Markov Analysis: What It Is, Uses, and Value

What Is Markov Analysis?

Markov analysis is a method used to forecast the value of a variable whose predicted value is influenced only by its current state, and not by any prior activity. In essence, it predicts a random variable based solely upon the current circumstances surrounding the variable.

Markov analysis is often used for predicting behaviors and decisions within large groups of people. It was named after Russian mathematician Andrei Andreyevich Markov, who pioneered the study of stochastic processes, which are processes that involve the operation of chance. Markov first applied this method to predict the movements of gas particles trapped in a container.

Understanding Markov Analysis

The Markov analysis process involves defining the likelihood of a future action, given the current state of a variable. Once the probabilities of future actions at each state are determined, a decision tree can be drawn, and the likelihood of a result can be calculated.

Markov analysis has several practical applications in the business world. It is often employed to predict the number of defective pieces that will come off an assembly line, given the operating status of the machines on the line. It can also be used to predict the proportion of a company's accounts receivable (AR) that will become bad debts.

Companies may also use Markov analysis to forecast future brand loyalty of current customers and the outcome of these consumer decisions on a company's market share. Some stock price and option price forecasting methods incorporate Markov analysis, too.

Advantages and Disadvantages of Markov Analysis

The primary benefits of Markov analysis are simplicity and out-of-sample forecasting accuracy. Simple models, such as those used for Markov analysis, are often better at making predictions than more complicated models. This result is well-known in econometrics.

Unfortunately, Markov analysis is not very useful for explaining events, and it cannot be the true model of the underlying situation in most cases. Yes, it is relatively easy to estimate conditional probabilities based on the current state. However, that often tells one little about why something happened.

In engineering, it is quite clear that knowing the probability that a machine will break down does not explain why it broke down. More importantly, a machine does not really break down based on a probability that is a function of whether or not it broke down today. In reality, a machine might break down because its gears need to be lubricated more frequently.

In finance, Markov analysis faces the same limitations, but fixing problems is complicated by our relative lack of knowledge about financial markets. Markov analysis is much more useful for estimating the portion of debts that will default than it is for screening out bad credit risks in the first place.

An Example of Markov Analysis

Markov analysis can be used by stock speculators. Suppose that a momentum investor estimates that a favorite stock has a 60% chance of beating the market tomorrow if it does so today. This estimate involves only the current state, so it meets the key limit of Markov analysis.

Markov analysis also allows the speculator to estimate that the probability the stock will outperform the market for both of the next two days is 0.6 * 0.6 = 0.36 or 36%, given the stock beat the market today. By using leverage and pyramiding, speculators attempt to amplify the potential profits from this type of Markov analysis.