An experimental test of efficient coding shows that experience tends to crowd out the effect of descriptive information on optimal allocation of coding resources, from @caryfrydman and @lawrence_j_jin https://t.co/hmF1IW9427

A model of the law of small numbers reconciles the disposition effect with return extrapolation and makes new predictions about investor behavior, confirmed empirically, from @lawrence_j_jin and @peng_cameron https://t.co/lsG4GbOMEp

Michael Jensen’s top paper has 130,000 citations. A towering figure in corporate finance; he deserved a Nobel. It is very sad to hear of his passing🙏

Although I have only seen Kahneman present once when I was a graduate student at Yale, his research has had a profound impact on my academic career---the majority of my work relates to biased beliefs and non-traditional preferences, discovered by Kahneman and Tversky. R.I.P.

This is very exciting. Great move for Berkeley!

The probability weighting function from prospect theory is malleable in the manner predicted by efficient coding, from @caryfrydman and @lawrence_j_jin https://t.co/uOZmPKLbBY

Thank you, @CFCamerer, for constantly promoting our work and providing us with great comments/insights.

Prospect Theory's famous “S-shaped” value function is a cornerstone of behavioural economics. Surprisingly, more than 40 years after its inception, there seem to be a lot of remaining puzzles about it. Or... perhaps not anymore. A 🧵that may change your view on Prospect Theory.

A lot to be done in cognitive economics; I view this as part of the third generation of behavioral models (cognitive noise, memory, attention, and learning algorithms).

Finally set up a new website: https://t.co/u3y8NjBn2F My paper on reinforcement learning (with N. Barberis) is finally ready to be posted online: https://t.co/uEZI46bVZ0 Comments are very welcome!

Hope it goes very well!🙏

I am sharing a "gofundme" link for people interested in helping the families who lost their loved ones in the recent Texas school shooting. Texas Elementary School Shooting Victims Fund

In July 2022, I will be joining Cornell’s Johnson College of Business as an Associate Professor of Finance. I look forward to working with a superb set of new colleagues; I am also grateful to all the support I have received from Caltech.❤️🙏

#QJE Feb 2022 #4, “Efficient Coding and Risky Choice” by Frydman (@caryfrydman) and Jin (@lawrence_j_jin),

An ambitious move by Harvard. My work with Barberis on reinforcement learning is also in this direction. I’m very excited about developing this line of research!

Glad that this is out! Return extrapolation not only explains facts about asset prices and return expectations, but also facts about cash-flow expectations. Question remains: what are the deeper mechanisms for forming expectations about different economic variables?

Katrin is a good scholar. Please take a look at her application if you are hiring:-)

An open question is: with an alternative theory that implements loss aversion, can it lead to different conclusions? For instance, can a different model, calibrated to experimental findings of a high degree of loss aversion, work "better" with finance applications...? 5/5

All this has led me to think that the degree of loss aversion in financial markets is lower than previously thought. However, observations from @sdpbht point to another possibility: the TK (1992) implementation of loss aversion itself might have some issues. 4/5

The disposition effect is too strong in realization utility models if lambda = 2.25; CPT has a worse performance when explaining anomalies in the cross-section if lambda = 2.25 (compared to 1.5); recent experimental studies also suggest a lower estimate of lambda. 3/5