Can Social Media Sentiment Predict Stock Market Success? Experts vs. Everyday Users

Kai Mendoza in Business & Economy December 2025 • 4 min read.

"A deep dive into how financial expertise on Weibo impacts the accuracy of stock market predictions. Can the wisdom of the crowds compete with expert analysis?"

The stock market is a complex beast, influenced by everything from national policies and exchange rates to unforeseen events like epidemics. Navigating this landscape requires a keen understanding of market dynamics, and investors are constantly seeking an edge to predict future trends.

In recent years, sentiment analysis – gauging public opinion from sources like social media – has emerged as a popular tool for stock market prediction. The premise is simple: by analyzing the overall sentiment expressed in online commentary, one can potentially forecast market movements. Many approaches rely on general comments on stocks, short text and news from social media.

But what if we could refine this approach by considering the source of the sentiment? Does the opinion of a seasoned financial expert hold more weight than that of an average social media user? This is the question that this work attempts to answer. By comparing the predictive power of sentiment expressed by verified financial advisors versus regular users on the Weibo platform, this article seeks to determine whether expertise truly translates to more accurate market forecasts.

Expert Sentiment vs. The Crowd: Unveiling Prediction Accuracy

Stock market prediction using social media sentiment analysis with expert financial analysts.

This work dives into how a user's financial background impacts the accuracy of sentiment-based stock market forecasting using artificial neural networks. It's a novel approach that combines neural networks to assess sentiment-based stock market predictions, factoring in the financial background of those generating the sentiment.

The team used Weibo, China's largest social media platform, to gather sentiment data. Weibo is unique because it allows users to provide qualification certificates as proof of their professional background. This feature allowed them to divide Weibo users into two distinct groups:

Authorized Financial Advisors (AFA): Users who have provided qualification certificates, verifying their expertise.
Unauthorized Financial Advisors (UFA): Users without verified financial backgrounds.

They then analyzed comments from both groups related to the Hong Kong Hang Seng Index, using sentiment analysis to gauge their opinions on the market. This sentiment data was then fed into a Long Short-Term Memory (LSTM) model, a type of neural network designed for time-series prediction, to forecast market movements.

Key Finding: Expertise Matters

The results showed a significant difference: stock market predictions based on sentiment from the AFA group were 39.67% more accurate than those based on the UFA group. The AFA group's predictions also demonstrated a high accuracy rate of 87% when compared to existing approaches. This suggests that the wisdom of financial experts on social media can indeed provide a more reliable signal for stock market prediction.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1007/978-3-030-80568-5_40,

Title: Do Weibo Platform Experts Perform Better At Predicting Stock Market?

Subject: q-fin.st cs.ai cs.lg cs.si

Authors: Ziyuan Ma, Conor Ryan, Jim Buckley, Muslim Chochlov

Published: 12-02-2024

Everything You Need To Know

What is sentiment analysis, and how can it be applied to stock market prediction?

Sentiment analysis is a technique used to gauge public opinion from sources like social media. In the context of stock market prediction, it involves analyzing the overall sentiment expressed in online commentary about stocks or market trends to forecast potential market movements. The underlying idea is that positive sentiment might indicate an upward trend, while negative sentiment could suggest a downward trend. This approach often relies on general comments on stocks, short text, and news from social media platforms to assess market sentiment.

How were users on Weibo categorized based on their financial expertise for stock market prediction analysis?

On Weibo, users were divided into two primary groups based on whether they had verified their financial qualifications. Those who provided qualification certificates were categorized as Authorized Financial Advisors (AFA), indicating their verified expertise. Users without verified financial backgrounds were classified as Unauthorized Financial Advisors (UFA). This distinction allowed for a comparison of sentiment and predictive accuracy between experts and non-experts.

What is an LSTM model, and how was it utilized in the stock market prediction analysis using social media sentiment?

A Long Short-Term Memory (LSTM) model is a type of neural network specifically designed for time-series prediction. In the stock market analysis, the LSTM model was used to forecast market movements based on sentiment data gathered from Weibo. After analyzing comments from both Authorized Financial Advisors (AFA) and Unauthorized Financial Advisors (UFA) to gauge their opinions on the market, this sentiment data was then fed into the LSTM model. Because LSTM networks are designed to learn from sequence data it made them ideal for analyzing how sentiment changed over time and how those changes corresponded to changes in the Hang Seng Index.

What were the key findings regarding the accuracy of stock market predictions based on sentiment from Authorized Financial Advisors (AFA) versus Unauthorized Financial Advisors (UFA) on Weibo?

The key finding was that stock market predictions based on sentiment from the AFA group were significantly more accurate than those based on the UFA group. Specifically, predictions from the AFA group were found to be 39.67% more accurate. Furthermore, the AFA group's predictions demonstrated a high accuracy rate of 87% when compared to existing approaches. This supports the idea that the sentiment and insights from financial experts on social media can provide a more reliable signal for predicting stock market movements.

What implications does the finding that Authorized Financial Advisors (AFA) provide more accurate stock predictions have for the use of social media sentiment in financial forecasting?

The finding that AFA sentiment leads to more accurate predictions highlights the importance of considering the source and expertise of sentiment in financial forecasting. It suggests that not all social media sentiment is created equal, and that the opinions of verified experts can be a valuable signal. This has implications for how financial institutions and individual investors approach sentiment analysis. Rather than relying solely on broad public sentiment, focusing on the sentiment of verified professionals could lead to more reliable and profitable trading strategies. This could also lead to platforms prioritizing and verifying financial experts to improve the quality of financial discourse and prediction accuracy.