Market inefficiency is a period in the world of trading where there are immense opportunities. Nevertheless, along with the opportunities a trader needs to be confident about things such as its speculative ability, a strategy that helps to buy and sell at the right time etc.
In short, in the case of an inefficient market, it is extremely important to follow the right approach and that is what we are going to focus on in this blog that covers:
- What is an inefficient market?
- Factors that lead to market inefficiency
- Examples of market inefficiencies in the past
- What is the Efficient Market Hypothesis?
- Types of Efficient Market Hypothesis
- Implications of an efficient market
- Market efficiency vs Market inefficiency
- How to trade in an inefficient market?
- Advantages of trading in an inefficient market
- Disadvantages of trading in an inefficient market
What is an inefficient market?
An inefficient market is nothing but the condition in the financial markets where the particular security’s price doesn't trade at its true value. Hence, the market functions in a non-efficient manner.
This non-efficient price of the tradeable items occurs when a recent event/news or speculation of an event/news renders the market with lesser or higher price of the securities or the tradeable items compared with the actual or fair value.
For instance, Black Tuesday or Wall Street Crash 1929 was one of the worst market crashes when investors traded around16 million shares on the New York Stock Exchange in a single day.
The very next day panic selling began and eventually there were no buyers for the stocks. Since the investors could not assess the actual value of the stocks, the panic followed through and this event eventually led to what is known as the Great Depression.
Also, there are various factors that lead to market inefficiencies that we will discuss next.
Factors that lead to market inefficiency
There are several factors because of which the market becomes inefficient. The two types of factors are as follows:
The external factors are such which occur due to the following:
- Market anomalies due to crisis
- Earnings release
Market anomalies due to crisis
The market anomalies arise due to the news updates such as natural or man-made crises. For instance, the Ukraine-Russia war in 2022 is causing market anomalies leading to the price of crude oil reaching an all-time high. This high price is an anomaly since it is not equal to the fair value of crude oil.
On the contrary, FMCG (Fast Moving Consumer Goods) is one of the industries that is poorly affected owing to the increase in the prices of key inputs in the products.
For instance, Russia and Ukraine together account for more than a quarter of global wheat exports, while Ukraine alone makes up almost half of the exports of sunflower oil. But because of the lack of exports in the war scenario, the key inputs, i.e., wheat, sunflower oil etc. have become expensive.
Consequently, the FMCG products have become expensive and the demand for the same has gone down. Hence, the inflation has made the speculators doubt the favourable returns of the FMCG sector leading to poor performance in the market.
Let us see the performance of both FMCG and Crude oil in the graph below. NESGJ (Nestle Futures) and BRN2! (Brent Crude Oil Futures) both can be seen in the graph below.
It is clearly visible that crude oil is trending at a much higher price than usual, whereas Nestle’s share price is depicting the condition of the FMCG sector as it is trending at a much lesser price as compared to its value.
Earnings announcement is the information relating to the company’s performance over a period. If the announcement portrays strong earnings, the investors and traders become optimistic about its performance and the stock prices go up (and vice versa).
For an instance let us assume that company A’s stocks are worth $40 but because of the current earnings announcement (which is affected by a scenario such as war, tsunami, etc.) company A’s stock is trending at $20.
Here, the price is trending at a much lower price as compared to the actual worth or value because of a temporary crisis. Conversely, the price can also be trending at abnormal or artificially high rates because of a bubble. Hence, that bubble will also be considered market inefficiency.
Recommended read: Impact of earnings announcements on share prices: Switzerland
Moving on to the next factor leading to market inefficiency, let us find out how there are some investor-related factors (solely dependent on the response by the investors in the market). These are as follows:
- Price speculation
- Investor’s reaction to the news of crisis
Speculation of traders leads to market inefficiencies because, in the case of an efficient market, the actual value is equal to the share prices.
But, in case the traders speculate prices to go up or down in the coming time because of an upcoming event (for instance, the election result), the shareholders can buy or sell the shares accordingly. This can lead to market inefficiency.
Investor’s reaction to the news of crisis
Whenever a news makes an impact on the valuation of tradeable items, if the investor reaction is delayed it leads to market inefficiency.
This delay in the reaction gives the opportunity to many traders to take such positions that can give favourable returns. But, in this little window (until the investors react and make the value equal to the price), huge losses can also accrue if the speculation, followed by the news, goes wrong (like the dotcom bubble burst).
Examples of market inefficiencies in the past
Coming to the practical examples next, let us head to see the market inefficiencies in the past. The examples go as follows:
Dotcom bubble (1996 - 2001)
The dotcom bubble, also known as the dotcom boom as well as the Internet bubble, was basically a stock market bubble. This bubble occurred in the 1990s as a result of excessive speculation that the firms operating online are to witness massive growth in the coming time.
This expectation led to a tremendous amount of investment influx in the internet-based firms despite the firms showing little to no growth potential. Eventually, the dotcom bubble burst, leading to the economic recession in 2001.
Let us see the image below to view the timeline for which the dotcom bubble existed.
The image above clearly shows how from 1996 onward, the speculation regarding internet-based firms showed a lot of optimism about their growth potential. Although, post 2001 a reality check made the traders realise that the internet firms were being overvalued which led to a crash.
Hence, the market was inefficient until the firms’ actual value (with the assumption that the entire information with regard to assets, liabilities, profits and losses etc. of the firm is available) could be reflected in the share prices.
Great recession (2007-2009)
The Great Recession was the sharpest decline in economic activity. Known to be a significant downfall, the recession took place because of the U.S. housing market that went down significantly after a boom.
In this slump, a large amount of mortgage-backed securities and derivatives lost a significant value. The slump occurred because of the:
- Rising home prices due to a real estate bubble that lasted for a short while
- Increase in subprime mortgages
- Loose lending practice
But soon the bubble burst and the banks suffered huge losses due to subprime mortgage crisis leading to shutting down of some banks.
Let us take a look at the image below that shows how mortgage lending could not be curbed. Hence, the housing prices in the U.S. were artificially high until 2007 after which the bubble finally burst, leading to the value loss for mortgage-backed securities.
Hence, it is clearly visible that there were huge losses on the subprime shares of mortgage securities that eventually led to the Great Recession.
What is the Efficient Market Hypothesis?
The efficient market hypothesis (EMH) also known as the efficient market theory, is a hypothesis that states that share prices reflect the entire information of the company/asset to be able to value it correctly.
Hence, it implies that the Efficient Market Hypothesis believes that the stocks always trade at a fair value on the exchanges. Also, if the stocks’ prices are equal to their value, there are no opportunities for the traders to buy an undervalued stock and sell the same when the prices are inflated.
Implications of an efficient market
In such a perfect or efficient market scenario, it is impossible to beat the market by selecting a stock with the potential to reach a higher price in the future. The strategies that a trader/investor can deploy here are:
- The risk-bearing strategy in which a higher risk can be taken in the expectation of a higher return. But a higher risk implies that the risk of losses is also higher.
- Also, one must strategically diversify the investment across the stocks to maintain a portfolio with stocks bearing different levels of risk. This kind of diversification can help in case some stocks’ returns are more favourable than others.
Recommended read: MARKET EFFICIENCY – DEFINITION, TESTS AND EVIDENCE
Types of Efficient Market Hypothesis
There are three forms of Efficient Market Hypothesis (EMH):
Weak Form Efficient Market Hypothesis
The entire past information is priced into securities in this form. Fundamental analysis of securities can provide the information to produce returns above market averages in the short term. But the fundamental analysis does not provide a long-term advantage, and technical analysis doesn’t work.
Semi-Strong Form Efficient Market Hypothesis
Semi-Strong Form Efficient Market Hypothesis implies that neither fundamental analysis nor technical analysis can provide any significant advantage. It also suggests that new information is instantly reflected in the price of the securities.
Strong Form Efficient Market Hypothesis
The entire information, both public and private, is reflected in the price of stocks. Therefore, no investor can gain an edge over the market. Strong form Efficient Market Hypothesis does not say it's impossible to get an abnormally high return. That's because there are always outliers included in the averages.
Efficient Market Hypothesis does not say that you can never outperform the market. It says that there are outliers who can beat the market averages. But there are also outliers who lose big to the market. The majority is closer to the median. Those who "win" are lucky; those who "lose" are unlucky.
Market efficiency vs Market inefficiency
Let us see the notable difference between market efficiency and market inefficiency now.
Entire information regarding the company (the value of the company) is known
Entire information regarding the company (the value of the company) is not known
The actual value of the company is reflected in its price (price is equal to the actual value)
Actual value of the company is not reflected in the price (price is either abnormally high or low)
No opportunities to gain an edge over other traders (by buying at a less price and selling at a higher later) in the market since everyone knows the actual price
Lots of opportunities are created since a trader can speculate, do the analysis and can arbitrage
Price remains stagnant
Price moves to create an efficient market eventually as the actual value comes to be known
How to trade in an inefficient market?
In case of an inefficient market, the trading practice can be modified according to the situation.
Since an inefficient market provides a lot of opportunities for the traders to speculate and decide upon the price of the tradeable assets, it is the right time to expect favourable returns.
Even though an inefficient market is a right time to trade, one must be confident about the ability to bargain and speculate the prices since the tradeable assets can be overvalued or undervalued in such a scenario.
Let us see some of the methods to trade in an inefficient market that is as follows:
- Statistical arbitrage
- Sentiment analysis
Arbitrage is the process of simultaneously transacting multiple financial securities to make a profit from the difference in prices.
This can be done in various ways such as:
- the purchase and sale of the same securities in different markets (Spatial Arbitrage) or
- simultaneous buying and selling of spot prices and futures contract of security or
- buying the stock of a company being acquired while selling the stock of the acquiring company (Merger Arbitrage).
Arbitrage is a risk-free strategy, although this is not always the case. There is always a possibility of execution risk, i.e. risk due to high volatility in the market and a sudden change in price makes it impossible to close the trade at a profitable price. Other risks involved are counterparty risk and liquidity risk.
For instance, a company ABC’s stock trades at $10 per share on the London Stock Exchange (LSE) and the same stock trades at $10.5 on the New York Stock Exchange (NYSE) , an arbitrage strategy would be to purchase the stock at $10 on the LSE and sell it for $10.5 on the NYSE, making a profit of $0.5 per share.
Stat arb algorithms monitor financial instruments that are historically known to be statistically correlated or cointegrated, and any deviations in the relationship indicate trading opportunities.
Stat arb involves statistics, quantitative methods and a computational approach for data mining which can be traded algorithmically at high frequency. Statistical arbitrage includes different types of strategies such as pairs trading, index arbitrage, basket trading or delta-neutral strategies.
These strategies vary depending on the number, types, and weights of instruments in a portfolio and its risk-taking capacity.
For instance, one of the most popular examples of stat arb is Pepsi vs Coca-Cola stocks. Both stocks belong to the same sector, or type of business, and move in tandem as the same market events affect their prices.
Any deviations in the movement of prices in this pair, for instance, if Pepsi stock rises considerably compared to that of Coca-Cola, then one might short the Pepsi stock and long the Coca-Cola stock in anticipation to book profit.
Speculation is a successful practice when the tradeable items in the financial markets do not reflect the correct price because the entire information that could reveal the worth of the tradeable items is not available.
The information might be unavailable due to factors such as a new entity, a technology that is newly introduced, misinformation by social media or news etc. Such factors may not have an immediate reaction in the market.
Hence, in such a scenario, speculation about the prices takes place for deciding whether to buy or sell the tradeable item. Buying takes place in case the stock price is expected to go up in the coming time and selling takes place in case the price is expected to fall.
An individual/trader/investor with the ability to speculate on the basis of:
- Insider information where a trader or a group know the next move of the firm which can decide its profitability
- Own assessments with the help of experiences in the past
- Assessment on the basis of news updates etc.
For instance, a company A is trending at $40, and a trader, with the help of its own assessment or assessment on the basis of news updates, speculates it to go up and buys the shares of the company.
Here, the price can actually go up giving favourable returns or can even go down leading to losses. One needs to be confident about the decisions to make in such a scenario.
Sentiment analysis is the analysis to gauge the market attitude or generic emotions towards particular security, using the news reports, blog posts, social media trends etc.
This process usually involves the usage of natural language processing tools, machine learning, or statistics. Natural language processing (NLP) in simple terms refers to the use of computers to process text in a natural language such as English.
The objective here is to extract information from unstructured or semi-structured data found in these tweets, blogs, and articles and classify them as positive, negative or neutral.
A relationship (or correlation) is drawn between these classifications and the market movements using statistics or machine learning. The sentiment analysis algorithm is then used to infer whether the market sentiment is bullish or bearish. Based on such analysis trading decisions are made.
Advantages of trading in an inefficient market
- Imperfect information in an inefficient market helps to gain an investment edge. Since the actual value of the stocks is not known to other traders, in such a scenario, doing a thorough analysis of the stocks, speculating and then taking a position can be beneficial.
- Value investing is also possible in an inefficient market scenario since it is possible that the traders are underestimating the value of a stock. But, with the strategies such as insider trading, analysis etc. one can indulge in value investing and gain an edge over other traders.
- Growth investing implies investing in a company’s stock that shows the potential to grow in the future. In an inefficient market, a stock may not show its actual value, but with the help of a growth investing strategy, it is possible to speculate on a company’s growth potential and thus, take a position.
Disadvantages of trading in an inefficient market
- Usually, speculators and arbitrageurs are the prominent players in this market and poor judgment can lead to immense losses.
- More often than not, major news releases influence prices in the financial markets positively or negatively. However, in an inefficient market, the prices of tradeable items do not entirely react immediately to the news which can lead to a miscalculation with regard to profitable opportunities with a trading position.
- A noticeable delay may be experienced. That little window creates an opportunity for the minor players to make a profit but, at the same time, huge losses can also accrue.
Market inefficiency may sound like an imperfect scenario, but it is actually favourable since it renders the traders with an opportunity to speculate and gain favourable returns.
If a market is perfect or efficient with all the information of the company available, there will be no opportunities for the traders to gain on the buying or selling of the stocks.
In an efficient market, the actual value will always be known and no trader will gain an edge. Also, we discussed the factors that lead to an inefficient market, the past incidents and the advantages along with disadvantages of trading in an inefficient market.
If you also want to learn how to utilise an inefficient market with the help of statistical arbitrage strategies, explore the course on Statistical Arbitrage Trading. With the help of statistical concepts such as co-integration, ADF test etc. you will be able to identify the trading opportunities after completing this course. This course also includes learning to create trading models using spreadsheets and Python and to backtest the trading strategies for a great trading experience! Happy learning!
Disclaimer: All investments and trading in the stock market involve risk. Any decision to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.