Auditing Algorithmic Bias in Transformer-Based Trading

• URL: http://arxiv.org/abs/2510.05140v1
• Date: Wed, 01 Oct 2025 21:20:26 GMT
• Title: Auditing Algorithmic Bias in Transformer-Based Trading
• Authors: Armin Gerami, Ramani Duraiswami,
• Abstract summary: We employ a transformer model for prediction, and introduce a metric based on Partial Information Decomposition (PID) to measure the influence of each asset on the model's decision making.<n>Our analysis reveals two key observations: first, the model disregards data volatility entirely, and second, it is biased toward data with lower-frequency price movements.
• Score: 10.235738752130803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer models have become increasingly popular in financial applications, yet their potential risk making and biases remain under-explored. The purpose of this work is to audit the reliance of the model on volatile data for decision-making, and quantify how the frequency of price movements affects the model's prediction confidence. We employ a transformer model for prediction, and introduce a metric based on Partial Information Decomposition (PID) to measure the influence of each asset on the model's decision making. Our analysis reveals two key observations: first, the model disregards data volatility entirely, and second, it is biased toward data with lower-frequency price movements.

Related papers

• Multivariate Forecasting of Bitcoin Volatility with Gradient Boosting: Deterministic, Probabilistic, and Feature Importance Perspectives [1.8352113484137627]
  This study investigates the application of the Light Gradient Boosting Machine (LGBM) model for both deterministic and probabilistic forecasting of Bitcoin realized volatility.<n>We evaluate the performance of LGBM-based models and compare them with both econometric and machine learning baselines.
  arXiv  Detail & Related papers  (2025-11-25T09:26:13Z)
• Optimization of Deep Learning Models for Dynamic Market Behavior Prediction [4.594360512414794]
  We study multi-horizon demand forecasting on e-commerce transactions using the UCI Online Retail II dataset.<n>We present a hybrid sequence model that combines multi-scale temporal convolutions, a gated recurrent module, and time-aware self-attention.<n>Results show consistent accuracy gains and improved on peak/holiday periods.
  arXiv  Detail & Related papers  (2025-11-24T13:30:52Z)
• Partial multivariate transformer as a tool for cryptocurrencies time series prediction [0.0]
  We show that a partial-multivariate strategy achieves significant statistical accuracy, effectively balancing informative signals with noise.<n>Lower prediction error did not consistently translate to higher financial returns in simulations.<n>This finding challenges the reliance on traditional error metrics and highlights the need to develop evaluation criteria more aligned with real-world financial objectives.
  arXiv  Detail & Related papers  (2025-11-22T21:59:32Z)
• Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank [50.9530591265324]
  Training two-tower models on clicks collected by well-performing production systems leads to decreased ranking performance.<n>We theoretically analyze the identifiability conditions of two-tower models, showing that either document swaps across positions or overlapping feature distributions are required to recover model parameters from clicks.<n>We also investigate the effect of logging policies on two-tower models, finding that they introduce no bias when models perfectly capture user behavior.
  arXiv  Detail & Related papers  (2025-06-25T14:47:43Z)
• STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading [55.02735046724146]
  In financial trading, factor models are widely used to price assets and capture excess returns from mispricing.<n>We propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM.<n>Storm extracts features of stocks from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level, and represents the factors as multi-dimensional embeddings.
  arXiv  Detail & Related papers  (2024-12-12T17:15:49Z)
• Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models [10.79035001851989]
  This paper proposes a prediction framework based on the Time Series Transformer model.<n>We compare the model with LSTM, GRU, CNN, TCN and RNN-Transformer models.<n>The experimental results show that the Time Series Transformer model outperforms other models in both mean square error (MSE) and mean absolute error (MAE) evaluation indicators.
  arXiv  Detail & Related papers  (2024-12-04T08:15:27Z)
• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling.<n>Yet their widespread adoption poses challenges regarding data attribution and interpretability.<n>We develop an influence functions framework to address these challenges.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction [25.711345527738068]
  multimodal methods have faced two drawbacks. They often fail to yield reliable models and overfit the data due to their absorption of information from the stock market. Using multimodal models to predict stock volatility suffers from gender bias and lacks an efficient way to eliminate such bias. Our comprehensive experiments on robustness-world financial audio datasets reveal that this method exceeds the performance of current state-of-the-art solution.
  arXiv  Detail & Related papers  (2024-07-03T18:40:53Z)
• Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens [10.103561529332184]
  We focus on multi-modal time-series forecasting, where imprecision due to noisy or incorrect data can lead to erroneous predictions. We introduce a rating methodology to assess the robustness of Multi-Modal Time-Series Forecasting Models.
  arXiv  Detail & Related papers  (2024-06-12T17:39:16Z)
• 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)
• 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)
• 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)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)

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.