на главную поиск contacts

The Noise Trader Approach to Finance

Опубликовано на портале: 13-05-2005
Journal of Economic Perspectives. 1990.  Vol. 4. No. 2. P. 19-33. 
This paper reviews an alternative to the efficient markets approach that we and others have recently pursued. Our approach rests on two assumptions. First, some investors are not fully rational and their demand for risky assets is affected by their beliefs or sentiments that are not fully justified by fundamental news. Second, arbitrage - defined as trading by fully rational investors not subject to such sentiment - is risky and therefore limited. The two assumption together imply that changes in investors sentiment are not fully countered by arbitrageurs and so affect security returns. We argue that this approach to financial markets is in many ways superior to the efficient markets paradigm.
Our case for the noise trader approach is threefold. First, theoretical models with limited arbitrage are both tractable and more plausible than models with perfect arbitrage. The efficient markets hypithesis obtains only as an extreme case of perfect riskless arbitrage that unlikely to apply in practice. Second, the investors sentiment/ limited arbitrage approach yields a more accurate description of financial markets than the efficient markets paradigm. The approach not only explains the available anomalies, but also readly explains board features of financial markets such as trading volume and actual investment strategies. Third, and most importantly, this approach yields new and testable implications about asset prices, some of which have been proved to be consistent with the data. It is absolutely not true that introducing a degree of irrationality of some investors into models of financial markets "eliminates all discipline and can explain anything".

текст статьи в формате pdf на сайте JSTOR:
Ключевые слова

См. также:
Рустем Махмутович Нуреев, Е.В. Маркин
TERRA ECONOMICUS. 2009.  Т. 7. № 3. С. 10-28. 
Geert Bekaert, Campbell R. Harvey, Christian Lundblad
NBER Working Paper Series. 2001.  w8245.