Something I feel like is missing from this post is what does LMH predict about markets, or what is the evidence of LMH? Like what is an example of an empirical observation about the stock market that contradicts EMH, but is consistent with LMH? The “Consequence” section is very abstract, I’d be interested in hearing something more concrete. For example, can LMH explain the result from De Bondt & Thaler (1985), “Does the Stock Market Overreact?”
On the contradiction point: LMH isn’t looking for a contradiction from EMH. More so, it’s claiming that when you model friction, cognitive cost, etc. realistic market parameters correctly, the most efficient markets that emerge from the real world will still be, at best, lazy.
The abstract research thesis here is that LMH theory should give us information about which directions to extend EMH-based economical models towards, to make them more accurate about real world markets.
(Meta: I considered giving more examples in the original post, but I felt like the terms I use are very easy to overload. I aim to write a post that is primarily about examples in the future—something like “Lazy strategies” that talks about instances of lazy decisionmaking in the real world, and it’s consequences.)
Skimming the paper: > While the overreaction hypothesis has considerable a priori appeal, the obvious question to ask is: How does the anomaly survive the process of arbitrage? There is really a more general question here. What are the equilibria conditions for markets in which some agents are not rational in the sense that they fail to revise their expectations according to Bayes’ rule? Russell and Thaler 24 address this issue. They conclude that the existence of some rational agents is not sufficient to guarantee a rational expectations equilibrium in an economy with some of what they call quasi-rational agents. (The related question of market equilibria with agents having heterogeneous expectations is investigated by Jarrow 13.) While we are highly sensitive to these issues, we do not have the space to address them here. Instead, we will concentrate on an empirical test of the overreaction hypothesis.
The paper explicitly sets aside the question of why this inefficiency persists. LMH is an attempt to explain why this inefficiency makes sense from the perspective of individual economic agents, why the behavior that generates it is generally adaptive for the agent, even when it loses them money in markets specifically.
Exploring: Claude pointed me into the direction of McLean & Pontiff: Does Academic Research Destroy Stock Return Predictability? (2015). Then I came across McLean, Pontiff, Reilly: Taking sides on return predictability (2025), which states:
> We assess how nine different categories of market participants trade relative to a comprehensive forecasted-return variable based on 193 predictors. Firms and short sellers tend to be the smart money—both sell stocks with low-forecasted returns, and their trades predict returns in the intended direction. Retail investors trade against forecasted returns. Retail investors’ and institutions’ trades predict returns opposite to the intended direction. This poor trading performance is driven by trades in stocks with either high- or low-forecasted returns. The forecasted-return variable predicts returns more strongly in stocks with more intense retail trading, consistent with retail investors exacerbating mispricing.
Of course, cherry-picking is easy. But this is the kind of result that seems consistent with LMH—active retail investors hold concentrated positions (which implies attention and amplifies reactivity), and are focused on more publicly available information than non-retail investors (since they don’t have the kind of investment in their own market modelling that non-retail money has), so they focus on local opportunities. The retail investors who do the most market actions are eager. Eager-local loses to eager-global (institutional investing, who have better, but more expensive models), and to lazy-local (investing in index funds / fire and forget investing).
Just to make it concrete what LMH contributes, besides terminology: I think behavioural economics would predict that “retail investors are more impulsive than institutional investors, therefore they will overreact, having worse returns”. The LMH addition here are these claims: - “It is adaptive for an economic agent to pay more attention and be more reactive in places where a lot of their portfolio has been invested.” (This is clearly rational regarding housing or employment!) - “It is adaptive for an agent to act more frequently in environments with fast feedback loops.” (And in environments that are adversarial over this, such as gambling or markets where HFT and better models than yours exist, this is a losing strategy.)
The pattern in both cases: the eager-local strategy is generally adaptive, and markets are one of the specific domains where it isn’t. Behavioural economics documents this category of failures. LMH aims to explain why the failing strategy was originally selected for.
Something I feel like is missing from this post is what does LMH predict about markets, or what is the evidence of LMH? Like what is an example of an empirical observation about the stock market that contradicts EMH, but is consistent with LMH? The “Consequence” section is very abstract, I’d be interested in hearing something more concrete. For example, can LMH explain the result from De Bondt & Thaler (1985), “Does the Stock Market Overreact?”
On the contradiction point: LMH isn’t looking for a contradiction from EMH. More so, it’s claiming that when you model friction, cognitive cost, etc. realistic market parameters correctly, the most efficient markets that emerge from the real world will still be, at best, lazy.
The abstract research thesis here is that LMH theory should give us information about which directions to extend EMH-based economical models towards, to make them more accurate about real world markets.
(Meta: I considered giving more examples in the original post, but I felt like the terms I use are very easy to overload. I aim to write a post that is primarily about examples in the future—something like “Lazy strategies” that talks about instances of lazy decisionmaking in the real world, and it’s consequences.)
Skimming the paper:
> While the overreaction hypothesis has considerable a priori appeal, the obvious question to ask is: How does the anomaly survive the process of arbitrage? There is really a more general question here. What are the equilibria conditions for markets in which some agents are not rational in the sense that they fail to revise their expectations according to Bayes’ rule? Russell and Thaler 24 address this issue. They conclude that the existence of some rational agents is not sufficient to guarantee a rational expectations equilibrium in an economy with some of what they call quasi-rational agents. (The related question of market equilibria with agents having heterogeneous expectations is investigated by Jarrow 13.) While we are highly sensitive to these issues, we do not have the space to address them here. Instead, we will concentrate on an empirical test of the overreaction hypothesis.
The paper explicitly sets aside the question of why this inefficiency persists. LMH is an attempt to explain why this inefficiency makes sense from the perspective of individual economic agents, why the behavior that generates it is generally adaptive for the agent, even when it loses them money in markets specifically.
Exploring:
Claude pointed me into the direction of McLean & Pontiff: Does Academic Research Destroy Stock Return Predictability? (2015). Then I came across McLean, Pontiff, Reilly: Taking sides on return predictability (2025), which states:
> We assess how nine different categories of market participants trade relative to a comprehensive forecasted-return variable based on 193 predictors. Firms and short sellers tend to be the smart money—both sell stocks with low-forecasted returns, and their trades predict returns in the intended direction. Retail investors trade against forecasted returns. Retail investors’ and institutions’ trades predict returns opposite to the intended direction. This poor trading performance is driven by trades in stocks with either high- or low-forecasted returns. The forecasted-return variable predicts returns more strongly in stocks with more intense retail trading, consistent with retail investors exacerbating mispricing.
Of course, cherry-picking is easy. But this is the kind of result that seems consistent with LMH—active retail investors hold concentrated positions (which implies attention and amplifies reactivity), and are focused on more publicly available information than non-retail investors (since they don’t have the kind of investment in their own market modelling that non-retail money has), so they focus on local opportunities. The retail investors who do the most market actions are eager. Eager-local loses to eager-global (institutional investing, who have better, but more expensive models), and to lazy-local (investing in index funds / fire and forget investing).
Just to make it concrete what LMH contributes, besides terminology: I think behavioural economics would predict that “retail investors are more impulsive than institutional investors, therefore they will overreact, having worse returns”.
The LMH addition here are these claims:
- “It is adaptive for an economic agent to pay more attention and be more reactive in places where a lot of their portfolio has been invested.” (This is clearly rational regarding housing or employment!)
- “It is adaptive for an agent to act more frequently in environments with fast feedback loops.” (And in environments that are adversarial over this, such as gambling or markets where HFT and better models than yours exist, this is a losing strategy.)
The pattern in both cases: the eager-local strategy is generally adaptive, and markets are one of the specific domains where it isn’t. Behavioural economics documents this category of failures. LMH aims to explain why the failing strategy was originally selected for.