
DoubleAdapt is an end-to-end framework that adapts both the data and the model to cope with distribution shifts in the online environment for stock trend forecasting.


coremsg

doubleadapt-a-meta-learning-approach-to-incremental-learning-for-stock-trend-forecasting


DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting


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The core message of this article is that the hyperbolic geometry of complex networks provides a more accurate representation of the Indian stock market compared to Euclidean embeddings, enabling improved detection of market stability/volatility, early identification of market changes, and natural clustering of market sectors.


uncovering-the-geometry-of-the-indian-stock-market-through-scale-free-network-analysis


Uncovering the Geometry of the Indian Stock Market through Scale-free Network Analysis



Effective prompting strategies can significantly improve the performance of large language models in generating market commentary from time-series stock price data.


generating-market-comments-from-stock-price-sequences-a-comparative-study-of-prompting-strategies


Generating Market Comments from Stock Price Sequences: A Comparative Study of Prompting Strategies
