Research
On-device research index

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.

168,657 papers · 148 categories

Trend · papers per month

3570104139 · May 202619922001200920172026
48 results for Trading Volume

A dynamic herding model with interactions of trading volumes is introduced. At time tt, an agent trades with a probability, which depends on the ratio of the total trading volume at time t1t-1 to its own trading volume at its last trade. The price return is determined by the volume imbalance and number of trades. The …

2008-03-06abs ↗pdf ↗

Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there exist mutual information and correlation between price and trading volume. We are curious about fractal features of this correlation and need to know how structures in different scales translate information. To expl…

2019-03-05abs ↗pdf ↗

We study the statistical properties of the recurrence intervals ττ between successive trading volumes exceeding a certain threshold qq. 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…

2010-02-06abs ↗pdf ↗

In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss (2001) define the optimal trading strategy to liquidate a fixed volume of a single security under price uncertainty. Yet there exist situations, such as in the power market, in which the volume to be traded can only be estimated and…

2018-10-28abs ↗pdf ↗

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.

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.

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…

2013-09-10abs ↗pdf ↗

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…

2013-02-13abs ↗pdf ↗

We empirically analyze the price and liquidity responses to trade signs, traded volumes and signed traded volumes. Utilizing the singular value decomposition, we explore the interconnections of price responses and of liquidity responses across the whole market. The statistical characteristics of their singular vectors …

2017-11-21abs ↗pdf ↗

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.

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…

2011-03-08abs ↗pdf ↗

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.

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.

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.

In this article we analyse linear correlation and non-linear dependence of traded volume, vv, of the 30 constituents of Dow Jones Industrial Average at different value scales. Specifically, we have raised vv to some real value αα or ββ, which introduces a bias for small (α,β<0 α, β<0) or large (α,β>1α, β>1) values. Our r…

2007-02-21abs ↗pdf ↗

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…

2005-12-24abs ↗pdf ↗

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…

2013-12-11abs ↗pdf ↗

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.

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.

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.

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…

2015-06-04abs ↗pdf ↗

Graph-based multi-view model predicts trading volume movement from various sources.

problem Lack of comprehensive understanding of trading volume movement from different sources.
method Graph-based approach incorporating long-term, short-term, and sudden event information.
result Our method outperforms strong baselines by a large margin.

The scaling properties of the time series of asset prices and trading volumes of stock markets are analysed. It is shown that similarly to the asset prices, the trading volume data obey multi-scaling length-distribution of low-variability periods. In the case of asset prices, such scaling behaviour can be used for risk…

2005-01-13abs ↗pdf ↗

The paper validates a classifier for identifying intraday regime shifts in MNQ futures.

problem Developing reliable trading signals from intraday regime shifts in MNQ futures.
method Constructed a composite day-classification system using three observable conditions.
result Classifier-positive days exhibit distinct intraday behavior but fail to generate profitable trading signals.