Exploring Market Hypotheses: From Efficient to Interactive

Manogane Sydwell

Jun 17, 20204 min read

Updated: Jul 4, 2022

Depending on who you talk to, financial markets are either impossible to beat or predictable and therefore allow for profitable speculative trades. In this article, we will cover a variety of market hypotheses, from those which are covered extensively in academia to some of the more exotic ones. Let us begin!

The Efficient Market Hypothesis

This theory states that the asset prices reflect all available information and that consistent alpha generation is not a possible feat to achieve. Alpha is what measures the performance of an investment when compared to a benchmark such as the ALSI Top40, which is considered to measure the market's movement as a whole. The efficient market hypothesis has been used to justify investments in passive strategies. This would entail placing funds into an Index or Exchange Traded Funded. John Bogle, founder of The Vanguard Group, was a notable practitioner of this theory.

According to Nobel Laureate Eugene Fama, who is responsible for this theory, financial markets can be categorized as three types of efficient.

Weak form efficient entails that past prices, historical events, and trends cannot be used to predict future prices. This form of market efficiency also states that stock prices reflect all available information. According to semi-strong efficiency, technical and fundamental analysis are useless when predicting a stocks future price movement. Only material nonpublic information is useful when predicting stock price movements. Finally, according to strong form efficiency, stock prices reflect all public and private information; successful stock price prediction is not possible in such an instance.

Despite the merits of this theory, it has received criticism from notable figures in the world of Investments,including but not limited to Warren Buffet and Jim Simons. Behavioral Psychology as a discipline provides a better approach to interacting with the stock market when juxtaposed to the efficient market hypothesis.

The Fractal Market Hypothesis

Put simply, a fractal is a never-ending cycle. Fractals can be described as infinitely complex patterns that are self-similar across different time scales. Propelled by recursion, fractals are moving pictures of dynamic systems - the visual representations of chaos! As an idea, fractals are pretty amazing to think about. The python simulation below is a visual demonstration of the fractal nature of trees.

Another object to consider with fractal properties is a coastline. Coastlines are not just curved, but rather, they are infinitely crooked. Irrespective of how much one zooms into a coastline, the kinks seen do not disappear. The video below provides an explanation of this phenomenon.

At the heart of the fractal market hypothesis is a focus on the heterogeneity of investors, mainly with their investment horizons in mind. The market for a particular asset consists of investors with different time frames ranging from milliseconds for some and decades to others. Because of this difference in time horizons, investors treat the inflowing information about the particular asset differently.

This theory helps explain why the market looks similar across different time horizons, and it is also receives substantial mathematical support.

The Interactive Agent Hypothesis

As was discussed in a previous article, an agent-based model is a subset of computational models that are utilized to mimic the action and interactions of autonomous agents with the intention of visualizing their impact on an entire system.

The advantage that this idea has over the efficient market hypothesis is that it allows for agents to take on more realistic characteristics than the latter hypothesis. For instance, the assumptions of the efficient market hypothesis imply that investors are rational, transaction costs are non-existent when buying and selling shares, and that luck is the only way through which one can outperform the market.

The interactive agent hypothesis, in line with ideas proposed in behavioral finance, argues that investors are “normal” and not rational. The assumption of normality allows for investors to now be influenced by their biases, and also to make cognitive errors that can lead to wrong decisions being made. I was first introduced to behavioral finance when I studied Advanced Corporate Finance in my postgraduate studies and deeply appreciated the use of more realistic investor assumptions.

In line with Kaizoji, these more realistic assumptions correspond with the opinion of John Maynard Keynes, who stated that stock price changes have their origin in the collective crowd behavior of many interacting agents rather than the fundamental values of firms which can be derived from careful analysis of present conditions and future prospects of firms. Below is a demonstration of an interactive agent model using Adaptive Modeler. You can read more about it here.

Conclusion

In this article we discussed a variety of hypotheses which view the market from an iridescent perspective. Some hypotheses are more introductory than others when their assumptions are examined while others are not. Irrespective of what you choose to follow, it is recommended that you do so wholeheartedly.The fractal tree simulation see above can be found on my github .Complement this article with the following video.