English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Predicting stock market is vital for investors and policymakers, acting as abarometer of the economic health. We leverage social media data, a potentsource of public sentiment, in tandem with macroeconomic indicators asgovernment-compiled statistics, to refine stock market predictions. However,prior research using tweet data for stock market prediction faces threechallenges. First, the quality of tweets varies widely. While many are filledwith noise and irrelevant details, only a few genuinely mirror the actualmarket scenario. Second, solely focusing on the historical data of a particularstock without considering its sector can lead to oversight. Stocks within thesame industry often exhibit correlated price behaviors. Lastly, simplyforecasting the direction of price movement without assessing its magnitude isof limited value, as the extent of the rise or fall truly determinesprofitability. In this paper, diverging from the conventional methods, wepioneer an ECON. The framework has following advantages: First, ECON has anadept tweets filter that efficiently extracts and decodes the vast array oftweet data. Second, ECON discerns multi-level relationships among stocks,sectors, and macroeconomic factors through a self-aware mechanism in semanticspace. Third, ECON offers enhanced accuracy in predicting substantial stockprice fluctuations by capitalizing on stock price movement. We showcase thestate-of-the-art performance of our proposed model using a dataset,specifically curated by us, for predicting stock market movements andvolatility.

Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices