Prediction of Hidden Liquidity in theLimit Order Book of GLOBEX Futures

Christensen, Hugh L.; Woodmansey, Robert

doi:10.3905/jot.2013.8.3.068

Cited by 9 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They use book depth for the S&P 500 E-mini futures contract to construct a measure of illiquidity. Christensen and Woodmansey (2013) create an algorithm to predict the hidden quantity resulting from iceberg orders and apply it to the S&P 500 E-mini futures contract.…”

Section: Databasementioning

confidence: 99%

Depth Characteristics for the Electronic Futures Limit Order Book

Aidov

Daigler

2015

Journal of Futures Markets

View full text Add to dashboard Cite

Prior literature provides limited information on the depth characteristics of futures markets, especially for U.S. exchanges, due to the lack of historical depth data from floor trading. A proprietary database remedies this problem by providing a five-deep electronic futures limit order book. Our results show a large amount of depth beyond the best level, as well as more concentrated activity during the day than the night. We document symmetry between the bid and ask sides of the book, although no evidence of equality between the depth levels exists. Most important is that our results often contradict equity depth research. Overall, the structure of the limit order book helps us to understand the depth allocation by active traders in futures markets.

show abstract

Section: Databasementioning

confidence: 99%

Depth Characteristics for the Electronic Futures Limit Order Book

Aidov

Daigler

2015

Journal of Futures Markets

View full text Add to dashboard Cite

show abstract

“…More specifically, the authors utilize SimpleMKL and the more recent LPBoostMKL methods for training a multi‐class SVM. Christensen and Woodmansey () develop a classification method based on the Gaussian kernel in order to identify iceberg orders for GLOBEX.…”

Section: Machine Learning For Hft and Lobmentioning

confidence: 99%

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Ntakaris

Magris

Kanniainen

et al. 2018

Journal of Forecasting

115

106

View full text Add to dashboard Cite

Managing the prediction of metrics in high‐frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high‐frequency limit order markets for mid‐price prediction. We extracted normalized data representations of time series data for five stocks from the Nasdaq Nordic stock market for a time period of 10 consecutive days, leading to a dataset of ∼4,000,000 time series samples in total. A day‐based anchored cross‐validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state‐of‐the‐art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large‐scale dataset can serve as a testbed for devising novel solutions of expert systems for high‐frequency limit order book data analysis.

show abstract

“…The detection of these large orders is of particular interest to market makers, as the supply-demand balance represented by the LOB is a key indicator of future security returns. In a previous paper, we published on a pattern-recognition approach to predicting the existence of these orders in the LOB (Christensen and Woodmansey 2013). Knowledge of the L3 structure presents another mechanism by which these orders could be detected, by direct observation of the (near) simultaneous iceberg order peak and refresh messages, as they appear in the L3 LOB.…”

Section: Discussionmentioning

confidence: 98%

“…Of note in the standard view, orders at each price (depth) level are aggregated to a net, single volume and individual orders are not visible. This standard view rebuild algorithm is detailed in Christensen and Woodmansey (2013). Figure 8 shows a 3D snapshot of the standard view of the L2 LOB from an equity index future on GLOBEX.…”

Section: The Statistical Problem With Rebuilding the Limit Order Bookmentioning

confidence: 99%

“…Applied areas of interest include high-frequency trading (Avellaneda and Stoikov 2008), optimally executing large orders (Obizhaeva and Wang 2013), market making (Menkveld 2011), hidden volume detection (Christensen and Woodmansey 2013) and short-term price prediction (Cont et al 2010). Research into modelling various characteristics of the LOB behaviour includes book resiliency (Large 2007), buy/sell intensity (Hall and Hautsch 2007), dynamical behaviour (Roşu 2009), shape (Gu et al 2008) and * Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rebuilding the limit order book: sequential Bayesian inference on hidden states

Christensen

Turner

Hill

et al. 2013

Quantitative Finance

Self Cite

View full text Add to dashboard Cite

The limit order book of an exchange represents an information store of market participants' future aims and for many traders the information held in this store is of interest. However, information loss occurs between orders being entered into the exchange and limit order book data being sent out. We present an online algorithm which carries out Bayesian inference to replace information lost at the level of the exchange server and apply our proof of concept algorithm to real historical data from some of the world's most liquid futures contracts as traded on CME GLOBEX, EUREX and NYSE Liffe exchanges.

show abstract

Prediction of Hidden Liquidity in theLimit Order Book of GLOBEX Futures

Cited by 9 publications

References 21 publications

Depth Characteristics for the Electronic Futures Limit Order Book

Depth Characteristics for the Electronic Futures Limit Order Book

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Rebuilding the limit order book: sequential Bayesian inference on hidden states

Contact Info

Product

Resources

About