DUCKQUANT / THE QUANT & AI DIRECTORY
1,058 resources, ranked by in-directory PageRank
Evaluates pretrained time-series foundation models on cross-sectional financial return forecasting.
Fine-tunes a pretrained time-series foundation model on financial data for forecasting tasks.
Open probabilistic time-series foundation model based on a lagged decoder-only transformer.
Adds sparse mixture-of-experts routing to time-series foundation models.
Extends the Chronos foundation model to universal multivariate and covariate-aware forecasting.
Financial LLM framework producing interpretable stock movement predictions with rationales.
Uses LLM prompting to extract explainable return-predictive factors from news text.
Survey of multimodal financial foundation models combining text, tables, audio and time series.
Comprehensive Chinese financial benchmark covering knowledge, reasoning and safety for LLMs.
End-to-end causal discovery model for news-driven stock movement prediction.
Combines graph relations with a Mamba state-space model for stock movement prediction.
Multi-graph convolution exploiting sector and supply-chain relations for stock prediction.
Hierarchical graph attention network predicting stock movement from relational data.
PPO-based adaptive weighting of LLM-generated alpha factors in quantitative trading.
Constructs a Japanese financial benchmark for evaluating large language models.
Japanese financial instruction dataset for adapting LLMs to Japanese finance tasks.
Introduces event-level financial sentiment analysis with a new annotated benchmark.
Case study combining multimodal data for stock price prediction on the Russian market.
Publication: A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM. Published Nov 21, 2019.
In the era of big data, deep learning for predicting stock market prices and trends has become even more popular than before. We collected…
Publication: Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks. Published Mar 11, 2019.
Publication: Deep Hedging. Published Feb 8, 2018.
Artificially intelligent tools for naturally creative humans.
Publication: Deep Stock Predictions. Published Jun 8, 2020.
Publication: Stock Market Prediction Based on Generative Adversarial Network. Published 2019.
Publication: AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks. Published Jul 24, 2019.
Sharang, Abhijit, and Chetan Rao. "Using machine learning for medium frequency derivative portfolio trading." arXiv preprint arXiv:1512.06228 (2015)
Xiong, Ruoxuan, Eric P. Nicholas, and Yuan Shen. "Deep Learning Stock Volatilities with Google Domestic Trends." arXiv preprint arXiv:1512.04916 (2015)
Ahuja, Saran, et al. "Limit order trading with a mean reverting reference price." arXiv preprint arXiv:1607.00454 (2016)
Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep Portfolio Theory
Claude skill for trading journaling: captures thesis, plan, and emotion at entry into the user's own Notion database, with weekly reviews that grade decisions…
K Kulkarni, T Diamandis, T Chitra - arXiv preprint arXiv:2207.11835, 2022 - arxiv.org. … Maximal Extractable Value (MEV) refers to excess value captured by…
value — A Obadia, A Salles, L Sankar, T Chitra... - arXiv preprint arXiv ..., 2021 - arxiv.org. ... whether there exists extractable value…
Authors: Alexey Khoroshilov, Alexey Chernysh, Orkhan Ekhtibarov, Nini Kamkia, Dmitry Zmitrovich. Large language models have demonstrated strong performance on…
Authors: Xueyuan Lin, Cehao Yang, Ye Ma, Ming Li, Rongjunchen Zhang, Yang Ni, Xiaojun Wu, Chengjin Xu, Jian Guo, Hui Xiong. Recently, large language…
Authors: Liang Hu, Jianpeng Jiao, Jiashuo Liu, Yanle Ren, Zhoufutu Wen, Kaiyuan Zhang, Xuanliang Zhang, Xiang Gao, Tianci He, Fei Hu, Yali Liao, Zaiyuan…
