What Does Robust Mean?

Robust is a characteristic describing a model's, test's or system's ability to effectively perform while its variables or assumptions are altered, in order for a robust concept to operate without failure under a variety of conditions.

For statistics, a test is claimed as robust if it still provides insight to a problem despite having its assumptions altered or violated. In economics, robustness is attributed to financial markets that continue to perform despite alterations in market conditions. In general, being robust means a system can handle variability and remain effective.

Understanding Robust

Financial models are an integral part of running a corporation. From the corporate executives of large multinational corporations, to the franchise owner of the local burger restaurant, decision-makers need up-to-date information presented to them in a model form that best reflects the activities of the business. Investors also use financial models to analyze and forecast the value of corporations to determine if they are viable prospective investments.

Business Financial Models

Business financial models focus mainly on the fundamentals of a corporation/business, such as revenues, costs, profits and other financial ratios. A model is considered to be robust if its output and forecasts are consistently accurate even if one or more of the input variables or assumptions are drastically changed due to unforeseen circumstances. For example, a specific cost variable may sharply increase due to a severe decrease in supply resulting from some sort of natural disaster. Another commonly unforeseen circumstance is when major countries go to war. This has effects on all sorts of financial variables, which cause models that are not robust to function erratically. A robust model will continue to provide executives and managers with effective decision-making tools, and investors with accurate information on which to base their investment decisions.

Robust Trading Models

While investors analyze a corporation’s fundamental data in order to find securities that are priced below market value and are therefore perceived to be a good investment, traders analyze a security’s price data using technical analysis to forecast price movements that result from disparities in the security’s supply and demand of the moment. Traders that use computerized trading systems to analyze and trade markets using technical analysis do so by developing, testing, and optimizing statistical models based on the application of technical indicators to the price data of a security. Some of the more popular indicators include moving average crossover, moving average convergence-divergence (MACD), Bollinger Bands and relative strength index (RSI), just to name few. A trading model is considered robust if it is consistently profitable when applied to various securities and in all market conditions including up trends, down trends, and range-bound markets. Very often, a trading model will function very well in a specific market condition or time period.

However, when market conditions change, or the model is applied to another time period or the future, the model fails horribly, and losses are realized. This is usually the result of a trading model that is not robust.