Modified Extreme Learning Machine Algorithm with Deterministic Weight Modification for Investment Decisions Based on Sentiment Analysis
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Date
2024-10
Journal Title
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Publisher
Avinashilingam
Abstract
The trading of stocks contributes to the growth of the commodity
economy by driving a significant quantity of capital into the stock market, which improves
the organic configuration of corporate capital through capital concentration. Consequently,
the stock market is seen as a measure of the financial activity of a nation or area.
Specifically, since it can precisely depict the supply dynamics of the stock market, the
trading price of the stock frequently acts as a measure of the price and quantity of the
stock. Timely and precise stock price prediction and analysis are essential for both
investor decision-making and the constancy of the national economy by increasing returns
and decreasing risks. Consequently, researching stock projections can help depositors
make wise decisions that will advance society and yield rewards for themselves.
The intricacy of financial time series presents challenges that ML can handle with
its strong data processing skills. Consequently, there are a lot of opportunities for ML and
finance together, but there hasn't been enough research done in this field. Furthermore, the
stock market is not entirely objective and does not always follow scientific principles due
to humans' emotional, psychological, and behavioral traits. Recent studies have also
demonstrated that investor sentiment may play a significant influence in stock market
investing.The present study proposed a modified extreme learning machine (ELM) algorithm
with deterministic weight adjustment to increase the precision and dependability of
sentiment analysis-based investment decision-making. To capture investor mood, the
approach incorporates financial sentiment research from news articles, social media, and
market patterns. With deterministic weight initialization (DWM), the ELM algorithm
achieves more consistent model performance than standard ELM techniques that use
random weight initialization. The suggested model is a potent tool for sentiment-driven
investing strategies since it shows improved prediction accuracy, quicker learning, and
robustness in financial forecasting.
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Keywords
Computer Science