Intraday liquidity patterns in limit order books

Malik, Azeem Tariq; Ng, Wing Lon

doi:10.1108/sef-11-2011-0093

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…The impact of algorithmic-trading on market microstructure is a more recent phenomenon in the financial market literature. Because it requires tick-by-tick data and high computing power, it has started to grow only very recently (Malik and Lon Ng, 2014;O'Hara, 2015). The implications of algorithmic-trading for the liquidity of financial markets constitute another potential area of interest to academics, investor community, policymakers and regulators.…”

Section: Sef 372mentioning

confidence: 99%

Liquidity of financial markets: a review

Tripathi

Dixit

Vipul

2019

SEF

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Purpose The purpose of this study is to systematically review and analyze the literature in the area of liquidity of financial markets. The study summarizes the key findings and approaches and highlights the research gaps in the extant literature. Design/methodology/approach A variety of reputed databases are utilized to select 100 research papers, from a large pool of nearly 3,000 research papers spanning between 1972 and 2018 using systematic literature review methodology. The selected research papers are organized to provide an in-depth analysis and an account of the ongoing research in the area of liquidity. The study uses bibliometric network visualization and word-cloud analyses to compile and analyze the literature. Findings The study summarizes the recent approaches in the liquidity research on aspects such as methodologies followed, variables applied, sub-areas covered, and the types of economies and markets covered. The article shows that the literature on liquidity in the emerging markets (e.g. China and India) is deficient. Overall, the following research areas related to liquidity need further exploration in the context of emerging markets: liquidity beyond the best bid-ask quotes, intraday return predictability using microstructure variables (e.g. order imbalances), impact of algorithmic-trading and volatility of liquidity. Originality/value To the best of authors’ knowledge, in the recent past, a detailed account of the literature on liquidity has not been published. It provides a comprehensive collection and classification of the literature on the liquidity of financial markets. This would be helpful to the future researchers, academics and practitioners in the area of financial markets.

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Section: Sef 372mentioning

confidence: 99%

Liquidity of financial markets: a review

Tripathi

Dixit

Vipul

2019

SEF

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“…Malik and Markose [2] developed a method to determine mid-term, intraday price trends based on extracted evolutions of demand and supply price curves from full-depth limit order book snapshots. Lipinski and Brabazon [3] concluded, after investigating thousands of order-book snapshots, that order book shape patterns may contain valuable information for automatic trading.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks applied to high-frequency market microstructure forecasting

Doering

Fairbank

Markose

2017

2017 9th Computer Science and Electronic Engineering (CEEC)

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Abstract-Highly sophisticated artificial neural networks have achieved unprecedented performance across a variety of complex real-world problems over the past years, driven by the ability to detect significant patterns autonomously. Modern electronic stock markets produce large volumes of data, which are very suitable for use with these algorithms. This research explores new scientific ground by designing and evaluating a convolutional neural network in predicting future financial outcomes. A visually inspired transformation process translates high-frequency market microstructure data from the London Stock Exchange into four market-event based input channels, which are used to train six deep networks. Primary results indicate that convolutional networks behave reasonably well on this task and extract interesting microstructure patterns, which are in line with previous theoretical findings. Furthermore, it demonstrates a new approach using modern deep-learning techniques for exploiting and analysing market microstructure behaviour.

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“…Their findings show that such order imbalances cause liquidity issues that last for up to ten minutes. Malik & Lon Ng (2014) analyse the asymmetric intra-day patterns of LOBs. They apply regression with a power transformation on the notional volume weighted average price (NVWAP) curves in order to conclude that both sides of the market behave asymmetrically to market conditions.…”

Section: Regression Analysismentioning

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

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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.

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Intraday liquidity patterns in limit order books

Cited by 5 publications

References 27 publications

Liquidity of financial markets: a review

Liquidity of financial markets: a review

Convolutional neural networks applied to high-frequency market microstructure forecasting

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

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