QuantNet learns global market trends to improve trading strategies.
problem Developing global trading strategies from multiple markets' data.
method QuantNet integrates transfer and meta-learning to learn market-agnostic trends and market-specific strategies.
result QuantNet outperformed top baseline strategies by 51% Sharpe and 69% Calmar ratios.
Hybrid ML ensemble predicts market risk and generates alpha.
problem Forecasting short-horizon market risk and generating alpha.
method Neural networks and tree-based voting models integrated for trading signal.
result Sharpe ratio of 2.51 and annualized CAPM alpha of +0.28.
MFIN networks improve crypto trading with multiple features.
problem Selecting and processing multiple features for effective trading.
method End-to-end framework using Multi-Factor Inception Networks (MFINs).
result MFINs learn uncorrelated, higher-Sharpe strategies not captured by traditional factors.
Agent learns to trade currency pairs with improved risk management.
problem Improving systematic FX trading performance with online transfer learning.
method Online inductive transfer learning using feature representation from Gaussian mixture model to a reinforcement learning agent.
result Annualized portfolio information ratio of 0.52, compound return of 9.3%.
Buying or selling assets leads to transaction costs for the investor. On one hand, it is well know to all market practionaires that the transaction costs are positive on average and present therefore systematic loss. On the other hand, for every trade, there is a buy side and a sell side, the total amount of asset and …
The aim of this work is to create systematic trading strategies built upon several financial crisis indicators based on the spectral properties of market dynamics. Within the limitations of our framework and data, we will demonstrate that our systematic trading strategies are able to make money, not as a result of pure…
Measures strategy durability through minimum regime performance, revealing trade-offs between efficiency and resilience.
problem Systematic investing strategies are vulnerable to regime changes, affecting their effectiveness and performance.
method Introduces minimum regime performance (MRP) to quantify the durability of systematic strategies, capturing how performance deteriorates under changing market conditions.
result Higher long-term Sharpe ratios do not always correlate with higher MRP, highlighting a new dimension of portfolio fragility.
The effect of proportional transaction costs on systematically generated portfolios is studied empirically. The performance of several portfolios (the index tracking portfolio, the equally-weighted portfolio, the entropy-weighted portfolio, and the diversity-weighted portfolio) in the presence of dividends and transact…
Deep RL applied for Indian stock trading strategies.
problem Designing profitable trading strategies for Indian stock markets.
method Applied deep reinforcement learning to ten Indian stock datasets.
result Models' performance compared and evaluated.
Paper uses LLMs for sector allocation, showing better returns.
problem Automated trading sector allocation inefficiencies.
method Systematic analysis of macroeconomic data and sentiment.
result LLM-based sector allocation outperforms traditional strategies.
Neural nets analyze crypto markets for multi-timeframe trading.
problem High-frequency trading in cryptocurrency markets.
method Multi-timeframe trend analysis and high-frequency direction prediction networks.
result Positive risk-adjusted returns through machine learning.
Paper proves using historical trading info improves trading strategies.
problem Improving trading strategies through historical data.
method Develops a new strategy using self-generated historical trading information.
result A new strategy consistently outperforms existing ones.
Paper develops an AI-driven framework for systematic investing.
problem Manual prompts limit model adaptability and data snooping biases.
method Closed-loop system with self-evolving AI, out-of-sample validation, and economic rationale.
result Long-short portfolios on factor signals outperform with Sharpe ratio 3.11 and return 59.53%.
Enhanced pairs trading with Black-Litterman model outperforms market indexes.
problem Underperformance of pairs trading in volatile or distressed markets.
method Integrated Black-Litterman model with pairs trading strategy.
result Superior performance compared to S\&P 500 index under various market conditions.
Pipeline for comparing trading algorithms in finance and crypto.
problem Disconnected research and applications in algorithmic trading.
method General pipeline for designing, programming, and evaluating trading strategies.
result Systematic comparison of trading algorithms in finance and crypto.
An algorithm was recently introduced by INTECH for the purposes of estimating the trading-profit contribution of systematic rebalancing to the relative return of rules-based investment strategies. We apply this methodology to analyze the size factor through the use of equal-weighted portfolios. These strategies combine…
New formulas forecast fractional Brownian motion for financial trading.
problem Forecasting financial log-prices following fractional Brownian motion.
method Theoretical formulas for accuracy metrics in fBm forecasting.
result Optimal trading strategies in fBm framework identified.
Stop-loss rules are often studied in the financial literature, but the stop-loss levels are seldom constructed systematically. In many papers, and indeed in practice as well, the level of the stops is too often set arbitrarily. Guided by the overarching goal in finance to maximize expected returns given available infor…
This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.
problem Return divergence among accounts after trade allocation.
method Systematic treatment of trade allocation risk, presenting a uniform return allocation method.
result Uniform allocation of returns irrespective of the number of accounts and trade sizes.
The paper proposes a new risk model for foundation models in finance.
problem Understanding how foundation models affect trading strategies' risk and return.
method An extension of the CAPM, separating systematic and idiosyncratic risks.
result Monte Carlo dropout measures the epistemic risk of foundation models.
TraderTalk uses LLMs to simulate human trading interactions in financial markets.
problem Simulating realistic human trading interactions in financial markets.
method Hybrid ABM with LLM-generated behaviors for detailed conversations.
result Successfully replicates trade-to-order volume ratios in financial markets.
Model analyzes trading frictions in cap-and-trade markets, showing how they interact to affect market effectiveness.
problem Analyzing how trading frictions impact cap-and-trade market effectiveness.
method Developed a dynamic stochastic model with multiple trading frictions, characterized access choices in closed form, and quantified using EU ETS data.
result Trading frictions interact to amplify or dampen market responses, and their combined effect is non-additive.
Financial markets change their behaviours abruptly. The mean, variance and correlation patterns of stocks can vary dramatically, triggered by fundamental changes in macroeconomic variables, policies or regulations. A trader needs to adapt her trading style to make the best out of the different phases in the stock marke…
Survey of AI in finance covering models, strategies, and knowledge systems.
problem Challenges in applying AI to financial markets, especially in high-frequency trading.
method Systematic analysis of financial AI across predictive models, decision frameworks, and knowledge augmentation systems.
result Critical trade-offs and gaps between theoretical advances and practical implementation in financial AI.
This study evaluates price improvements in order flow auctions on Ethereum.
problem Improving trading outcomes in blockchain-based trading platforms.
method Utilized open-source tools to attribute price improvements to specific system inputs.
result Auction-enhanced interfaces can provide statistically significant improvements in trading outcomes, averaging 4-5 basis points.
Vecchia approximations provide the best accuracy-runtime trade-off for Gaussian process approximations.
problem High computational cost of Gaussian processes for large data sets.
method Systematic comparison of different Gaussian process approximations.
result Vecchia approximations consistently provide the best accuracy-runtime trade-off.
Trading bubbles form when traders adapt to price mismatches.
problem Self-sustained price bubbles driven by adaptive trading behavior.
method Multi-agent model illustrating price bubble formation and statistical properties.
result Price bubbles can be driven by adaptive investment strategies.
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…
ATLAS uses LLMs to adaptively trade by optimizing prompts and coordinating agents.
problem Adapting LLMs for real-time financial decision-making in noisy markets.
method ATLAS integrates structured market data, uses Adaptive-OPRO for prompt optimization, and employs multi-agent coordination.
result Adaptive-OPRO consistently outperforms fixed prompts in financial trading.
New system resists meme coin copy trading bots.
problem Manipulative bots exploit copy trading in illiquid meme coins.
method Multi-agent architecture with LLM and CoT reasoning.
result System outperforms other methods in prediction and economic performance.
We run experimental asset markets to investigate the emergence of excess trading and the occurrence of synchronised trading activity leading to crashes in the artificial markets. The market environment favours early investment in the risky asset and no posterior trading, i.e. a buy-and-hold strategy with a most probabl…
The purpose of these notes is to provide a systematic quantitative framework - in what is intended to be a "pedagogical" fashion - for discussing mean-reversion and optimization. We start with pair trading and add complexity by following the sequence "mean-reversion via demeaning -> regression -> weighted regression ->…
Proposes FACT, a diagnostic for understanding group fairness trade-offs.
problem Group fairness notions often conflict with each other, requiring a cost in model performance.
method Characterizes trade-offs via the fairness-confusion tensor and optimizes accuracy and fairness objectives.
result Demonstrates the use of FACT on synthetic and real datasets to understand accuracy-fairness trade-offs.
This paper studies liquidity providers in decentralized exchanges.
problem Understanding how liquidity providers behave in DEXes.
method Analyzed operations on Uniswap, measured investment strategy, returns, and risks.
result Liquidity providers benefit from transaction fees and determine their strategy based on market changes.
The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms all relying, primary, on aspects of time-series analysis. Recently, after visiting the trading floor of a leading financial institution, we noticed that traders always execute their trade or…
New algorithm improves asset ranking for better cross-sectional portfolios.
problem Sub-optimal ranking of assets in cross-sectional systematic strategies.
method Learning-to-rank algorithms to enhance portfolio construction.
result Modern machine learning ranking algorithms boost Sharpe Ratios by approximately threefold.
TraDE uses self-attention for better density estimation of tabular and image data.
problem Improving density estimation for tabular and image data.
method Self-attention-based architecture trained with a penalized maximum likelihood objective.
result TraDE produces significantly better density estimates than existing methods.
We present a model of worldwide crisis contagion based on the Google matrix analysis of the world trade network obtained from the UN Comtrade database. The fraction of bankrupted countries exhibits an \textit{on-off} phase transition governed by a bankruptcy threshold κ related to the trade balance of the countries. …
Enhances pairs trading with neural networks and Kalman Filters.
problem Inaccurate linear models in pairs trading lead to suboptimal performance.
method Augments Kalman Filter with Neural Networks to improve financial indicator extraction.
result Empirically shows improved trading performance compared to benchmarks.
Most previous works usually explained adversarial examples from several specific perspectives, lacking relatively integral comprehension about this problem. In this paper, we present a systematic study on adversarial examples from three aspects: the amount of training data, task-dependent and model-specific factors. Pa…
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.
Factor Engine simplifies financial factor computation and analysis in Python.
problem Efficient computation and analysis of financial factors.
method Modular, extensible Python library with decorators, integrates with data science ecosystem.
result Mispricing factors computed by Factor Engine and Stata implementation are highly similar.
Margin trading in which investors purchase shares with money borrowed from brokers is blamed to be a major cause of the 2015 Chinese stock market crash. We propose a cascading failure model and examine how an increase in margin trading increases share price vulnerability. The model is based on a bipartite graph of inve…
Study models opaque financial markets using multi-agent simulation.
problem Challenges in financial markets with obscured data availability.
method Multi-agent simulation with small-scale meta-heuristic methods.
result Captures bilateral market dynamics of OTC trading.
Order-flow entropy predicts price magnitude without directionality.
problem Predicting price magnitude in financial markets.
method Real-time order-flow entropy computed from a 15-state Markov transition matrix.
result Order-flow entropy predicts the magnitude of intraday returns with high accuracy.
MountainLion uses LLMs to interpret financial data and generate investment strategies.
problem Challenges in integrating heterogeneous data for financial trading.
method Multi-modal LLM-based agents that process textual and visual data.
result Improves returns and investor confidence through interpretable investment framework.
A new approach to continuous-time universal portfolios using pathwise Itô calculus.
problem Continuous-time version of Cover's universal portfolio strategies.
method Pathwise Itô calculus approach to establish existence and properties of universal portfolio strategies.
result The universal portfolio strategy's portfolio value process is the average of all values of constant rebalanced strategies.
Time and Sales of corn futures traded electronically on the CME Group Globex are studied. Theories of continuous prices turn upside down reality of intra-day trading. Prices and their increments are discrete and obey lattice probability distributions. A function for systematic evolution of futures trading volume is pro…