An Effective Gated and Attention-Based Neural Network Model for Fine-Grained Financial Target-Dependent Sentiment Analysis

Jiang, Mengxiao; Wang, Jianxiang; Lan, Man; Wu, Yuanbin

doi:10.1007/978-3-319-63558-3_4

Cited by 5 publications

(1 citation statement)

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“…Regarding financial management, these results help explain the success of algorithmic investing, and it lends support to alternative trading schools, in particular technical analysis, which assumes that patterns are present in past financial time series, and that has been a key component in recent efforts of application of machine learning, including recurrent neural networks, to finance [43,44,45].…”

Section: Discussionmentioning

confidence: 63%

Financial Risk and Returns Prediction with Modular Networked Learning

Gonçalves

2018

Preprint

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An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations are used to build a volatility-sensitive interval prediction for financial returns, which is evaluated on three major financial indices and shown to be able to predict financial returns with higher than 80% success rate in interval prediction in both training and testing, raising into question the Efficient Market Hypothesis. The agent is introduced as an example of a class of artificial intelligent systems that are equipped with a Modular Networked Learning cognitive system, defined as an integrated networked system of machine learning modules, where each module constitutes a functional unit that is trained for a given specific task that solves a subproblem of a complex main problem expressed as a network of linked subproblems. In the case of neural networks, these systems function as a form of an "artificial brain", where each module is like a specialized brain region comprised of a neural network with a specific architecture.

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Section: Discussionmentioning

confidence: 63%