The paper "The Noise Trader Approach to Finance" by Andrei Shleifer and Lawrence H. Summers challenges the efficient markets hypothesis (EMH) by introducing the concept of "noise traders." Noise traders are investors whose demand for risky assets is influenced by beliefs or sentiments that are not fully justified by fundamental news. The authors argue that arbitrage, defined as trading by fully rational investors, is risky and limited, leading to changes in investor sentiment affecting security returns. The paper highlights two main assumptions: first, some investors are not fully rational and their demand for risky assets is influenced by non-fundamental factors; second, arbitrage is risky and limited. These assumptions imply that changes in investor sentiment are not fully counteracted by arbitrageurs, affecting security returns. The authors provide three main arguments for their noise trader approach: 1. **Theoretical Models**: Models with limited arbitrage are more tractable and plausible than those with perfect arbitrage. 2. **Description of Financial Markets**: The approach better explains anomalies and broad features of financial markets, such as trading volume and investment strategies. 3. **Testable Implications**: The approach yields new and testable implications about asset prices, some of which have been validated by data. The paper also discusses the limits of arbitrage, including fundamental risk and the unpredictability of future resale prices. It provides evidence that arbitrage does not completely counter responses of prices to fluctuations in uninformed demand. Examples include the inclusion of new stocks into the S&P 500 index and the January effect, where small stocks outperform market indices in January. The authors further explore the role of investor sentiment, noting that shifts in demand can be driven by pseudo-signals, inflexible trading strategies, and popular models. These shifts can be correlated across noise traders, leading to aggregate demand shifts. The paper argues that noise traders can earn higher expected returns despite their risk-taking, and that arbitrageurs may even benefit from positive feedback trading. Finally, the paper discusses the implications of unpredictable investor sentiment and positive feedback trading. It suggests that assets subject to unpredictable swings in investor sentiment should yield higher average returns than similar assets not subject to such whims. The paper also explains the pricing of closed-end mutual funds and the overreaction of stock prices to news. In conclusion, the paper presents an alternative to the efficient markets paradigm, emphasizing the roles of investor sentiment and limited arbitrage in determining asset prices. It argues that this approach has significant explanatory power and provides new testable implications about security returns.The paper "The Noise Trader Approach to Finance" by Andrei Shleifer and Lawrence H. Summers challenges the efficient markets hypothesis (EMH) by introducing the concept of "noise traders." Noise traders are investors whose demand for risky assets is influenced by beliefs or sentiments that are not fully justified by fundamental news. The authors argue that arbitrage, defined as trading by fully rational investors, is risky and limited, leading to changes in investor sentiment affecting security returns. The paper highlights two main assumptions: first, some investors are not fully rational and their demand for risky assets is influenced by non-fundamental factors; second, arbitrage is risky and limited. These assumptions imply that changes in investor sentiment are not fully counteracted by arbitrageurs, affecting security returns. The authors provide three main arguments for their noise trader approach: 1. **Theoretical Models**: Models with limited arbitrage are more tractable and plausible than those with perfect arbitrage. 2. **Description of Financial Markets**: The approach better explains anomalies and broad features of financial markets, such as trading volume and investment strategies. 3. **Testable Implications**: The approach yields new and testable implications about asset prices, some of which have been validated by data. The paper also discusses the limits of arbitrage, including fundamental risk and the unpredictability of future resale prices. It provides evidence that arbitrage does not completely counter responses of prices to fluctuations in uninformed demand. Examples include the inclusion of new stocks into the S&P 500 index and the January effect, where small stocks outperform market indices in January. The authors further explore the role of investor sentiment, noting that shifts in demand can be driven by pseudo-signals, inflexible trading strategies, and popular models. These shifts can be correlated across noise traders, leading to aggregate demand shifts. The paper argues that noise traders can earn higher expected returns despite their risk-taking, and that arbitrageurs may even benefit from positive feedback trading. Finally, the paper discusses the implications of unpredictable investor sentiment and positive feedback trading. It suggests that assets subject to unpredictable swings in investor sentiment should yield higher average returns than similar assets not subject to such whims. The paper also explains the pricing of closed-end mutual funds and the overreaction of stock prices to news. In conclusion, the paper presents an alternative to the efficient markets paradigm, emphasizing the roles of investor sentiment and limited arbitrage in determining asset prices. It argues that this approach has significant explanatory power and provides new testable implications about security returns.