There’s nothing new under the sun!!!

Is an old and well-known statement – but I have got news for you!!

Just in case you haven’t heard, there is now something called OpenAI o3. So, in the context of financial modelling, I posed a few questions just to see what it can do.

Q1. – What are the reasoning capabilities of OpenAI o3?

ChatGPT said:

Yes, there are early indications and reports about the reasoning capabilities of OpenAI’s o3 model, emphasizing its advanced problem-solving skills. Here’s a summary of what is known so far:

Enhanced Reasoning Abilities

1. Complex Problem Solving:
   • OpenAI o3 is reportedly optimized for advanced reasoning tasks, excelling in domains such as mathematics, coding, and scientific inquiry.
   • It shows a notable improvement in multi-step reasoning and the ability to manage highly abstract tasks compared to its predecessors.
2. Structured Decision Making:
   • The model demonstrates enhanced capabilities in breaking down complex queries into manageable components, leading to more accurate and reliable outcomes.
3. Scientific and Logical Reasoning:
   • Its ability to simulate scientific reasoning and logic makes it a strong contender for use in academic and professional research.
   • o3 appears to reduce errors in logical deductions and mathematical proofs.

Efficiency and Generalization

1. Broader Knowledge Application:
   • The model can generalize better across diverse knowledge domains, even in
     situations with incomplete or ambiguous information.
   • Its performance in handling novel or edge-case scenarios is reportedly superior.
2. o3-mini vs. Full o3 Model:
   • o The smaller variant, o3-mini, is designed to achieve high efficiency, targeting less resource-intensive applications while maintaining strong reasoning performance.

Benchmarks and Early Testing

• Early test results indicate that o3 outperforms existing models like GPT-4 in standardized reasoning benchmarks.
• Some researchers have noted its potential to approach human-level reasoning in specific controlled tasks.

Expected Use Cases

• Education: Automated tutors with deeper reasoning for complex topics.
• Healthcare: Diagnostics and decision-support tools.
• Engineering and Coding: High-precision problem-solving in design, development, and troubleshooting.
• Scientific Research: Assisting with hypothesis generation and experimental design.

If you’re interested in further updates or specific benchmarks, OpenAI plans to share results after the current phase of internal safety evaluations and external researcher feedback.

Question to ChatGPT

Q2. My special interests lie in the development of complex financial models for a variety of projects, such as low-cost housing, real estate (commercial and retail), mining, renewable energy etcetera.

The development of the models requires the modeler to research all the relevant drivers of financial performance, or to comprehend and extract from information provided by the client, all the relevant drivers -such as revenue, costs, overheads, time series, capital expenditure, funding, inflation expectancy, interest rates.

If one was to provide a document, (a model specification) containing all the relevant information, could Open AI o3, extract and categorise all pertinent information?

ChatGPT said:

Yes, OpenAI’s o3 model is expected to excel at tasks like extracting and categorizing pertinent information from documents, given its advanced reasoning capabilities and improvements in understanding structured and unstructured data.

For a project like developing financial models, o3 could potentially:

1. Extract Key Information: Identify and extract relevant financial drivers (e.g., revenue, costs, CAPEX, funding details) from a detailed document.
2.  Categorize Drivers: Organize extracted information into categories such as revenue streams, operational costs, capital expenses, funding structure, and macroeconomic assumptions (e.g., inflation and interest rates).
3. Provide Contextual Insights: Highlight assumptions, dependencies, or critical variables that may influence financial performance.
4. Generate Summaries: Create concise summaries for each financial category or driver.

Steps to Utilize Such Features

1. Document Submission: Provide the input as text or a structured file (e.g., PDF, Word, or Excel).
2. Specific Instructions: Clarify the type of categorization or additional context required (e.g.sensitivity analysis, time-series organization).
3. Automated Output: Use the model's analysis to generate a categorized framework for financial modelling.

While OpenAI o3 isn’t yet publicly available, models like GPT-4 (or o2) can already perform many of these tasks effectively, albeit with less precision in complex reasoning compared to o3.

Makes you think, doesn’t it. What’s next?

Colin Human CA(SA)
Goalfix Financial Modellers
colin@goalfix.co.za
1 Jan 2025