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Financial Markets Prediction with Deep Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Financial Markets Prediction with Deep Learning./
Author:	Wang, Jia.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	113 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:	Dissertations Abstracts International82-12B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28417551
ISBN:	9798515200138

Financial Markets Prediction with Deep Learning.
Wang, Jia.

Financial Markets Prediction with Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 113 p.

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Thesis (Ph.D.)--University of Massachusetts Lowell, 2021.

This item must not be sold to any third party vendors.

Financial markets are a complex dynamical system. The complexity comes from the interaction between a market and its participants. The integrated outcome of activities of the entire participants determines the market trends, in turn, the market trends affect activities of participants. These interwoven interactions make financial markets keep evolving and thus extremely difficult to be predicted. Inspired by the Adaptive Markets Hypothesis (AMH), this thesis explores predicting financial markets with deep learning approaches. In particular, (1) We design customized 1-D convolution neural networks named Cross-Data-Type 1-D CNNs (CDT 1-D CNNs) to extract local features instead of technical indicators, which can prevent our approach from being affected by human biases; (2) We introduce sequence-to-sequence framework (Seq2Seq) with attention mechanisms to extract temporal features in order to promptly adopt to the environment changes of financial markets; (3) We also introduce Kullback-Leibler divergence as an extra regularizer to alleviate overfitting problems. To our best knowledge, the integrated system of all the above approaches, called Convolutional LSTM based Variational Sequence-to-Sequencemodel with Attention (CLVSA), is the first deep learning approach without technical indicators to predict financial markets.Although it is the common sense in finance and economics that prices reflect all information, investigating the sentiment data is still informative for traders. we thus introduce TRMI data to investigate whether or not the sentiment data provides signals that are more directional than price movements. A series of experiments indicate that sentiment data does not only provide informative features to prediction systems, but it also contains the extra information which prices and volume do not reflect. With a subtle method to fuse TRMI data and historical trading data, we upgrade CLVSA to dual-CLVSA, which outperforms CLVSA 9.3% by average annual return and 0.91 by Sharpe ratio on SPDR S&P 500 ETF Trust. We also reveal the details about how TRMI data working in dual-CLVSA with the analysis of two cases.

ISBN: 9798515200138Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Data mining

Financial Markets Prediction with Deep Learning.
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520 $a Financial markets are a complex dynamical system. The complexity comes from the interaction between a market and its participants. The integrated outcome of activities of the entire participants determines the market trends, in turn, the market trends affect activities of participants. These interwoven interactions make financial markets keep evolving and thus extremely difficult to be predicted. Inspired by the Adaptive Markets Hypothesis (AMH), this thesis explores predicting financial markets with deep learning approaches. In particular, (1) We design customized 1-D convolution neural networks named Cross-Data-Type 1-D CNNs (CDT 1-D CNNs) to extract local features instead of technical indicators, which can prevent our approach from being affected by human biases; (2) We introduce sequence-to-sequence framework (Seq2Seq) with attention mechanisms to extract temporal features in order to promptly adopt to the environment changes of financial markets; (3) We also introduce Kullback-Leibler divergence as an extra regularizer to alleviate overfitting problems. To our best knowledge, the integrated system of all the above approaches, called Convolutional LSTM based Variational Sequence-to-Sequencemodel with Attention (CLVSA), is the first deep learning approach without technical indicators to predict financial markets.Although it is the common sense in finance and economics that prices reflect all information, investigating the sentiment data is still informative for traders. we thus introduce TRMI data to investigate whether or not the sentiment data provides signals that are more directional than price movements. A series of experiments indicate that sentiment data does not only provide informative features to prediction systems, but it also contains the extra information which prices and volume do not reflect. With a subtle method to fuse TRMI data and historical trading data, we upgrade CLVSA to dual-CLVSA, which outperforms CLVSA 9.3% by average annual return and 0.91 by Sharpe ratio on SPDR S&P 500 ETF Trust. We also reveal the details about how TRMI data working in dual-CLVSA with the analysis of two cases.
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