ProgramsGraduate Consulting Experience (Summer 2025)ProjectsSageFusion AI - Analyzing Statistical Distributions of the stock ma...

Project Title: SageFusion AI - Analyzing Statistical Distributions of the stock market assuming a non-normal distribution

SageFusion

Details
Project Title SageFusion AI - Analyzing Statistical Distributions of the stock market assuming a non-normal distribution
Project Topics Data Management Entrepreneurship Information Technology (IT) Research, Analysis, Evaluation
Skills & Expertise Emerging Tech
Project Synopsis: Challenge/Opportunity
Well, I own three companies: www.investmentscy.com, www.sagefusion.co, and https://www.therapharma.consulting/
Project Synopsis: Activities/Actions Required
Action Items:
  1. Market Data Analysis and Statistical Distributions
    • Gather publicly available financial data on historical stock market trends.
    • Analyze year-over-year changes in statistical distributions for specific ticker symbols.
    • Compare assumptions of normal distribution versus actual data.
  2. Algorithmic Risk Model Enhancement
    • Develop new AI-driven risk modeling techniques based on real-world distribution patterns.
    • Test different machine learning approaches to improve forecasting.
    • Identify potential trading strategies derived from data-driven insights.
  3. Data Licensing and Thought Leadership
    • Assess the commercial value of refined investment models for licensing.
    • Explore opportunities for publishing white papers and research on financial risk modeling.
    • Identify potential clients and partners in the financial services and government sectors.
  4. Implementation & Testing
    • Conduct backtesting using historical data to measure the effectiveness of new models.
    • Optimize algorithms based on test results and stakeholder feedback.
    • Provide strategic recommendations on integrating findings into Sage Fusion’s treasury management platform.
Project Synopsis: Expected Results
Measuring Success:
  • Identification of inaccuracies in traditional risk modeling assumptions.
  • Development of AI-enhanced models that improve investment decision-making.
  • Backtested performance improvements in risk assessment accuracy.
  • Potential revenue opportunities from data licensing and consulting.
  • Published findings and reports demonstrating insights for financial professionals.
Milestones & Deliverables
Milestone 1: Market Data Collection & Distribution Analysis
  • Goal: Examine financial market data to challenge traditional normal distribution assumptions.
  • Guiding Questions:
    • How do stock price distributions evolve over time?
    • What statistical anomalies exist in current risk models?
    • How can we visualize year-over-year changes in market data?
    • What are the implications of non-normal distribution for risk modeling?
    • How do AI models improve accuracy in detecting these changes?
  • Suggested Deliverable:Market distribution analysis report with visualized trends.
Milestone 2: Development of AI-Driven Risk Models
  • Goal: Enhance traditional risk models using AI and advanced statistical techniques.
  • Guiding Questions:
    • What machine learning methods are most effective for risk prediction?
    • How can non-normal distributions be incorporated into trading algorithms?
    • What are the limitations of existing financial risk models?
    • How can AI improve dynamic portfolio management?
    • How do proposed models compare in performance to industry standards?
  • Suggested Deliverable:Prototype AI model with initial backtesting results.
Milestone 3: Commercial Feasibility & Data Licensing Strategy
  • Goal: Explore monetization opportunities through data licensing and financial research applications.
  • Guiding Questions:
    • Which institutions could benefit from access to refined risk models?
    • How can Sage Fusion monetize proprietary financial insights?
    • What pricing structures exist for data licensing in financial markets?
    • How do regulatory considerations impact commercial adoption?
    • What marketing strategies will position Sage Fusion as a leader in investment science?
  • Suggested Deliverable:Business case and go-to-market strategy for data licensing.
Milestone 4: Final Testing & Integration with Sage Fusion
  • Goal: Validate models through real-world testing and prepare for integration into Sage Fusion’s treasury management platform.
  • Guiding Questions:
    • How do backtested results compare to traditional risk modeling?
    • What refinements are needed before implementation?
    • How will integration impact Sage Fusion’s overall product offering?
    • What client feedback can be gathered for further optimization?
    • What long-term research directions should be pursued based on project findings?
  • Suggested Deliverable:Final AI risk model, backtesting validation report, and integration plan.
Suggested Resources from Sage Fusion
  • Historical investment models and previous research attempts.
  • Proprietary market datasets for back testing AI-driven risk models.
    • Access to Sage Fusion’s treasury management platform for integration testing.
  • Past reports and financial statements relevant to risk modeling.

Project Timeline

Touchpoints & Assignments Date Type

Applications Closed for Students

 Apr 17 2024 Event

Students Upload Resume

 May 10 2024 Action Item

Students Upload Signed "Fordham Unpaid Internship Agreement"

 May 10 2024 US/Eastern (UTC-04:00) Event

Teams Finalized, Projects Assigned

 May 10 2024 Event

Industry Partners to Provide Each Offer Letter to Each Student

 May 17 2024 Event

Kickoff Eval

 May 24 2024 US/Eastern (UTC-04:00) Evaluation

Goal Date for CPT Approval

 May 31 2024 Event

Projects Launch!

 Jun 03 2024 Event

Temp Check

 Jun 12 2024 Evaluation

Temp Check

 Jun 28 2024 Evaluation

Temp Check

 Jul 10 2024 Evaluation

Projects End

 Jul 26 2024 Event

End of Project Self Reflection

 Jul 26 2024, 12:00 PM Evaluation

Upload your Résumé

 May 16 2025 Action Item

TEMPERATURE CHECK: How's the project going so far?

 Jun 20 2025 Evaluation

PROJECT REFLECTION: Can you give us your feedback?

 Jul 26 2025 Evaluation

End of Project Peer Evaluation

 Jul 26 2025 Evaluation

Program Managers

Name Organization