Search results

The purpose of this paper is to examine the practices of professors teaching the introductory class in investments.

A sample of 101 syllabi of the first investments course taught in various AACSB accredited business schools around the country was collected. Several dimensions of course content are summarized: the primary textbook selections, other required and recommended materials including the use of spreadsheets, financial calculators, financial magazines such as the Wall Street Journal (WSJ), and/or stock market games, grading policies including assessment components and their weights, and course contents based on various investments topics.

Classroom practices of investments professors differ considerably. There is virtually nothing that is universally applied by investments faculty. There are, however, many areas such as key content to include in the course where a large majority tends to agree with each other.

While there is obviously not a single right way to teach investments, many professors may be able to improve their classes or their assessment methods by trying some of the things that others are doing. Including stock market games, for example, might enlighten the students and encourage more classroom discussion.

The authors present the results of a survey on how Korean firms evaluate new projects and estimate their capital costs. The authors report how Korean firms’ capital budgeting practices compare to other developed countries and to best practices in the field of finance.

The authors survey CFOs of major Korean firms on their capital budgeting practices. The authors then compare the results against the US and European firms and best practices of leading firms and financial advisors.

The authors find that the capital budgeting practices of Korean firms are as strong as or stronger than firms in developed markets. A majority of Korean firms use best practices techniques such as NPV, IRR and the CAPM for project evaluation and cost of equity estimation. Chaebol affiliation results in somewhat stronger capital budgeting practices. The authors also find that other factors, such as company size, leverage, CEO age and CEO education, impact capital budgeting practices.

This paper is the first article that comprehensively examines Korean firms' capital budgeting practices.

The authors investigate how the stock market reacts to financial restatements using the restatements data from the United States Government Accountability Office (GAO-06-678). In particular, the purpose of this paper is to examine the long-run equity performance of the restating firms, for holding periods of one to five years after the announcements of restatements.

This paper measures the long-run stock performance of restating firms with the buy-and-hold abnormal returns and time-series regression analyses based on Fama–French’s (1993) three-factor model and Carhart’s (1997) four-factor model.

The authors find that restating firms significantly underperform in the long run compared with their peers matched by industry, size and book-to-market. Restating firms’ underperformance is confirmed with time-series regression analyses based on Fama–French’s (1993) three-factor model and Carhart’s (1997) four-factor model. Further, the authors find the negative long-run abnormal performance of restating firms is primarily driven by large firms. The authors also report that self-prompted restatements and improper revenue accounting-triggered restatements result in worse long-run abnormal performance.

This paper is the first paper that thoroughly investigates the long-run stock returns of the firms that restate financial statements by fully considering the size effect.

The purpose of this paper is to provide a plausible explanation for the “sell in May” anomaly observed in US stock markets. A heretofore unexplained strategy of selling stock in May and not returning to the market until November has been shown to outperform a simple strategy of buying and holding stock all year long.

The authors compare the seasonal performance of three US size-based portfolios for the May–October and November–April periods considering whether or not they were in years with US congressional elections, which occur every two years.

While the sell-in-May effect appears to persist in the long run, the authors find that the anomaly is not present in non-election years. There is no significant difference between the May–October and November–April stock returns in non-election years. The observed sell-in-May effect is driven by poor stock returns in the May–October periods leading up to US presidential or congressional elections and subsequent strong performance in the November–April periods immediately following elections.

The paper offers an election-year effect as an explanation of the sell-in-May anomaly that has been observed in the US stock market. Other possible explanations of the effect, such as seasonal affective disorder, the weather, and daylight savings time, have not gained widespread acceptance.

Aims to test to determine whether the selection of the historical return time interval (monthly, quarterly, semiannual, or annual) used for calculating real estate investment trust (REIT) returns has a significant effect on optimal portfolio allocations.

Using a mean‐variance utility function, optimal allocations to portfolios of stocks, bonds, bills, and REITs across different levels of assumed investor risk aversion are calculated. The average historical returns, standard deviations, and correlations (assuming different time intervals) of the various asset classes are used as mean‐variance inputs. Results are also compared using more recent data, since 1988, with, data from the full REIT history, which goes back to 1972.

Using the more recent REIT datarather than the full dataset results in optimal allocations to REITs that are considerably higher. Likewise, using monthly and quarterly returns tends to understate the variability of REITs and leads to higher portfolio allocations.

The results of this study are based on the limited historical return data that are currently available for REITs. The results of future time periods may not prove to be consistent with the findings.

Numerous research papers arbitrarily decide to employ monthly or quarterly returns in their analyses to increase the number of REIT observations they have available. These shorter interval returns are generally annualized. This paper addresses the consequences of those decisions.

It has been shown that the decision to use return estimation intervals shorter than a year does have dramatic consequences on the results obtained and, therefore, must be carefully considered and justified.

Notes US efforts to make documents easier to read and reviews previous research on readability. Presents a study of the readability of the investment objective sections of two mutual fund prospectuses, using a sample of college students, the Cloze Readability Procedure and the Flesch readability analysis. Finds the Cloze test scores well below the “moderately readable” level of the Flesch assessment “difficult”. Analyses the relationships between students’ understanding and their training, investment experience, financial information, gender etc.; and notes that they misjudged their ability to read the prospectuses accurately. Calls for pressure to make the mutual funds improve their literature and for universities to provide training impersonal finance.

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 describe some optimization exercises which have proved to be very useful for introducing students to Markowitz‐style mean‐varience optimization.

This paper describes two exercises that walk students through the process of gathering security price and dividend data, estimating the parameters of the joint distribution of asset returns, and then using a portfolio optimizer to construct mean‐variance efficient portfolios. It describes the basic methodology, and the more complex formulations of the portfolio optimization problem that are used in practice.

Portfolio selection is typically taught in finance courses as an abstract solution to a system of equations, and does little to connect the portfolio construction process to Exchange Traded Funds, stocks, bonds and other assets that are traded in markets. This study offers a practical approach to teaching portfolio optimization, that starts with gathering market data and shows how a quadratic optimization system is used to construct mean‐variance optimal portfolios.

The exercises in this case study prepare students to construct mean‐variance efficient portfolios for asset allocation with Exchange Traded Funds, and for building stock and bond portfolios, using market data and a portfolio optimizer.

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.