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.
Rajas Paresh Kshirsagar Reviewer
08 Oct 2024 04:33 PM
Approved
Relevance and Originality
This review work addresses a critical area in financial analysis by investigating the complex dynamics of stock markets. By employing Long Short-Term Memory (LSTM) as a machine learning algorithm, it presents an original approach to understanding the various factors that influence stock prices. The intention to deliver a comprehensive solution through the application of multiple machine learning techniques enhances its relevance, making it a significant contribution to both academic research and practical investment strategies.
Methodology
The methodology focuses on the use of LSTM to analyze stock market data across different conditions. However, a more detailed description of the dataset selection process and the specific factors included in the analysis would improve the clarity of the methodology. Additionally, comparing LSTM's performance with other machine learning models would provide a more robust framework, allowing for a clearer understanding of its strengths and limitations in stock market prediction.
Validity & Reliability
To establish the validity and reliability of the proposed model, the evaluation of its performance based on accuracy and precision is crucial. The article should elaborate on the metrics used for this assessment, ensuring that the findings can be replicated. Addressing potential biases within the dataset and discussing the robustness of the results would further strengthen the study's credibility, illustrating its applicability in real-world investment scenarios.
Clarity and Structure
The structure of the review is logical, effectively guiding readers through the content. However, clarity could be enhanced by defining technical terms and simplifying complex concepts for a broader audience. Incorporating subheadings or bullet points for key sections would improve readability and facilitate better navigation of the material.
Result Analysis
The assertion that the proposed model achieves higher accuracy and precision is compelling, yet the result analysis would benefit from a more in-depth examination. Including specific numerical results and visual representations, such as graphs or tables, would provide clearer insights into the findings. Discussing the implications of these results for investors and acknowledging any limitations encountered would offer a more comprehensive perspective, enriching the overall contribution of the research.
IJ Publication Publisher
Done Sir
Rajas Paresh Kshirsagar Reviewer