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.
Nishit Agarwal Reviewer
08 Oct 2024 04:31 PM
Approved
Relevance and Originality
The review work addresses a crucial topic in financial analysis by exploring the complexities of stock market dynamics. By employing Long Short-Term Memory (LSTM) as a machine learning approach, the research offers an original perspective on how various factors influence stock prices. The intent to provide a comprehensive solution through the integration of multiple machine learning techniques enhances its relevance, making it a notable contribution to both academic discourse and practical investment strategies.
Methodology
The methodology focuses on utilizing LSTM to assess stock market data across different conditions. While the approach is innovative, the article would benefit from a more detailed explanation of the dataset selection process and the specific variables included in the analysis. Additionally, comparing LSTM's performance with other machine learning models would strengthen the methodological rigor and provide a clearer context for its advantages.
Validity & Reliability
To ensure the validity and reliability of the proposed model, the research highlights performance evaluation based on accuracy and precision against an existing dataset. However, elaborating on the specific metrics used for this evaluation would enhance transparency. Discussing potential biases in the dataset and the robustness of the findings would further solidify the credibility of the results, demonstrating their applicability in real-world investment scenarios.
Clarity and Structure
The structure of the review is logical, effectively guiding the reader from the introduction to the methodology. However, clarity could be improved by defining technical terms and simplifying complex concepts to make them accessible to a wider audience. Incorporating subheadings or bullet points for key sections would enhance readability and allow for easier navigation of the content.
Result Analysis
The assertion that the proposed model yields more accurate and precise results is compelling. However, a deeper analysis of the results is needed, including specific numerical outcomes and visual representations, such as graphs or tables. Discussing the implications of these findings for investors, along with any limitations encountered during the research, would provide a more nuanced perspective and enrich the overall contribution of the work.
IJ Publication Publisher
Ok Sir
Nishit Agarwal Reviewer