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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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48 results for Stock tick

Paper predicts tick value changes' impact on market microstructure.

problem Assessing the effects of changing tick values in stock exchanges.
method Applied methodology from Dayri and Rosenbaum (2015) to forecast costs and optimal tick values.
result Accurate predictions of future market and limit order costs after tick value changes.

MiFID II impacts European stock liquidity and price formation.

problem Impact of MiFID II on European stock liquidity and price formation.
method Analyzed effects of MiFID II on European stock markets, focusing on intraday and closing auction liquidity and tick size changes.
result Closing auction volumes increased and price formation became more efficient after MiFID II.

The analysis which assumes that tick by tick data is linear may lead to wrong conclusions if the underlying process is multiplicative. We compare data analysis done with the return and stock differences and we study the limits within the two approaches are equivalent. Some illustrative examples concerning these two app…

2001-11-28abs ↗pdf ↗

This study examines lead-lag relationships in Chinese futures markets using high-frequency data.

problem Understanding high-frequency trading dynamics and information flow in futures markets.
method High-frequency tick-by-tick data analysis of lead-lag relationships between different maturity futures contracts.
result The near-month futures lead longer-dated contracts by one tick, with a negative feedback effect on the leading asset.

Study shows market quality improves with larger orders, not smaller tick sizes or higher trading frequencies.

problem Impact of order book tick sizes, metaorders, and trading frequencies on market quality.
method Multi-agent reinforcement learning model to simulate stock market dynamics.
result Market quality benefits from larger orders but not from smaller tick sizes or higher trading frequencies.

This study examines how financial tick data becomes more random with time aggregation.

problem Investigating the randomness of financial tick data over time.
method Applied statistical randomness tests from NIST and TestU01 batteries to ultra-high frequency financial data.
result Financial tick data becomes increasingly random as the aggregation level of transaction time increases.

A tick size is the smallest increment of a security price. It is clear that at the shortest time scale on which individual orders are placed the tick size has a major role which affects where limit orders can be placed, the bid-ask spread, etc. This is the realm of market microstructure and there is a vast literature o…

2010-09-13abs ↗pdf ↗

Model stock price dynamics using semi-Markov processes.

problem Model stock price dynamics through a semi-Markov process.
method Use semi-Markov process with Poisson random measure, establish existence and uniqueness of solution, derive HJB equation.
result Obtain expressions for optimal controls and value function using HJB equation.

Study improves Hayashi-Yoshida estimator for high-dimensional stock covolatility.

problem Inconsistent performance of Hayashi-Yoshida estimator in high dimensions.
method Analyzed the limiting spectral distribution of the Hayashi-Yoshida estimator.
result Established the connection between the estimator's spectrum and the true covariance matrix in high dimensions.

Recent academic work has developed a method to determine, in real time, if a given stock is exhibiting a price bubble. Currently there is speculation in the financial press concerning the existence of a price bubble in the aftermath of the recent IPO of LinkedIn. We analyze stock price tick data from the short lifetime…

2011-05-28abs ↗pdf ↗

A novel framework extracts essential factors from order flow data for high-frequency trading.

problem Challenges in extracting and utilizing order flow data due to its large volume and limitations of traditional techniques.
method Proposes a Context Encoder and Factor Extractor for unsupervised learning of important signals from order flow data.
result Extracts superior factors from order flow data, improving stock trend prediction and order execution tasks.

One of the principal statistical features characterizing the activity in financial markets is the distribution of fluctuations in market indicators such as the index. While the developed stock markets, e.g., the New York Stock Exchange (NYSE) have been found to show heavy-tailed return distribution with a characteristi…

2006-07-03abs ↗pdf ↗

In order to investigate the origin of large price fluctuations, we analyze stock price changes of ten frequently traded NASDAQ stocks in the year 2002. Though the influence of the trading frequency on the aggregate return in a certain time interval is important, it cannot alone explain the heavy tailed distribution of …

2006-06-18abs ↗pdf ↗

Queue imbalance predicts mid-price movement direction.

problem Predicting mid-price movement direction in limit order books.
method Logistic regression and local logistic regression fits between queue imbalance and mid-price movement direction.
result Queue imbalance provides significant predictive power for mid-price movement direction.

In this paper one studies the distribution of log-returns (tick-by-tick) in the Lisbon stock market and shows that it is well adjusted by the solution of the equation, {dpxdx=βqpxq(βqβq)pxq\frac{dp_{x}}{d| x|}=-β_{q^{\prime }}p_{x}^{q^{\prime}}-(β_{q}-β_{q^{\prime}}) p_{x}^{q}}, which corresponds to a generalization of the differential …

2004-03-24abs ↗pdf ↗

Based on the tick-by-tick price changes of the companies from the U.S. and from the German stock markets over the period 1998-99 we reanalyse several characteristics established by the Boston Group for the U.S. market in the period 1994-95, which serves to verify their space and time-translational invariance. By increa…

2002-08-12abs ↗pdf ↗

Lead/lag relationships are an important stylized fact at high frequency. Some assets follow the path of others with a small time lag. We provide indicators to measure this phenomenon using tick-by-tick data. Strongly asymmetric cross-correlation functions are empirically observed, especially in the future/stock case. W…

2011-11-30abs ↗pdf ↗

Motivated by empirical data, we develop a statistical description of the queue dynamics for large tick assets based on a two-dimensional Fokker-Planck (diffusion) equation, that explicitly includes state dependence, i.e. the fact that the drift and diffusion depends on the volume present on both sides of the spread. "J…

2013-04-25abs ↗pdf ↗

Study analyzes order transitions in high, medium, and low market cap stocks using Markov chains.

problem Understanding order transitions in stocks of different market caps.
method First-order discrete-time Markov chain model applied to NASDAQ100 stocks.
result Limit orders exhibit higher inertia during opening hours but decrease in subsequent hours, while market orders increase.

In addressing the question of the time scales characteristic for the market formation, we analyze high frequency tick-by-tick data from the NYSE and from the German market. By using returns on various time scales ranging from seconds or minutes up to two days, we compare magnitude of the largest eigenvalue of the corre…

2003-11-05abs ↗pdf ↗

Financial markets can be seen as complex systems in non-equilibrium steady state, one of whose most important properties is the distribution of price fluctuations. Recently, there have been assertions that this distribution is qualitatively different in emerging markets as compared to developed markets. Here we analyse…

2006-06-26abs ↗pdf ↗

Study applies Hawkes volatility to mid-price process for real-time risk management.

problem Lack of studies on Hawkes volatility for tick-level price dynamics.
method Derived variance formula for unmarked and marked Hawkes models, applied to mid-price process.
result Reliable results and high predictive power of intraday Hawkes volatility.

Study finds strong power-law cross-correlations between trading activity and volume traded, not returns.

problem Understanding power-law cross-correlations between trading activity and volume traded in the stock market.
method Empirical data from tick-by-tick recordings of 31 stocks, detrending daily patterns, multifractal detrended cross-correlation analysis (MFCCA).
result Strongest power-law cross-correlations exist between trading activity and volume traded, weaker between returns and other quantities.

In this work, we provide a framework linking microstructural properties of an asset to the tick value of the exchange. In particular, we bring to light a quantity, referred to as implicit spread, playing the role of spread for large tick assets, for which the effective spread is almost always equal to one tick. The rel…

2012-07-26abs ↗pdf ↗

The study examines how limit-order book resilience changes after effective market orders in Chinese stocks.

problem Understanding the resilience of limit-order books after liquidity shocks.
method Empirical analysis of order flow data from Chinese stocks, focusing on bid-ask spread, LOB depth, and order intensity.
result Traders are more likely to submit effective market orders when the bid-ask spread is low, same-side depth is high, and opposite-side depth is low.

Deep learning predicts cryptocurrency price movements with 78% accuracy.

problem Predicting price formation in cryptocurrency markets with high volatility and illiquidity.
method Applied deep learning to predict mid-price changes on live tick-level cryptocurrency data.
result Achieved 78% accuracy in predicting mid-price movement of Bitcoin vs USD.

Simulates realistic execution and costs in limit order books.

problem Realistic simulation of limit order books for large-tick assets.
method Tractable representation of spread and volume imbalance; calibrated event timing; feedback mechanism for market impact.
result Simulator yields realistic behavior and sensitivity to execution parameters.

The paper optimizes RV estimation by efficient sampling in time-changed diffusion models.

problem Improving realized variance (RV) estimation in time-changed diffusion models.
method Theoretical analysis and simulations of hitting time and realized business time sampling schemes.
result Realized business time sampling is empirically most efficient for high noise levels.

Study uses Hawkes and diffusion models to analyze stock price dynamics.

problem Analyzing volatility and price dynamics in ultra-high-frequency stock data.
method Combined symmetric Hawkes and diffusion models with maximum likelihood estimation.
result Model provides accurate volatility estimation and dynamics of parameters.