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Knowledge of spreadsheet tools like Microsoft Excel is a valuable skill to have in today’s job market. The preliminary assessment of a group of business school students shows that most of them struggle to perform simple tasks in a spreadsheet. The purpose of this paper is to propose using student tutors to teach these skills.

The authors identify students proficient in Excel as tutors and organize one-on-one peer tutoring lessons. The authors compare the Excel competency level of students prior to and after the tutoring sessions.

The results suggest that most students with minimal Excel skills significantly improve their competency level after tutoring.

The proposed hands-on approach appears to be effective in helping students acquire basic Excel capabilities.

Currently many jobs for undergraduate finance majors require that the student demonstrate advanced Excel modeling skills. The purpose of this paper is to illustrate and explain the Excel Best Practices which should enhance their financial modeling efficiency.

The focus is a way to teach the Excel Best Practices when teaching financial modeling with Excel 2007. It uses a chronological modeling procedure that is consistent with current learning theory and the way students should use these Excel Best Practices. A capital budgeting replacement problem is used to illustrate many of the Excel Best Practices.

It was found that using a chronological modeling procedure is consistent with current learning theory.

Using the procedures mentioned in this paper should result in efficient financial modeling. Efficient models are created in less time, have fewer errors, if any, and are designed for ease of use.

Except for word processing, the electronic spreadsheet is probably the most useful general purpose software for the microcomputer user. Almost all spreadsheets are now packaged in combination with other applications, such as database systems and graphic capabilities. To become proficient in the development of spreadsheet applications, one must be prepared to invest a certain amount of time and effort, but the user will be rewarded by the results. In addition to a general discussion of spreadsheets, the authors critically review Microsoft Excel, version 2.0 (for the IBM PS/2, PC‐AT, or compatibles), noting enhancements over earlier spreadsheets. A sidebar presents spreadsheet templets that can be used to manage a library retrospective conversion project.

The purpose of this paper is to inform the general management community on the qualitative and visualization capabilities available to them through Excel.

The paper provides a basic overview and illustration of a subset set of qualitative capabilities, along with some brief tutorials and tips on how to make use of these capabilities. Discussions are aimed at novices and those with experience in Excel alike. A reference for more in‐depth discussions and guidance is provided.

Access to novel and powerful capabilities that have traditionally been under‐utilized in Excel are within grasp of any management research or practitioner, provided they know where to find some basic guidance and are intrepid enough to test it out. The wave of new, tech‐savvy management users is likely to have a pivotal impact on the way technology‐assisted decision making is thought of in the future.

The opportunity to open the eyes of a wider audience to the convenience and power of Excel as a development, decision support, research and teaching tool is of critical value. This work suggests that such awareness may be instrumental in changing the climate of organizational technology perspectives across a wide range of fields of practice.

During the past several decades, the decision-making process and the decision-makers’ role in it have changed dramatically. Because of this, the use of analytical tools, such as Excel, have become an essential component of most organizations. The analytical tools in Excel can provide today’s decision-maker with a competitive advantage. We will illustrate several powerful Excel tools that facilitate the decision support process.

The purpose of this paper is to develop an algorithm to harvest user specified information on finance portals and compile it into machine‐readable datasets for quantitative analysis.

The Visual Basic macro language in Microsoft Excel is applied to develop code that is not constrained by the single‐query function of Excel. The core of the algorithm is built around the splitting of the URL connector line and the placement of a continuously updating variable into which are looped as many tickers as there are in the input list. The output is then written to non‐overlapping cells.

Numerical information placed on major finance websites can be harvested into structured machine‐readable datasets by applying this algorithm.

One significant change in Microsoft Excel 2007 is that the worksheet is expanded from 224 to 234 cells, or to be more specific, from 256 (IV) columns × 65,536 rows (28 × 216) to 16,384 (XFD) × 1,048,576 (214 × 220). These new limits while allowing for a larger number of tickers, still constrain a single worksheet to 16,384 columns. For five fields per ticker that translates into roughly 3,200 ticker symbols.

The algorithm extends user accessibility to websites that do not provide the facility of simultaneous downloading of information on multiple stock tickers. Furthermore, the procedure automates the downloading of multiple pieces of information (fields) and entire tables per ticker (record).

An exhaustive literature search did not find any paper that discusses a multiple ticker algorithm for web harvesting.

The purpose of this paper is to present a practice briefing in the form of a user’s manual for Excel-based simulation of real estate risk. Based on a generic discounted cash flow model, the simulation incorporates the often ignored heavy tail behaviour of real estate investments, and consolidates Jensen’s inequality. The briefing attempts to explain a model that permits the user to decide whether to include extreme events in real estate risk modelling and how extreme these events may be. Practitioners can generate a variety of modelling outcomes and then choose risk comfort zones in which to contemplate a range of returns.

The paper provides an overview of the underlying mathematical concepts and challenges, as well as on the perspectives on their application from the current academic literature. It offers a step-by-step walk-through of the Excel model (the model being downloadable at: www.mathestate.com).

Existing models for real estate risk modelling fall short with respect to realistic simulation of the probability of extreme events due to challenges in the implementation of stable laws. These former barriers to the implementation of stable laws have been overcome by providing a unique combination of Excel-resident functionalities with a stable pseudo random number generator.

Investment advisers no longer need expensive add-ins to estimate risk. The presented Excel model is more robust than common approaches as it considers distribution shapes that are not otherwise easily available. The only apparent limitation is that users need to be familiar with the most basic functionality of Excel.

Practitioners are provided with an easy-to-use Excel model that does not require further software add-ins. The model simulates real estate investment returns, based on a more realistic inclusion of risk behaviour. It allows specifying how much extreme value behaviour characterizes the volatility in future projections modelled to guide investment decisions.

Risk is a cost to society. Many recent news events demonstrate the importance of including extreme values in modelling. The paper attempts to contribute to more realistic risk estimation in real estate investment.

This briefing introduces a real estate risk simulation model that includes using stable laws, using Excel, a familiar and widely used platform. Such a model has not previously been reported in the academic or practitioners’ literature.

The purpose of this paper is to propose a way to introduce regression analysis into courses with minimal start‐up time. Doing so makes it less likely that introducing both the software and the estimation technique will create discontinuity in the flow of the material being covered.

This paper discusses an Excel workbook that reduces the amount of time students must use to become adept at estimating model parameters.

The workbook provides a set of macros that guides students through the implementation of ordinary least squares (OLS) estimation and provides them with information that is not part of standard Excel output. It also conducts high‐low analysis.

Using this program can reduce the difficulties encountered in having students conduct the valuable exercise of model estimation.

This paper presents a decomposition forecast of stock prices using time series of weekly stock price data as implemented in Excel. The following decomposition components are presented, analyzed, and interpreted including a moving average, a trend, a periodic function, and two shock variables including a triangular shock variable and a level change. The results of the individual components are compared and a discussion of each component’s efficiency is provided. The trend component is statistically significant over the forecast time. The moving average component displays a bi-modal error distribution over varying spans of the moving average and forecast periods. The first mode coincides with random walk behavior with an optimal span and forecast period of one. The second mode is more interesting and applicable for investing beyond the short-term with an optimal spans and forecast periods beyond 75 weeks. The periodic sine function well captures the typical U.S. business cycle of 4–5 years and significantly improves model performance. Finally, the significant outliers remaining from the decomposition are diagnosed and modeled with a triangular shock variable for the bust and recovery associated with the 2008 financial crisis. The model presented does a good job of decomposing the analytical components in forecasting stock prices and provides a useful illustration of Excel methods.