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Stanford CS 007-2 (2021): Personal Finance for Engineers / Behavioral Finance

These are the slides from the 2nd session of the Stanford University class, CS 007 "Personal Finance for Engineers," given on September 28, 2021. This seminar covers the topic of Behavioral Finance.

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Stanford CS 007-2 (2021): Personal Finance for Engineers / Behavioral Finance

  1. 1. PERSONAL FINANCE FOR ENGINEERS CS 007: SESSION 2
  2. 2. PREDICTABLY IRRATIONAL CS 007 * DesignHacks.co: 188 Known Cognitive Biases *”Predictably Irrational” is the title of a book by Dan Ariely
  3. 3. BEHAVIORAL FINANCE How many of you think you are rational with your money? (show of hands)
  4. 4. YOU ARE NOT RATIONAL ANCHORING MENTAL ACCOUNTING CONFIRMATION & 
 HINDSIGHT BIAS GAMBLER’S FALLACY HERD BEHAVIOR OVERCONFIDENCE OVERREACTION & 
 AVAILABILITY BIAS LOSS AVERSION
  5. 5. WHY BEHAVIORAL ECONOMICS? • A number of economic frameworks assume that humans evaluate financial decisions consistently & rationally • Daniel Kahneman & Amos Tversky (1960s) • 2002 Nobel Memorial Prize in Economic Sciences • Prospect Theory
  6. 6. THREE THEMES IN BEHAVIORAL ECONOMICS • Heuristics 
 Humans make a vast majority of their decisions using mental shortcuts or rules of thumb. • Framing 
 Humans use anecdotes & stereotypes to understand & respond to events • Market Inefficiencies 
 Mis-pricing or non-rational decision making
  7. 7. ANCHORING • People estimate answers to new & novel problems with a bias towards reference points • Tversky & Kahneman (1974) 
 (quick multiplication) • Dan Ariely 
 (social security numbers & prices) • Common examples: • Price you bought a stock at • High point for a stock
  8. 8. MENTAL ACCOUNTING • Money is fungible, but people put it into separate “mental accounts” • Also known as “bucketing” • Example: Lost Movie Tickets • Example: “Found Money” • Real world problems: 
 Vacation Fund & Credit Card Debt
  9. 9. CONFIRMATION & HINDSIGHT BIAS • Very different biases, but often conflated with each other. • Confirmation Bias 
 We selectively seek information that supports pre-existing theories, and we ignore / dispute information that challenges or disproves them. • Hindsight Bias 
 We overestimate our ability to predict the future based on the “obviousness” of the past. • Combination of the two is particularly bad.
  10. 10. GAMBLER’S FALLACY • We see patterns in independent, random chains of events. • We believe that, based on a series of previous events, an outcome is more likely than odds actually suggest. • Example: Dinner Party & Coin Flips • Real odds might be 51/49, but we tend to jump to 80/20. • Likely cause: the rarity of “independent events” in day-to-day experience.
  11. 11. HERD BEHAVIOR • We have a tendency to mimic the actions of the larger group • Example: Building Psych Experiment • Example: Empty Supermarket • Crowd psychology may be a contributor to bubbles. • Bucking the crowd creates stress & fatigue. It gets harder, not easier. • Easier to be “wrong with everyone” than “right and alone” • No gets fired for buying IBM?
  12. 12. OVERCONFIDENCE • In “Behaving Badly” (2016), 74% of investment managers believed that they deliver above-average performance. • Dunning-Kruger Effect. The more poorly you perform, the more you over-estimate your performance. • Capability in one domain can lead to overconfidence in others. • Humility is a virtue.
  13. 13. RECENCY & AVAILABILITY BIAS • Recency Bias 
 We overreact to recent events • Example: Celebrity Illness • Availability Bias 
 We assume that the data we have been provided is representative of the entire data set. • The combination is particularly bad. • Studies show checking stock prices daily leads to more trading & worse results on average. • Worse for engineers, because we are immersed in “game changers” & “it is different this time”
  14. 14. YOU HAVE $1,000 AND YOU MUST PICK ONE OF THE FOLLOWING GAMES You have a 100% chance of gaining $500. B You have a 50% chance of gaining $1,000, and a 50% chance of gaining $0. A OR
  15. 15. NOW, YOU HAVE $2,000 AND YOU MUST PICK ONE OF THE FOLLOWING GAMES B A OR You have a 50% chance of losing $1,000, and a 50% chance of losing $0. You have a 100% chance of losing $500.
  16. 16. LOSS AVERSION (PROSPECT THEORY) • We hate losses more than we love winning • Average loss aversion across multiple studies is between 2:1 and 3:1 • Affects our views on a wide range of situations, including career decisions, dating, purchasing, investing and taxes. • We even hate being responsible for decisions that could result in a loss • Example: “Sunk Cost” mistakes with investments • Currently under debate!* * https://blogs.scientificamerican.com/observations/why-the-most-important-idea-in-behavioral-decision-making-is-a-fallacy/?amp
  17. 17. IT’S OK TO NOT BE RATIONAL
  18. 18. IT’S OK TO NOT BE RATIONAL • As Dan Ariely beautifully put it, the key is that humans are predictably irrational • Know your own flaws, and you can set up systems to help account for them • Self awareness is key 
 (yes, my Mom is a psychologist)
  19. 19. ADDITIONAL COURSES & MATERIAL • Econ 178: Behavioral Economics • Econ 278: Behavioral & Experimental Economics • ACCT 618 (GSB): Market Efficiency & Informational Arbitrage • Coursera / Duke: Behavioral Finance 
 https://www.coursera.org/learn/duke-behavioral-finance
  20. 20. QUESTIONS CS 007
  21. 21. NEXT WEEK: GETTING PAID • Compensation • Different models & methods • Public vs. Private Companies • Stock Options vs. Restricted Stock • Understanding Equity Compensation • Taxes

These are the slides from the 2nd session of the Stanford University class, CS 007 "Personal Finance for Engineers," given on September 28, 2021. This seminar covers the topic of Behavioral Finance.

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