Week 1: Introduction to Behavioral Finance

1. What this course is about

Behavioral Finance is the study of the psychological and behavioral factors that influence financial decisions and the functioning of markets. The course combines theory, empirical studies and real-world examples so that you understand the mechanisms that shape investor behavior.

The lecture gives a roadmap of the topics that the rest of the course covers:

• Efficient Market Hypothesis (EMH): the idea that prices of financial products incorporate all available information, so that it is impossible to beat the market systematically.
• Rational and irrational decisions: cognitive biases such as anchoring (sticking to the first piece of information you receive) and attitudes to risk such as risk aversion.
• Prospect Theory and heuristics: the experiments of Kahneman and Tversky, and the mental shortcuts people use to simplify decisions when a perfect solution is not required.
• CAPM, noise traders and bubbles: how bubbles contradict the EMH and how some traders profit from them.
• Seasonality, momentum and reversal: recurring patterns in market returns, such as calendar effects, trends that continue (momentum) and trends that turn around (reversal).
• Overconfidence, underreaction and herding: overestimating the accuracy of your own information, stocks that react only slowly to news (for example after a merger), and investors who all move in the same direction like a herd. Black Monday (1987) and the collapse of Lehman Brothers (2008) are used as examples.
• Sentiment, animal spirits and behavioral funds: investment strategies based on behavioral finance, market mood, and recent phenomena such as meme coins.

Course objectives and assessment

The three objectives are: (1) understanding what Behavioral Finance discusses, (2) understanding better how financial markets work and what role human psychology plays, and (3) analysing and evaluating the academic literature on the subject.

2. What is Behavioral Finance?

Traditional finance starts from the model of the rational investor and the efficient market. Behavioral Finance contrasts with that model and tries to explain phenomena that the traditional view struggles with: bubbles, financial crises, return anomalies and herding behavior.

Thinking, Fast and Slow: the two systems

The lecture uses Daniel Kahneman's idea of two modes of thinking (from his book Thinking, Fast and Slow):

Intuition: most of the time System 1 runs the show because it is cheap and quick. That works fine for everyday life, but in financial markets the quick answer is often biased. Many of the biases in this course are System 1 errors that System 2 did not catch. Week 3 treats the two systems in detail.

Why does it matter?

1. Traditional theories explain how markets should work.
2. Reality shows deviations: bubbles, crises, overreactions.
3. Behavioral finance bridges this gap, because traditional models are imperfect.
4. It places great weight on investor psychology and on subjective rules of behavior.

What famous investors said

• Jason Zweig's point is that investing is not about beating other people at their game, but about mastering yourself at yours. What threatens an investor most is his own conduct, not the other people trading.
• Benjamin Graham observed that great achievements require excitement, yet on Wall Street excitement tends to end in disaster. The same emotion that drives an entrepreneur forward does damage when you invest.
• Warren Buffett's advice: be fearful while others are greedy, and greedy while others are fearful. This is a contrarian guideline: when the mood of the market reaches an extreme, prices are probably off.

3. "Traditional" finance and its assumptions

Traditional finance deals with funds, investment and risk management. Its basic assumptions are complete information and rational investors.

Everything is a cycle

The slide "Everything is a cycle" shows a market cycle chart drawn on the dot-com period (roughly 1998 to 2004, index rising from about 1,000 to above 4,500 and falling back). It labels the emotional phases investors go through: 1 Hope, 2 Optimism, 3 Belief, 4 Thrill, 5 Euphoria (the top), then 6 Complacency, 7 Anxiety, 8 Denial, 9 Panic, 10 Capitulation, 11 Anger, 12 Depression (the bottom) and finally 13 Disbelief when the new rise starts. The point is that prices follow emotions as much as fundamentals: people buy most eagerly at the top (euphoria) and sell at the bottom (capitulation), which is the opposite of what a rational investor would do.

Homo Economicus

The rational agent of economic theory, Homo Economicus, has four properties (the slide title jokingly calls them "hilarious jokes to tell yourself"):

1. Fully rational.
2. Calculates all possible outcomes.
3. Maximises utility.
4. Time-consistent (his preferences today about the future do not change when the future arrives).

Behavioral finance questions each of these. Real people have limited attention, cannot compute all outcomes, and often change their minds (for example, planning to save but spending when the money arrives).

4. Expected Utility Theory

Expected Utility Theory (EUT) by von Neumann and Morgenstern (1947) is the benchmark model of rational choice under uncertainty. It says that a rational decision maker chooses the option that maximises the expected value of a utility function, not the expected value of money.

The key theorem states that anyone whose preferences meet the four axioms can be described by a utility function. Those preferences can then be put on a numerical scale, and the person will always pick the option that yields the greatest expected utility.

The four axioms

In the notation below, L, M and N are lotteries (risky options). "L ⪰ M" means "L is at least as good as M".

1. Completeness. The individual has clearly defined preferences and can always compare two options: either L ⪰ M or L ⪯ M (or both, which means indifference). Example: asked to choose between a holiday and a laptop of equal price, you can always say which you prefer or that you do not care.
2. Transitivity. Preferences are consistent across three choices: if L ⪰ M and M ⪰ N, then L ⪰ N. Example: if you prefer shares to bonds and bonds to cash, you must prefer shares to cash. Without transitivity someone could be "money pumped" by trading in a circle.
3. Continuity. If L ⪯ M ⪯ N, there is a probability p between 0 and 1 such that a mix pL + (1 − p)N is equally good as M. There is an "equilibrium point" where a gamble between the worse and the better option is exactly as attractive as the middle option. Example: if €0 ⪯ €50 ⪯ €100, there is some chance p of getting €0 (and 1 − p of €100) that makes you indifferent to receiving €50 for sure.
4. Independence. Take any M and any p in [0,1). Whenever L ⪯ N, it follows that (1 − p)L + pM ⪯ (1 − p)N + pM. If you blend both alternatives with an identical third lottery M at an identical probability, your ordering should stay the same, since the shared component "cancels out". The Allais paradox (Week 2) demonstrates that people break precisely this axiom.

Worked example: the lottery with U(W) = √W

The slides compare a certain amount with a lottery for an investor whose utility function is the square root of wealth. Square-root utility is concave: each extra euro adds less utility than the previous one (diminishing marginal utility). That is what makes a person risk-averse.

What the slide concludes: option A offers certainty together with the higher utility (10.95), while option B yields a lower expected utility (10.23), so the risk-averse investor picks the sure option A. Within EUT the curvature of U(W) accounts for risk aversion entirely. Later, behavioral finance shows that risk attitudes depend on more than how curved the wealth function is, for instance on whether results are presented as gains or as losses.

5. Behavioral biases: a first overview

The lecture sorts behavioral biases into two groups:

• Mental accounting: treating money differently depending on its source or purpose, for example being careless with a bonus but careful with salary.
• Herding: copying what other investors do instead of using your own information.
• Emotional gap: making decisions under strong emotions (fear, greed, excitement) that you would not make in a calm state.
• Overconfidence: overestimating your knowledge or the precision of your forecasts.
• Anchoring: relying too much on the first number you saw, such as the price you bought a stock at.
• Recency bias: giving too much weight to the most recent events.
• Familiarity bias: preferring what you know, such as shares of your employer or of home-country firms, which leads to poor diversification.
• Loss aversion: losses hurt more than equally large gains please.
• Confirmation bias: searching for information that confirms what you already believe.

Why the split matters: cognitive errors can often be reduced by better information and education, while emotional biases are harder to correct and usually need rules or commitment devices (for example automatic investing plans).

Three pillars of Behavioral Finance (Shefrin, 2002)

1. Heuristics: people decide on the basis of approximate judgments (rules of thumb) rather than strict rational analysis.
2. Framing: the way a problem is presented influences the decision maker and his action.
3. Market inefficiencies: some observed anomalies have explanations that conflict with rational expectations theory and with market efficiency.

These three pillars are a useful structure for a project: most research questions in behavioral finance concern one heuristic or framing effect and test whether it creates a market inefficiency.

6. Two classic examples

The endowment effect: WTP versus WTA

The slide illustrates this with a concert ticket (System of a Down, Stade de France, 4 July 2026, fan club rate). Before buying, you might be willing to pay, say, €80 for it. Once you own it, you might refuse to sell it for less than €150. Under traditional theory WTP and WTA should be almost equal, because the ticket gives the same enjoyment either way. The gap is explained by loss aversion: giving up something you own is coded as a loss, and losses weigh more than gains.

Why it matters for investors: investors tend to overvalue stocks they already hold and are reluctant to sell them, which reduces trading and contributes to the disposition effect (Week 2).

Framing: the Asian disease problem (Kahneman and Tversky, 1981)

A disease is expected to affect 600 people. Participants choose between two programmes:

• A: 200 people out of the 600 are saved for sure.
• B: with probability 1/3 (33%) all 600 people are saved, and with probability 2/3 (66%) no one is saved.

Result: 72% chose A, 28% chose B. People prefer certainty when the options are described as gains (lives saved).

A second group saw the same problem with a different frame:

• C: 400 of the 600 will die.
• D: a 1/3 chance that nobody dies and a 2/3 chance that all 600 die.

Result: only 22% chose C and 78% chose D.

Interpretation: in a gain frame people are risk-averse; in a loss frame they become risk-seeking. Rational theory (EUT) says the frame should not matter, because the final outcomes are identical. This reversal is one of the key motivations for Prospect Theory in Week 2. In finance, the same investment can look attractive or unattractive depending on whether it is presented as "90% chance of keeping your capital" or "10% chance of losing it".

7. The Efficient Market Hypothesis (EMH)

The EMH was formalised by Fama (1970). Basic principle: financial markets are efficient in incorporating all available information into the prices of financial assets.

• Security prices reflect all available information.
• Changes in asset prices are random and unpredictable, because only new (unexpected) information moves prices.
• Markets respond immediately and fully to new information, so prices are always "correct": all information is already discounted.

Three forms of efficiency

Each form includes the previous one. Example: if a company announces surprisingly high earnings at 9:00, the semi-strong form says the price jumps immediately to the new correct level, so buying at 9:05 after reading the news gives no excess return.

Implications for investors

• If the EMH holds, it is impossible to beat the market systematically.
• Technical analysis is ineffective (weak form).
• Fundamental analysis is of limited use (semi-strong form).
• Insider trading is useless (strong form).

The practical consequence is passive investing: buy a cheap, diversified index fund instead of trying to pick winners.

Other assumptions behind the EMH

1. There is a large number of investors, traders and analysts who actively take part in the market and constantly analyse every available piece of information.
2. Participants try to maximise their utility and have rational expectations, which they update correctly when new information arrives.
3. Investors react quickly and accurately to new information, and no single investor can influence a stock's price.
4. Information is available to all participants at the same time, at no cost, and arrives randomly.

"And if some gamble?" The defence of the EMH

Supporters of the EMH accept that some investors are not rational, but argue that this does not matter, for two reasons:

• If irrational investors trade randomly (their errors are uncorrelated) and there are many of them, their trades cancel each other out and do not affect prices.
• Even if they did push prices away from fair value, arbitrage by rational investors (buying underpriced assets and selling overpriced ones) would push prices back, neutralising the irrational traders.

Behavioral finance attacks both points: biases are often shared by many investors at once (so they do not cancel out), and arbitrage is risky and costly (so it is limited). These arguments return later in the course with noise traders and limits to arbitrage.

Criticism of the EMH

• Failures in practice: markets sometimes overreact or underreact to new information; bubbles and crashes are hard to explain under the EMH.
• Investor psychology: behavioral finance argues that investors often decide based on emotions rather than logic.

Case: "From dusk till dawn", the 2025 tariff announcements

Two Bloomberg intraday charts of the S&P 500 illustrate how strongly markets react to news. After the US "Liberation Day" tariff announcement on 2 April 2025, the index fell sharply overnight and into 3 April (from roughly 5,670 to about 5,400 on the chart). On 9 April 2025, when a pause of the tariffs was announced, the index jumped within minutes from about 4,950 to about 5,450, a rise of around 9.5% in one afternoon.

How to read this: the jump itself is consistent with the EMH (prices react immediately to new public information). But the size of the swings, both down and up within a week, raises the question whether prices were "correct" at both moments, or whether fear and relief (System 1 reactions) amplified the moves. This is a good illustration of overreaction to news.

8. The random walk

The slide "A Random Walk Down Wall Street" (the title of Burton Malkiel's book) shows 100 simulations of a Gaussian random walk over 100 periods. Each path starts at zero and moves up or down by a random step each period. After 100 steps the paths spread out to between roughly −20 and +30, and some look like clear "trends" even though they are pure chance.

• Prices follow a random path.
• Returns are unpredictable.
• There is no pattern to exploit.

Example: a stock at €50 with daily shocks of +€1 or −€1 with 50% probability each. After a run of five "up" days the price is €55, but the expected price tomorrow is still €55. The run does not make another up day more or less likely. People who see such a run often believe in a trend (or in a coming reversal); both are errors if prices are truly a random walk.

9. Market anomalies

An anomaly is a pattern in returns that is predictable and that the EMH cannot easily explain. Examples from the slides:

• January effect: stocks (especially small stocks) historically earned higher returns in January than in other months.
• Momentum: stocks that did well over the past months tend to keep doing well in the next months.
• Reversal: over longer horizons (several years), past losers tend to outperform past winners. De Bondt and Thaler (1985) are the classic reference and are covered later in the course.
• Overreaction and underreaction: prices move too much on some news and too little on other news, and then correct.

"September is the worst month for the S&P 500"

The last slide (source: Zerohedge) shows a heat map of monthly S&P 500 returns from 2008 to 2025, with a 17-year average row on top. The September column is highlighted: its average return is negative (about −1.2%), the weakest of all months, while months such as July, October, November and December show positive averages (December around +2.6% on the chart). Individual years vary a lot: for example September 2008 was about −9.1% and September 2022 about −9.3%.

Interpretation: if a calendar month predictably has lower returns, a trader could exploit it, which should not be possible in an efficient market. Two cautions are useful for a project: (1) with only 17 observations per month, a seasonal average can easily be the result of a few extreme years (sample-size problem, Week 3); (2) once an anomaly is widely known, arbitrage may weaken it. Testing whether such an effect is statistically significant and persists out of sample is a typical behavioral finance research design.

10. Putting it together

Week 1 sets up the central tension of the course. On one side stand Homo Economicus, Expected Utility Theory and the Efficient Market Hypothesis: rational agents, consistent preferences and prices that reflect all information. On the other side stand the facts: framing effects, the endowment effect, emotional market cycles, extreme reactions to news and return anomalies. Behavioral finance does not claim that markets are always wrong; it claims that psychology causes systematic, predictable deviations, and that arbitrage cannot always remove them.