Study shows non-synchronous trading and portfolio effects explain market index behavior.
problem Explaining the difference in market efficiency between NYSE index types.
method Long-term analysis of NYSE indexes, rolling window variance tests, portfolio simulations.
result Joint effects of portfolio and non-synchronous trading explain index behavior.
NAPLES resolves lead-lag analysis challenges in non-synchronous high-frequency data.
problem Challenges in analyzing lead-lag effects due to non-synchronous observations and high-frequency data.
method NAPLES (Negative And Positive lead-lag EStimator) resolves these challenges.
result NAPLES has a strong correlation with actual lead-lag effects, including those triggered by macroeconomic announcements.
Estimates lead-lag relationships in high-frequency financial markets without interpolation.
problem Lag relationships in high-frequency financial markets with non-synchronous data.
method Proposes a novel estimation procedure for scale-by-scale lead-lag relationships.
result Identifies two types of lead-lag relationships at different time scales.
New method for estimating lead-lag times between non-synchronously observed point processes.
problem Estimating lead-lag relationships between non-synchronously observed point processes.
method Formulate lead-lag estimation as CPCF shape estimation; propose kernel density estimation-based lead-lag time estimator.
result Proposed method delivers superior numerical performance and effective lead-lag time estimation.
The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-…
In practice daily volatility of portfolio returns is transformed to longer holding periods by multiplying by the square-root of time which assumes that returns are not serially correlated. Under this assumption this procedure of scaling can also be applied to contributions to volatility of the assets in the portfolio. …
We consider a simple model of rational agents competing in a single product market described by simple linear demand curve. Contrary to accepted economic theory, the agents' production levels synchronise in the absence of conscious collusion, leading to a downward spiraling of market total production until the monopoly…
New method identifies whether equity return predictability is due to magnitude shrinkage or directional reversal.
problem Determining the nature of equity return predictability (directional reversal vs magnitude shrinkage).
method Developed the Fourier-Residue Identity (FRI) to decompose return autocorrelation into sign and magnitude channels.
result The lag-1 autocorrelation in SPY is driven entirely by magnitude shrinkage, not directional reversal.
A streaming algorithm estimates quadratic covariation from financial data efficiently.
problem Estimating quadratic covariation from ultra-high-frequency financial data with limited memory.
method Formulated multi-scale, realized kernel, pre-averaging, and modulated realized covariance estimators with fixed bandwidth.
result Fixed bandwidth estimators require higher bandwidth for positive semidefiniteness.
We present a systematic study of various statistical characteristics of high-frequency returns from the foreign exchange market. This study is based on six exchange rates forming two triangles: EUR-GBP-USD and GBP-CHF-JPY. It is shown that the exchange rate return fluctuations for all the pairs considered are well desc…
This work investigates the impact of staleness in distributed ML systems and offers insights into convergence.
problem The effects of staleness on the convergence of distributed machine learning algorithms are inconclusive and challenging to monitor.
method Extensive experiments with various ML models and algorithms under delayed updates.
result The empirical findings reveal the diverse effects of staleness on ML algorithm convergence and match the best-known convergence rate.
Optimizes control of synchronization in networked oscillators using genetic programming.
problem Optimizing control of synchronization in complex networked systems.
method Multi-objective genetic programming-based symbolic regression.
result Learned interpretable control functions for driving systems from synchronized to non-synchronized states.
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task is a phase-shifted periodic time series. In particular, we develop a novel Bayesian nonparametric model capturing a mixture of Gaussian proc…
New method linearizes nonlinear coupled oscillators on graphs.
problem Predicting global synchronization in nonlinear coupled oscillators on graphs.
method Latent dynamic filters learned through supervised matrix factorization.
result Latent dynamics filters enable effective prediction of global synchronization.
This paper examines how regional trade agreements affect global trade relationships.
problem The relationship between regional trade agreements and global trade purity.
method Defined and decomposed synthesized trade resistance, separated natural and artificial factors, used expectation maximization algorithm to optimize parameters, and quantified trade purity indicator.
result Regional trade agreements contribute to the relative prosperity of EU and NAFTA countries, but weaken the role of trade unions and accelerate multilateral trade liberalization.
This study analyzes stock trading networks to quantify price impacts based on trader positions.
problem Quantifying the immediate price impact of trades in stock markets.
method Constructed stock trading networks using k-shell decomposition to classify traders and compare different market segments. result Institutional traders have lower price impacts compared to individuals at the same positions in the trading network.
World trade atlas reveals hyperbolic geometry of international connections.
problem Understanding the complex architecture of international trade networks.
method Gravity model predicting trade channels and analysis of trade distances.
result Trade distances are hyperbolic, not flat, reflecting a hierarchical international trade system.
Paper predicts international trade flows using machine learning and factorization models.
problem Predicting international bilateral trade flows with PTAs.
method Two-stage approach combining SHAP Explainer and Factorization Machine models.
result Enhanced predictive accuracy and deeper insights into trade dynamics.
Analysis shows preference for Chinese yuan in global trade network.
problem Analyzing the preference of countries to trade in US dollars or Chinese yuan.
method Mathematical analysis of world trade network using Ising spin interactions.
result Majority of countries prefer trading in Chinese yuan due to network structure.
Complex financial instruments allow insider trading without actual trades.
problem How to commit insider trading without executing a trade.
method Use of financial instruments to replicate the economic effect of trading.
result Insider trading can be committed without an actual trade.
Model predicts trading strategies based on latent demand and price impact.
problem Predicting strategic trading behavior of investors with private targets.
method Equilibrium model of dynamic trading, learning, and pricing by strategic investors.
result Trading strategies are a combination of target following, liquidity provision, and front-running based on latent demand and price pressure.
Optimizes trading pairs of stocks to reduce cross-impact costs.
problem Minimizing costs from trades of one stock affecting another.
method Develops a strategy to minimize cross-impacts by optimizing trading rates and periods.
result An optimal trading strategy for stock pairs is found.
A dynamic herding model with interactions of trading volumes is introduced. At time t, an agent trades with a probability, which depends on the ratio of the total trading volume at time t−1 to its own trading volume at its last trade. The price return is determined by the volume imbalance and number of trades. The …
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…
Model reconstructs and simulates international trade networks under policy changes.
problem Reconstructing and simulating international trade networks under policy changes.
method Maximizing entropy based on local trade information to reconstruct networks; simulating structural changes using trade tariffs.
result Trade networks can be successfully reconstructed and simulated under policy changes.
Automated trading system with preprocessing and reinforcement learning.
problem Efficient portfolio trading for individual investors.
method Feature preprocessing, recurrent reinforcement learning, automated trading algorithm.
result System outperforms other strategies in profit and drawdown.
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
problem The profitability of technical trading rules in stock markets is controversial.
method Proves the upper bound of cumulative return and investigates the profitability of technical trading rules using bootstrap methodology.
result Technical trading rules are not better than random trading and less profitable than the market.
The paper classifies trades into types based on proximity and measures their impact on stock prices.
problem Understanding the impact of high-frequency trades on stock prices and their predictability.
method Classifies trades into five types based on proximity, measures conditional order imbalance (COI), and develops trading strategies.
result Strong positive correlations between contemporaneous returns and COIs, and positive associations with future returns for isolated trades.
Study high-frequency trading patterns in cryptocurrencies.
problem Understanding automated trading algorithms in cryptocurrency markets.
method Analyzes intraday trading data of cryptocurrencies, focusing on returns, volumes, and volatility.
result Provides insights into predictability of economic value in cryptocurrency markets.
Developed Forex trading heuristics with high profit potential.
problem Reduced trade opportunities due to technical indicator values.
method Machine learning simulation of 10 years of Forex data.
result Optimized trade parameters for 118 pips daily profit.
Paper examines costs of using wrong price impact models in trading.
problem Misspecifying price impact models in trading predictions.
method Derives formulas for misspecification costs and applies to trading data.
result Misspecification costs are asymmetric, affecting profits and losses.
This paper surveys cryptocurrency trading research, covering various aspects.
problem Understanding the unique nature and behavior of cryptocurrencies as assets.
method Comprehensive review of 146 research papers on cryptocurrency trading.
result Identifies promising open opportunities in cryptocurrency trading.
Brazil proposes a new BRICS trade currency to dominate international trade.
problem Dominance of BRICS currency in international trade.
method Mathematical model of influence battle between three currencies, using trade flows and weights in global trade.
result By 2012, about 58% of countries preferred trading with the BRICS currency.
Trade finance history traced from medieval origins to modern markets.
problem Evolution and standardization of trade finance products.
method Historical analysis of market structures and regulatory changes.
result Global trade finance market evolved from local to centralized, then decentralized.
CREDIT learns to master pair trading with risk-aware RL, outperforming existing methods.
problem Challenges in applying RL to pair trading due to temporal correlations and risk considerations.
method Risk-aware recurrent reinforcement learning (RL) with bidirectional GRU and temporal attention.
result CREDIT achieves significant profit in pair trading over five years of U.S. stock data.
Develops strategies to minimize trading costs in volatile markets.
problem Minimizing trading costs in volatile markets with uncertain asset price paths.
method Constructs dynamic, pathwise optimal trade execution strategies using random Young differential equations.
result Good trade execution strategies minimize trading costs in a pathwise sense, not just expected costs.
Trading strategies are limited by position limits, leading to a finite number of unique strategies.
problem Limiting the number of long and short positions in trading strategies.
method Formulas and distributions derived for the number of unique trading strategies, transactions, and do-nothing actions.
result A discrete distribution of actions and their properties are presented.
Using the United Nations COMTRADE database \cite{comtrade} we construct the Google matrix G of multiproduct world trade between the UN countries and analyze the properties of trade flows on this network for years 1962 - 2010. This construction, based on Markov chains, treats all countries on equal democratic grounds …
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.
Study reveals inefficiencies in EU carbon trading market.
problem Inefficiencies in carbon trading market undermine emission reduction goals.
method Analysis of granular transaction data from 2005-2020.
result 40% of firms never trade in a given year, and many trade only during high-price months.
Automated trading systems on developed and emerging capital markets are studied in this paper. The standard for developed market is automated trading system with 40-days simple moving average. We tested it for the index SIX Industrial for 1000 and 730 trading days of the slovak emerging capital market. The Buy and Hold…
Optimal transport and neural networks improve trade modeling accuracy.
problem Capturing subtler drivers of trade beyond supply and demand.
method Employing optimal transport and deep neural networks to learn a time-dependent cost function from data.
result Consistently outperforms traditional gravity models in accuracy.
Improved trading strategy using macroeconomic forecasts.
problem Optimizing trading strategies based on yield curve mean-reversion.
method Factored in machine learning forecasts of macroeconomic variables to optimize trading signals.
result Clear improvement in APR over evaluation period.
Paper examines trade/no trade patterns in illiquid stocks, highlighting effects of varying zero returns probabilities.
problem Detecting long-run trade/no trade effects in illiquid stocks with varying zero returns probabilities.
method Proposes a framework considering constant and time-varying zero returns probabilities, analyzing trade/no trade categorical sequences.
result Long-run trade/no trade effects may be spuriously detected in presence of non-constant zero returns probabilities.
We develop a polynomial method to optimize trading in markets with transaction costs.
problem Optimizing trading strategies in markets with proportional transaction costs.
method Polynomial approximation of the residual value function to determine optimal trading strategies.
result Identify the trade-off between trading frequency and trade sizes for satisfactory agreement with theoretically optimal strategies.
Study identifies key trades predicting market movements.
problem Predicting future market price movements.
method Optimized neural network predictor to identify influential trades.
result Trades with specific characteristics significantly impact future price predictions.
RL enhances cryptocurrency trading profits.
problem Enhancing cryptocurrency trading profits through dynamic scaling.
method Combining RL with pair trading, using new reward shaping and observation/action spaces.
result RL-based trading achieved 9.94% to 31.53% annualized profits, vs. 8.33% for traditional methods.
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.