Finance

An efficient market is defined as a market that accurately and fully reflects all available information in the form of prices. This would make it impossible for any investor to consistently generate higher returns than the market average i.e. no investor can beat the market consistently except by luck.

Market efficiency can be further explained in the form of 3 types of efficiencies price, operational and allocation efficiency. In case of capital markets efficiency is usually determined by price efficiency. Price efficiency can be understood as prices of assets being determined by expected future cash flows, and that any data or information that may have any impact on the future cash flows is available to all investors and reflected in the quoted price of the security also any new information will be instantly reflected in adjusted price. Operational efficiency refers to the cost of transactions. Allocation efficiency is the extent to which capital is directed to the most competitive or profitable sectors of the economy.

The efficient market is therefore characterized by rational (profit seeking) investors, free and accurate information availability i.e. no informational discrepancy between investors.

Efficient Market Hypothesis is stated in three forms weak, semi-strong and strong. The weak form states that the historical price and performance is not reflective of future price and performance and therefore investors cannot predict trends. The semi-strong form states that all public or commonly known facts and information are reflected in the market price. Public information includes financial accounts, press releases etc. The strong form dictates that all information i.e. both public and private is available and known by all investors and reflected in the equilibrium prices of the securities. Private information includes information not available to general public. The strong form eliminates distinction between public and private information. Due to the unrealistic demands of the strong form all markets generally fall between semi strong and weak forms.

Monte Carlo simulation model works by integrating together probability distributions and sensitivities for a set of variables in determining the probability distribution of the NPV of the project. A computer is programmed to select a random value for each variable from a range of values, which are specified in the beginning for each variable. Variables are factors, which cannot be predicted with certainty these may include sales volume, sales price, variable costs, overhead costs, inflation and prices of inputs such as oil etc. The program calculates the values the NPV for the project based on the selected set of values and stores the result i.e. NPV in its memory. This can be understood as the first simulation run, the program will repeat this process i.e. select another random set of values and calculate another NPV, a number of times, perhaps 1000 or even more if required. The result is a set of NPVs that are approximately normally distributed. Unlike the scenario or situational analysis that only generates a single or few NPVs, the Monte Carlo simulation generates a range of NPVs with the mean NPV, standard deviation and also the NPV probability distribution.

The key benefit of Monte Carlo simulation is that it allows the analyst to test the project feasibility against a set of possible changes. Similar to other sensitivity analysis techniques except that due to numerous reruns, the simulation results tend to be more comprehensive and rigorous. A 3 situational analysis (optimistic, realistic, pessimistic) simply reveals the conditions under which the NPV is positive and its expected value. However a simulation will reveal the probability of a positive NPV, NPV distribution skewness, overall risk of the project (standard deviation), and the maximum potential upside and downside. In short the results of the simulation are important because they offer much more detail than a simple NPV or IRR analysis.