Forecasting sales, expenses and stock market values by quarterly
financial statement ratio analysis: a microcomputer software
development model

doi:10.1108/02686909510079620

Login

Welcome:
Guest

Search for:

Browse:

Resources:

Managerial Auditing Journal

ISSN: 0268-6902

Online from: 1986

Subject Area: Accounting and Finance

Content: Latest Issue | Latest Issue RSS | Previous Issues

Options: To add Favourites and Table of Contents Alerts please take a Emerald profile

Forecasting sales, expenses and stock market values by quarterly financial statement ratio analysis: a microcomputer software development model

Document Information:
Title:	Forecasting sales, expenses and stock market values by quarterly financial statement ratio analysis: a microcomputer software development model
Author(s):	Avi Rushinek, (Professor in the Department of Accounting, at the University of Miami, Coral Gables, Florida, USA.), Sara F. Rushinek, (Professor in the Department of Computer Information Systems, at the University of Miami, Coral Gables, Florida, USA.)
Citation:	Avi Rushinek, Sara F. Rushinek, (1995) "Forecasting sales, expenses and stock market values by quarterly financial statement ratio analysis: a microcomputer software development model", Managerial Auditing Journal, Vol. 10 Iss: 2, pp.7 - 33
Keywords:	Budgeting, Financial statements, Models, Ratios, Sales forecasting, Statistics
Article type:	Case study
DOI:	10.1108/02686909510079620 (Permanent URL)
Publisher:	MCB UP Ltd
Abstract:	Presents a case study demonstrating financial statement ratio analysis (FSRA). This analysis matches company to industry data and builds sales forecasting models. FSRA imputes forecast standards of sales and costs, and applies them to a budgeted financial statement variance analysis for the EE (electronic and electrical) industry. Develops the concept of industry base standards, integrating them into the more traditional statistical and accounting concepts of quality control standards. Provides an implementation example, and reviews possible improvements to the current methodology and approach. Uses a similar methodology to forecast the stock market value with some exceptions. Models sales and costs of an individual company and an industry based largely on aggregate industry databases. For this purpose, uses a multivariate linear trend regression analysis for the sales forecasting model. Defines and tests related hypotheses and evaluates their significance and confidence levels. For an illustration uses the EE industry and the APM company. Also demonstrates a microcomputer-based FSRA software that speeds, facilitates, and helps to accomplish the stated objectives. The FSRA software uses industry financial statement databases, computes financial ratios and builds forecasting models.