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Dynamic datasets and market environments for financial reinforcement learning
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building...
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Deep Reinforcement Learning Model for Stock Portfolio Management Based on Data Fusion
Deep reinforcement learning (DRL) can be used to extract deep features that can be incorporated into reinforcement learning systems to enable...
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EvoFolio: a portfolio optimization method based on multi-objective evolutionary algorithms
Optimal portfolio selection —composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and...
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Supervised actor-critic reinforcement learning with action feedback for algorithmic trading
Reinforcement learning is one of the promising approaches for algorithmic trading in financial markets. However, in certain situations, buy or sell...
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Stock Market Intraday Trading Using Reinforcement Learning
In this study, Reinforcement Learning (RL) techniques are used to develop trading strategies for the stock market. Conventional trading strategies...
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Deep Reinforcement Learning for Stock Trading with Behavioral Finance Strategy
Stock trading is a challenging task and has attracted extensive attention from artificial intelligence researchers. Deep Reinforcement Learning (DRL)...
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Ensemble Strategy Based on Deep Reinforcement Learning for Portfolio Optimization
Although deep reinforcement learning for portfolio optimization has attracted the attention of more and more researchers, existing research focuses...
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Dynamic stock-decision ensemble strategy based on deep reinforcement learning
In a complex and changeable stock market, it is very important to design a trading agent that can benefit investors. In this paper, we propose two...
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To learn or not to learn? Evaluating autonomous, adaptive, automated traders in cryptocurrencies financial bubbles
Financial bubbles represent a severe problem for investors. In particular, the cryptocurrency market has witnessed the bursting of different bubbles...
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Evaluation of Deep Reinforcement Learning Based Stock Trading
Stock is one of the most important targets in investment. However, it is challenging to manually design a profitable strategy in the highly dynamic...
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Deep Reinforcement Learning with Comprehensive Reward for Stock Trading
Stock trading is one of economically research hotspots. In the past decades, many researchers used machine learning methods to simply predict the...
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Early portfolio pruning: a scalable approach to hybrid portfolio selection
Driving the decisions of stock market investors is among the most challenging financial research problems. Markowitz’s approach to portfolio...
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A survey on model-based reinforcement learning
Reinforcement learning (RL) interacts with the environment to solve sequential decision-making problems via a trial-and-error approach. Errors are...
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Intelligence — Dynamics and Representations
The paper explores a biologically inspired definition of intelligence. Intelligence is related to whether behavior of a system contributes to its...
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Acyclic Hypergraphs and Relational Databases (A Survey)
Structural properties of hypergraphs and relevant features of relational databases having acyclic schemes were intensively investigated during 80s....
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On the interaction between plan recognition and intelligent interfaces
Plan recognition is an active research area in automatic reasoning, as well as a promising approach to engineering interfaces that can exploit models...