Learning from others,reacting, and market quality |
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| Institution: | 1. Department of Clinical Embryology, Kasturba Medical College, Manipal, Manipal University, Manipal, 576 104, Karnataka, India;2. Department of Anatomy, Kasturba Medical College, Manipal University, Manipal, 576 104, Karnataka, India;3. Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal University, Manipal, 576 104, Karnataka, India;1. Division of Business Administration, College of Business, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759, South Korea;2. School of Business Administration, College of Business and Economics, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, South Korea;3. Department of Business Management, Osan University, 45 Cheonghak-ro, Osan-si, Gyeonggi-do 18119, South Korea;4. Price College of Business, University of Oklahoma, 307 West Brooks, Norman, OK 73019, United States;1. School of Environmental Science and Engineering, Fujian Normal University, Fuzhou, 350007, Fujian Province, China;2. Environmental Contaminants Group, Future Industries Institute, University of South Australian, Mawson Lakes, SA, 5095, Australia;1. Department of Statistics, The Chinese University of Hong Kong, Hong Kong;2. Department of Mathematics and Statistics, School of Decision Science, The Hang Seng University of Hong Kong, Hong Kong;3. School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia |
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| Abstract: | Traders pay attention to one another but are unable to perfectly deduce each others’ beliefs from transactions alone. This explains why markets are hard to beat and also why trading occurs at all. Even when traders react rationally to the actions of others, they cannot arrive easily at a common posterior assessment of value. We model a realistic market composed of traders who combine their own private information with rational learning about the information possessed by others. We compare phenomena in this market with an otherwise identical market populated by traders who receive the same private information but ignore other traders. Using simulation to engender greater realism, we find that learning usually reduces volatility, increases the accuracy of the market price as a forecast of value, reduces trading volume, and decreases the prevalence of bubbles. However, for some combinations of market conditions, learning can have the opposite effect. The marginal influences of eight different market conditions, ranging from information heterogeneity through resource diversity, are estimated. Prices, volatility, volume, and bubbles exhibit subtle and complex responses to market conditions. |
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