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What is a time series?

A financial time series is a set of financial data collected over a specific period of time [1]. Time series analysis, also known as ‘series analytics’ or ‘sequential analysis’, is often used in macroeconomics and financial econometrics to make accurate forecasts on financial markets. One example of this application would be performing financial regression analysis on financial time series to predict financial returns. In financial markets, financial time series may include data such as price of a financial security over time or indices of the stock market which is based on financial securities including stocks and bonds.

What are some types of financial data used for financial forecasting?

There are many different types of financial information that financial forecasters use to forecast financial returns. Financial time series data which financial forecasters commonly analyse include sales, profits, workforce numbers, exchange rates, interest rates and inflation rates.

Time series in finance is used in financial regression analysis to predict financial returns. An example of financial time series data includes the S&P 500 index which is one commonly used financial index in financial markets to measure stock market performance.

What are some different types of financial forecasting?

There are many different techniques that financial forecasters use to analyse financial time series, each with its own benefits or drawbacks. These include: moving average or MA, exponential smoothing models (ESM), autoregressive models (AR), Box-Jenkins models, linear regression models, neural networks and decision trees [2].

The main types of financial forecasts that forecasters focus on are point forecast, interval forecast and distribution forecast [3]. A point forecast is a single financial value such as the financial return on an investment. An interval forecast is a range of financial values such as financial returns over a given time period with a certain probability. A distribution forecast uses an estimated financial distribution that will be used to allocate financial resources [2].

There are also two main types of forecasts: extrapolative and interpretative. An extrapolative model is based solely on historical data and does not make assumptions for future scenarios, whereas an interpretive model makes predictions based off of analysis or analysis combined with external knowledge [1].

What is financial forecasting?

Financial forecasting looks at financial time series to predict the future financial performance of organisations [1]. Financial forecasters use financial models in order to obtain forecasts which can be used for financial decision making.

Forecast verification’ is the process of evaluating forecasts with actual financial outcomes either in terms of financial returns or financial values. Some examples of financial forecasting include financial time series analysis which uses regression equations to forecast financial returns based on historical information, and market sentiment analysis where forecasters focus on releasing reports that provide an accurate financial outlook.

An example of a business that may use financial forecasting is a start-up company who wants to plan when they will need additional capital for future projects or expansions. Another example would be when companies make yearly budget decisions when allocating resources like inventory, staffing levels and advertising campaigns .

A way to benefit from using financial forecasting methods in finance is that financial forecasts can help financial managers make better-informed financial decisions. For example, financial forecasters use financial return data to estimate the financial performance of a portfolio and to forecast future financial returns which could be used by financial managers when deciding whether or not they should stay invested in an asset .

Characteristics of financial time series

Time series are financial data that show numeric or alphanumeric financial values that occur at different times. It can be thought of as ‘a sequence of financial observations over time’. There are three main types of financial time series: finite, periodic and continuous .

A financial time series with a finite number of terms is referred to as a finite financial time series. An example would be stock prices on Monday morning compared to Friday evening for one week.

A financial time series which has an infinite number of terms is called a continuous financial time series. These kinds of financial timeseries include interest rates, exchange rates and inflation rates.

Time Series modelling volatility approaches

The financial time series forecasting models that are used to estimate financial forecasts include the autoregressive integrated moving average (ARIMA), Box-Jenkins and financial econometrics models. The financial econometrics model is based on financial time series analysis which uses regression equations to forecast financial returns; it can be used for both qualitative and quantitative data. Auto Regressive Integrated Moving Average (ARIMA) includes an autoregressive term, a moving averaging term and an integration process. This model is usually applied when there are no significant differences between the historical errors of the financial time series being analysed .

Box-Jenkins uses statistical techniques including multiple regression equations in order adjust for the issues which arise from financial time series which have a trend, a seasonal effect and a random error.

Financial econometrics models use financial forecasting equations to calculate financial forecasts that are based on financial returns from previous periods. They can be used for both financial forecasting qualitative financial data; qualitative financial data has numerical values that represent non-numeric items such as growing trends, seasons or political standpoints.

Merits and demerits of volatility models

Merits

The advantages of using financial econometrics models to analyse financial data include: they can be used for both qualitative and quantitative data; there is no need to use conversion factors to convert these kinds of financial time series into their natural units; they are flexible in terms of being able to incorporate non-stationary financial time series; there is no need to calculate seasonal components which are often difficult to calculate and financial returns data can be easily used in financial econometrics models.

The advantages of using an Auto Regressive Integrated Moving Average (ARIMA) model to analyse financial data include: it ensures that a financial time series has stationary properties which means that financial returns data can be used in financial return equations; seasonal components are automatically removed from financial time series which reduces the need to calculate them and there is no need to calculate conversion factors to convert financial time series into their natural units .

Demerits

The disadvantages of financial econometrics models include: they cannot deal with non-stationary financial time series very well; they do not produce accurate financial forecasts for long-term financial forecasting when compared to ARIMA or Box-Jenkins models ; the results are sensitive to changes in input values when using these types of financial forecasting models .

The disadvantages of using an ARIMA financial forecasting model include: it cannot deal with non-stationary financial time series very well; financial forecasts are not very accurate for long term financial forecasting when compared to Box-Jenkins models; the results are sensitive to changes in input values when using these types of financial forecasting models based on regression analysis .

Box Jenkins uses statistical techniques including multiple regression equations in order adjust for the issues which arise from financial time series which have a trend, a seasonality effect and a random error . The financial forecasts generated using financial econometrics models are based on financial returns from previous periods.

Financial Returns

financial return is the change in value over some specified period; positive when the value goes up and negative when the value goes down . The financial return equation will show how much of financial return came from changes in prices (P) and how much came from dividends (D). For example, if the percentage returns were 0% this would mean that financial returns were zero .if the percentage rates were 10% it would indicate financial returns of 10%. The financial return equation is:

P = (R – D) x 100

where R= financial returns and D= financial dividends.

Financial time series which do not have an explicit financial start or financial end-date are called periodic financial times series. They occur everyday, week or year for example.

Diagnostic Tests

Tests for financial time series include the Durbin Watson Statistical Test, the Breusch-Pagan/Godfrey-Lavyser Lagrange Multiplier Test which are used to test for autocorrelation. This is important because financial time series are not stationary .Therefore they must be tested to see whether or not they meet the assumptions required of an econometric model.

Examples of time series analysis in business and economics

Financial time series include but are not limited to financial return data, financial returns on investment and financial dividends. A financial return equation is used to analyze financial time series which explains what proportion of financial returns came from changes in prices (P) and how much came from dividends (D).

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