Forecasting Stock Market Movement Direction Using Sentiment Analysis and Support Vector Machine

Rui Ren · Desheng Dash Wu

257 citations0/0 files storedIEEE Systems Journal

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Financial Markets and Investment Strategies Sentiment Analysis and Opinion Mining

Abstract

Investor sentiment plays an important role on the stock market. User-generated textual content on the Internet provides a precious source to reflect investor psychology and predicts stock prices as a complement to stock market data. This paper integrates sentiment analysis into a machine learning method based on support vector machine. Furthermore, we take the day-of-week effect into consideration and construct more reliable and realistic sentiment indexes. Empirical results illustrate that the accuracy of forecasting the movement direction of the SSE 50 Index can be as high as 89.93% with a rise of 18.6% after introducing sentiment variables. And, meanwhile, our model helps investors make wiser decisions. These findings also imply that sentiment probably contains precious information about the asset fundamental values and can be regarded as one of the leading indicators of the stock market.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 257 citations exist upstream.

References

2006Investor Sentiment and the Cross‐Section of Stock Returns 2011Twitter mood predicts the stock market 2007Giving Content to Investor Sentiment: The Role of Media in the Stock Market 1994The Sharpe Ratio 2003From Efficient Markets Theory to Behavioral Finance 2000Irrational Exuberance 2004Forecasting stock market movement direction with support vector machine 2007Sports Sentiment and Stock Returns

Cited by

2021A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions 2021Artificial Intelligence Applied to Stock Market Trading: A Review 2023Machine learning approaches in stock market prediction: A systematic literature review 2022Deep Learning-based Integrated Framework for stock price movement prediction 2020Stock price forecast based on combined model of ARI-MA-LS-SVM 2021A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.