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A Double-Layer Neural Network Framework for High-Frequency Forecasting

Published: 12 January 2017 Publication History

Abstract

Nowadays, machine trading contributes significantly to activities in the equity market, and forecasting market movement under high-frequency scenario has become an important topic in finance. A key challenge in high-frequency market forecasting is modeling the dependency structure among stocks and business sectors, with their high dimensionality and the requirement of computational efficiency. As a group of powerful models, neural networks (NNs) have been used to capture the complex structure in many studies. However, most existing applications of NNs only focus on forecasting with daily or monthly data, not with minute-level data that usually contains more noises. In this article, we propose a novel double-layer neural (DNN) network for high-frequency forecasting, with links specially designed to capture dependence structures among stock returns within different business sectors. Various important technical indicators are also included at different layers of the DNN framework. Our model framework allows update over time to achieve the best goodness-of-fit with the most recent data. The model performance is tested based on 100 stocks with the largest capitals from the S8P 500. The results show that the proposed framework outperforms benchmark methods in terms of the prediction accuracy and returns. Our method will help in financial analysis and trading strategy designs.

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      Published In

      cover image ACM Transactions on Management Information Systems
      ACM Transactions on Management Information Systems  Volume 7, Issue 4
      January 2017
      74 pages
      ISSN:2158-656X
      EISSN:2158-6578
      DOI:10.1145/3026477
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 12 January 2017
      Accepted: 01 November 2016
      Revised: 01 August 2016
      Received: 01 March 2016
      Published in TMIS Volume 7, Issue 4

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      Author Tags

      1. High-frequency forecasting
      2. S8P 500
      3. neural networks

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