Modelling financial volume curves with hierarchical Poisson processes

• URL: http://arxiv.org/abs/2406.19402v1
• Date: Sat, 1 Jun 2024 12:03:57 GMT
• Title: Modelling financial volume curves with hierarchical Poisson processes
• Authors: Creighton Heaukulani, Abhinav Pandey, Lancelot F. James,
• Abstract summary: A common strategy is to trade a desired quantity across many orders in line with the expected volume curve throughout the day. We introduce a hierarchical Poisson process model for the intensity functions of admixtures of inhomogenous Poisson processes, which represent the trading times of the stock throughout the day. We demonstrate the method on datasets from the Trade and Quote repository maintained by Wharton Research Data Services.
• Score: 0.8624680612413765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling the trading volume curves of financial instruments throughout the day is of key interest in financial trading applications. Predictions of these so-called volume profiles guide trade execution strategies, for example, a common strategy is to trade a desired quantity across many orders in line with the expected volume curve throughout the day so as not to impact the price of the instrument. The volume curves (for each day) are naturally grouped by stock and can be further gathered into higher-level groupings, such as by industry. In order to model such admixtures of volume curves, we introduce a hierarchical Poisson process model for the intensity functions of admixtures of inhomogenous Poisson processes, which represent the trading times of the stock throughout the day. The model is based on the hierarchical Dirichlet process, and an efficient Markov Chain Monte Carlo (MCMC) algorithm is derived following the slice sampling framework for Bayesian nonparametric mixture models. We demonstrate the method on datasets of different stocks from the Trade and Quote repository maintained by Wharton Research Data Services, including the most liquid stock on the NASDAQ stock exchange, Apple, demonstrating the scalability of the approach.

Related papers

• Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis [89.60263788590893]
  Post-training Quantization (PTQ) technique has been extensively adopted for large language models (LLMs) compression. Existing algorithms focus primarily on performance, overlooking the trade-off among model size, performance, and quantization bitwidth.
  arXiv  Detail & Related papers  (2025-02-18T07:35:35Z)
• 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. We propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM. 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)
• 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)
• Deep Learning Enhanced Realized GARCH [6.211385208178938]
  We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and distills modeling advances from financial econometrics, high frequency trading data and deep learning.
  arXiv  Detail & Related papers  (2023-02-16T00:20:43Z)
• Stock Trading Volume Prediction with Dual-Process Meta-Learning [20.588377161807916]
  We propose a dual-process meta-learning method that treats the prediction of each stock as one task under the meta-learning framework. Our method can model the common pattern behind different stocks with a meta-learner, while modeling the specific pattern for each stock across time spans with stock-dependent parameters.
  arXiv  Detail & Related papers  (2022-10-11T13:35:20Z)
• 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)
• Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization [13.26093613374959]
  We develop two datasets with high rate of trading signals. We perform a machine learning simulation of 10 years of Forex price data over three low-margin instruments and 6 different OHLC granularities. As a result, we develop a specific and reproducible list of most optimal trade parameters found for each instrument-granularity pair.
  arXiv  Detail & Related papers  (2021-05-11T00:36:02Z)
• Universal Trading for Order Execution with Oracle Policy Distillation [99.57416828489568]
  We propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. We show that our framework can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information.
  arXiv  Detail & Related papers  (2021-01-28T05:52:18Z)
• Numerical Simulation of Exchange Option with Finite Liquidity: Controlled Variate Model [0.0]
  Trading in our market model has a direct impact on the asset's price. Two-dimensional Milstein scheme is implemented to simulate the pair of assets prices. Time complexity of these numerical schemes is included.
  arXiv  Detail & Related papers  (2020-06-14T02:19:18Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)
• A posteriori Trading-inspired Model-free Time Series Segmentation [0.0]
  Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a state-of-the-art model-based top-down approach fitting Gaussian models. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset.
  arXiv  Detail & Related papers  (2019-12-16T06:14:03Z)

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.