Research papers

List of research papers and books about AI cases in Financial Markets.

Portfolio Optimization and Management

Zhipeng Liang, et. al. Adversarial Deep Reinforcement Learning in Portfolio Management [code] [code] #RL #DDPG #PPO #PG #AdversarialLearning #StockMarket #China #CNN #SharpeRatio
Zhengyao Jiang, et. al. A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem [code] [other repos] #RL #LSTM #CNN #EIIE #BTC #crypto
Olivier Jin, Hamza El-Saawy. Portfolio Management using Reinforcement Learning #RL #DQL #BetaIndex #SharpeRatio #StockMarket #SnP500
Wonsup Shin, et. al. Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning #RL #DQL #CNN #crypto #binance #USDT
Derek Snow. Machine Learning in Asset Management [repo] #RL #SupervisedLearning #UnsupervisedLearning #UsefulLinks
Frank Z. Xing, et. al. Discovering Bayesian Market Views for Intelligent Asset Allocation [code]
Bernhard Pfaff. Financial Risk Modelling and Portfolio Optimization with R #book
Farzan Soleymani, Eric Paquet. Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder — DeepBreath
Gang Huang, et. al. Deep reinforcement learning for portfolio management based on the empirical study of chinese stock market
Matthew Dixon, Igor Halperin. G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning

Brian Ning, et. al. Double Deep Q-Learning for Optimal Execution
Yuriy Nevmyvaka, et. al. Reinforcement Learning for Optimized Trade Execution
Adamantios Ntakarisa, et. al. Feature Engineering for Mid-Price Prediction with Deep Learning
Paraskevi Nousia, et. al. Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data
Faisal Qureshi. Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach [code]
Alec N.Kercheval, Yaun Zhang. Modeling high-frequency limit order book dynamics with support vector machines
E. S. Ponomarev, et. al. Using Reinforcement Learning in the Algorithmic Trading Problem [code]
Huang, Chien-Yi. Financial Trading as a Game: A Deep Reinforcement Learning Approach [code]
Ayman Chaouki, Stephen Hardiman, et. al. Deep Deterministic Portfolio Optimization [code]
Zihao Zhang, et. al. Deep Reinforcement Learning for Trading
Xiao Li, Weili Wu. A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction #graph #crypto #blockchain-info #price-prediction

Ziniu Hu, et. al. Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction [code] [code]
Huicheng Liu. Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network [code]
Tian Guo, et. al. Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders
Wei Luo, Feng Yu. Recurrent Highway Networks with Grouped Auxiliary Memory [code]
Pushpendu Ghosh, et. al. Forecasting directional movements of stock prices for intraday trading using LSTM and random forests [code]
Venkata Sasank Pagolu, et. al. Sentiment Analysis of Twitter Data for Predicting Stock Market Movements [code]
Raehyun Kim, et. al. HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction [code]
Johan Bollen, et. al. Twitter mood predicts the stock market [code]
Lior Sidi. Improving S&P stock prediction with time series stock similarity.

Hyeong Kyu Choi. Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model [code]
Krauss, Christopher, et. al. Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
S.E. Yi, et. al. A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series
Thomas Hollis, et. al. A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series
Liao Zhu, et. al. News-Based Sparse Machine Learning Models for Adaptive Asset Pricing

Xiang Gao. Deep reinforcement learning for time series: playing idealized trading games [code]
Hassan Ismail Fawaz, et. al. Deep learning for time series classification: a review [code]
Aditya Kusupati, et. al. FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network [code]
Bryan Lim, et. al. Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting [code]