Transparent Peer Review By Scholar9
Stock Market Analysis Using Machine Learning
Abstract
The main aim of this review work is to dive in to the intricate world of analysing the stock market and its dynamics where the stock prices are affected by multiple factors. The strategy involves the use of a machine learning algorithm, Long Short-Term Memory ( LSTM ), a neural network which to analyse stock market and its different factors in various market conditions. The performance of the algorithm proposed is evaluated by comparing accuracy and precision by a existing dataset. The main aim of this research review is to analyse and provide a inclusive solution to the analysis of stock markets by applying different machine learning techniques in various scenarios. Therefore, LSTM has been applied to the model which can be further used to analyse the stock markets and its dynamics which can make it easier for an investor to buy or sell the shares knowing the conditions and threats beforehand. The proposed model provides a more accurate and precise result on comparing it with the other existing datasets and models. Evaluating a models performance is the most important part of any research work.
Sivaprasad Nadukuru Reviewer
08 Oct 2024 04:30 PM
Approved
Relevance and Originality
This research work addresses a pertinent issue in financial analysis by delving into the complexities of stock market dynamics. The focus on using Long Short-Term Memory (LSTM) as a machine learning algorithm adds originality, highlighting an innovative approach to understanding how multiple factors influence stock prices. The attempt to provide an inclusive solution through various machine learning techniques further emphasizes its relevance, making it a significant contribution to both academic research and practical applications in investing.
Methodology
The methodology emphasizes the application of LSTM to analyze stock market data under diverse conditions. While the article outlines the algorithm's use, it would benefit from a more thorough explanation of the dataset selection process and the specific factors considered in the analysis. Furthermore, a comparison with other machine learning methods would strengthen the methodological framework, allowing readers to appreciate the advantages and limitations of using LSTM specifically.
Validity & Reliability
To assess the validity and reliability of the proposed model, the article mentions evaluating accuracy and precision against an existing dataset. However, it should elaborate on the metrics employed for this evaluation, ensuring that the results can be replicated. Including a discussion on potential biases in the data and the robustness of the findings would enhance the credibility of the research, reinforcing the claims of its applicability in real-world scenarios.
Clarity and Structure
The article is structured logically, transitioning from the introduction to the methodological approach effectively. However, clarity could be improved by defining technical terms and concepts to cater to a broader audience. Additionally, using subheadings or bullet points for critical sections would enhance readability and allow readers to navigate the content more easily.
Result Analysis
The claim that the proposed model offers superior accuracy and precision is compelling, yet the analysis of results could be more robust. Providing specific numerical outcomes, along with visual aids like charts or tables, would better illustrate the findings. Discussing the implications for investors and acknowledging any limitations in the model’s performance would offer a more comprehensive perspective, enriching the overall contribution of the research work.
IJ Publication Publisher
Thank You Sir
Sivaprasad Nadukuru Reviewer