Focus: Human behavioral biases in financial decision-making and the potential for AI-powered financial advisors
Key Finding: 50% of people lack basic financial literacy, but systematic biases affect even educated investors
Interactive experiments demonstrate loss aversion, uncertainty aversion, and unconscious bias
Vision: AI financial advisors that are trustworthy, available 24/7, and free for all users
Over 50% of people globally cannot answer three basic finance questions, highlighting the urgent need for accessible financial guidance
Loss aversion, uncertainty aversion, and irrational decision-making patterns systematically lead to poor financial choices
LLMs could provide 24/7, trustworthy, personalized financial advice at no cost, revolutionizing access to financial guidance
Demonstrating systematic biases in human financial decision-making
Description: Two urn experiment demonstrating human preference for known probabilities over unknown ones
Setup: Urn A: 50 red, 50 black balls (known). Urn B: Unknown distribution of red and black balls
Finding: Most people prefer Urn A despite identical expected values, showing uncertainty aversion
Description: Investment scenarios revealing different risk preferences for gains versus losses
Setup: Choice A vs B (gains): $240K sure vs 25% chance of $1M. Choice C vs D (losses): $750K sure loss vs 75% chance of $1M loss
Finding: Risk-averse for gains, risk-seeking for losses - violating expected utility theory
Description: Interactive demonstration revealing non-standard utility functions
Setup: Three sequential gambles to extract individual utility curves
Finding: Real human utility functions are S-shaped (convex for losses, concave for gains), not purely concave
Description: Visual recognition experiment showing data-dependent decision making
Setup: Pixelated images with increasing resolution asking 'friend or foe' identification
Finding: Default to 'foe' when uncertain - evolutionary survival mechanism
Description: Cocktail party scenario revealing unconscious bias in decision-making
Setup: Jose (gay Latino Democrat) vs Susan (white Christian Republican) for various roles
Finding: Immediate biased judgments despite limited information - over 1 million possible personality combinations
From rule-based systems to narrative-processing models
Description: Rules-based AI with extensive programming but minimal data
Characteristics: Rigid, enumerated states, analytical optimization
Example: Complex decision trees for every possible scenario
Limitations: Extremely expensive, limited adaptability
Description: Data-driven AI with simple algorithms processing large datasets
Characteristics: Pattern recognition, collaborative filtering
Example: Amazon's 'people who bought this also bought' recommendations
Advantages: Scalable, effective for known patterns
Description: Narrative-based AI processing stories and context, not just facts
Characteristics: Story processing, contextual understanding, generative capabilities
Example: ChatGPT providing sophisticated financial analysis
Potential: Human-like reasoning with vast knowledge access
Modern AI approaches using large datasets and narrative processing are actually closer to human intelligence than traditional rule-based systems. Human brains store stories and narratives, not isolated facts.
Demonstrating rapid improvement in AI financial advice capability
Query: What should I do if I lose 25% of my life savings?
Response: Generic advice including 'consider dollar cost averaging'
Assessment: Inappropriate - violates suitability principle
Issue: One-size-fits-all advice for distressed investors
Query: What should I do if I lose 25% of my life savings?
Response: Sophisticated, personalized advice impressing professional advisors
Assessment: Impressive quality matching human financial advisors
Breakthrough: Demonstrated potential for AI financial guidance
Query: Fundamental analysis of Moderna stock at $133.40
Response: Comprehensive analysis concluding 'not ideal for immediate returns'
Assessment: Accurate prediction - stock fell to $32.92
Significance: Demonstrated superior analysis capability
ChatGPT 4.0's analysis of Moderna at $133.40 recommended against investment due to "declining revenues, ongoing losses, and limited profitability."
Result: Stock fell to $32.92 - a 75% decline, validating the AI's fundamental analysis.
Description: People are more sensitive to losses than equivalent gains
Manifestation: Risk-seeking behavior when facing losses
Consequence: Rogue trading, doubling down on losing positions
Description: Preference for known risks over unknown uncertainties
Manifestation: Avoiding unfamiliar investments despite potential
Consequence: Suboptimal portfolio diversification
Description: Emotional reactions to market downturns
Manifestation: Selling investments during market lows
Consequence: Crystallizing losses and missing recoveries
Description: Making decisions based on limited information
Manifestation: Investing based on recent performance or media coverage
Consequence: Chasing trends and missing fundamentals
Professional traders systematically exploit these human biases to create arbitrage opportunities. The $10,000 "lying on the sidewalk" in the loss aversion experiment represents real money that sophisticated traders extract from biased individual investors.
Facts Approach: Cannot efficiently store isolated facts
Narrative Approach: Optimized for story-based information processing
Example: Remembering historical events through stories, not dates
Facts Approach: Daily price movements and statistics
Narrative Approach: Market stories, investor sentiment, economic narratives
Example: Trump's daily decisions affecting entire market sentiment
Facts Approach: Company fundamentals and financial ratios
Narrative Approach: Growth stories, competitive positioning, market trends
Example: Moderna's COVID vaccine success story vs. financial metrics
Facts Approach: Historical volatility and correlation data
Narrative Approach: Risk stories, crisis scenarios, behavioral patterns
Example: 2008 financial crisis narrative vs. statistical models
Education should shift from fact memorization to narrative understanding. Elementary school focuses on facts (state capitals), but graduate education emphasizes stories and creative ideas. AI enables this transition by handling fact retrieval while humans focus on meaning-making.
Vision for the future of accessible financial guidance
24/7 portfolio tracking and market analysis
Access to all published financial news and research
Instant access anytime, anywhere
Programmed to prioritize client's best interests
No management fees or commissions
"What if your financial advisor knew how your portfolio was performing at all times, read every piece of published news, was available 24/7, was completely trustworthy, and was free? We believe this is achievable within 2-3 years given the pace of LLM advancement."
Ensuring AI doesn't perpetuate or amplify human biases
Complexity: Training data contains historical biases
Providing advice appropriate for each individual's situation
Complexity: Regulatory requirement for personalized guidance
Avoiding false or misleading financial information
Complexity: Financial advice requires high accuracy standards
Balancing AI capabilities with human judgment
Complexity: Financial decisions have life-changing consequences
Personalized advice for 401(k) and IRA optimization
Current Problem: Most people lack access to professional advice
Immediate support during market downturns
Current Problem: Emotional decisions during volatility
Teaching financial literacy through interactive scenarios
Current Problem: 50% financial illiteracy rate globally
Detecting and preventing financial scams
Current Problem: Increasing sophistication of financial fraud
Given current market uncertainty driven by unpredictable policy changes, Professor Lo recommends reducing market exposure until new patterns emerge. "When you don't know the rules, take some chips off the table."
"The best investment you could possibly make is investing in your own intellect" - Professor Lo's final advice to students.