Alphanomics: Bridging Finance, Economics, and Behavioral Science

Alphanomics is a financial theory that argues that the efficient market hypothesis of classic economic and financial theory is mistaken. Borrowing ideas from notions of market efficiency, fundamental analysis, and behavioral economics, alphanomics examines how assets are mispriced to help investors improve their performance.

What Is Alphanomics?

Alphanomics is a portmanteau of “alpha,” the Greek letter used in finance to stand for a security’s excess returns over a benchmark, and the second part of “economics.” It refers to principles that can be used to discern mispriced assets or when generating alpha. Alphanomics is largely a response to the efficient market hypothesis (EMH) long dominant in finance and investing.

Efficient Market Hypothesis (EMH)

A major principle of classical economics, which became a key assumption in the rise of fundamental analysis in finance and grew into the efficient market hypothesis (EMH) of Eugene Fama in the 1960s, is that asset prices reflect all available information in the market.

For the EMH, the market efficiently distributes information to adjust prices, such that investors should theoretically be unable to outperform the market in the long term. The market is not a place for gambling, but is rational and should be approached as such. However, many investors, at least in practice, don’t take this to be the case. Otherwise, they would not try time and again to outperform the market through better investment choices or trade timing.

The upshot of EMH is that prices in this model help, without regulatory or other interference, to decide where best to put resources and indicate relative scarcity. This informational role is one that Friedrich Hayek in the 1940s argued made market-based economies an advance over others since the market as a whole was better than any set of individuals at allocating value and resources. Thus, the efficiency of markets is key to many arguments for unfettered free markets.

There are three versions of the EMH for how much information is priced into the present costs of assets.

• Weak: Present prices incorporate all past price information. As such, technical analysis, which uses this information to anticipate future price moves, would not provide an advantage.
• Semi-strong: Prices incorporate all the information in the public domain, not just past prices. This includes company announcements, annual reports, and any other public disclosures. Here, fundamental analysis also wouldn’t have much of an advantage.
• Strong: All information, public and private, is fully reflected in asset prices. This view suggests that even insiders with private information can’t achieve superior returns.

Critics of EMH argue that market prices are caused by all sorts of irrational behavior, among other factors, which leads to price anomalies and opportunities for above-average returns.

Alphanomics’ Reply to Efficient Market Hypothesis (EMH)

Alphanomics argues that purely efficient markets are not what traders and people in the field deal with. Instead, investor sentiment, information asymmetry (buyers knowing more than sellers, and vice versa), and so on play significant roles in the prices of stocks and corporate finance decisions.

For proponents of alphanomics, EMH is a theory often presumed by academic researchers as a prior fact to frame how they then interpret their empirical studies. Alphanomics starts by removing this assumption and examining what actual traders are doing in the market. It’s not saying EMH is simply wrong so much as putting it aside to detect other influences upon market behavior.

The upshot, for proponents of alphanomics, is that investors can and do outperform the market if they can understand when the market is inefficient. These proponents are not arguing that markets are completely inefficient. Their take is more nuanced: Individual trades are perhaps the market becoming more efficient, and alphanomics looks at the gap between what efficiency theory calls for and the mispricing that exists in everyday trading.

Principles of Alphanomics

To understand alphanomics, an investor needs to understand the key tenets of the theory.

Traders Act on Inefficiencies

Proponents of alphanomics say that EMH is inaccurate: Markets don’t begin as efficient, but might aim to become more efficient. This, of course, means that inefficiencies shape prices in the market at any given time.

A typical defense of EMH is the existence of arbitrage (essentially, the purchase or sale of similar assets to make up for short-term market inefficiencies), which ensures that prices eventually are at their proper level.

Financial incentives exist to uncover that and take advantage if an asset is priced improperly. Thus, investors put great effort into uncovering all relevant information about a security or market just as others are also doing so, and everyone does this until the security or market reaches its correct value. Of course, the argument for EMH at its crudest is circular: The right price is the market price, and we know it’s the price that should be reached (even if it’s just one moment) because it’s the market price.

According to alphanomics, the market must have inefficiencies for arbitrage to exist. If the market were efficient at all times, there would be no opportunity for anyone to conduct arbitrage and earn a return. Therefore, significant numbers of active traders and arbitrageurs indicate that the market is inefficient.

Abnormal Returns Do Not Require Risk

Another common argument of EMH is that for an investor to earn a higher return, the only option is to accept a higher level of risk. If an asset appears to outperform predictions, then it must be because of an unknown risk factor.

Proponents of alphanomics argue the opposite. Charles M.C. Lee and Eric C. So, whose 2015 paper “Alphanomics: The Informational Underpinnings of Market Efficiency” is a founding text of alphanomics, contend that recent research on predicting stock returns is difficult to fit within the efficient market framework. For Lee and So, this research suggests that firms with the typical metrics to label them healthier and safer, with lower risk and better fundamentals, tend to earn higher subsequent returns. This runs counter to the idea that higher risks should correspond to higher expected returns, a foundational principle in finance.

In fact, Lee and So argue, a great proportion of abnormal returns occur around the times when firms release their earnings reports. This is hard to explain in terms of EMH’s discussion of risk since asset-pricing models don’t predict these short-window price movements.

Momentum studies, which document price drifts following corporate news releases, pose a particular challenge for EMH’s risk-based models. These studies, Lee and So say, show that stock prices continue to move in the direction of an earnings surprise, dividend announcement, or stock split, which again is counter to the efficient market framework, which assumes that prices adjust quickly to new information.

Psychology and Investor Sentiment (Noise Traders) Inform Asset Prices

Market inefficiency doesn’t rule out saying how assets are priced. Alphanomics argues that noise traders—those said to trade investments based on market noise rather than on purported value—are key in helping set asset prices.

Noise trading, which is responsible for the massive volume of daily trading, was proposed by EMH defenders to explain the existence of arbitrage and why asset prices and intrinsic value often vary. Despite their sway in the market, these investors are an annoyance to EMH since they are not rational actors but follow crowd sentiment or emotional cues from the market. In short, they confuse noise for information.

EMH scholars depict noise traders as, at best, naive (not knowing how the market really works) and, at worst, preyed upon by those more knowledgeable. Individual noise traders, though, should be quickly weeded out if EMH is correct—bankrupted by market movements they don’t understand.

Alphanomics on Arbitrage and Market Efficiency

Alphanomics proponents argue that early theorists behind EMH didn’t account enough for the incentives for information acquisition and arbitrage, which are crucial for going out and investigating the best prices in the first place. This means that they might misunderstand the role of noise traders. For the price discovery process to work, there must be enough incentive for others to spend time researching and acting on this information.

By helping set up mispricing, noise traders indirectly enable an active arbitrage market since professional arbitrageurs are employed to capitalize on these price differences. This arbitrage activity, in turn, should play a critical role in moving asset prices closer to their intrinsic value, thus aiding the price discovery process. The key insight here is that noise traders, by creating potential mispricing, provide the fuel that drives arbitrage, which is fundamental to price correction and, by extension, market efficiency.

This nuanced understanding challenges conventional EMH by suggesting that mispricing is not merely a market failure but a crucial driver in correcting inefficient pricing. The persistence of professional arbitrageurs in the market, despite EMH predictions of their obsolescence, is said to lend credence to the alphanomics argument. There is always a need for active asset management, not as a vain endeavor but as a rational response to the persistence of noise traders and the resulting mispricing that they engender.

In this light, alphanomics views the give and take between noise traders and arbitrageurs as a dynamic ecosystem that self-regulates and moves the market toward efficiency over time. This is a departure from EMH’s more static view that assumes markets are efficient before any investigation, which doesn’t recognize the essential role of mispricing in correcting market prices toward what they count as efficient. This means that the reliability of prices that were supposed to be the starting point for EMH analyses depends on the existence of mispricing in the market first.

EMH adherents also suggest that active asset managers are often merely clever marketers with no role in enhancing market efficiency since it’s efficient already, and they are simply taking advantage of noise traders. Despite the argument that naive investors (noise traders) would be weeded out in a competitive market, the continued existence of professional arbitrageurs suggests that there are still market inefficiencies beyond just new noise traders arriving to replace those who left. For alphanomics, persistent spending on active asset management indicates a regular need for market corrections.

Otherwise said, cause and effect are reversed in EMH for alphanomics theorists: Mispricing causes people to come into the market to take advantage. Only after that might there be a move toward what EMH theorists consider the efficient price. Again, alphanomics isn’t opposed to EMH but sees market efficiency as something built toward, not as, the point of departure.

How Alphanomics Might Influence Investment Decisions

Alphanomics argues that to understand asset prices, we need more than the tools of EMH, including concepts from behavioral economics related to investor psychology and sentiment. This means there is an opportunity for investors to earn greater returns than the market. That can influence how a person chooses to invest.

While alphanomics might suggest markets are inefficient, this should not be confused with thinking that markets are unpredictable. As Lee and So portray it, the field seeks out “the source of predictability in asset returns.” Like many fields in finance, alphanomics underscores the importance of well-informed strategies for investing.

By delving into anomalies and inefficiencies in the market, alphanomics could help investors find investment opportunities that arise from mispricing. Studying why an investment is mispriced can guide you in capitalizing on this and similar opportunities.

Alphanomics’ emphasis on looking at the incentives for acquiring information and the role of arbitrage in price discovery could persuade you to invest in assets or strategies with strong arbitrage mechanisms. As alphanomics explores alternative explanations for market anomalies and tests behavioral models, it may encourage investment in emerging behavioral finance models or firms that leverage behavioral insights in their investment strategies.

The field of alphanomics could lead to revising some traditional investment theories, which may prompt investors and asset managers to re-evaluate their investment frameworks and consider incorporating alternative or complementary approaches in their decision-making processes.

Another way that alphanomics could impact investment decisions is when it says that risk is not the primary determinant of potential returns. According to alphanomics, it may, in fact, be better to invest in less risky companies because they tend to produce better long-term returns. That could lead investors to focus more on established, blue-chip companies rather than smaller, less-established ones.

Case Studies: Successful Use of Alphanomics

Alphanomics is a relatively new concept first discussed in Lee and So’s 2015 paper. That means it has not yet seen significant real-world use, successful or unsuccessful.

The volatility of shares in AMC and GameStop in 2021 are, for alphanomics proponents, specific real-world examples that show its importance. So argues that these companies and similar meme stocks, which had massive spikes in their prices, are a prime example of the power that investor sentiment has on asset prices. They also show, he argues, the inability of arbitrage to enforce market efficiency to ensure that prices and intrinsic value remain the same.

Criticisms of and Challenges in Alphanomics

As with any theory about asset prices and investing, there are many limits to and criticisms of alphanomics.

One challenge is determining the information already priced into an asset. If EMH is inaccurate, some investors might have access to information that others do not that has yet to be priced in. However, it’s often difficult for even the best investors to know whether their knowledge is relatively exclusive, making it difficult to trade on that information.

Another challenge is how to gauge investor sentiment. Given that investor beliefs are said by alphanomics to play a large role in the mismatch between intrinsic value and price, knowing how investors feel about a security is important. Finding effective ways to measure that sentiment and how susceptible a firm is to changes in industrywide or marketwide sentiment is difficult.

The Bottom Line

Alphanomics seeks to better understand why asset prices change in the ways that they do. Rather than presuming the market is efficient, alphanomics argues that the existence of arbitrage is an effective but imperfect try to push it toward efficiency. Ultimately, prices are influenced by many factors, including investor sentiment and noise.