Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression

• URL: http://arxiv.org/abs/2202.12578v1
• Date: Fri, 25 Feb 2022 09:33:10 GMT
• Title: Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression
• Authors: Diksha Garg, Pankaj Malhotra, Anil Bhatia, Sanjay Bhat, Lovekesh Vig, Gautam Shroff
• Abstract summary: We consider learning a trading agent acting on behalf of a firm earning revenue in a foreign currency (FC) and incurring expenses in the home currency (HC) The goal of the agent is to maximize the expected HC at the end of the trading episode by deciding to hold or sell the FC at each time step in the trading episode. We propose a novel supervised learning approach that learns to forecast the top-K future FX rates instead of forecasting all the future FX rates.
• Score: 19.942711817396734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider learning a trading agent acting on behalf of the treasury of a firm earning revenue in a foreign currency (FC) and incurring expenses in the home currency (HC). The goal of the agent is to maximize the expected HC at the end of the trading episode by deciding to hold or sell the FC at each time step in the trading episode. We pose this as an optimization problem, and consider a broad spectrum of approaches with the learning component ranging from supervised to imitation to reinforcement learning. We observe that most of the approaches considered struggle to improve upon simple heuristic baselines. We identify two key aspects of the problem that render standard solutions ineffective - i) while good forecasts of future FX rates can be highly effective in guiding good decisions, forecasting FX rates is difficult, and erroneous estimates tend to degrade the performance of trading agents instead of improving it, ii) the inherent non-stationary nature of FX rates renders a fixed decision-threshold highly ineffective. To address these problems, we propose a novel supervised learning approach that learns to forecast the top-K future FX rates instead of forecasting all the future FX rates, and bases the hold-versus-sell decision on the forecasts (e.g. hold if future FX rate is higher than current FX rate, sell otherwise). Furthermore, to handle the non-stationarity in the FX rates data which poses challenges to the i.i.d. assumption in supervised learning methods, we propose to adaptively learn decision-thresholds based on recent historical episodes. Through extensive empirical evaluation, we show that our approach is the only approach which is able to consistently improve upon a simple heuristic baseline. Further experiments show the inefficacy of state-of-the-art statistical and deep-learning-based forecasting methods as they degrade the performance of the trading agent.

Related papers

• $φ$-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models [58.217707070069885]
  This paper presents a novel Fairness Direct Preference Optimization (FaiDPO or $$-DPO) framework for continual learning in LMMs.<n>We first propose a new continual learning paradigm based on Direct Preference Optimization (DPO) to mitigate catastrophic forgetting by aligning learning with pairwise preference signals.<n> Extensive experiments and ablation studies show the proposed $$-DPO achieves State-of-the-Art performance across multiple benchmarks.
  arXiv  Detail & Related papers  (2026-02-26T04:14:33Z)
• FineFT: Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading [39.845446417892525]
  The Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading (FineFT) is a novel ensemble framework with stable training and proper risk management.<n>We show FineFT outperforms 12 SOTA baselines in 6 financial metrics, reducing risk by more than 40% while achieving superior profitability compared to the runner-up.
  arXiv  Detail & Related papers  (2025-12-29T11:56:33Z)
• Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets [57.179679246370114]
  In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices.<n>During deployment, these agents trade in live markets where their own transactions can shift asset prices, a phenomenon known as market impact.<n>Traditional robust RL approaches address this model misspecification by optimizing the worst-case performance over a set of uncertainties.<n>We develop a novel class of elliptic uncertainty sets, enabling efficient and tractable robust policy evaluation.
  arXiv  Detail & Related papers  (2025-10-22T18:22:25Z)
• Finance-Grounded Optimization For Algorithmic Trading [0.0]
  We first introduce financially grounded loss functions derived from key quantitative finance metrics, including the Sharpe ratio, Profit-and-Loss (PnL), and Maximum Draw down.<n>Our findings demonstrate that the proposed loss functions, in conjunction with turnover regularization, outperform the traditional mean squared error loss for return prediction tasks.<n>The study shows that financially grounded metrics enhance predictive performance in trading strategies and portfolio optimization.
  arXiv  Detail & Related papers  (2025-09-04T09:18:54Z)
• FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making [58.04602111184477]
  FinHEAR is a framework for Human Expertise and Adaptive Risk-aware reasoning.<n>It orchestrates specialized agents to analyze historical trends, interpret current events, and retrieve expert-informed precedents.<n> Empirical results on financial datasets show that FinHEAR consistently outperforms strong baselines across trend prediction and trading tasks.
  arXiv  Detail & Related papers  (2025-06-10T04:06:51Z)
• Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF [67.48004037550064]
  We propose an active learning approach to efficiently select prompt and preference pairs. Our method evaluates the gradients of all potential preference annotations to assess their impact on model updates. Experimental results demonstrate that our method outperforms the baseline by up to 5% in win rates against the chosen completion.
  arXiv  Detail & Related papers  (2025-03-28T04:22:53Z)
• FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting [58.70072722290475]
  Financial time series (FinTS) record the behavior of human-brain-augmented decision-making. FinTSB is a comprehensive and practical benchmark for financial time series forecasting.
  arXiv  Detail & Related papers  (2025-02-26T05:19:16Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Detecting Toxic Flow [0.40964539027092917]
  This paper develops a framework to predict toxic trades that a broker receives from her clients. We use a proprietary dataset of foreign exchange transactions to test our methodology. We devise a strategy for the broker who uses toxicity predictions to internalise or to externalise each trade received from her clients.
  arXiv  Detail & Related papers  (2023-12-10T09:00:09Z)
• NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading [0.0]
  NoxTrader is a sophisticated system designed for portfolio construction and trading execution. The underlying learning process of NoxTrader is rooted in the assimilation of valuable insights derived from historical trading data. Our rigorous feature engineering and careful selection of prediction targets enable us to generate prediction data with an impressive correlation range between 0.65 and 0.75.
  arXiv  Detail & Related papers  (2023-10-01T17:53:23Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints [51.12047280149546]
  A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints. We formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. We demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
  arXiv  Detail & Related papers  (2022-12-23T22:29:08Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
  We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Reinforcement Learning for Systematic FX Trading [0.0]
  We conduct a detailed experiment on major cash pairs, accurately accounting for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to our recurrent reinforcement learner. This is despite forcing the model to trade at the close of the trading day 5pm EST, when trading costs are statistically the most expensive.
  arXiv  Detail & Related papers  (2021-10-10T09:44:29Z)
• Interpretability in Safety-Critical FinancialTrading Systems [15.060749321774136]
  In 2020, some of the world's most sophisticated quant hedge funds suffered losses. We implement a gradient-based approach for precisely stress-testing how a trading model's forecasts can be manipulated. We find our approach discovers seemingly in-sample input settings that result in large negative shifts in return distributions.
  arXiv  Detail & Related papers  (2021-09-24T17:05:58Z)
• Deep Portfolio Optimization via Distributional Prediction of Residual Factors [3.9189409002585562]
  We propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors. We demonstrate the efficacy of our method on U.S. and Japanese stock market data.
  arXiv  Detail & Related papers  (2020-12-14T04:09:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.