<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>NLP | Wenjie Lan</title><link>https://drwenjielan.github.io/tag/nlp/</link><atom:link href="https://drwenjielan.github.io/tag/nlp/index.xml" rel="self" type="application/rss+xml"/><description>NLP</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 01 Dec 2023 00:00:00 +0000</lastBuildDate><image><url>https://drwenjielan.github.io/media/icon_hu7729264130191091259.png</url><title>NLP</title><link>https://drwenjielan.github.io/tag/nlp/</link></image><item><title>A Project on the prediction of systematic risk in stock market based on multibranch LSTM model with multidimensional heterogeneous perspective</title><link>https://drwenjielan.github.io/project/multi_lstm/</link><pubDate>Fri, 01 Dec 2023 00:00:00 +0000</pubDate><guid>https://drwenjielan.github.io/project/multi_lstm/</guid><description>&lt;p>This project adopts multi-dimensional, heterogeneous data—covering contagion effects, textual information, and fundamentals—to construct a feature set. Contagion indicators are derived from global market indices, capturing inter-market risk structures and CoES-based correlation vectors. Text features come from A-share financial news, while fundamental indicators encompass macroeconomic, stock market, and foreign exchange data, plus historical systemic risk.&lt;/p>
&lt;p>A multi-branch LSTM model is then proposed to predict systemic risk in the A-share market. Three independent LSTM branches extract information from each modality, and a separate convolutional-LSTM branch learns holistic knowledge. Results show that contagion-network features significantly enhance model performance, and the multi-branch LSTM effectively supports the monitoring and early warning of systemic risk in the stock market.&lt;/p></description></item></channel></rss>