Finance

1. Alexandros Iosifidis
2. Moncef Gabbouj
3. Adamantios Ntakaris
4. Dat Thanh Tran
5. Juho Kanniainen
6. Martin Magris
7. Weiwei Jiang

Citations in the corpus

• Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “Popular machine learning methods in HFT include regression analysis [1], [2], [3], [4], [5], multilayer feed forward network [6], [7], [8], convolutional neural network [9] , recurrent neural network [10], [11], [12].”
• Benchmark Dataset for Mid-Price Prediction of Limit Order Book data (2017, methodology): “Detollenaere and D’ Hondt [16] apply adaptive Least Absolute Shrinkage and Selection Operator (LASSO)(1) for variable selection which best explains transaction cost of the split order.”