.png){kind=icon}
.png){kind=icon}
Return
Learning to be overconfident
.png){kind=icon}
PRE
AI
DOI：10.1093/rfs/14.1.1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We develop a multiperiod market model describing both the process by which traders learn about their ability and how a bias in this learning can create overconfident traders. A trader in our model initially does not know his own ability. He infers this ability from his successes and failures. In assessing his ability the trader takes too much credit for his successes. This leads him to become overconfident. A trader's expected level of overconfidence increases in the early stages of his career. Then, with more experience, he comes to better recognize his own ability. The patterns in trading volume, expected profits, price volatility, and expected prices resulting from this endogenous overconfidence are analyzed.
Keywords:
FINANCIAL-MARKETS
TRADERS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.4
Papers:
2.8K
Citations:
3.0W
Organization
No organization information available