ChatGPT for Finance and Investment Professionals

This lecture offers a high-level overview of ChatGPT and its GPT-4 or GPT-3.5 foundations by OpenAI, explaining deep neural networks, training on web data, pattern recognition, and text generation.

Compare ChatGPT and search engines to see direct, conversational answers versus link-based results; learn how keywords and ranking shape search, and how AI and search will integrate.

Compare artificial intelligence and human intelligence, highlighting ChatGPT's non-deterministic responses, data-driven strengths in scale and speed, and human intuition, feelings, and creativity in finance and investing.

Create a ChatGPT account on openai.com, log in or sign up with Google, Microsoft, Apple, or email, and learn the web chat interface and default GPT 3.5 model.

Provide July 2024 update on OpenAI models and interface. Highlight GPT-4 variants (Omni, mini) and free vs plus plans for investing and trading prompts with data analysis, browsing, and DALL-E.

Explore the landscape of GPT models and products, including ChatGPT, GPT-3.5 and GPT-4, plus plugins, browsing, APIs, and fine-tuning for finance and investment applications.

Explore the OpenAI website to discover ChatGPT, DALL-E, and other GPT models; learn about safety, data privacy, API tokens and pricing, and how to use the API, playground, and documentation.

Explore how tokens power language models like GPT, from tokenization and token IDs to how spelling, capitalization, and punctuation influence output, and learn token limits that shape prompts and responses.

Learn how prompt engineering shapes ai responses by crafting clearer prompts, mastering five techniques—temperature setting, length of the responses, few-shot learning, iterative refinement—and the bonus aggregation and codification for finance.

Explore five essential prompt engineering techniques for finance, starting with explicit instruction to craft precise, unambiguous prompts that guide ChatGPT to retrieve targeted financial data.

Explore temperature settings to balance creativity and determinism in ChatGPT, from low, deterministic outputs to high creativity, with default 0.7, tailored for finance tasks like quarterly analysis and recession strategies.

Learn to control ChatGPT response length with prompt engineering, delivering concise or thorough outputs for finance and investment scenarios, from quick summaries to deep analyses.

Discover how few-shot learning guides ChatGPT with a few examples to generate consistent, finance-focused reports and newsletters. Compare few-shot with explicit instruction to tailor outputs to firm standards.

Learn how iterative refinement uses a feedback loop to improve ChatGPT responses, refining prompts to deepen analysis—from an Excel valuation model to emerging market risk and mitigation.

Convert lengthy analyses into easy-to-read tables that compare asset classes—public equity, private equity, fixed income, and more—by profitability, risk, liquidity, investment horizon, and retail accessibility across upswing and downturn.

Explore the limits and pitfalls of using ChatGPT in finance and investment, including real-time data gaps, lack of predictive power, bias, and the need for human oversight and regulatory ethics.

Understand real-time data availability in ChatGPT for finance and investing, comparing free and plus versions, training data cutoffs, and web browsing options for live market information.

ChatGPT cannot predict future market movements; it uses training data to inform algorithmic trading insights, so treat forecasts as reflections of past patterns and supplement with human expertise.

Navigate biases and misinformation in chatgpt, acknowledge garbage in, garbage out, recognize the black-box limits, and apply cross-referencing with multiple trusted sources to ensure reliable financial insights.

Balance AI tools with human oversight to preserve judgment in finance; leverage AI for data processing while valuing human intuition, creativity, and nonpublic insights that drive trust and decision quality.

Examine how finance and investment professionals must comply with ethics and regulations when using ChatGPT, guided by CFA Institute standards, with sources disclosure and prudent care.

Explore how tokens and 4096-token limits shape ChatGPT conversations, and learn to overcome boundaries by chunking data, segmenting prompts, and summarizing dialogue in finance analysis.

Explore advanced features for plus users in finance and investment contexts, including custom GPTs, file uploads, Python-based data analysis with pandas and NumPy, chart analysis, DALL-E 3, and web browsing.

Compare GPT-3.5 and GPT-4 performance in finance tasks using ChatGPT Plus, highlighting 3.5’s speed and 4’s stronger reasoning and longer context in amortizing loan calculations.

Discover how plugins became custom GPTs by July 2024, explore them with a plus subscription, and see Wolfram GPT and the pdf plugin now operate as custom GPTs.

Explore unleashing custom GPTs and AI PDF plugins to read and analyze large PDFs, extract highlights, and deliver finance-focused insights from annual reports and fleet financing data.

Explore how the wolfram alpha plugin supports finance and investment tasks, from stock data retrieval and financial calculations to bond and option pricing, portfolio analysis, and irr/npv visualization.

Explore advanced data analysis with ChatGPT and Python, uploading csv stock data, normalizing to 100, computing returns and correlations, and visualizing results with seaborn heatmaps.

Copy and paste ChatGPT's Python code to load the stocks data set into a pandas data frame in a Jupyter notebook. Use the appendix to install Python.

Explore how GPT-4 analyzes financial charts and images to extract holding-period returns from the S&P 500, highlight long-term volatility, and improve investment interpretation.

Explore how DALL-E 3 creates images from text prompts and how ChatGPT enhances prompt refinement for finance visuals, including real estate charts and a bull dominating a bear in styles.

Explore how ChatGPT uses Bing browsing to fetch and summarize the latest financials, including Apple’s third-quarter results and iPhone performance, with sources and metrics.

Explore the July 2024 update to ChatGPT, Pharo, and Pharo mini, featuring built-in web browsing, Dall-E three image creation, advanced data analysis, and customizable GPTs for finance and investment workflows.

Learn to create a GPT from scratch with the GPT builder and save Market Analyst Pro for daily Dow Jones stock analysis using web browsing and technical indicators.

Discover the free web ChatGPT browser extension that provides web access, six-source Google searches, AI-assisted summaries, and easy installation on Chrome, Edge, or Firefox.

Explore goal-based investing and advanced prompt engineering with ChatGPT in a case study, featuring portfolio design, risk assessment, and asset allocation for wealth and retirement planning.

Define how to tailor ChatGPT with custom instructions for your target audience and role, setting tone and response length, and reuse across chats for goal-based investing and asset allocation.

Select GPT-4 for longer prompts when available and feed Peter Miller's background, interview, and risk and decision-making questionnaires in meaningful chunks to plan a tailored financial analysis and asset allocation.

Explore the pre-response inquiry by reversing roles, having ChatGPT request missing information to sharpen analysis and provide context from cash, debts, inflation expectations, and liquidity preferences.

Present a goal-based prompting framework for retirement planning, clarifying goals, scope, exclusions, and anchoring risk tolerance, with bucketed asset allocation across equities, fixed income, real estate, cash, and gold.

Apply iterative refinement and chain prompting to improve wealth planning, rebalance buckets by time horizon and goal importance, and boost charity bucket with higher returns via private equity.

Finalize a wealth and retirement plan for Peter Miller using goal-based investing and present an overview table with bucket amounts, horizons, asset allocation, estimated returns, risk, liquidity, and goal descriptions.

Develop self-assessment and reflective thinking to critically review ChatGPT investment analyses, uncover biases and errors, and improve risk assessment, asset-class diversification, and recommendations.

Learn prompt re-engineering to optimize ChatGPT prompts and responses, including starting fresh chats, testing with background info, and comparing prompts to improve future finance and investment prompts.

Explore excel modelling and deal pitching in a wind case, applying a 15-year amortizing loan with 4.45% rate, dsr 1.5, upfront 1% fee, commitment 0.25%, and agency fee of 10,000.

Learn how to use ChatGPT to structure a 15-year amortizing loan with 4.45% interest, 1.5 DSCR, and maximize the initial loan amount, using Excel Solver to reach zero ending balance.

Discover an integrated Excel model for loan sculpting with backward induction. Calculate the initial loan amount using simple formulas and adjust interest rate to keep the end balance zero.

Draft a response letter for an offshore wind park RFP debt facility; showcase loan terms and a track record of over 10 billion in infrastructure loans using ChatGPT.

Explore a Python coding case study on bug fixing a stock analysis tool with ChatGPT. Learn to load, analyze, and visualize multiple stocks using Yahoo Finance in a Jupyter notebook.

Fix a Python bug in a financial instrument class using ChatGPT, diagnosing a missing assignment in renaming a column. Compare in-place and reassignment for stock analytics on Apple and GE.

ChatGPT helps analyze and improve a financial instrument class by adding error handling, date and ticker validation, docstrings, inline comments, and robust Yahoo Finance data handling.

Extend a financial instruments class to analyze multiple tickers using pandas, loading data with Yahoo Finance, computing mean returns, standard deviation, and annualized performance.

Define a Python class named financial instrument with a dunder init to initialize ticker, start date, and end date for each instance, illustrated by Apple from 2015-01-01 to 2019-01-01.

Define a getdata method to download stock price data from Yahoo Finance for a given period using the finance library, convert it to a dataframe, and auto-download on instantiation.

Build a financial instrument class that creates a price data frame and automatically adds a daily log returns column at instantiation, e.g., for Apple.

Explore how the double underscore wrapper defines an object's string representation for a financial instrument, enabling intuitive displays of the ticker and start and end dates to improve user experience.

Explore visualizing finance data by plotting price charts and log returns, noting volatility clusters and normality checks via histograms, using a class method to plot prices and returns for Apple.

Explore encapsulation in object oriented programming, illustrating how protected attributes with a leading underscore guard the ticker and price data from unintended changes in Python.

learn how to implement a set method to update a protected ticker attribute, re-fetch price data from Yahoo Finance, and compute log returns after changing the ticker.

Learn to compute mean and risk of returns with configurable frequency, using daily to monthly data, including resampled prices, log returns, and annualized performance metrics.

Install Python and the data science ecosystem with Anaconda, a comprehensive package manager for data science, machine learning, and algo trading, offering cross-platform setup and fewer conflicts with Python installations.

Open Anaconda Navigator, launch Jupyter Notebook, and write Python code in a notebook. Explore the base environment and installed packages like numpy and pandas, using shift enter to run cells.

Discover the fundamentals of Jupyter notebooks for Python coding, including cells, edit and command modes, markdown content, keyboard shortcuts, and kernel management to avoid common errors.