Adaptive Market Hypothesis – Definition

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Adaptive Market Hypothesis Definition

Adaptive market hypothesis is a model which combines the principles of the effective market hypothesis with those of behavioral finance. This theory was suggested by MIT Professor Andrew Lo in 2004. The behavioral finance was established after an observation was made which concluded that people are not rational as the economic and market theories assume.

Professor Lo claims that there are some behaviors exhibited by the investors such as loss avoidance, boldness, and overreaction which agrees with the evolutionary theories of human behavior such as natural selection, adaptations, and competition. Also, fear and greed which are seen as the main factor why these investors fail to think rationally are also determined by these evolutionary forces.

A Little More on What is an Adaptive Market Hypothesis

While efficient market hypothesis principles are irrational, behavioral finance principles are rational; the adaptive market hypothesis tries to reconcile the two. The hypothesis states that people make the best prediction on trial and error bases. For instance, an investor may come up with a certain strategy and use it, if the strategy succeeds, the investor is likely to use it again but if it fails, he is likely to try a different approach.

Efficient Market Hypothesis

The efficient market hypothesis was developed by Eugene Fama who claims that it is impossible to conquer the market since stocks always sell at their best fair price. This makes it difficult for the investors to sell stocks at inflated prices and also it is impossible to purchase low valued stock.

Generally, It is difficult to conquer the market by applying a stock selection or market selection and the only possible way an investor can use to conquer the market and expect higher returns is by chance.

Eugene Fama did a study with Kenneth French showed that the abnormal distribution of US mutual funds is the same as what was expected even when the fund managers have had no skills.

Behavioral Finance

Behavioral finance suggests a psychology-based approach which explains the differences in stock market such as the rise and falls of stock prices. The main aim of this theory is to understand why humans make specific financial choices. It claims that the structure of the market affects the individuals financial decision and overall the market consequences.

According to MIT Professor Lo, the Adaptive market hypothesis can be concluded in five main principles:

  • Humans are not rational or irrational always but they are biological units whose behaviors and features are based on evolutionary forces.
  • ¬†Humans have behavioral preferences resulting into substandard decisions. However, they are able to learn from their past experience.
  • Humans have the ability to think intellectually and predict the future based on their previous experience of environmental changes.
  • ¬†Financial market changes are determined by the way humans behave, adapt to each other and the way they adapt to the social, economic, political, cultural and natural environment where they live.
  • Survive is the most crucial factor that drives competition, adaptation, and innovation.

Example of Adaptive Market Hypothesis

In 2018, a study was done on the assessment of the adaptive market hypothesis in the Bitcoin market. The authors argued that there was a market efficiency difference which could not be explained by efficient market hypothesis only. The authors concluded that it was important to embrace the use of the adaptive market hypothesis in the evaluation of bitcoin.

Reference for ‚ÄúAdaptive Market Hypothesis‚ÄĚ ‚Äļ Investing ‚Äļ Financial Analysis…/the-adaptive-markets-hypothesis-a-financial-ecosystem…

Academics research on ‚ÄúAdaptive Market Hypothesis‚ÄĚ

The adaptive markets hypothesis: Market efficiency from an evolutionary perspective, Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. One of the most influential ideas in the past 30 years is the Efficient Markets Hypothesis, the idea that market prices incorporate all information rationally and instantaneously. However, the emerging discipline of behavioral economics and finance has challenged this hypothesis, arguing that markets are not rational, but are driven by fear and greed instead. Recent research in the cognitive neurosciences suggests that these two perspectives are opposite sides of the same coin. In this article I propose a new framework that reconciles market efficiency with behavioral alternatives by applying the principles of evolution – competition, adaptation, and natural selection – to financial interactions. By extending Herbert Simon’s notion of “satisficing” with evolutionary dynamics, I argue that much of what behavioralists cite as counterexamples to economic rationality – loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases – are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, the Adaptive Markets Hypothesis offers a number of surprisingly concrete implications for the practice of portfolio management.

Calendar effects, market conditions and the Adaptive Market Hypothesis: Evidence from long-run US data, Urquhart, A., & McGroarty, F. (2014). Calendar effects, market conditions and the Adaptive Market Hypothesis: Evidence from long-run US data. International Review of Financial Analysis, 35, 154-166. In this paper, we examine the Adaptive Market Hypothesis (AMH) through four well-known calendar anomalies in the Dow Jones Industrial Average from 1900 to 2013. We use subsample analysis as well as rolling window analysis to overcome difficulties with each method type of analysis. We also create implied investment strategies based on each calendar anomaly as well as determining which market conditions are more favourable to the calendar anomaly performance. The results show that all four calendar anomalies support the AMH, with each calendar anomaly’s performance varying over time. We also find that some of the calendar anomalies are only present during certain market conditions. Overall, our results suggest that the AMH offers a better explanation of the behaviour of calendar anomalies than the Efficient Market Hypothesis.

The adaptive markets hypothesis: evidence from the foreign exchange market, Neely, C. J., Weller, P. A., & Ulrich, J. M. (2009). The adaptive markets hypothesis: evidence from the foreign exchange market. Journal of Financial and Quantitative Analysis, 44(2), 467-488. We analyze the intertemporal stability of excess returns to technical trading rules in the foreign exchange market by conducting true, out-of-sample tests on previously studied rules. The excess returns of the 1970s and 1980s were genuine and not just the result of data mining. But these profit opportunities had disappeared by the early 1990s for filter and moving average rules. Returns to less-studied rules also have declined but have probably not completely disappeared. High volatility prevents precise estimation of mean returns. These regularities are consistent with the Adaptive Markets Hypothesis (Lo (2004)), but not with the Efficient Markets Hypothesis.

Adaptive market hypothesis: evidence from the REIT market, Zhou, J., & Lee, J. M. (2013). Adaptive market hypothesis: evidence from the REIT market. Applied Financial Economics, 23(21), 1649-1662. We tests two important implications for Real Estate Investment Trust (REIT) market efficiency from the adaptive markets hypothesis (Lo, 2004): first, market efficiency is not an all-or-none condition but is a characteristic that varies continuously over time; second, market efficiency is dependent upon market conditions. By using the automatic variance ratio test of Choi (1999), and the automatic portmanteau test of Escanciano and Lobato (2009), we confirm both implications for the US REIT market. The degree of REIT return predictability is found to be time varying. More specifically, it appears to be declining over time, which implies that the REIT market has become more efficient. Furthermore, we show that the return predictability is indeed influenced by market conditions. The level of market development appears to be the primary driver for REIT market efficiency. Other factors like inflation and the overall equity market volatility also have impacts. Finally, we demonstrate that the REIT regulatory changes in the early 1990s have greatly improved market efficiency.

Are stock markets really efficient? Evidence of the adaptive market hypothesis, Urquhart, A., & McGroarty, F. (2016). Are stock markets really efficient? Evidence of the adaptive market hypothesis. International Review of Financial Analysis, 47, 39-49. This study examines the adaptive market hypothesis in the S&P500, FTSE100, NIKKEI225 and EURO STOXX 50 by testing for stock return predictability using daily data from January 1990 to May 2014. We apply three bootstrapped versions of the variance ratio test to the raw stock returns and also whiten the returns through an AR-GARCH process to study the nonlinear predictability after accounting for conditional heteroscedasticity through the BDS test. We evaluate the time-varying return predictability by applying these tests to fixed-length moving subsample windows and also examine whether there is a relationship between the level of predictability in stock returns and market conditions. The results show that there are periods of statistically significant return predictability, but also episodes of no statistically significant predictability in stock returns. We also find that certain market conditions are statistically significantly related to predictability in certain markets but each market interacts differently with the different market conditions. Therefore our findings suggest that return predictability in stock markets does vary over time in a manner consistent with the adaptive market hypothesis and that each market adapts differently to certain market conditions. Consequently our findings suggest that investors should view each market independently since different markets experience contrasting levels of predictability, which are related to market conditions.

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