arXiv · arXiv q-fin · 2021
A wide variety of deep reinforcement learning (DRL) models have recently been proposed to learn profitable investment strategies. The rules learned by these models outperform the previous strategies specially in high frequency trading environments. However, it is shown that the quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading r…
Mehran Taghian, Ahmad Asadi, Reza Safabakhsh
arXiv · arXiv · 2026
Financial observations are continuous, heterogeneous, and noisy, whereas decoder-only next-token models are usually built around discrete symbolic inputs. We introduce Vector-Input Autoregressive Inference for Ordinal-Return Modeling (VAIOM), a decoder-only Transformer for probabilistic next-return modeling on one-hour foreign-exchange bars. VAIOM separates input representation from output likelihood: continuous mult…
Yiming Ma, Xinyu Chen
arXiv · arXiv q-fin · 2021
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating capitals. However, a vast majority of existing RL methods focus on the relat…
Shuo Sun, Wanqi Xue, Rundong Wang, Xu He, Junlei Zhu
arXiv · arXiv q-fin · 2023
The impact of non-deterministic outputs from Large Language Models (LLMs) is not well examined for financial text understanding tasks. Through a compelling case study on investing in the US equity market via news sentiment analysis, we uncover substantial variability in sentence-level sentiment classification results, underscoring the innate volatility of LLM outputs. These uncertainties cascade downstream, leading t…
Boyang Yu
arXiv · arXiv q-fin · 2022
Traditional approaches to financial asset allocation start with returns forecasting followed by an optimization stage that decides the optimal asset weights. Any errors made during the forecasting step reduce the accuracy of the asset weightings, and hence the profitability of the overall portfolio. The Portfolio Transformer (PT) network, introduced here, circumvents the need to predict asset returns and instead dire…
Damian Kisiel, Denise Gorse
arXiv · arXiv q-fin · 2020
Systematic financial trading strategies account for over 80% of trade volume in equities and a large chunk of the foreign exchange market. In spite of the availability of data from multiple markets, current approaches in trading rely mainly on learning trading strategies per individual market. In this paper, we take a step towards developing fully end-to-end global trading strategies that leverage systematic trends t…
Adriano Koshiyama, Sebastian Flennerhag, Stefano B. Blumberg, Nick Firoozye, Philip Treleaven
arXiv · arXiv · 2025
We propose ARBITER, a risk-neutral neural operator for learning joint SPX-VIX term structures under no-arbitrage constraints. ARBITER maps market states to an operator that outputs implied volatility and variance curves while enforcing static arbitrage (calendar, vertical, butterfly), Lipschitz bounds, and monotonicity. The model couples operator learning with constrained decoders and is trained with extragradient-st…
Jian'an Zhang
arXiv · arXiv · 2023
We present a novel process for generating synthetic datasets tailored to assess asset allocation methods and construct portfolios within the fixed income universe. Our approach begins by enhancing the CorrGAN model to generate synthetic correlation matrices. Subsequently, we propose an Encoder-Decoder model that samples additional data conditioned on a given correlation matrix. The resulting synthetic dataset facilit…
Szymon Kubiak, Tillman Weyde, Oleksandr Galkin, Dan Philps, Ram Gopal
arXiv · arXiv q-fin · 2024
The over-the-counter (OTC) government bond markets are characterised by their bilateral trading structures, which pose unique challenges to understanding and ensuring market stability and liquidity. In this paper, we develop a bespoke ABM that simulates market-maker interactions within a stylised government bond market. The model focuses on the dynamics of liquidity and stability in the secondary trading of governmen…
Alicia Vidler, Toby Walsh
arXiv · arXiv · 2024
This paper presents a new approach to volume ratio prediction in financial markets, specifically targeting the execution of Volume-Weighted Average Price (VWAP) strategies. Recognizing the importance of accurate volume profile forecasting, our research leverages the Transformer architecture to predict intraday volume ratio at a one-minute scale. We diverge from prior models that use log-transformed volume or turnover…
Hanwool Lee, Heehwan Park
arXiv · arXiv q-fin · 2025
Cross-market portfolio optimization has become increasingly complex with the globalization of financial markets and the growth of high-frequency, multi-dimensional datasets. Traditional artificial neural networks, while effective in certain portfolio management tasks, often incur substantial computational overhead and lack the temporal processing capabilities required for large-scale, multi-market data. This study in…
Amarendra Mohan, Ameer Tamoor Khan, Shuai Li, Xinwei Cao, Zhibin Li
arXiv · arXiv q-fin · 2025
In response to growing demand for resilient and transparent financial instruments, we introduce a novel framework for replicating private equity (PE) performance using liquid, AI-enhanced strategies. Despite historically delivering robust returns, private equity's inherent illiquidity and lack of transparency raise significant concerns regarding investor trust and systemic stability, particularly in periods of height…
E. Benhamou, JJ. Ohana, B. Guez, E. Setrouk, T. Jacquot
arXiv · arXiv q-fin · 2022
Intrinsic time is an example of an event-based conception of time, used to analyze financial time series. Here, for the first time, we reveal the connection between intrinsic time and physical time. In detail, we present an analytic relationship which links the two different time paradigms. Central to this discovery are the emergence of scaling laws. Indeed, a novel empirical scaling law is presented, relating to the…
James B. Glattfelder, Anton Golub
arXiv · arXiv q-fin · 2026
The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge evaluation toward interactive trading simulations. However, existing frameworks for evaluating real-time trading largely overlook a critical failure mode: the severe behavioral instability of LLMs in sequential decision-making under financial uncertainty. Through extensive experiments, we s…
Wentao Zhang, Mingxuan Zhao, Jincheng Gao, Jieshun You, Huaiyu Jia
arXiv · arXiv q-fin · 2023
Public finances are one of the fundamental mechanisms of economic governance that refer to the financial activities and decisions made by government entities to fund public services, projects, and operations through assets. In today's globalized landscape, even subtle shifts in one nation's public debt landscape can have significant impacts on that of international finances, necessitating a nuanced understanding of t…
Kapil Panda
arXiv · arXiv q-fin · 2023
This paper proposes a novel meta-learning approach to optimize a robust portfolio ensemble. The method uses a deep generative model to generate diverse and high-quality sub-portfolios combined to form the ensemble portfolio. The generative model consists of a convolutional layer, a stateful LSTM module, and a dense network. During training, the model takes a randomly sampled batch of Gaussian noise and outputs a popu…
Kamer Ali Yuksel