Order book characteristics and the volume–volatility relation: Empirical evidence from a limit order market

Næs, Randi; Skjeltorp, Johannes A.

doi:10.1016/j.finmar.2006.04.001

Cited by 116 publications

(16 citation statements)

References 18 publications

(6 reference statements)

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“…Joulin et al [63] analyzed the one-minute data of 163 USA stocks and found that news and trading volume play a minor role in causing large price changes, which is thus conjectured to be caused by the vanishing of liquidity. Naes and Skjeltorp [64] studied the order flow data from the Oslo Stock Exchange in Norway and found that price fluctuations are positively correlated with trade number, a component of trading volume, and negatively correlated with different liquidity measures.…”

Section: Introductionmentioning

confidence: 99%

Determinants of immediate price impacts at the trade level in an emerging order-driven market

Zhou¹

2012

New J. Phys.

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Common wisdom argues that, in general, large trades cause large price changes, whereas small trades cause small price changes. However, for extremely large price changes, the trade size and news play a minor role, while liquidity (especially price gaps on the limit order book) is a more influential factor. Hence, there might be other factors influencing the immediate price impacts of trades. In this paper, through mechanical analysis of price variations before and after a trade of arbitrary size, we identify that the trade size, the bid-ask spread, the price gaps and the outstanding volumes at the bid and ask sides of the limit order book have an impact on the changes in prices. We propose two regression models to investigate the influence of these microscopic factors on the price impact of buyer-initiated partially filled trades, seller-initiated partially filled trades, buyer-initiated filled trades and seller-initiated filled trades. We find that they have quantitatively similar explanatory powers and these factors can account for up to 44% of the price impacts. Large trade sizes, wide bid-ask spreads, high liquidity at the same side and low liquidity at the opposite side will cause a large price impact. We also find that the liquidity at the opposite side has a more influential impact than the liquidity at the same side. Our results shed new light on the determinants of immediate price impacts.

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Section: Introductionmentioning

confidence: 99%

Determinants of immediate price impacts at the trade level in an emerging order-driven market

Zhou¹

2012

New J. Phys.

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“…Almgren and Lorenz () explain price momentum by modeling Brownian motion with a drift whose distribution is updated based on Bayesian inference. Næs and Skjeltorp () show that the order book slope measures the elasticity of supplied quantity as a function of asset prices related to volatility, trading activity, and an asset's dispersion beliefs.…”

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

110

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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“…We believe the new approach is universal and can be applied to other fields where a proper assessment of the relative amount of signal variation is critical (e.g. financial markets [33][34][35] , or neuroscience 36,37 ). Additionally, the number of sources annotated by political bias was relatively low and skewed towards the US which significantly decreased the reliability and generality of the results, although giving a more homogeneous dataset to analyze.…”

Section: Discussionmentioning

confidence: 99%

A calibrated measure to compare fluctuations of different entities across timescales

Chołoniewski

Sienkiewicz

Dretnik

et al. 2020

Sci Rep

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A common way to learn about a system’s properties is to analyze temporal fluctuations in associated variables. However, conclusions based on fluctuations from a single entity can be misleading when used without proper reference to other comparable entities or when examined only on one timescale. Here we introduce a method that uses predictions from a fluctuation scaling law as a benchmark for the observed standard deviations. Differences from the benchmark (residuals) are aggregated across multiple timescales using Principal Component Analysis to reduce data dimensionality. The first component score is a calibrated measure of fluctuations—the reactivityRA of a given entity. We apply our method to activity records from the media industry using data from the Event Registry news aggregator—over 32M articles on selected topics published by over 8000 news outlets. Our approach distinguishes between different news outlet reporting styles: high reactivity points to activity fluctuations larger than expected, reflecting a bursty reporting style, whereas low reactivity suggests a relatively stable reporting style. Combining our method with the political bias detector Media Bias/Fact Check we quantify the relative reporting styles for different topics of mainly US media sources grouped by political orientation. The results suggest that news outlets with a liberal bias tended to be the least reactive while conservative news outlets were the most reactive.

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Order book characteristics and the volume–volatility relation: Empirical evidence from a limit order market

Cited by 116 publications

References 18 publications

Determinants of immediate price impacts at the trade level in an emerging order-driven market

Determinants of immediate price impacts at the trade level in an emerging order-driven market

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

A calibrated measure to compare fluctuations of different entities across timescales

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