QUANTITATIVE METHODS FOR BUSINESS

Quantitative Methods for Business



Quantitative Methods for Business

P1. Forecasting Approaches

Moving Averages

Moving Average is probably the best known and extensively used technical analysis indicator, which is effective in capturing the trend of financial markets in easily identified and interpreted way. Moving average is a technical analysis tool, effective in identifying dynamic support and resistance levels, and the development of many trading systems. In relation to this, simple moving averages calculate the average values ??through the latest in a series of data (Guo, Huang & He, 2008).

Simple moving average can be determined as the mean of the closing prices for the numbers of period. For case in point, simple moving average of 5 days is the outcome of the sum of closing prices recorded in the last 5 days divided by 5.

= 21 + 22 + 23 + 24 + 25 = 115

= 115/ 5 = 23

If in the same example, the following closing price is 26, then it is added to calculate the moving average and the oldest days price series will be removed that is 21. Then, the new five days moving average will be determined in following way:

= 22 + 23 + 24 + 25 + 26 = 120

= 120/ 5 = 24

So the last 2 days simple moving average moved from 23 to 24. As the old days will be removed, new days are included to the series and by consequence the moving average will keep on changing.

Weighted Moving Averages

The weighted moving average is used to solve the problem of equal weights. This indicator is calculated as the sum of all closing prices divided by a certain time period and multiplying the sum of the amounts that is weights of each day. For case in point, for an average period of five days, the closing price of today will be multiplied by five, of the last on the fourth and so on until the first day of the scale period is reached. These values ??are then summed and divided by the sum of multipliers (Hsien-Chung, 2003).

Exponential Smoothing

The exponential smoothing methods have been around since 1950 and are still the most popular forecasting methods used in the commercial and industrial sector. However, the use of exponential smoothing was very limited, because it was necessary for computational aid. Today, the equations of exponential smoothing methods to estimate the parameters and generate the predictions are quite intuitive and easy to understand. As a consequence, this forecasting method has been implemented in several commercial applications (Navon & Samorodnitsky, 2009).

The area of exponential smoothing has undergone a substantial revolution in the past decade. On this subject, much has been discussed in numerous magazine articles, for researchers and practitioners using the technique enough to compare time series models and find better predictions (Navon & Samorodnitsky, 2009).

P2. Steps for Linear Programming Problem Formulation

Linear programming modeling or the problem formulation is the method of converting verbal statements of the problem into the mathematical ...