The Physical Foundation of Human Mind and a New Theory of Investment
<|reference_start|>The Physical Foundation of Human Mind and a New Theory of Investment: This paper consists of two parts. In the first part, we develop a new information theory, in which it is not a coincidence that information and physical entropy share the same mathematical formula. It is an adaptation of mind to help search for resources. We then show that psychological patterns either reflect the constraints of physical laws or are evolutionary adaptations to efficiently process information and to increase the chance of survival in the environment of our evolutionary past. In the second part, we demonstrate that the new information theory provides the foundation to understand market behavior. One fundamental result from the information theory is that information is costly. In general, information with higher value is more costly. Another fundamental result from the information theory is that the amount of information one can receive is the amount of information generated minus equivocation. The level of equivocation, which is the measure of information asymmetry, is determined by the correlation between the source of information and the receiver of information. In general, how much information one can receive depends on the background knowledge of the receiver. The difference in cost different investors are willing to pay for information and the difference in background knowledge about a particular information causes the heterogeneity in information processing by the investment public, which is the main reason of the price and volume patterns observed in the market. Many assumptions in some of the recent models on behavioral finance can be derived naturally from this theory.<|reference_end|>
@article{chen2005the,
    title={The Physical Foundation of Human Mind and a New Theory of Investment},
    author={Jing Chen},
    journal={arXiv preprint arXiv:cs/0512027},
    year={2005},
    archivePrefix={arXiv},
    eprint={cs/0512027},
    primaryClass={cs.IT math.IT}
}