A dynamic herding model with interactions of trading volumes is introduced. At time , an agent trades with a probability, which depends on the ratio of the total trading volume at time to its own trading volume at its last trade. The price return is determined by the volume imbalance and number of trades. The …
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48 results for “traded volumes”
Study examines how trading volumes and transactions affect stock volatility.
problem Understanding the impact of trading volumes and transactions on stock volatility.
method Used GARCH models to analyze daily stock data of the Tokyo Stock Exchange.
result GARCH effects are not always removed by adding trading volumes or transactions, suggesting they don't fully represent information arrivals.
Market crowd trading behavior and volume impact stock prices in China.
problem Little known about the role of trading volume in market behavior.
method Adaptive hypotheses tested on Chinese stock market data.
result Market crowd trades efficiently and achieves agreement on prices.
Develops a model for optimal trading with uncertain volume targets.
problem Optimal trading strategy under uncertain volume targets.
method Model incorporating risk term related to volume uncertainty.
result Delayed trades can be optimal for risk-averse traders.
Study predicts intraday stock trading volume using ML models.
problem Predicting intraday trading volumes in equity markets.
method Used machine learning models with HF predictors.
result Intraday stock trading volume is highly predictable.
Study on price-volume correlation fractal features and market type effects.
problem Understanding the fractal features and market type effects of price-volume correlation.
method Applied MF-DXA method to analyze price, trading volume, and their coupling.
result Price, trading volume, and price-volume coupling exhibit power law and multifractal properties.
We study the statistical properties of the recurrence intervals between successive trading volumes exceeding a certain threshold . The recurrence interval analysis is carried out for the 20 liquid Chinese stocks covering a period from January 2000 to May 2009, and two Chinese indices from January 2003 to April 2…
We investigate the temporal correlations and multifractal nature of trading volume of 22 liquid stocks traded on the Shenzhen Stock Exchange in 2003. We find that the trading volume exhibit size-dependent non-universal long memory and multifractal nature. No crossover in the power-law dependence of the detrended fluctu…
Motivated by how transaction amount constrain trading volume and price volatility in stock market, we, in this paper, study the relation between volume and price if amount of transaction is given. We find that accumulative trading volume gradually emerges a kurtosis near the price mean value over a trading price range …
The paper proposes a new order slicing strategy to reduce market impact in large-volume trading.
problem Significant market impact and slippage in large-volume trading.
method Volatility-volume-based order slicing strategy using Exponential Weighted Moving Average and Markov Chain Monte Carlo simulations.
result Improves trade execution efficiency and reduces market impact.
The paper examines how market trade values and volumes affect price autocorrelation.
problem Understanding the impact of market trade values and volumes on price autocorrelation.
method Derives the dependence of price statistical moments and volatility on trade values and volumes, and assesses statistical moments and correlations by conventional frequency-based probabilities.
result Highlights the impact of market trade randomness on price statistical moments and autocorrelation.
The study examines statistical properties of market price and liquidity responses.
problem Understanding the statistical properties of market price and liquidity responses.
method Utilized singular value decomposition to analyze interconnections and statistical characteristics of responses.
result Traded volumes play a critical role in price changes induced by liquidity changes.
Market-based asset price probability depends on trade volumes and values, improving forecasts and reliability.
problem Limited accuracy of frequency-based asset price statistical moments.
method Derive market-based variance and 3rd statistical moment from trade values and volumes, accounting for trade volume randomness.
result Market-based statistical moments improve price probability forecasts and reliability.
Employing a recent technique which allows the representation of nonstationary data by means of a juxtaposition of locally stationary patches of different length, we introduce a comprehensive analysis of the key observables in a financial market: the trading volume and the price fluctuations. From the segmentation proce…
In this study, we investigate the statistical properties of the returns and the trading volume. We show a typical example of power-law distributions of the return and of the trading volume. Next, we propose an interacting agent model of stock markets inspired from statistical mechanics [24] to explore the empirical fin…
Improved stock volume prediction using Kalman Filters with various hidden states.
problem Improving accuracy of intraday trading volume prediction.
method Extended Kalman Filter with various hidden states for different stocks, using cross-validation to determine optimal state number.
result Demonstrated improved accuracy through comparison experiments and numerical analysis.
Modeling trading volume curves using hierarchical Poisson processes.
problem Predicting trading volume curves for financial instruments.
method Hierarchical Poisson process model based on hierarchical Dirichlet process with MCMC algorithm.
result Demonstrated scalability on NASDAQ stocks, including Apple.
The distribution of trade sizes and trading volumes are investigated based on the limit order book data of 22 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. We observe that the size distribution of trades for individual stocks exhibits jumps, which is caused by the number preference…
This paper conducts an empirically study on the trade package composed of a sequence of consecutive purchases or sales of 23 stocks in Chinese stock market. We investigate the probability distributions of the execution time, the number of trades and the total trading volume of trade packages, and analyze the possible s…
On the distribution of high-frequency stock market traded volume: a dynamical scenariocond-mat.stat-mech
This manuscript reports a stochastic dynamical scenario whose associated stationary probability density function is exactly a previously proposed one to adjust high-frequency traded volume distributions. This dynamical conjecture, physically connected to superstatiscs, which is intimately related with the current nonex…
Study shows significant changes in trading volume and volatility patterns after 2008 financial crisis.
problem Non-stationary intraday statistical properties of trading volume and volatility.
method Analysis of blue chip equities trading volume and volatility over 2003-2014, split into semesters.
result Trading volume and volatility patterns changed significantly after 2008, with faster morning recovery and steeper afternoon.
Markowitz simplified portfolio returns assuming constant trade volumes.
problem Understanding portfolio returns and variance in markets with variable trade volumes.
method Investor observes market trades, models portfolio as single security, derives portfolio return and variance.
result Markowitz's equation for portfolio returns and variance is a simplified approximation of real markets with constant trade volumes.
Using high-frequency time series of stock prices and share volumes sizes from January 2002-May 2009, this paper investigates whether the effects of the onset of high-frequency trading, most prominent since 2005, are apparent in the dynamics of the dollar traded volume. Indeed it is found in almost all of 14 heavily tra…
Regulated Bitcoin futures led to higher volatility and trading volume.
problem Estimating the impact of regulated Bitcoin futures on volatility and volume.
method Employed a new causal approach, C-ARIMA.
result Regulated Bitcoin futures increased Bitcoin volatility by more than double.
The study challenges the reliability of VaR due to market randomness.
problem Reliability and accuracy of VaR predictions are compromised by market randomness.
method Introduces market-based probabilities of price and return, dependent on trade values and volumes.
result Market-based price volatility is more accurate than frequency-based VaR predictions.
Equilibrium price and optimal insider trading strategy under stochastic liquidity with long memoryq-fin.MF
Extended Kyle model with long memory trading volume, finds excessive price volatility.
problem Understanding insider trading dynamics with long memory trading volume.
method Extended Kyle model with stochastic noise trading volatility and long memory trading volume.
result Excessive price volatility due to insider's aggressive trading strategy in high noise trading volume.
A Volume-Price-Adjusted MACD Trading Strategy with Sensitivity Calibration for U.S. Equity Indicesq-fin.TR
Study improves MACD trading strategy with volume and price adjustments.
problem Signal lag and false signals in traditional MACD trading rules.
method Develops VP-MACD framework with sensitivity calibration.
result Proposed framework outperforms baseline MACD in profitability and risk-adjusted return.
New method corrects Markowitz variance for trading volume fluctuations.
problem Incorrect risk estimates from Markowitz variance in trading environments.
method Modeling portfolio variance based on trade volume fluctuations.
result Market-based variance can significantly differ from Markowitz variance.
Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Predictionq-fin.TR
A new distillation framework predicts stock trading volumes more accurately with less model size.
problem Predicting stock trading volumes using regression models without class correlations.
method Transformed regression model into a probabilistic forecasting model, matching distributions and correlational relationships.
result Framework achieves superior prediction accuracy with significantly smaller model size.
Reinforcement learning is explored as a candidate machine learning technique to enhance existing analytical solutions for optimal trade execution with elements from the market microstructure. Given a volume-to-trade, fixed time horizon and discrete trading periods, the aim is to adapt a given volume trajectory such tha…
New framework uses trading volume instead of volatility for stock pricing.
problem Improving stock price dynamics understanding and market data gap.
method Proposes a new stock pricing model using trading volume instead of volatility, based on two hypotheses.
result The new framework can be applied to option pricing and points to a new direction in finance.
Local fluctuations of the signed traded volumes and the dependencies of demands: a copula analysisq-fin.TR
Copulas reveal strong positive dependencies in stock demand fluctuations due to volume imbalances.
problem Analyzing dependencies of stock demands using local volume fluctuations.
method Copula analysis of empirical data to model dependence structures.
result Large local fluctuations of signed traded volumes increase positive dependencies in demand but slightly lower negative ones.
Yet on statistical properties of traded volume: correlation and mutual information at different value magnitudesphysics.data-an
In this article we analyse linear correlation and non-linear dependence of traded volume, , of the 30 constituents of Dow Jones Industrial Average at different value scales. Specifically, we have raised to some real value or , which introduces a bias for small () or large () values. Our r…
A Structural Matrix Autoregressive Model for the Joint Dynamics of Volume, Volatility, and Returnsecon.EM
Proposes a Structural Matrix Autoregressive model for joint analysis of asset returns, realized volatility, and trading volume.
problem Joint analysis of asset returns, realized volatility, and trading volume
method Structural Matrix Autoregressive model
result Volatility is primary driver of trading activity, with informational shocks incorporated through price variability.
In this pre-print we explore the multi-fractal properties of 1 minute traded volume of the equities which compose the Dow Jones 30. We also evaluate the weights of linear and non-linear dependences in the multi-fractal structure of the observable. Our results show that the multi-fractal nature of traded volume comes es…
Market Impact Paradoxesq-fin.TR
The market impact (MI) of Volume Weighted Average Price (VWAP) orders is a convex function of a trading rate, but most empirical estimates of transaction cost are concave functions. How is this possible? We show that isochronic (constant trading time) MI is slightly convex, and isochoric (constant trading volume) MI is…
The paper sets limits on the accuracy of macroeconomic forecasts based on statistical moments and trade volumes.
problem Uncertainty in predicting macroeconomic variables like prices and returns.
method Defines theoretical lower bounds of uncertainty and upper limits on forecast accuracy based on statistical moments and trade volumes.
result Accuracy of forecasts of probabilities of macroeconomic variables doesn't exceed Gaussian approximations.
Market-based portfolio variance measures risks using trade data.
problem Measuring portfolio risks using traditional methods ignores trade volume randomness.
method Uses time series of trades with securities and portfolio to assess variance.
result Portfolio variance can be decomposed into securities' contributions, accounting for trade volume randomness.
We study the relationship between price spread, volatility and trading volume. We find that spread forms as a result of interplay between order liquidity and order impact. When trading volume is small adding more liquidity helps improve price accuracy and reduce spread, but after some point additional liquidity begins …
New model explains price, volume, and waiting times in financial markets.
problem Understanding price, volume, and waiting times in financial markets.
method Generalized semi-Markov chains with endogenous index process and copulae for dependence.
result Model accurately reproduces empirical evidence from Italian stock market data.
This paper examines how wash traders exploit market conditions in Bitcoin, finding strategic timing and spillover effects.
problem Wash trading in cryptocurrency markets to inflate volume and manipulate market conditions.
method Analysis of 18 million Mt. Gox transactions, exogenous demand shock study.
result Wash trading intensifies in low legitimate trading volume and responds to demand shocks, indicating strategic behavior.
We consider a few quantities that characterize trading on a stock market in a fixed time interval: logarithmic returns, volatility, trading activity (i.e., the number of transactions), and volume traded. We search for the power-law cross-correlations among these quantities aggregated over different time units from 1 mi…
The paper analyzes how automated market makers can retain trading fees.
problem How automated market makers can sustainably retain a portion of trading fees.
method Modeling to determine the optimal take rate for AMMs to maximize their revenue.
result AMMs can sustainably set a non-zero take rate if they have loyal trade volume.
This paper presents a quantitative analysis of the relationship between the stock market returns and corresponding trading volumes using high- frequency data from the Polish stock market. First, for stocks that were traded for suffciently long period of time, we study the return and volume distributions and identify th…
Hawkes processes for credit indices time series analysis: How random are trades arrival times?stat.AP
Researchers use Hawkes processes to analyze credit trades, revealing self-excitement and volume impacts.
problem Understanding the dynamics of credit market trades and their interactions.
method Simple method for fitting multidimensional Hawkes processes with exponential kernels using maximum likelihood non-convex optimization.
result Quantification of self-excitement and volume impacts in credit trades.
Unified market-based description of returns and variances of trades.
problem Market-based variance of trades and market portfolio.
method Unified market-based approach to describe returns and variances of trades and market portfolio.
result Market-based variance accounts for random volumes of trades and differs from Markowitz's portfolio variance.
The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contrib…
Quintet Volume Projectionq-fin.TR
Models predict equities' daily trading volume using Bayesian methods.
problem Predicting equities' daily trading volume accurately.
method Bayesian econometric methods combining historical and current inputs.
result Unified approach for predicting total, remaining, intra-day, close auction, and seasonal volumes.