In today's fast-paced digital era, where information is abundant and readily accessible, it can be challenging to discern the reliability of financial advice provided online. The purpose of this project is to assess the credibility of financial advisers and determine the most reliable sources of information in the realm of stock investments. By leveraging the power of data and technology, this project aims to provide insights into the trustworthiness of various sources and aid in making informed investment decisions.
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The project begins by collecting a comprehensive list of publicly listed stocks on the New York stock exchanges. This step ensures that the analysis covers a wide range of companies and allows for a more accurate assessment of the reliability of financial advice related to these stocks.
The next crucial phase involves scraping the most recent news articles about investing advice. These articles serve as a valuable resource for understanding the prevailing sentiment and recommendations in the market. By analyzing the content of these articles, the project identifies which companies are mentioned and collects relevant data for further analysis.
The data collected from over 300 articles is stored in a CSV file, located in the "data-sheets" folder of this repository. This dataset becomes the foundation for subsequent processing and analysis.
To ensure the reliability of the collected data, a series of post-processing steps are performed. It is important to filter out any inaccurate or falsely identified stocks that may have been included in the initial dataset. Statistical measurements are applied to identify outliers and potentially erroneous entries, which are then removed from the dataset.
By carefully curating the dataset, the project aims to maintain a high level of accuracy and credibility in the subsequent analysis.
The final step in this project involves creating a news-based stock portfolio using a paper money investment of $100,000. The selection of stocks for the portfolio is based on the frequency of their mentions in the collected articles. The more a company is mentioned, the higher its representation in the portfolio.
The project identifies the 20-30 most commonly reported stocks and allocates the percentage of investment for each stock in a proportional manner. This approach ensures that stocks with higher mentions receive a larger portion of the portfolio, reflecting the perceived confidence and recommendation associated with those stocks.
This pioneering endeavor exhibits a medley of skills that showcase the project's multidimensional nature:
The project seamlessly amalgamates a comprehensive stock list and recent news articles, demonstrating adeptness in web scraping techniques.
The post-processing phase underscores the mastery of data curation, where noise is eliminated and outliers are tamed through statistical prowess.
The crux of the project's value lies in extracting insights from the data. Skills in data analysis and visualization lay the groundwork for constructing a meaningful portfolio.
Crafting a portfolio isn't a random exercise. The project elegantly showcases an understanding of investment principles by tying portfolio weightage to sentiment frequency.
The project's ReadMe isn't just an afterthought; it's a testament to the ability to communicate complex concepts to a broader audience, while providing the necessary context and warnings.
The creation of this news-based stock portfolio serves as a valuable tool for assessing the reliability of financial advisers and the credibility of online investment information sources. By leveraging data scraping techniques and statistical analysis, this project enables investors to make more informed decisions based on real-time sentiment and expert recommendations.
It is important to note that while this project provides insights into the prevailing sentiment and popularity of certain stocks, it should not be regarded as financial advice. Investing in the stock market carries inherent risks, and individuals should consult with professional financial advisors before making any investment decisions.
By leveraging the power of technology and data-driven analysis, this project aims to empower investors with a deeper understanding of the investment landscape and the credibility of online financial advisers.