New findings challenge the importance of forecast accuracy in battery storage optimization, highlighting the role of rank correlation instead.
problem The challenge of optimizing battery storage dispatch decisions in multi-market electricity trading using forecast accuracy metrics.
method A hierarchical three-layer optimization system trading in multiple markets (FCR, aFRR, day-ahead, intraday) with real market data.
result Rank correlation (Kendall tau) is a better predictor of intraday dispatch value than forecast accuracy (MAE), with a threshold of tau around 0.85-0.95 capturing up to 97-100% of perfect-foresight revenue.
Following the recent literature on make take fees policies, we consider an exchange wishing to set a suitable contract with several market makers in order to improve trading quality on its platform. To do so, we use a principal-agent approach, where the agents (the market makers) optimise their quotes in a Nash equilib…
We employ a 2x3 factorial experiment to study two central factors in the design of prediction markets (PMs) for idea evaluation: the overall design of the PM, and the elasticity of market prices set by a market maker. The results show that 'multi-market designs' on which each contract is traded on a separate PM lead to…
The paper analyzes profitable bidding strategies for BESS in day-ahead and intraday markets.
problem Optimizing profitability of Battery Energy Storage Systems (BESS) in day-ahead and intraday markets.
method Employing the rolling intrinsic approach to model continuous intraday markets, accounting for bid-ask spreads and liquidity constraints.
result Multi-market bidding strategies outperform single-market participation, and relaxing daily cycling constraints can unlock additional value.
Study finds non-monotonic Value of Information in dynamic multi-market monopoly.
problem Investigates non-monotonicity in Value of Information for a price-setting monopolist.
method Uses a Bayesian inverse problem with Kalman-Bucy-Stratonovich filter in a dynamic discrete model.
result Non-monotonic relationship between signal variance and Value of Information.
LiveTradeBench evaluates LLMs in live trading environments.
problem Static benchmarks fail to assess real-world trading ability.
method Live data streaming, portfolio management abstraction, multi-market evaluation.
result LLMs show distinct portfolio styles and adapt to live signals.
SNNs optimize cross-market portfolios with neuromorphic computing, reducing computational overhead and improving returns.
problem Complex cross-market portfolio optimization with high-frequency, multi-dimensional datasets.
method Leaky Integrate-and-Fire neuron dynamics, adaptive thresholding, spike-timing-dependent plasticity, lateral inhibition, hierarchical clustering, population-based spike encoding, multiple decoding strategies.
result SNNs deliver superior risk-adjusted returns and reduced volatility compared to ANN benchmarks, with improved computational efficiency.
Paper optimizes battery storage in multiple energy markets for better profits.
problem Optimizing battery storage participation in multiple energy markets to balance supply and demand.
method Developed a joint bidding strategy combining intraday and frequency markets using mixed integer linear programming and a learned classifier strategy.
result The LCS increases overall profits by over 4% compared to static strategies and by more than 3% over a naive dynamic benchmark.
Generalization of the minority game to more than one market is considered. At each time step every agent chooses one of its strategies and acts on the market related to this strategy. If the payoff function allows for strong fluctuation of utility then market occupancies become inhomogeneous with preference given to th…
Kronos improves financial time series analysis with a pre-trained model.
problem Limited application of large-scale models to financial candlestick data.
method Unified, scalable pre-training framework for financial K-line modeling.
result Kronos excels in financial tasks like price forecasting and volatility prediction.
Market confidence is essential for successful investing. By incorporating multi-market into the evolutionary minority game, we investigate the effects of investor beliefs on the evolution of collective behaviors and asset prices. When there exists another investment opportunity, market confidence, including overconfide…
This study optimizes energy storage scheduling under price uncertainty, balancing risk and reward.
problem Optimizing energy storage operation under price uncertainty and risk.
method Two-stage stochastic risk-constrained approach using conditional value-at-risk.
result Increasing risk aversion leads to substantial benefits in terms of risk reduction and expected reward.
Optimizes leveraged staking strategies in decentralized finance.
problem Maximizing returns on staked assets in decentralized lending platforms.
method Developed a mathematical framework to optimize leveraged staking strategies, reducing the multi-market problem to convex allocation over market exposures.
result Rebalanced leveraged positions can achieve up to 6.2% APY, significantly higher than unleveraged staking.
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.
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.
Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into k classes using the k-shell decomposition m…
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.
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.
Here, we present the World Trade Atlas 1870-2013, a collection of annual world trade maps in which distance combines economic size and the different dimensions that affect international trade beyond mere geography. Trade distances, which are based on a gravity model predicting the existence of significant trade channel…
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…
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.
We extend the framework of trading strategies of Gatheral [2010] from single stocks to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cros…
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.
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.
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.
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.
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.
This paper uses RL for better financial trading.
problem Improving financial trading algorithms.
method Deep Q Learning applied to quantitative trading.
result RL can outperform traditional trading algorithms.
Algorithm combines ESG ratings with pairs trading for sustainable investing.
problem Lack of socially responsible investment solutions.
method Integrates ESG data with pairs trading strategy using technical indicators.
result Model generates positive returns while adhering to ESG principles.
Develops a new flexible grid trading model using ANN and SSO.
problem Improving automated trading strategies in financial markets.
method Combines SSO algorithm with ANN for optimizing trading parameters.
result Provides a robust and efficient trading model with better returns.
Study improves trading decisions by predicting profit and loss outcomes.
problem Inconsistent profitability of machine learning forecasts in financial markets.
method Developed a novel algorithm for forecasting profit and loss outcomes, integrating with market trend predictions.
result Significantly improved performance of trading strategies, including traditional and algorithmic trading.
Trading-R1 uses LLMs for financial trading, improving risk-adjusted returns.
problem Lack of interpretability and trust in AI for finance.
method Supervised fine-tuning and reinforcement learning with a curriculum.
result Improved risk-adjusted returns and lower drawdowns compared to other models.
Generalizes insider trading model to multiple assets.
problem Modeling informed trading in a multi-asset context.
method Formulated an infinite-dimensional Bayesian trading game.
result Obtained a parsimonious equilibrium with closed-form solutions.