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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

2870 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: A1106109119/2019©BEIESP DOI: 10.35940/ijeat.A1106.109119 Abstract: In the financial world, stock trading is one of the most crucial activities. Investors make educated guesses to predict stock market trends by analyzing news, studying the company history, industrial history and a lot of other data. successfully predicting the stock market trends and investing in the right shares at the right time can maximize the investor’s profit or at least minimize the losses. Stock market price data is generated in huge volumes and is affected by various diverse factors. This work proposes two models to predict the stock market prices. The first model is an LSTM model that employs a backpropagation optimized LSTM network to forecast future stock prices. The second model is a hybrid model that combines an SVM model, KNN model and a Random Forest classifier using the Majority Voting algorithm to predict stock market trends. Both models have a sentiment analyzer to factor the news influencing the stock market using Natural Language Processing (NLP). The project aims to help investors who are new to the stock market and don’t possess sufficient knowledge to make share investments as well the experienced investors by predicting stock market trends.

Predicting Stock Market Trends using Hybrid SVM Model and LSTM with Sentiment Determination using Natural Language Processing