We present a model of market competition in which consumers' attention is drawn to the products' most salient attributes. Firms compete for consumer attention via their choices of quality and price. Strategic positioning of a product affects how all other products are perceived. With this attention externality, depending on the cost of producing quality some markets exhibit "commoditized" price salient equilibria, while others exhibit "de-commoditized" quality salient equilibria. When the costs of quality change, innovation can lead to radical shifts in markets, as in the case of decommoditization of the coffee market by Starbucks. In the context of financial innovation, the model generates the phenomenon of "reaching for yield".

Building on a textbook description of associative memory (Kahana 2012), we present a model of choice in which a choice option cues recall of similar past experiences. Memory shapes valuation and decisions in two ways. First, recalled experiences form a norm, which serves as an initial anchor for valuation. Second, salient quality and price surprises relative to the norm lead to large adjustments in valuation. The model unifies many well-documented choice puzzles, including the attribution and projection biases, inattention to hidden attributes, background contrast effects, and context-dependent willingness to pay. Unifying these puzzles on the basis of selective memory and attention to surprise yields multiple new predictions.

We present a theory of choice among lotteries in which the decision maker's attention is drawn to (precisely defined) salient payoffs. This leads the decision maker to a context-dependent representation of lotteries in which true probabilities are replaced by decision weights distorted in favor of salient payoffs. By specifying decision weights as a function of payoffs, our model provides a novel and unified account of many empirical phenomena, including frequent risk-seeking behavior, invariance failures such as the Allais paradox, and preference reversals. It also yields new predictions, including some that distinguish it from prospect theory, which we test.

We present a simple model of asset pricing in which payoff salience drives investors' demand for risky assets. The key implication is that extreme payoffs receive disproportionate weight in the market valuation of assets. The model accounts for several puzzles in finance in an intuitive way, including preference for assets with a chance of very high payoffs, an aggregate equity premium, and countercyclical variation in stock market returns.

We provide a novel account of experimental evidence for the endowment effect using the salience mechanism (Bordalo, Gennaioli, and Shleifer, 2011). The two-stage procedure implemented in experiments implies that the endowed good and other goods are evaluated in different contexts. We describe conditions under which the standard effect occurs, but also account for recent evidence such as a reverse endowment effect for bads and a role for reference prices in modulating the WTA-WTP gap.

We present a model of credit cycles arising from diagnostic expectations—a belief formation mechanism based on Kahneman and Tversky's representativeness heuristic. Diagnostic expectations overweight future outcomes that become more likely in light of incoming data. The expectations formation rule is forward looking and depends on the underlying stochastic process, and thus is immune to the Lucas critique. Diagnostic expectations reconcile extrapolation and neglect of risk in a unified framework. In our model, credit spreads are excessively volatile, overreact to news, and are subject to predictable reversals. These dynamics can account for several features of credit cycles and macroeconomic volatility.

We present a theory of context-dependent choice in which a consumer’s attention is drawn to salient attributes of goods, such as quality or price. An attribute is salient for a good when it stands out among the good’s attributes, relative to that attribute’s average level in the choice set (or generally, the evoked set). Consumers attach disproportionately high weight to salient attributes and their choices are tilted toward goods with higher quality/price ratios. The model accounts for a variety of disparate evidence, including decoy effects, context-dependent willingness to pay, and large shifts in demand in response to price shocks.

We address the joint hypothesis problem in cross-sectional asset pricing by using measured analyst expectations of earnings growth. We construct a firm-level measure of Expectations Based Returns (EBRs) that uses analyst forecast errors and revisions and shuts down any cross-sectional differences in required returns. We obtain three results. First, variation in EBRs accounts for a large chunk of cross-sectional return spreads in value, investment, size, and momentum factors. Second, time variation in these spreads is predictable from that in EBRs, holding constant scaled price variables (as proxies for time varying required returns). Third, firm characteristics often seen as capturing risk premia predict disappointment of expectations and low EBRs. Overall, return spreads typically attributed to exotic risk factors are explained by predictable movements in non-rational expectations of firms’ earnings growth.

We introduce diagnostic expectations into a standard setting of price formation in which investors learn about the fundamental value of an asset and trade it. We study the interaction of diagnostic expectations with learning from prices and speculation (buying for resale). With diagnostic (but not with rational) expectations, these mechanisms lead to price paths exhibiting three phases: initial underreaction, then overshooting (the bubble), and finally a crash. With learning from prices, the model generates price extrapolation as a by-product of beliefs about fundamentals, lasting only as the bubble builds up. When investors speculate, even mild diagnostic distortions generate substantial bubbles.

We revisit La Porta's finding that returns on stocks with the most optimistic analyst long‐term earnings growth forecasts are lower than those on stocks with the most pessimistic forecasts. We document the joint dynamics of fundamentals, expectations, and returns of these portfolios, and explain the facts using a model of belief formation based on the representativeness heuristic. Analysts forecast fundamentals from observed earnings growth, but overreact to news by exaggerating the probability of states that have become more likely. We find support for the model's predictions. A quantitative estimation of the model accounts for the key patterns in the data.