Direct investments in securities: A primer

Direct investment plans (commonly known as DRIPs) let investors bypass traditional investment channels and avoid problems such as high transactions costs and the relatively large dollar amounts necessary to purchase certain assets. While no one expects these plans to answer all of the modern investor's needs, DRIPs probably appeal to the buy-and-hold clientele seeking the lowest possible transactions costs. ; This article discusses DRIPs, describing how the financial services industry has evolved to meet the needs of the small investor. The author identifies the remaining limitations on this sort of investment, noting that mutual funds continue to offer convenience and unmatched diversification for small accounts. He then presents reasons why companies might offer DRIPs. For example, companies that face political or regulatory scrutiny may want a broad, stable ownership base. Such shareholders also tend to vote with management, offering potential as a takeover defense. Finally, a broad ownership base provides opportunities for cross-selling. ; The article also identifies empirical differences between companies that offer DRIPs and those that do not. The analysis shows that large companies, more mature companies, and companies in industries that are subject to relatively high levels of regulation are more likely to offer the plans. ; Finally, the discussion speculates about the future of direct investments. One obvious tool for DRIP investors is the Internet. Broker-run DRIPs provide another evolutionary direction.Investments ; Saving and investment

Asset allocation and section 529 plans

Previous research has concluded that prespecified asset allocations used by many Section 529 college savings plans are suboptimal. We extend this research to show that though it may be true, it is true for reasons other than those asserted in previous research. In addition, it tends to deflect attention from other investment options and strategies.Investments ; Saving and investment

Merchant acquirers and payment card processors: a look inside the black box

Each year, hundreds of millions of credit and debit cardholders make billions of transactions worth trillions of dollars. Yet few consumers are aware that such transactions travel through, and are made possible by a highly evolved group of intermediaries that sign up merchants to accept cards, handle card transactions, manage the dispute-resolution process, and, along with regulatory agencies, set rules that govern card transactions. ; This article demystifies the “Black Box” of the transactions process for payment cards. After describing a simple transaction with a private-label card, the author then considers the complications introduced by general-purpose cards, such as Visa and MasterCard, emphasizing the key roles of merchant acquirers and card processors. ; Merchant acquirers, who sign up merchants to accept cards and who provide or arrange for processing, bear most of the risk of loss if merchants fail to make good on credit transactions disputed by customers. To guard against such losses, acquirers carefully evaluate the credit quality of merchants seeking or using the acquirers’ services. ; The article delineates some of the risk factors associated with specific industries, merchant types, and transactions that influence the price merchants pay for acquirers’ services. Finally, the article discusses some ways that merchant acquirers manage risk, especially the risk of fraud.Payment systems ; Credit cards ; Risk

Expected returns to stock investments by angel investors in groups

Angel investors invest billions of dollars in thousands of entrepreneurial projects annually, far more than the number of firms that obtain venture capital. Previous research has calculated realized internal rates of return on angel investments, but empirical estimates of expected returns have not yet been produced. Although calculations of realized returns are a valuable contribution, expected returns, rather than realized returns, drive investment decisions. We use a new data set and statistical framework to produce the first empirical estimates of expected returns on angel investments. We also allow for the time value of money, which previous research has typically ignored. Our sample of 588 investments spans the 1972–2007 period and contains 419 exited investments. We conduct extensive tests to explore potential bias in the data set and conclude that the evidence in favor of bias is tenuous at best. Our results suggest that angel investors in groups can expect to earn returns that are on the order of returns on venture capital investments. Estimated net returns are about 70 percent in excess of the riskless rate per year for an average holding period of 3.67 years. This estimate is reasonable compared to Cochrane's (2005) estimate of 59 percent per year for venture capital investments, which tend to be in lower-variance, later-stage projects. Returns have a large variance and are heavily skewed, with many losses and occasional extraordinarily high returns.

A discrete choice model of dividend reinvestment plans: classification and prediction

We study 852 companies with dividend reinvestment plans in 1999 matched by total assets to 852 companies without such plans. We use discrete choice methods to predict the classification of these companies. We interpret the misclassified companies as being likely to switch their plan status. That is, if a firm's financial data suggest that a company should have had a dividend reinvestment plan in 1999 but did not, then we expect that it would be more likely to institute a plan than the other companies in the sample. Conversely, if it did have a plan but the financial data suggest that it should not, then we expect that the company would be more likely to drop the plan. We use data from 2004 to explore this conjecture and find evidence supporting it. Our model is an economically and statistically reliable predictor of changes in plan status. We also identify which variables have the most influence on a company's decision whether or not to offer a plan.

Analyzing imputed financial data: a new approach to cluster analysis

The authors introduce a novel statistical modeling technique to cluster analysis and apply it to financial data. Their two main goals are to handle missing data and to find homogeneous groups within the data. Their approach is flexible and handles large and complex data structures with missing observations and with quantitative and qualitative measurements. The authors achieve this result by mapping the data to a new structure that is free of distributional assumptions in choosing homogeneous groups of observations. Their new method also provides insight into the number of different categories needed for classifying the data. The authors use this approach to partition a matched sample of stocks. One group offers dividend reinvestment plans, and the other does not. Their method partitions this sample with almost 97 percent accuracy even when using only easily available financial variables. One interpretation of their result is that the misclassified companies are the best candidates either to adopt a dividend reinvestment plan (if they have none) or to abandon one (if they currently offer one). The authors offer other suggestions for applications in the field of finance.

Financial market frictions

Market frictions, which exist even in efficient markets and change over time, impede trade but also offer profit opportunities. To provide a framework for understanding market frictions, the authors classify frictions into five categories.Financial markets

Can simple models explain Zipf’s law for all exponents?

H. Simon proposed a simple stochastic process for explaining Zipf’s law for word frequencies. Here we introduce two similar generalizations of Simon’s model that cover the same range of exponents as the standard Simon model. The mathematical approach followed minimizes the amount of mathematical background needed for deriving the exponent, compared to previous approaches to the standard Simon’s model. Reviewing what is known from other simple explanations of Zipf’s law, we conclude there is no single radically simple explanation covering the whole range of variation of the exponent of Zipf’s law in humans. The meaningfulness of Zipf’s law for word frequencies remains an open question.Peer ReviewedPostprint (published version