Financial crises inflict significant human as well as economic hardship. This paper focuses on the human fallout of capital market stress. Financial stress-induced behavioral changes can manifest in higher suicide and murder-suicide rates. We find that these rates also correlate with the Gross Domestic Product (GDP) growth rate (negatively associated; a -0.25% drop [in the rate of change in annual suicides for a +1% change in the independent variable]), unemployment rate (positive link; 0.298% increase), inflation rate (positive link; 0.169% increase in suicide rate levels) and stock market returns adjusted for the risk-free T-Bill rate (negative link; -0.047% drop). Suicides tend to rise during periods of economic turmoil, such as the recent Great Recession of 2008. An analysis of Centers for Disease Control and Prevention (CDC) data of more than 2 million non-natural deaths in the US since 1980 reveals a positive correlation with unemployment levels. We find that suicides and murder-suicides associated with adverse market sentiment lag the initial stressor by up to two years, thus opening a policy window for government/public health intervention to reduce these negative outcomes. Both our models explain about 73 to 76% of the variance in suicide rates and rate of change in suicide rates, and deploy a total of four widely available independent variables (lagged and/or transformed). The results are invariant to the inclusion/exclusion of 2008 data over the 1980–2016 time series, the period of our study. The disconnect between rational decision making, induced by cognitive dissonance and severe financial stress can lead to suboptimal outcomes, not only in the area of investing, but in a direct loss of human capital. No economic system can afford such losses. Finance journal articles focus on monetary alpha, which is the return on a portfolio in excess of the benchmark; we think it is important to be aware of the loss of human capital as a consequence of market instability. This study makes one such an attempt.

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is an associate professor of finance at the University of Maine in Orono, ME, and founding president of Cloud Epsilon LLC. pankaj.agrrawal@maine.edu T his article seeks to extend classic investing, which is often limited to only domestic equities or to a mix of equities and bonds, to a wider array of lower-correlation non-equity assets. The ready availability of highly liquid index exchange-traded funds (ETFs) on assets such as domestic equities, international equities, treasury/sovereign bonds, real estate, gold bullion, and foreign currencies has the potential to extend our availability set and the resulting risk-return (σ−μ) efficient frontier beyond what is possible with only equity-only portfolios. This article shows that. This could take us a step closer toward meeting the all-inclusive, but elusive, "true market portfolio" (Roll [1977]) and thinking "out of the equity box" that we seem to be perpetually trapped in.Investors stand to gain from the additional diversification made feasible by extending into a multi-asset-class (MAC) portfoliobased covariance matrix. The shrinkage of the asset covariance structure (Choueifaty, Froidure, and Reynier [2013]) and an overall reduction in the cross-correlations of the constituent assets (Willenbrock [2011]) have the potential to produce efficient frontiers that would not be possible with pure-equity-based portfolios. Eventually, that would be an efficiency gain for the investors.The article uses the Markowitz [1956] mean-variance optimization (MVO) process to show that the efficient frontier of a MAC portfolio dominates not only the capitalizationweighted Russell 1000 Index but an all-equity mean-variance optimization (MVO) efficient frontier as well. The risk-adjusted Sharpe ratios (Sharpe [1987]) and the beta of the MAC portfolio are developed and reported as well. Furthermore, in addition to the MVO portfolios, a 1/N equal-weighted portfolio is created from the constituents of the MAC portfolio and plotted in the σ−μ space. The alternate portfolios are then tested for relative portfolio efficiency by the application of the exact Gibbons, Ross, and Shanken [1989] GRS W-test. LITERATURE AND DATABesides risk-return efficiency, a tradable implementation of the diversified MAC portfolio approach was also a consideration in the design of this study. To enable that, a set of highly liquid ETFs were selected (Agrrawal and Clark [2009]) that represented six major asset classes. Roll [2013] wrote that "across asset classes, ETF heterogeneity might be acceptable… though it is not that impressive within each class" and discussed the equity, bond, commodities, and currency asset classes. The Blake, Lehmann, and Timmermann [1999] strategic allocation study on U.K. pension funds included real estate as an additional asset class. Black and Litterman [1992] showed how quantitative asset allocation models could significantly improve The Journal of Index Investing 2013.4.2:83-94. Downloaded from www.iijournals.com by NEW YORK UNIVERSITY on 07/23/15.It is illegal to make unautho...

Purpose-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. Design/methodology/approach-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. Findings-Numerical information placed on major finance websites can be harvested into structured machine-readable datasets by applying this algorithm. Research limitations/implications-One significant change in Microsoft Excel 2007 is that the worksheet is expanded from 2 24 to 2 34 cells, or to be more specific, from 256 (IV) columns  65,536 rows (2 8  2 16) to 16,384 (XFD)  1,048,576 (2 14  2 20). 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. Practical implications-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). Originality/value-An exhaustive literature search did not find any paper that discusses a multiple ticker algorithm for web harvesting.