How to build a behavioural finance course: a complete guide for lecturers

Central question: What does standard finance assume, and where does behavioural finance add explanatory power without turning every mistake into irrationality?

Open the course with a benchmark rather than a catalogue of biases. Students need the expected-utility, Bayesian-updating and efficient-market ideas that behavioural claims are measured against. The objective is not to declare standard finance wrong, but to show when cognitive limits, emotions, social context and institutional frictions create systematic departures that matter for financial decisions.

What this concept covers

• Rational-choice and market-efficiency benchmarks as useful null models.
• Bounded rationality, normal wants, cognitive shortcuts and emotional errors.
• Individual-level behaviour versus aggregate market outcomes.
• Positive explanation versus normative advice: describing behaviour is not the same as recommending it.
• Evidence standards: laboratory experiments, field data, surveys, administrative records and market data.

Learning outcomes

By the end of this concept, students should be able to:

• State the benchmark assumptions that a behavioural explanation seeks to relax.
• Distinguish a bias from a preference, constraint or information problem.
• Evaluate whether a behavioural claim is supported by the evidence presented.
• Separate investor-level behaviour from claims about market inefficiency.

Supports Course Outcomes 1, 2 and 9.

How to teach it

• Begin with a standard investment-choice problem and ask what a fully rational investor would do.
• Give groups four explanations for the same behaviour: preferences, information, constraints and bias.
• Use Statman's second-generation framing to discuss investors as normal rather than simply irrational.
• Require students to identify what evidence would discriminate between competing explanations.

Case-style example

Ridgeway Pension Trust must allocate £100 million between a global equity index and a cash fund. Its committee expects equities to return 7% with 18% volatility and cash 3% with near-zero volatility, yet several members resist equities after a recent market fall. Students calculate the benchmark allocation under a stated 60/40 policy, then write two explanations for the resistance. One may be rational risk tolerance; the other may be recency or loss salience. Students discover that behaviour alone does not identify the mechanism.

Where students struggle

Students often label any choice they dislike as a bias. Push them to ask whether the behaviour could be explained by preferences, constraints, incentives or information before using behavioural language.

Reading and a quick check

Use Statman, Behavioral Finance: The Second Generation, Introduction and Chapter 1. Quick check: give one observed investor action and ask students to list one rational and one behavioural explanation, plus the evidence needed to distinguish them.

Where it leads

Once students can identify the benchmark and the evidential burden, they can study the central behavioural model of risky choice: prospect theory.

Central question: How do reference points, loss aversion and nonlinear probability weighting change financial choices under risk?

Prospect theory gives the course a disciplined model of choices that standard expected utility often describes poorly. Teach the value function, reference dependence and probability weighting together, then connect them to disposition, insurance, lottery-like securities and market anomalies rather than treating loss aversion as a slogan.

What this concept covers

• Reference points and gains versus losses.
• Loss aversion, diminishing sensitivity and the S-shaped value function.
• Probability weighting and attraction to small-probability outcomes.
• Realisation utility and links to the disposition effect.
• How prospect-theory predictions can be tested in asset-pricing and investor data.

Learning outcomes

By the end of this concept, students should be able to:

• Apply a reference point to classify outcomes as gains or losses.
• Predict how loss aversion can alter risk-taking around the reference point.
• Compare expected-utility and prospect-theory predictions for the same gamble.
• Critique a market-anomaly explanation based on prospect theory.

Supports Course Outcomes 2, 3 and 7.

How to teach it

• Draw the value function and ask students to locate equivalent gains and losses.
• Use matched gambles to show risk aversion in gains and risk seeking in losses.
• Change the reference point while keeping final wealth unchanged.
• Link the model to a recent empirical paper rather than presenting it as universally accepted.

Case-style example

Arden Wealth holds two £50,000 positions. Stock A is now worth £60,000 after purchase at £50,000; Stock B is worth £40,000 after the same purchase price. Analysts give both stocks identical forward distributions: 50% chance of +12% and 50% chance of -8%. Students compute expected returns, then choose which position to sell if the client needs £40,000 cash. Selling A is easy to defend under tax or momentum assumptions; holding B can reveal reference dependence. Students discover that identical future prospects need not generate identical choices when purchase price becomes a reference point.

Where students struggle

Students sometimes equate prospect theory with “people hate losses twice as much as gains,” then stop. The useful teaching move is to make them specify the reference point, domain, probability weighting and predicted choice.

Reading and a quick check

Assign Barberis, Jin and Wang (2021), Prospect Theory and Stock Market Anomalies, selectively alongside the textbook. Quick check: ask students to predict how a 20% loss changes risk appetite when the reference point is purchase price versus current wealth.

Where it leads

Reference dependence creates a foundation for the heuristics that shape how investors form probabilities and interpret noisy information.

Central question: When do useful mental shortcuts become predictable errors in valuation, forecasting and security selection?

Heuristics should be taught as efficient shortcuts that can fail in specific environments, not as evidence that investors are careless. Representativeness can encourage base-rate neglect, availability can overweight vivid information, and anchoring can make valuations insufficiently responsive to new evidence. The important question is when each shortcut changes a financial decision.

What this concept covers

• Representativeness, similarity judgements and base-rate neglect.
• Availability and the effect of vivid, recent or easily recalled information.
• Anchoring and insufficient adjustment from salient starting values.
• Law-of-small-numbers reasoning and over-extrapolation from short histories.
• Debiasing through base rates, reference classes, ranges and pre-specified update rules.

Learning outcomes

By the end of this concept, students should be able to:

• Diagnose which heuristic is most consistent with a stated decision process.
• Use base rates to challenge a representative story.
• Evaluate whether an anchor materially affected a valuation or forecast.
• Design a simple decision aid that reduces heuristic error.

Supports Course Outcomes 3, 4 and 9.

How to teach it

• Give students a glamorous company story before revealing sector base rates.
• Run an anchoring exercise by giving different groups different irrelevant starting numbers.
• Ask students to separate information that is vivid from information that is diagnostic.
• Use a reference-class forecast as a practical debiasing tool.

Case-style example

Helix Solar has grown revenue 45% annually for two years. A pitch deck anchors management’s valuation at 12x revenue, while comparable mature firms trade at 5x and only 20% of recent sector entrants sustained growth above 30% for a third year. Students build a 5x-9x range using the base rate, then write a second valuation beginning from the 12x anchor. One team may defend a premium for quality; another may call the premium narrative-driven. Students discover how a salient story can crowd out base-rate evidence.

Where students struggle

Students often name several biases at once. Require one mechanism, one observable prediction and one alternative explanation.

Reading and a quick check

Use Statman Chapters 3-4 and a short anchoring exercise. Quick check: present a forecast and ask which single piece of base-rate information would most change the conclusion.

Where it leads

Once students see how shortcuts affect beliefs, the course can examine a particularly consequential belief error: overconfidence.

Central question: Why can confidence exceed accuracy, and how does that affect trading, diversification and financial advice?

Overconfidence is more useful when decomposed into overprecision, overestimation and overplacement. Students can then connect excessive confidence to narrow forecast ranges, high turnover, concentration and resistance to disconfirming evidence. Self-attribution adds a dynamic: successes are credited to skill while failures are blamed on noise.

What this concept covers

• Overprecision, overestimation and overplacement.
• Calibration of probability forecasts and confidence intervals.
• Self-attribution and learning from wins versus losses.
• Turnover, concentration and belief in private signals.
• Team decision processes that challenge confidence without eliminating conviction.

Learning outcomes

By the end of this concept, students should be able to:

• Distinguish the main forms of overconfidence.
• Assess forecast calibration using stated probabilities and realised outcomes.
• Explain how self-attribution can sustain excessive trading.
• Recommend process controls that improve calibration.

Supports Course Outcomes 3, 6 and 9.

How to teach it

• Ask students for 90% confidence intervals on ten factual quantities, then score coverage.
• Compare a confident concentrated portfolio with a diversified mandate.
• Use a post-mortem template that records ex-ante reasons before outcomes are known.
• Ask teams to appoint a designated challenger to surface disconfirming evidence.

Case-style example

Northstar Capital’s analyst predicts five stocks will beat the market, assigning each a 70% success probability. After one quarter, four outperform; after one year, only two do. The analyst wants to double position sizes because the early results “proved the model.” Students calculate Brier-style calibration informally, compare the quarter and annual evidence, and decide whether to increase exposure. A skill story and a small-sample/self-attribution story are both defensible. Students discover why confidence must be evaluated against a pre-recorded forecast, not reconstructed after success.

Where students struggle

Students confuse confidence with competence. The teaching goal is to measure calibration and process quality rather than punish strong views.

Reading and a quick check

Use a calibration quiz plus Statman on cognitive errors. Quick check: ask students whether a 90% confidence interval that contains the truth 55% of the time is cautious or overprecise, and what investment behaviour it could encourage.

Where it leads

Confidence interacts with framing and account boundaries, which the next concept develops through mental accounting and narrow bracketing.

Central question: How do labels, presentation and account boundaries change choices that look equivalent in aggregate wealth?

Mental accounting explains why people separate money by source or purpose, while framing shows how equivalent outcomes can produce different choices depending on presentation. In finance, these mechanisms appear in dividend preferences, house money effects, debt repayment, retirement pots, budgeting and the tendency to evaluate one position rather than the total portfolio.

What this concept covers

• Mental accounts by source, purpose, asset and time horizon.
• Narrow bracketing and security-by-security evaluation.
• Framing of returns, fees, losses and probabilities.
• House money, sunk-cost and break-even effects where relevant.
• Portfolio-level aggregation as a debiasing technique.

Learning outcomes

By the end of this concept, students should be able to:

• Identify how an account boundary affects a financial choice.
• Reframe equivalent outcomes and predict when choices may change.
• Evaluate a portfolio decision at both position and total-wealth level.
• Design disclosure or advice that reduces misleading frames.

Supports Course Outcomes 2, 4 and 8.

How to teach it

• Present the same investment outcome as a percentage gain, cash gain and change in total wealth.
• Ask students to consolidate several labelled savings pots into a balance-sheet view.
• Compare a dividend preference with a total-return withdrawal policy.
• Use fee disclosures in pounds and basis points to discuss framing.

Case-style example

Maya has £120,000 invested: £80,000 in a diversified fund, £20,000 in employer stock and £20,000 in a “holiday account” held in cash. She also carries £12,000 of credit-card debt at 19%. Students calculate the expected one-year financing cost and compare it with a 4% cash yield. They recommend whether to preserve the labelled account or repay debt. Keeping liquidity can be defensible; preserving a mental account while paying 19% interest may not be. Students discover how labels can conceal the household balance-sheet decision.

Where students struggle

Students sometimes treat mental accounting as automatically irrational. Separate useful budgeting functions from costly isolation of decisions that should be evaluated together.

Reading and a quick check

Use Statman on wants and behavioural portfolios. Quick check: ask whether an emergency fund is a harmful mental account, a rational liquidity constraint or both depending on the facts.

Where it leads

Frames compete for scarce attention. The next concept examines how salience, memory and limited information processing determine what enters the decision at all.

Central question: Which information gets noticed, remembered and acted on, and what happens when visibility is mistaken for importance?

Modern behavioural finance increasingly studies the information-processing channel directly. Investors cannot process every security, headline or disclosure. Salient assets attract attention; remembered outcomes shape beliefs; digital interfaces can alter what users notice and how often they act. This is a strong bridge from classic biases to current market design.

What this concept covers

• Limited attention and the security-choice set.
• Salience, media coverage, extreme returns and attention-grabbing products.
• Memory retrieval, associative recall and belief formation.
• Digital engagement practices such as alerts, leaderboards and visual cues.
• Distinguishing informative attention from attention-induced trading.

Learning outcomes

By the end of this concept, students should be able to:

• Explain why attention can affect which securities investors consider.
• Evaluate whether a salient signal is economically diagnostic.
• Connect memory and recalled experiences to forecasts and trades.
• Critique interface features that may alter trading frequency or risk.

Supports Course Outcomes 4, 5 and 10.

How to teach it

• Give groups identical data with different headlines or orderings and compare decisions.
• Use the FCA trading-app experiment to discuss causal evidence on interface design.
• Ask students to build an attention budget: which five variables deserve monitoring and why?
• Contrast attention to a stock with information about its expected return.

Case-style example

PulseTrade is testing two interfaces with 9,600 simulated users. Version A shows a static watchlist; Version B adds flashing prices, push notifications and a points leaderboard. In a pilot, average monthly trades rise from 8 to 11 and the share allocated to high-volatility assets rises from 18% to 24%. Students compute the percentage changes, list causal threats if the pilot were observational, then propose a randomised test. Growth managers may prefer engagement; conduct teams may prioritise decision quality. Students discover why more attention is not automatically more information.

Where students struggle

Students tend to infer that any heavily traded asset is mispriced. Attention can affect consideration and trading without proving a persistent return anomaly.

Reading and a quick check

Assign Gödker, Jiao and Smeets (2025), Investor Memory, or Charles (2025), Memory Moves Markets. Quick check: ask students to separate an attention prediction from an asset-pricing prediction.

Where a simulation fits

The Portfolio Management Simulation can be used later, after formal portfolio theory, to contrast disciplined rebalancing with reactions to recent news. The simulation does not teach attention bias directly, so frame the behavioural learning in the pre-brief and debrief rather than claiming it is a behavioural-finance simulation.

Where it leads

Attention and memory operate in social settings too. The next concept adds peer beliefs, herding and sentiment.

Central question: When do investors learn rationally from others, and when do social signals amplify correlated mistakes or narrative-driven demand?

Herding is not simply “everyone doing the same thing.” Investors may rationally infer information from others, respond to common fundamentals or face career incentives. Behavioural herding requires a more specific mechanism, such as social imitation, reputation, echo chambers or sentiment contagion. Teach the competing explanations side by side.

What this concept covers

• Informational cascades and social learning.
• Reputational and career-based herding.
• Investor sentiment and correlated demand.
• Online communities, echo chambers and selective exposure.
• Separating common-information responses from social influence.

Learning outcomes

By the end of this concept, students should be able to:

• Distinguish rational social learning from behavioural imitation.
• Interpret evidence on echo chambers and correlated beliefs.
• Assess whether sentiment could affect demand when arbitrage is limited.
• Design a discussion or investment process that surfaces independent views.

Supports Course Outcomes 5, 7 and 9.

How to teach it

• Ask students to make a private estimate before seeing classmates’ answers, then reveal the distribution.
• Use an online-investor discussion example and ask which evidence would identify peer influence.
• Contrast a shared fundamental shock with a social cascade.
• Have teams record independent forecasts before group discussion to reduce conformity pressure.

Case-style example

CrowdAlpha’s 40 analysts independently estimate fair value for a stock before joining a shared chat. The median private estimate is £32, with an interquartile range of £28-£36. After one influential analyst posts a £50 target, the median revised estimate becomes £41 and dispersion falls. New company information during the interval is minimal. Students quantify the shift and propose tests for information versus conformity. A genuine private-signal interpretation remains possible; a social-influence interpretation also fits. Students discover why correlated beliefs need an identification strategy.

Where students struggle

Students often call price momentum “herding” without evidence about investor interaction. Make them separate the observed pattern from the proposed behavioural mechanism.

Reading and a quick check

Assign Cookson, Engelberg and Mullins (2023), Echo Chambers. Quick check: ask students for one observation consistent with both rational learning and herding, then one additional datum that could distinguish them.

Where it leads

Social signals feed into expectations. The next concept focuses on how investors form, extrapolate and update beliefs.

Central question: How do investors form return expectations, update them and translate beliefs into portfolio choices?

Beliefs are the link between information and action. Behavioural models often feature extrapolation, diagnostic expectations or sluggish updating, but evidence also shows substantial heterogeneity and incomplete pass-through from stated beliefs to portfolios. This concept teaches students to measure beliefs before claiming that beliefs explain trades.

What this concept covers

• Subjective return expectations and perceived risk.
• Extrapolation from past returns or earnings.
• Bayesian updating as a benchmark and departures from it.
• Belief heterogeneity, confidence and disagreement.
• The empirical link between expectations, trading and portfolio allocation.

Learning outcomes

By the end of this concept, students should be able to:

• Elicit and summarise subjective return beliefs.
• Compare Bayesian and extrapolative updating after new information.
• Evaluate how strongly observed portfolios reflect stated beliefs.
• Explain why disagreement can persist even when investors see similar data.

Supports Course Outcomes 4, 6 and 7.

How to teach it

• Have students record a return forecast and confidence interval before showing new information.
• Ask them to update quantitatively and explain the weighting given to the signal.
• Compare belief changes with proposed trades.
• Use a survey-based paper to show why expectations and holdings should be studied jointly.

Case-style example

Orion Investors expects a market return of 6% next year. After three consecutive monthly gains of 4%, one analyst raises the forecast to 12%; another remains at 6% because the gains contain little information about long-run expected returns. Students compute the annualised recent pace but then identify why annualising is not a forecast. They assign probabilities to two data-generating stories and recommend an equity-weight change. Students discover that a price history can support very different beliefs depending on the perceived process.

Where students struggle

Students often treat past return patterns as sufficient evidence of biased expectations. Push them to state the benchmark belief, information set and updating rule.

Reading and a quick check

Assign Giglio, Maggiori, Stroebel and Utkus (2021), Five Facts about Beliefs and Portfolios, or Andries et al. (2025). Quick check: ask why a strong correlation between optimism and equity holdings does not, by itself, establish causality.

Where it leads

Once students can model belief formation, they can ask whether systematic belief errors generate predictable price patterns and anomalies.

Central question: When does a return pattern challenge a benchmark, and when is an apparent anomaly a reward for risk, a data-mining artefact or a behavioural effect?

Anomalies are where behavioural finance can become sloppy. The right teaching sequence is evidence first, mechanism second. Students should learn how momentum, reversal, post-event drift and lottery-like demand are documented, then evaluate competing risk-based, institutional and behavioural explanations. They should also learn why published predictability can weaken after discovery.

What this concept covers

• Momentum, reversal and underreaction/overreaction patterns.
• Cross-sectional anomalies and multiple-testing concerns.
• Prospect-theory and extrapolation-based explanations.
• Risk-based and institutional alternatives.
• Out-of-sample testing, publication effects and economic significance after costs.

Learning outcomes

By the end of this concept, students should be able to:

• Describe an anomaly relative to a stated benchmark.
• Compare behavioural and non-behavioural explanations for return predictability.
• Assess whether a pattern is economically exploitable after risk and costs.
• Critique evidence for data mining or weak external validity.

Supports Course Outcomes 6, 7 and 9.

How to teach it

• Give students an anomaly fact sheet without the explanation and ask for three mechanisms.
• Require an out-of-sample and transaction-cost check.
• Use prospect theory as one model, not the default answer to every anomaly.
• Ask what result would falsify the preferred behavioural story.

Case-style example

QuantBridge reports that its “attention reversal” screen earned 6.0% annual alpha from 2008-2021. From 2022-2025 the same screen earns 1.1% before costs, with estimated turnover costs of 1.5% a year. Students calculate the post-cost result and decide whether the anomaly disappeared, was arbitraged away or was overfit. One group may defend regime change; another may reject the original claim. Students discover that statistical discovery is not the same as persistent, implementable mispricing.

Where students struggle

Students can mistake a named anomaly for a settled behavioural fact. Insist on benchmark, sample, mechanism, alternative explanation and implementability.

Reading and a quick check

Assign Barberis, Jin and Wang (2021) for a model-driven link between prospect theory and anomalies. Quick check: ask students to write the strongest non-behavioural alternative for one anomaly discussed in class.

Where it leads

Even if mispricing exists, it may persist only when rational traders face costs or risks that limit correction. That is the next concept.

Central question: Why might informed traders fail to eliminate mispricing, and what does that imply for claims about market efficiency?

Behavioural finance needs a market mechanism. If some investors are biased but sophisticated traders can costlessly arbitrage them, prices may still be close to fundamentals. Limits to arbitrage provide the bridge from investor psychology to market prices through fundamental risk, noise-trader risk, short-sale constraints, financing risk, horizons and delegated-management incentives.

What this concept covers

• Fundamental risk and imperfect substitutes.
• Noise-trader risk and uncertain correction timing.
• Short-sale, borrowing, liquidity and financing constraints.
• Agency and performance-evaluation risk for delegated managers.
• Relative versus absolute market efficiency.

Learning outcomes

By the end of this concept, students should be able to:

• Explain why apparent mispricing may be difficult to arbitrage.
• Map specific arbitrage risks to a proposed trade.
• Evaluate whether an inefficiency claim survives realistic implementation constraints.
• Distinguish “not perfectly efficient” from “easy excess returns.”

Supports Course Outcomes 7, 8 and 9.

How to teach it

• Give students an obviously overvalued security and ask them to design the short trade.
• Add borrow cost, a six-month investor evaluation horizon and a possible 30% further price rise.
• Ask what hedge would be required and whether a close substitute exists.
• Debate whether persistence of mispricing proves irrationality.

Case-style example

Meridian Fund believes NovaCloud is worth £40 but trades at £70. Short borrow costs 8% annually, the closest hedge has a 0.65 return correlation, and the fund faces investor redemptions if its portfolio falls 12% in any quarter. Students size a £10 million short, estimate annual borrow cost and stress a further rise to £95 before convergence. Shorting may still be attractive long term; avoiding it may be rational for a delegated manager. Students discover why identifying mispricing is easier than financing the trade that corrects it.

Where students struggle

Students often reason that if behavioural investors create mispricing, arbitrageurs must profit. The constraint set and horizon determine whether the trade is feasible.

Reading and a quick check

Use a recent anomaly or limits-to-arbitrage application and ask students to list the trade’s fundamental, financing and timing risks. Quick check: “If the price is wrong, why can it become more wrong first?”

Where it leads

With market mechanisms in place, the course can return to the individual investor and examine real portfolio choice, trading and household finance.

Central question: How do biases, beliefs and goals alter portfolio construction, trading, saving and advice at the household level?

This concept turns the earlier mechanisms into portfolio decisions. Students compare the normative portfolio benchmark with observed concentration, under-diversification, trading, disposition, home bias and goal-based accounts. The point is not to attribute every deviation to psychology but to identify which decisions are best explained by preferences, constraints, beliefs or behavioural frictions.

What this concept covers

• Behavioural portfolio theory and goal-based layers.
• Disposition effect and reference-dependent selling.
• Under-diversification, familiarity and home bias.
• Trading frequency, attention and overconfidence.
• Advice, defaults and interventions that improve household decisions.

Learning outcomes

By the end of this concept, students should be able to:

• Diagnose behavioural and non-behavioural drivers of a household portfolio.
• Evaluate a sell/hold decision without using purchase price as the only criterion.
• Compare a model portfolio with an observed allocation and explain the gap.
• Recommend a process-level intervention rather than a generic “be less biased” instruction.

Supports Course Outcomes 3, 8 and 10.

How to teach it

• Begin with mean-variance/CAPM as the normative baseline.
• Ask students to explain each portfolio deviation using at least two competing mechanisms.
• Use pre-commitment rebalancing rules and total-portfolio reporting as debiasing tools.
• Run the Portfolio Management Simulation only after students know the benchmark and use the debrief to surface behaviour under changing news.

Case-style example

Ellis Family Office targets 60% global equities and 40% bonds. After a technology rally, equities rise to 72% and technology reaches 28% of the full portfolio. The investment committee wants to keep the winners because “selling now would cut the upside,” while its written rule requires rebalancing above a 5-point drift. Students calculate the trades needed to return to 60/40, then defend either disciplined rebalancing or an evidence-based policy change. Students discover the difference between conviction and post-hoc justification.

Where students struggle

Students can assume a diversified benchmark is always personally optimal. Household constraints, taxes, human capital, liquidity needs and preferences matter alongside behavioural mechanisms.

Reading and a quick check

Assign Barber et al. (2022) on attention-induced trading or Giglio et al. (2021) on beliefs and portfolios. Quick check: ask what evidence would distinguish a deliberate concentrated strategy from overconfidence.

Where a simulation fits

The Portfolio Management Simulation is the strongest applied fit on this page. It teaches CAPM, covariance, diversification, mean-variance optimisation, Alpha, Sharpe Ratio and rebalancing rather than behavioural biases directly. For Behavioural Finance, use it as a decision environment: pre-register each team’s strategy, then debrief whether later trades reflected mandate-consistent updating, recency, salience, disposition-like attachment or overconfidence. Behavioural interpretation must come from the teaching design, not from a claim that the simulation itself measures bias.

Where it leads

The same behavioural mechanisms influence managers, founders, investors and capital-market intermediaries. The final concept broadens the lens to corporate and entrepreneurial finance and then asks how decisions can be designed better.

Central question: How do managerial, founder and investor psychology affect financing and corporate decisions, and how can process design improve judgement?

Behavioural finance is not only about retail investors. Managerial overconfidence can affect investment and financing, founders can anchor on valuation and control, and advisors can frame alternatives strategically. This final concept integrates behavioural corporate finance with debiasing, choice architecture and governance, giving students a practical capstone rather than ending with another list of biases.

What this concept covers

• Managerial and founder overconfidence in investment and financing.
• Anchoring, framing and motivated reasoning in valuations and negotiations.
• Escalation of commitment and sunk-cost effects.
• Choice architecture, pre-mortems, checklists, independent estimates and decision logs.
• Ethical boundaries between helpful design and manipulation.

Learning outcomes

By the end of this concept, students should be able to:

• Apply behavioural mechanisms to a corporate or funding decision.
• Evaluate how role incentives and framing influence negotiation positions.
• Design a debiasing process with observable decision checkpoints.
• Defend an intervention against ethical and governance concerns.

Supports Course Outcomes 5, 9 and 10.

How to teach it

• Use a startup funding negotiation to make anchoring and role incentives visible.
• Require private valuations before teams see counterparty positions.
• Run a pre-mortem before a major capital decision.
• Ask students to distinguish a nudge that improves deliberation from one designed primarily to increase activity.

Case-style example

Vertex Robotics needs £4 million. Founders anchor on a £20 million pre-money valuation because a competitor raised at that level; a VC offers £14 million pre-money with a 12% option pool and one board seat. Students calculate founder and investor ownership before and after the round, then negotiate two packages with equivalent headline economics but different control terms. Founders may rationally value control; investors may rationally demand protection. Students discover how anchors and frames interact with legitimate strategic preferences.

Where students struggle

Students may label hard bargaining as bias. Role incentives, bargaining power and preferences can explain disagreement without psychology. Behavioural claims should identify a decision-process mechanism beyond the outcome.

Reading and a quick check

Use the Startup Funding materials as an applied bridge and Statman on correcting errors. Quick check: ask students to design one pre-commitment rule, one independent-estimate step and one post-decision review for a capital-allocation committee.

Where a simulation fits

The Startup Funding Simulation fits as an applied negotiation environment after students study anchoring, overconfidence, framing and motivated reasoning. Students negotiate valuation, ownership, dilution, option-pool treatment, governance and investor protections. The platform records the negotiated package; the lecturer can compare teams’ opening anchors, concessions and final agreements, but should not infer a psychological bias from an outcome without supporting process evidence. The IPO Simulation is an optional extension for attention, optimism, investor demand and pricing discussions.

Where it leads

The course closes by integrating investor behaviour, market outcomes and decision design: students should be able to identify a mechanism, test it against alternatives and recommend a defensible financial process.

10-week course structure

Compress by pairing related mechanisms, not by removing the benchmark, market-mechanism or application stages.

14-week course structure

Use the additional time to separate risky-choice mechanisms, deepen empirical testing and give more space to simulation reflection and oral defence.

The Portfolio Management Simulation is the closest applied fit for Behavioural Finance, while remaining a portfolio-theory simulation at its core. Teams manage one of two client mandates - Hedge Fund or Pension Fund - across 25 global companies. They use CAPM and mean-variance optimisation to estimate expected return, beta, covariance, volatility and portfolio weights, then construct, trade and rebalance as new information arrives.

Why it fits here

By Session 8, students already know the normative portfolio benchmark and the behavioural mechanisms that can affect updating. That allows the debrief to compare a pre-registered strategy with later trades. The lecturer can ask whether a rebalance was supported by changed information and mandate objectives, or whether recency, salience, overconfidence or attachment to a prior position may have influenced the process.

What students do

• Watch the role and market briefing and confirm whether they manage a Hedge Fund or Pension Fund mandate.
• Download historical market data and use the supplied Excel model to calculate and interpret expected return, beta, covariance, volatility and portfolio weights.
• Choose an initial set of securities, allocations and cash position, with borrowing available where the role permits it.
• Review new company and market information over later quarters and decide whether to hold, buy, sell or rebalance.
• Monitor portfolio return and volatility; Hedge Fund teams focus on Alpha while Pension Fund teams focus on Sharpe Ratio.
• Review trade history and final results against teams with the same mandate during reflection.

Recommended course placement

• After Session 8, once students know diversification, CAPM/mean-variance ideas and the behavioural mechanisms used in the debrief.
• Across two classes if the course wants pre-registration, live decisions and a separate behavioural reflection.
• Before an individual memo that asks students to distinguish outcome luck from process quality.

Knowledge students should have before the simulation

• Basic CAPM, Sharpe Ratio and mean-variance optimisation.
• Expected return, beta, covariance, volatility and diversification.
• Behavioural concepts such as recency, salience, overconfidence and disposition-like attachment, taught separately from the simulation.

Delivery format

Suggested debrief questions

• Which trade was most clearly supported by new information?
• Where did a recent outcome receive more weight than the pre-registered process justified?
• Did the team change risk because the mandate changed, the evidence changed or confidence changed?
• Which losing position was hardest to sell, and what evidence justified holding it?
• Would the same process remain defensible if the realised return had been worse?
• What part of the optimiser was treated as a benchmark, and what part was overridden by judgement?

The Startup Funding Simulation places students on both sides of a seed-funding negotiation. One Startup team is paired with one VC team. Each side analyses the same financing problem from different role objectives, prepares an opening term sheet and negotiates funding amount, pre-money and post-money valuation, ownership, founder dilution, option-pool treatment, board representation, consent rights and investor protections.

Why it fits here

The negotiation makes valuation anchors, framing, motivated reasoning, role incentives and confidence observable enough to discuss without claiming that any final term proves a bias. The strongest design is to collect each team's independent opening rationale before the counterparty position is revealed, then compare how evidence, bargaining and concessions changed the final package.

What students do

• Receive a role-specific briefing as a Startup or VC team and identify the objectives and constraints they need to protect.
• Work independently through company materials and the funding ask, then build an opening term sheet.
• See the counterparty term sheet for the first time and classify gaps across economics, ownership/control and investor protections.
• Set a negotiation strategy by distinguishing essential terms, tradable terms and potential deal breakers.
• Negotiate provision by provision; the VC side enters negotiated terms and the Startup side records acceptance before a provision is final.
• Finish with a complete agreement or a recorded set of blockers, then compare valuation, ownership, dilution, governance and protection outcomes across the cohort.

Recommended course placement

• After Session 10, once anchoring, framing, overconfidence and behavioural corporate finance have been taught.
• Use independent pre-negotiation valuations and decision logs if behavioural interpretation is an explicit learning objective.
• Follow with an individual memo that separates bargaining preferences from evidence of a behavioural mechanism.

Knowledge students should have before the simulation

• No formal startup-finance prerequisite is required by the live simulation page.
• For this course placement, students should nevertheless know pre-money/post-money valuation, ownership and dilution well enough to distinguish finance mechanics from behavioural interpretation.
• Students should understand anchoring, framing, motivated reasoning and role-based incentives.

Delivery format

Suggested debrief questions

• What was your opening anchor, and what evidence supported it before you saw the other side?
• Which concession changed because of new information and which changed because of negotiation pressure?
• Did a high headline valuation distract the team from control or downside terms?
• Where did role objectives create legitimate disagreement that should not be called bias?
• Which term did the team defend most confidently, and was that confidence calibrated to the evidence?
• What process would you change to make a second negotiation less vulnerable to anchoring or motivated reasoning?

Grading with the Portfolio Management Simulation

The simulation records team-level portfolio construction, trades and performance metrics. Grade the quality of the financial process and behavioural reflection rather than final return alone.

• Ex-ante mandate and strategy: 20%
• Use of CAPM, diversification and risk evidence: 20%
• Rationale for rebalancing decisions: 25%
• Behavioural debrief with competing explanations: 25%
• Clarity and declared AI/tool use: 10%

Grading with the Startup Funding Simulation

The simulation records opening and final funding terms, negotiation status and comparative outcomes. Grade role-consistent reasoning, trade-offs and the evidence for any behavioural interpretation.

• Opening valuation and term rationale: 20%
• Understanding of ownership, dilution and governance: 20%
• Negotiation strategy and defensible concessions: 25%
• Behavioural analysis of anchors/frames versus legitimate role preferences: 25%
• Individual reflection or oral defence: 10%

Grading with the IPO Simulation

Use the IPO activity selectively. Students work through valuation, roadshow, price indications, firm bidding and allocation. A Behavioural Finance assessment should focus on how optimism, attention and demand were interpreted, while preserving the finance logic of pricing and allocation.

• Valuation and pricing rationale: 25%
• Interpretation of investor demand and information: 25%
• Evidence-based discussion of optimism/attention without overclaiming causality: 25%
• Allocation or market-design recommendation: 15%
• Individual defence: 10%

Moderation and free-riding: team outputs show what a group decided, not who contributed which argument. Add an individual memo, viva or reflection and use local moderation rules for material contribution concerns. Do not let leaderboard or negotiated outcome substitute for academic judgement.