Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways
T Lim - Artificial Intelligence Review, 2024 - Springer
The rapidly growing research landscape in finance, encompassing environmental, social,
and governance (ESG) topics and associated Artificial Intelligence (AI) applications …
and governance (ESG) topics and associated Artificial Intelligence (AI) applications …
Automated cryptocurrency trading approach using ensemble deep reinforcement learning: Learn to understand candlesticks
L Jing, Y Kang - Expert Systems with Applications, 2024 - Elsevier
Despite their high risk, cryptocurrencies have gained popularity as viable trading options.
Cryptocurrencies are digital assets that experience significant fluctuations in a market …
Cryptocurrencies are digital assets that experience significant fluctuations in a market …
Deep reinforcement learning applied to a sparse-reward trading environment with intraday data
Deep reinforcement learning (DRL) has made remarkable strides in empowering
computational models to tackle intricate decision-making tasks. In quantitative trading, DRL …
computational models to tackle intricate decision-making tasks. In quantitative trading, DRL …
[HTML][HTML] Statistical arbitrage trading across electricity markets using advantage actor–critic methods
In this paper, risk-constrained arbitrage trading strategies that exploit price differences
arising across short-term electricity markets, namely day-ahead (DAM), continuous intraday …
arising across short-term electricity markets, namely day-ahead (DAM), continuous intraday …
Deep deterministic policy gradient algorithm: A systematic review
Abstract Deep Reinforcement Learning (DRL) has gained significant adoption in diverse
fields and applications, mainly due to its proficiency in resolving complicated decision …
fields and applications, mainly due to its proficiency in resolving complicated decision …
A novel stock trading utilizing long short term memory prediction and evolutionary operating-weights strategy
X Huang, C Wu, X Du, H Wang, M Ye - Expert Systems with Applications, 2024 - Elsevier
In the realm of quantitative investing, predicting stock prices and developing effective trading
strategies are paramount. The inherent randomness of the stock market, however, limits the …
strategies are paramount. The inherent randomness of the stock market, however, limits the …
Quantitative day trading from natural language using reinforcement learning
It is challenging to design profitable and practical trading strategies, as stock price
movements are highly stochastic, and the market is heavily influenced by chaotic data …
movements are highly stochastic, and the market is heavily influenced by chaotic data …
Smart robotic strategies and advice for stock trading using deep transformer reinforcement learning
The many success stories of reinforcement learning (RL) and deep learning (DL) techniques
have raised interest in their use for detecting patterns and generating constant profits from …
have raised interest in their use for detecting patterns and generating constant profits from …
Neural network-based blended ensemble learning for speech emotion recognition
B Yalamanchili, SK Samayamantula… - … Systems and Signal …, 2022 - Springer
Abstract Speech Emotion Recognition (SER) identifies human emotion from short speech
signals that enable natural Human Computer Interactions (HCI). Accurate emotion prediction …
signals that enable natural Human Computer Interactions (HCI). Accurate emotion prediction …
Vision-based drl autonomous driving agent with sim2real transfer
To achieve fully autonomous driving, vehicles must be capable of continuously performing
various driving tasks, including lane keeping and car following, both of which are …
various driving tasks, including lane keeping and car following, both of which are …