Project Objective and Scope

The primary objective of this project is to empower students to utilize the AnaChart dataset—a comprehensive database of analyst stock price targets and ratings—to derive actionable insights relevant to financial research and investment analysis. By engaging with this real-world dataset, students will apply theoretical knowledge gained in the classroom to explore patterns, evaluate analyst behavior, and assess the predictive power of stock recommendations over time.

This project provides a practical, hands-on opportunity for students enrolled in finance, economics, or data analytics programs to develop and demonstrate their competencies in data handling, financial analysis, and critical thinking.

Dataset Overview

The AnaChart dataset is the largest of its kind available in the market, containing over 800,000 analyst recommendations collected over a 15-year period and continuously updated. For the scope of this educational project, free access will be granted to a curated subset of the dataset, limited to stocks included in the NASDAQ-100 index. This subset still offers significant depth and breadth for analysis while remaining manageable for class-based projects.

Please note:

• Computation costs incurred through querying the dataset on Google BigQuery are the responsibility of the participating university or department.
• AnaChart does not charge for access to the dataset for approved academic projects.
• For departments or institutions unable to cover BigQuery computation costs, AnaChart offers an alternative:
• Free Advanced-level membership accounts on anachart.com can be provided to students and faculty, enabling access to the dataset through the platform's web interface. To qualify, users must log in using a verified university email address, and the project must be formally approved for academic use.

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Tasks and Activities

Students will engage in a series of structured tasks that mirror the stages of a real-world data analysis workflow:

1. Data Familiarization

• Understanding the structure, fields, and context of the AnaChart dataset.
• Reviewing metadata to identify key variables for analysis.

1. Data Cleaning and Preparation

• Handling missing or inconsistent values.
• Filtering for relevant tickers within the NASDAQ-100.
• Transforming data for usability (e.g., converting dates, calculating returns, standardizing rating scales).

1. Exploratory Data Analysis (EDA)

• Identifying trends in analyst recommendations over time.
• Evaluating the distribution and frequency of price target updates.
• Analyzing correlation between analyst ratings and stock price performance.

1. Advanced Statistical and Financial Analysis

• Measuring analyst accuracy and consistency.
• Identifying potential biases or herding behavior among analysts.
• Backtesting the performance of recommendation-based strategies.

1. Visualization

• Creating charts, heatmaps, and dashboards to communicate insights.
• Using tools such as Python (Matplotlib, Seaborn), R, Tableau, or Power BI.

1. Interpretation and Recommendation

• Synthesizing insights into coherent investment theses.
• Developing data-driven recommendations for hypothetical investment portfolios.

Deliverables

Each student group (or individual, if applicable) will be responsible for producing the following:

• Project Proposal: A brief outline of the analysis plan, research questions, and methodologies.
• Data Analysis Notebook: A reproducible code-based report (e.g., Jupyter Notebook or R Markdown) showcasing all analysis steps and results.
• Final Report: A comprehensive written report that includes:
• Executive summary
• Methodology
• Key findings
• Visualizations
• Limitations and areas for further research
• Investment recommendations (if applicable)
• Presentation: A 10–15 minute presentation summarizing the project outcomes and insights.

Suggestions for Enhancement

To further enrich the project and align it with professional practice, consider:

• Bringing in a guest speaker from the investment research industry to provide context.
• Hosting a peer-review session where students evaluate each other’s findings.
• Incorporating ethical considerations, such as the implications of analyst influence on retail investors.
• Encouraging comparative analysis across sectors or time periods.

Here are some suggestions:

The project will culminate in two key deliverables: a comprehensive analytical report and a final presentation. Together, these outputs will demonstrate students’ ability to interpret complex financial data, derive actionable insights, and effectively communicate their findings.

1. Analytical Report

Each team (or individual, if assigned as a solo project) will produce a detailed report that includes:

• Executive Summary
• A high-level overview of the project, highlighting key questions, findings, and conclusions.
• Methodology
• A clear description of the data preparation and analytical techniques used (e.g., filtering, transformation, statistical modeling, regression, clustering, etc.).
• Data Analysis
• Visualizations (charts, graphs, heatmaps, etc.) that illustrate trends, patterns, and outliers.
• Statistical findings based on descriptive and inferential analysis.
• Comparisons across analysts, sectors, or time periods, if applicable.
• Insights and Interpretation
• In-depth discussion of what the data reveals about analyst behavior, stock performance, and potential market signals.
• Recommendations
• Data-driven investment recommendations or strategic insights, supported by the analysis.
• Limitations & Future Research
• A critical reflection on the project’s scope and potential areas for further investigation.
• Appendices
• Supplementary code (e.g., Python/R scripts), tables, or additional charts as needed.

2. Presentation

Students will also prepare and deliver a 10–15 minute presentation that communicates their project outcomes in a clear, engaging, and professional format. The presentation should:

• Summarize key research questions and approach
• Highlight major findings and insights
• Showcase select visualizations
• Provide high-level recommendations
• Invite discussion and questions from peers and instructors