Understanding market anomalies through alternative data

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Thomas Kuhno It was once written that scientific revolutions happen when substantial discrepancies are revealed. They are insoluble and do not fit into the current theoretical and empirical framework. Enough of these discrepancies can cause a paradigm shift, requiring a re-imagining of the scientific theory.

“Traditional” finance theory is partly based on the efficient market hypothesis (EMH), that is, markets and prices reflect all publicly available information at all times. There are some other assumptions, such as the idea that all investors are rational actors, but we will focus primarily on EMH.

Over the long years of market history, there have been many unexplained events. From various calendar-day or calendar-month-related changes in pricing to more complex anomalies such as momentum effects and price effects, these fluctuations are predictable but obscured by the lens of asset value theory.

theoretical undercurrents

Some of these discrepancies seem easy to understand, even if they cannot be explained by asset value theory. The January effect (i.e., increases in trading volume and share prices in late December and early January), appears to be closely related to tax-year changes.

Its primary interpretation also fits into the paradigm of a rational actor. Most investors, from retail to institutional, would like to reduce their tax burden each year as they try to maximize capital gains. Such balancing act may require some constructive accounting which is done in accordance with local laws. As a result, trading volume increases.

However, other discrepancies are much more disruptive. The momentum effect (i.e., the tendency for high-performing stocks to overperform and vice versa), fits neither into rational actors nor into the EMH framework. In fact, most momentum effect explanations are based on investor irrationality.

Incorporating the Momentum Effect into traditional finance means acknowledging that, at least in some way, the hypothesis outlined above is not true. This would shake the foundations of traditional asset pricing and financial theory.

However, there is another way to look at it. Much has been written about EMH’s notion of a correlation between publicly available information and price adjustments. But the definition of data may have become too narrow. In other words, discrepancies are because we have yet to adjust our information sources from which we try to explain certain phenomena.

efficient market hypothesis contender

When considering EMH, “publicly available information” is taken with reference to traditional data sources. These can be public filings of financial statements, company news, government data, etc.

Such sources have become so essential to our understanding of proper marketing practice that using personally held information is considered insider trading – a crime almost everywhere in the world. What has not been considered, however, is that these sources are far from perfect indicators of a company’s performance.

There are many other indicators of a company’s performance, some of which can be a bit difficult to convert to signals. Nevertheless, some researchers It is proposed that data such as vacant parking lot spaces Can be useful when forecasting retailer performance. On a theoretical level, such signals would be incorporated into regular, traditional sources as the company’s performance to be reflected at large. Such alternative data provide a small, bite-sized portion of the same signal as before.

If so, however, EMH should not be taken literally. Parking lot data is public, yet, some would say the markets have adjusted to it. Even if the same signal is included in a later dataset, reaching it early can create an alpha simply because the markets are inefficient.

Such incapacitation shall be considered an anomaly. This is so, however, because we haven’t considered that a new type of accessible data has emerged – alternative data.

Alternative data to explain the discrepancies

There is an interesting irony in the fact that alternative data may be able to explain market anomalies (something “alternative to the long-run theory”). Nevertheless, the inclusion of such data does not preclude EMH or rational actors. This might add to the explanation.

one of the following Proposed explanation for the speed effect is that investors overreact or underreact to certain news of the company. Regardless of how we treat these concepts (i.e., if they fit into the rational actor framework), it can only be an anomaly when considered through conventional data.

Investors express sentiment about news in a variety of ways, and there are ways to measure it, namely through natural language processing. These sentiments can be reflected in the way the company reacts to news, giving us a glimpse of what’s going on in the market. In the end, rationality remains because the goal is the same – to produce capital gains.

The momentum effect, in such a case, would not be an anomaly that conflicts with the efficient market hypothesis. If sentiment is out, the market adjusts to it through investor reactions to other public information.

Other effects may have similar explanations. The January Effect, as noted above, is probably influenced by taxation laws across the country. While it may be difficult to quantify the individual investment strategies that are used to reduce the tax burden, other data (i.e., laws) are available.

Again, rational actors will try to maximize capital gains. Tax burden reduction is just another strategy for which information is publicly available. The finance sector may have indirectly understood the practical value of alternative data. as As we have seen, many have adopted web scraping Purely for the value it brings to investment strategies. However, the theoretical changes have been left in the dust.

Market discrepancies may simply be because the current understanding of the relevant data does not hold. A question should be raised whether discrepancies or data appear earlier, however, this is outside the scope of the present discussion. We should look to expand our current beliefs about data and efficient markets to see if the discrepancies really go against traditional asset price theory.

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