I have an approximately 20,000 word work in progress ‘intro to financial theory for social scientists’ paper in progress. It is seeming like it will never be done though, so I decided to go ahead and start posting it in parts even if it is a little rough. It’s not as though I’m being graded. Citations will be available in the final version when I properly format my ‘book’ and turn it into a PDF. Otherwise available upon request.
The Efficient Market Hypothesis:
It will now be a useful exercise to consider how the previous intro on market laws might change the way a clever reader (yes, you!) views a stock market, if you were to accept everything so far as absolute truth. We are now aware that a discount factor that combines many factors, such as various types of risk and human impatience, is used to find the current value of a firm’s stock price. We now also know that assets with identical or extremely similar discount factors offer identical or extremely similar payoffs (Law of one Price). And that a mispricing in the market cannot exist (No Arbitrage). Everything is properly priced, all relevant information such as country specific risk is included in the discount factor, and there are no mispriced assets in markets or across markets. This suggests that markets are efficient. Or in other words, it means that we do not expect to receive a higher payoff from our investments than would be suggested from the discount factor. And that we do not expect to receive a higher payoff than would be expected due to our clever strategizing. If we consistently could pick stocks that offered higher returns than their discount factors suggested, we would be violating market efficiency. This leads us to the efficient market hypothesis. It was first presented by Eugene Fama in a seminal paper in 1970. It is mostly right, but often wrong. I will explain its strengths, weaknesses, and applications in depth. A clever student of finance should recognize that various economic efficiency laws have known flaws, yet still respect their importance.
There are three forms in total: weak, semi-strong, and strong. This was the most debated and important spectrum in financial economics for many decades, and the argument still exists, although it has toned down. The strong form suggests it is not possible to consistently and truly beat the market, as the market is always in equilibrium. If everything is properly priced there is no way to receive excess return vs. the market (without taking on more risk). The weak form does not require the market to be in constant equilibrium. In addition some forms of fundamental analysis can provide excess return. In this case fundamental analysis generally means analysis that focuses on the microeconomics of a firm, rather than just examining pure market data to look for pricing errors. And that while it is profitable to make money from inefficiency in the short run, this is not possible in the long-run to consistently make money. This is because inefficiencies will cease to exist, and overall excess return in the market must equal zero (for every winner there is a loser). The semi-strong form is a reasonable mix of the two.
Lastly, there are a number of important aspects that are true for all three: Future prices cannot be predicted from past prices to profit in the long-run; using technical analysis (or computer algorithms) cannot generate profit in the long-run; price movements in the market follow a random walk, (they move randomly) since the movements are based entirely on new information that is non-forecastable; there are no predictable patterns to asset prices; and that investors have rational expectations, meaning that even if some are wrong, on average the population is correct. I would like to reiterate that stock movements only occur based on new information that isn’t already contained in past prices. For example, if all investors are 100% certain Apple will have great quarterly earnings accuracy in ten days, they will buy Apple today. This means that when the quarterly earning comes out the stock will only move if the earning is above or below the current expectation. So if the quarterly earnings do end up being great, the stock will not move, because all investors already anticipated this event and bought Apple stock (thus increasing its share price before the official date of release). Now in reality investors do not have 100% confidence, and often have different guesses. However, it is for this reason an investor is more interested in the surprise of the actual release measured against the estimated release rather than the absolute earning.
The previous paragraph is the most important of this entire paper (re-read it). Unsurprisingly, if accepted fully, it renders every attempt at teaching an investor how to make easy money false. It suggests that all investments firms are not actually beating the market. It also suggests that the information your broker shares with you on ‘hot new investment products’ is useless. A famous hedge fund guru–Seth Klarman–warns against these investments advertisements in his famous investments book. He believed entirely in fundamental analysis of firms and markets, and warns investors not to listen to all the products being sold by Wall Street. Spotting the late night infomercial scams is not sufficient, most reasonably intelligent people know those are false. It is important to note that your broker has one goal: Making money off of you. For example, even if they don’t believe in technical analysis themselves they provide all the tools. Technical analysis day-traders spend lots of money on commission. Just because your broker or wealth-manager supports a type of trading does not mean it is sound. There was also an extremely influential book released in 1973 (shortly after Eugene Fama’s market efficiency paper) entitled A Random Walk down Wall St. by Burton Malkiel, a Princeton economist. This was the first mainstream push to warn all investors to only buy a market index and never attempt to beat the market or buy products that attempt to beat the market.
Beating the Market:
Efficiency and You
It is important to recognize that a stock market is simply a reflection of underlying firms and entities. As a result the stock market is a zero-sum game. If the entire S&P500 returns 5% in a year, the average return for all holders of the S&P500 must be 5% by definition. As a result imagine if one investor experienced a return of 6% by selling the S&P500 and buying it back a week later after it had decreased by 1%. This gain must come at the loss of another investor who happened to buy the S&P500 at exactly the wrong time. This same thought experiment can be extended towards the entire worldwide stock market. For every winner it is required that there is a loser. Now consider that trading infrastructure is expensive. Hiring a team to attempt to beat the S&P500 costs money. As a result the stock market is considered a zero-sum game. As a brief example, imagine a market with only two investors: every time an investor A wins money investor B must lose money. In addition both investors pay money for computers and a team of analysis. As a result investing simply costs money and is overall a losing game to play.
However, there is a contradiction inherent to belief in zero-sum market efficiency: Markets are man-made. As a result a market can only be efficient because investors make them efficient. Every aspect of a market is the function of a set of legal rules and regulations followed by investors entering the market and investing by the rules. Imagine if no person ‘played the game’ and attempted to beat the market. Instead they recognized it is a zero-sum game that costs money, and decided just to hold the market, such as passive indexes, instead of attempting to beat the market. The result would be the market would never react to incorporate new information. Reflect over the Arab Spring uprising. The moment a the social movement began the price of oil increased to incorporate the new risk to oil supplies. Some shrewd traders were able to capitalize on the events. If there were no active traders, there would be no way for the market to incorporate this new information.
Eugene Fama and Kenneth French found that there was a pattern in the price of the U.S. equity and U.S. bond market. They realized that if the dividend ratio of the U.S. equity market increased, the U.S. bond market inched up over the following two weeks. They found that this trend was consistent. Imagine that they then told colleagues at an investment firm, and that they then began paying close attention to this relationship and invested on it whenever it appeared. Slowly other investors became aware of it as well. They even began to invest in the bond market based on expectations of the dividend ratio before it came out: For example, if they anticipated there was a 70% chance the dividend ratio would increase by 30%, they had an equation to determine how many shares they ought to buy of the US bond market. Eventually, due to all these relationships becoming clear, the trend disappeared. As a result you can see that trends do not exist because the moment they do, investors will trade on them, thus eliminating the trend. It is important to note that the observations of market efficiency are not similar to physical laws, rather, they are observations of how the market works considering that clever investors will execute trades in all opportunities. While this theory has been accepted, recently Jeff Pontiff, a professor of Finance from Boston College released a working paper called Does Academic Research Destroy Stock Return Predictability?, where he found empirical evidence for pricing anomalies disappearing after discovery. As a result while many studies may find anomalies or trading strategies, one they are observed they should theoretically cease to exist. However, I am not writing this to teach anyone a clever trading strategy. Rather, I would like to teach you the theory behind a clever trading strategy, and how to tell if a strategy is clever or stupid.
I will begin by explaining some trading strategies that work, but that you could never hope to accomplish. I don’t say this as a joke, I’m entirely serious. There are firms that are primarily driven by pure quantitative programming, have teams of professors and PhDs, and infrastructure measured in the tens of millions. In some cases they have even moved closer to the official servers and hired physicists in general relativity to help them optimize their servers so that they can reduce their trade-delay. This is not sufficient to make money, however, it is necessary.
These algorithmic, quantitative, and high-frequency firms tend to trade to uphold ‘No Arbitrage.” Some firms specialize in ‘statistical arbitrage’ and search for asset mispricings across markets and asset classes. Such as two different markets having the same stock that are not exactly equal in price. There is also ‘event arbitrage’ where a firm will take advantage of their near instantaneous ability to place a trade due to their infrastructure and get ‘first dibs,’ for example, on a positive job report. However, there are many other firms that operate on the same general principle that do not fall into an explicit category or have proprietary information. Perhaps a firm has found a trend by analyzing data, or perhaps they enforce a documented trend. Often the most successful firms trade on an esoteric asset. For example, some hedge funds focus only on trading volatility. Their goal is to make sure derivative contracts on the S&P500 are all at the proper price. A fat fingered investor at his home would not notice a difference for hours after it happened, and even then be far too slow and costly to implement the trade. A high infrastructure hedge fund on the other hand has nearly zero transaction costs and is able to capitalize on the trade seconds after it occurs, capturing just cents per trade (cents per trade multiplied by thousands of trades adds up).
These strategies keep markets efficient and often work well, however, the firms that are successful are few and far between. In addition, there is not always room for new entrants. Often in these areas if a firm is too large they experience diseconomies of scale. If a fund is too large they lose agility and the ability to enter and exit positions gracefully without impacting the market. While these areas do exist, it is important to observe and understand how they work, but trying to replicate their style in any other environment is not reasonable.
There are many other investment firms that do not self classify as purely quantitatively driven. These firms often tend to trade by searching for some type of fundamental analysis. This is a fancy way of saying they are interested in the micro-economic foundations of the sectors, firms, and places they invest. These could range from small capitalization US tech firms to firms that exclusively trade emerging market debt. They play close attention and meticulously research their area of focus. As a result if an emerging market has a currency crisis or a new trend appears in US tech firms, they will either buy or sell assets to bring the market to efficiency.
It is important to note that these firms and investors that do contribute to market efficiency build their trading models on theory that interacts with reality. Some firms trade on algorithms that keep the price of stocks equal across different markets. This allows for more liquidity and no information asymmetry. Other firms might trade on international relations theory, perhaps their thought is a large oil exporting country has more political risk than others are aware. The goal is to absorb data of all forms and turn it into specific information through analysis. The opposite of trading this way is just buying and selling stocks for no good reason. This would place the investor in the category of a ‘noise trader.’ Essentially indicating that despite trading, the trades themselves are entirely meaningless towards market efficiency. An individual example would be someone buying Apple just because they think it sounds cool. An institutional example would be a firm selling Apple so that they can rebalance their mutual fund to an index. In both cases Apple wasn’t bought or sold for any tangible reason that recognizes the firms attempt at generating money. It was simple ‘noise’ and either had no reason, or had reason that was specific to the investor. As a result while this noise will marginally move the stock price up or down, since it is not reflective of any true change in the stock, it should be expected that another trader will buy (or sell) Apple back to its fair value.
If not for active investors attempting to beat the market, commodities and assets would not reflect new information in the world. This presents a paradox where the winning move might seem to suggest not to attempt to beat the market because it is a zero-sum game. For every winner there is a loser, plus expenses. However, if everyone follows this methodology the market would not be efficient and assets would not reflect new information. As a result active trading should exist, despite it appearing to be a zero-sum game, the expenses the firms incur allow them to engage in information and price discovery. At this point macroeconomic theory tends to give way to empirical observations. The academic literature tends to find that individual and institutional investors are net losers; meaning that after expenses they would have been better had they held a non-actively traded portfolio.
Individuals tend to lack the necessary resources to make educated active investments. Even if an individual investor was a professor of finance, without access to top level research and supporting teams it is too difficult to actively beat the market. On the other hand, mutual funds often suffer because they are too large. This is called diseconomies of scale. With one billion dollars it is possible to enter and exit positions. With ten billion dollars many opportunities are too small. In addition mutual funds have far more legal regulation, which can be crippling when trying to act quickly. In addition, Mutual funds tend to have small amounts of money from a very large amount of investors, which requires lots of legal and paperwork. As a result, while active mutual funds do tend to beat the market on average, once expenses are added they lose on average. In addition, if a winning mutual fund is spotted, often new cash flow will enter until the fund becomes too bloated. There are some arguments that certain mutual fund firms can outperform their peers in the long-run, however, the empirical evidence for this argument is weak.
Conversely, hedge funds and other similar investment vehicles tend to only hold money from small pools of high net-worth individuals or institutions, such as endowments. In addition, they tend to hold lower amounts of capital and often cap their funds if they fear diseconomies of scale. These firms tend to also employ the best talent. They can offer better compensation than mutual fund employees and also receive their compensation based on performance, whereas mutual funds charge a flat fee. As a result many of these firms have the best investors in the game, the top and most expensive infrastructure, and the ability to act quickly with little to no oversight. These are a few of the key points that explain why Hedge funds, on average, can perform very well. It also explains why on occasion they can go bankrupt fast. Many hedge funds used massive amounts of debt for leverage, one of many options mutual funds are forbidden from, and as a result when their investments went down even only one or two percent in 2008 they immediately went bankrupt and their investors lost all their money.
I have stressed throughout this that these are broad generalizations that do not explain exceptions. However, they are largely accepted in academic analysis of different market players. Part of the reason it is so important to conduct large empirical studies is due to the randomness of the market, as well as the statistical difficulty of measuring one person. It is not possible to examine an active and passive individual investor, an actively and passively traded mutual fund, and a passive hedge fund that focuses only on portfolio construction as well as a hedge fund that trades multiple times daily, over two years, and hope to explain who did well and who did not. It is not enough information, too short of a time frame, and too small of a sample size. As an example, I have witnessed too many students who are new to finance experience higher returns than the S&P500 for a year and think it suggests profound skill on their part. There always far too much I do not know about this students investment (and that he or she often does not know either), and even if I knew how much risk he took on, I still could not explain whether he won due to luck or skill in such a short time-frame.
Even if an active mutual fund has beat the market after expenses for a decade, due to the amount of active mutual funds that exist it is possible that fund was just one of the lucky few. If a portfolio manager for a mutual fund underperforms or outperforms for three years straight he or she will likely be fired or promoted. But the truth is that there is not enough information to justify either of those actions. As a result researchers tend to focus on larger and generalized groups, such as lumping all actively managed mutual funds in one group and comparing them to all passive mutual funds over five decades. An interesting paper on mutual fund performance did not examine funds, but instead examined portfolio manager returns through variables such as SAT score and education. This paper did find a positive correlation between education and excess returns–however–often the smarter students are recruited by the top firms, so it is difficult to be certain if this has any real merit. Overall, it is still overshadowed by the large research that find active mutual fund performance to be drab.
The reason the active and passive dichotomy exists is that it is an extremely useful classification. Those who research finance want to be able to examine the market as a whole throughout time. For this reason indices are made for nearly all asset classes in all regions. This then allows researchers to compare those markets as a whole, and compare active investors in them to the market itself. This dichotomy also allows firms to explain how their funds act. For example, there might be two complex retirement mutual funds. Each one becomes less risky every year until retirement. In the one that is is active, the portfolio is attempting to beat the market by picking and choosing. The passive portfolio will simply hold the market. However, each one likely is holding ten to twenty different markets across the world based on a portfolio optimization model.
If I have discouraged anyone from actively investing themselves or buying actively traded mutual funds, I will have accomplished my goal. To help achieve long-term financial stability the best strategy is to invest in a mix of passive assets that have a risk exposure that is most prudent for your goals. Many investors enjoy investing personal money that isn’t needed for retirement or stability actively in the market in the hopes of making lots of money. Some more educated investors might also enjoy long term gains from strategies such as value investing (made famous by Warren Buffet). While understanding the difference in active and passive is very useful for investing in a 401k or reading financial literature, when it comes to personal investments the need for simplification disappears. The truth is there are many different ‘active’ trading strategies. Buying and selling gold daily is different than choosing twenty firms based on research and trading every six years. If you do choose to invest actively be sure to recognize the benefit of low-fee and passive ETFs and mutual funds (often offered in a retirement plan) that are loosely optimized for your age, risk level, and desired goals. Then make active trades in addition to your already secure financial portfolio. It is possible to mix the two together and tweak a personal portfolio to reflect personal bets and investments, and many private wealth management firms aimed at high net worth individuals offer these services. However, it is generally a poor idea without very strong knowledge and resources.
Understanding Financial Products
It is difficult for an investor who has not formally studied financial economics to differentiate between good and bad investment products and strategies. This could be a reason why multi-billion dollar firms continue to have their retirement funds managed by overpriced firms. Being able to wade through all the different products and types of investment vehicles is very difficult. For example imagine you log on to your broker and they have two new ‘suggestions’ of investment products that you may buy: The first one is an ETF (an exchange traded fund that is very similar to a mutual fund) that tracks Chile and the second is a mutual fund targeted towards elderly investors that, the advertisement claims, are “not receiving high yields from their fixed income products.” Even if you are not interested in these products, it is still useful to have the skills to examine each one and determine if it is absolute trash or might have some merit for the right investor. I purposefully chose two very different products in this example.
A great first place to start is to run each product’s goal against a market efficiency test. The first ETF claims to follow a Chile index passively. This means it is not attempting to beat the market, just replicate the market. An index means that someone created a simple list of rules to categorize stocks, for example, list them in order of size. This type of ETF will proceed to hold these stocks based on the rules defined at the beginning. While there may be some small variations since the ETF needs to rebalance on occasion, it will generally follow the index. This ETF is executing a transparent plan.
To further analyze this ETF, I would expect that as a typical investor having high exposure to Chile would be unnecessary. However, the ETF seems to be reasonable. The goal is to allow you to hold a general piece of Chile’s overall economy. It is a simple but useful product to help an investor gain exposure to Chile based on a clear and available index. In addition, since it is traded passively the fees are likely to be lower. The reason being instead of hiring a team of highly compensated traders to attempt to beat a market, it is done by a couple people just following an index.
Now to properly analyze the mutual fund we will need information, let’s suppose that the mutual fund claims to achieve excess returns against its index by holding an 80/20 percent split of an investment grade bond index and dividend stock index. The product continues to explain that their goal is to actively trade the stocks to achieve greater returns. This means instead of strictly following the rules of the index, the portfolio manager will actively trade the stocks in the index while attempting to keep risk factors at the same level as each respective index. Another way to consider this is that the active traders on this mutual fund team are attempting to identify stocks that have been mispriced. Let’s think of this in discount factor terms: If a stock has claims on all future profits of a firm, its price is all those profits discounted to today based on their perceived risk as well as factors such as the interest rate and the time value of money. Now let’s imagine a stock has been discounted backwards and is currently at the price of $100. This means if we buy that stock we are entitled to an expected gain equal to the discount factor, since this is what investors demand for investing money in a risky stock instead of buying cupcakes today or investing in another less risky stock. The goal of a talented team of investors on this mutual fund team would be to somehow find what they think the true value of the stock would be by coming up with their own discount factor. If this team finds that they believe the discount factor ought to be 20% lower than the market discount factor, this means that the stock should be more expensive. This is because a lower relative discount rate means that a stock is not as risky as the market expects. However, if a market expects a stock to be risky they will demand it be a lower price as compensation for the risk. As a result this team would try to buy stocks that are cheap due to perceived risk–despite not actually being as risky as expected. If this seems incredibly complex compared to the passive ETF of Chile–it is because it is more complex.
The mutual fund does not pass evaluations as well. It is being sold as a solution to elderly investors who have had lackluster returns on fixed income products. We can initially take a reasonable guess that the product will achieve higher returns. Since stocks generally have higher returns than fixed income, this mutual fund is weighted more towards risk. This fund is also being actively traded. The portfolio manager is increasing the risk on this product to offer higher returns versus rival funds, which are aimed at elderly investors. This fund is also being actively traded, which often increases risk as well (despite claims to the contrary). Actively traded mutual funds, on average, increase the price by more than the returns are increased. It is not cheap hiring a team of highly compensated portfolio managers to attempt to beat the market. The end result is that this mutual fund will likely have higher returns, but this comes at a cost of higher fees and more pronounced risk. It is also important to note that some actively managed mutual funds that were sold as ‘safe’ had extremely risky positions prior to the financial crisis, ultimately falling 30-40%. Whereas similar passively managed mutual funds that followed the same index did not suffer as poorly. While this fund would increase returns, overall I would be hesitant to suggest an elderly investor buy products that attempt to beat the market. If he or she thought it prudent to increase overall portfolio risk, with guidance from an educated adviser, it would still make more sense to invest in a passive portfolio. While nearly all evidence suggests actively managed mutual funds fail their objectives, it would be a particularly poor idea for retired investors to hold these products.
My explanation so far on market efficiency is sufficient at an introductory guide. For readers that are acquainted with financial journalism and investment blogs I would like to add a few more substantial examples and explanations. This will help discern intelligent from unintelligent writers as well as teach some of the empirical tests that have allowed market efficiency theories to become so widely accepted.
To begin, the formal definition of efficiency is “information efficiency.” As I have mentioned before, the idea is any new information will be incorporated into the stock price today. In addition, if any ability to predict good or bad days existed, all investors would jump on the opportunity, and the strength of the prediction would cease to exist. The first generation of empirical tests found with extreme accuracy that this was indeed the case. An ordinary least squares (OLS) regression was run on annual market data from 1927-2008. The regression forecast the ability of past stock prices to predict the stock price in the next period. While the mathematical proofs to derive the theory behind OLS is quite complex, the equation is intuitive. The test measured if returns in the following year could be predicted by multiplying past returns by a ‘beta’ coefficient. The answer was essentially that they could not predict future prices on past information(the beta was 0.04 and the R squared was 0.002). To contrast this prediction T bills are very predictable, since interest rates are predictable.
There is a stark difference between simply reading this and searching for logical fallacies in investments articles or arguments. John Cochrane created a list of five examples for University of Chicago MBA students enrolled in his Advanced Investments course that sound like they could have come from one of many articles. The italics are my addition.
1. “The market declined temporarily because of proﬁt-taking. It will bounce back next week.” (If markets were to increase next week, investors would attempt to buy earlier to enjoy the gain, this would eventually lead to the market instantaneously increasing now)
2. “The stock price rises slowly as new information diﬀuses through the market.” (Information does not slowly diffuse through a market. There are hundreds of millions of dollars in hedge fund and active investment infrastructure all pursuing rigorous research searching for good deals today. That is not to say information does not diffuse in a market, but to suggest it will diffuse with a clear upwards or downwards trend is incorrect)
3. “The internet is the wave of the future. You should put your money in internet stocks.” (While the internet may be the wave of the future, the internet firms will still only compensate their shareholders for the risk that they are holding in aggregate. In addition if this were true investors would buy internet stocks until they were once again in equilibrium with other investment opportunities. Internet stocks did shoot up at first, but the initial investors took a large risk. Imagine an internet firm is offering a dividend of $10 a year per share. If the firm is part of the future and will offer great returns at low risk, investors would bid the price of the share up so that the $10 dividend only represents a more modest risk-adjusted gain in the traditional 4-10% realm.
4. “Buy stocks of strong companies, with good earnings and good earnings growth. They will be more proﬁtable and give better returns to stockholders.” (Debunking this statement may draw some flack from value investors. However, value investors search for underpriced firms. In this case the statement is suggesting strong companies with publicly known good earnings and good earnings growth will always give better stockholder returns. Once again, if this were true, speculators would buy the stock until the price increased, thus bringing it back into equilibrium.)
5. “The demand curve slopes down;” “Big trades have a lot of price impact.” “Stock prices fell today under a lot of temporary selling pressure,” “Some stocks fell too far in the crash because mutual funds and hedge funds had to unload them to meet redemptions” “Small losers fall in December as dentists harvest tax losses.” (See if you can do these ones yourselves)
Each phenomenon explained here could not exist due to competitive speculators. Some of these sentences though, in the right context, seem very reasonable. This is precisely why properly understanding market efficiency is such a trying task. It is surprising how often a common and flippant short-term market theory can violate efficiency. However, it seems as though most industry professionals are more caught trying to progress their careers and make money, than attempting to properly follow basic market theory. This isn’t surprising, nor is it wrong or different from nearly any other industry created by humans. The end goal for most participants in any field is a successful career. In defense of industry professionals, often they must offer products to fulfill market demand regardless of efficiency. It appears that customers of financial services enjoy fun services like a simple set of ‘technical analysis’ buttons on their trading website, or exciting new ETFs that offer synthetic ‘hedge fund’ exposure. However, as an individual investor, or student of finance who strives to truly understand the truths and falsehoods of the field, it is important to identify real research from attempts to sell financial products. To provide a counter-example of a short theory that might be on a finance website that is more rigerous consider the following:
6. “Buy stocks of strong companies in traditionally safe sectors (e.g. telecommunications and healthcare), with good earning and good growth for individuals who want consistent dividend payouts instead of high risk from large capital gains. They will be more likely to provide a stream of income that is less affected by turmoil from geopolitical events and European debt over the next two years.
In this example instead of suggesting strong companies will just ‘do better,’ I pointed to a theory of how these assets will perform and react to crises, and how this suits them to a particular investor. At the very core instead of making a broad claim that “one bucket of stocks will do better than another for vague reasons,” I am suggesting that an investor in term of lower risk dividend payouts should prefer to own firms such as Comcast and Procter and Gamble as opposed to smaller pharmaceutical firms and emerging market banking equities.
There is still much unknown about markets. Up until recently if a dividend price ratio (D/P ratio) declined, most academics thought it meant that the future dividends of an equity would be lower. A dividend price ratio is calculated by taking the current price and dividing it by the most recent dividend. If future dividend payments are expected to decrease, the price of the stock will decrease (note the last paid dividend cannot change, and only will change when the next dividend is paid, which usually occurs on periods denominated by months). Therefore if the dividend price ratio decreased, this valuation would suggest future dividends would decrease. However, empirical evidence and research over the past four decades strongly suggests a lower D/P ratio suggests higher future expected returns. Meaning that expectations of future dividends appear to fluctuate more than future dividends themselves. This is one of many different anomalies and valuations current financial economists study. With modern empirical tools it is possible to put these to the test and begin to evaluate true phenomenon.
To conclude this section: Markets are very efficient and reflect nearly all available information. However, this only exists because market participants buy and sell assets to reflect all new information. Most information and price discovery is conducted by high-resource and high-powered investments firms, not individual investors. Despite this efficiency, some pricing anomalies still exist in the stock market. If an asset class has higher (or lower) prices than academics suspect due to market efficiency, it might be due to the following reasons: A key risk factor has not been identified, there is an institutional factor that has not been considered, some investors are lacking information, it could be a result of cherry-picking data and have no theoretical significance, or perhaps it was entirely random and will revert back to the expected price.