Academic journal article Financial Services Review

Investment Performance of AAII Stock Screens over Diverse Markets

Academic journal article Financial Services Review

Investment Performance of AAII Stock Screens over Diverse Markets

Article excerpt

1. Introduction

As part of an investment process, investors consider the extent to which they use passive and active approaches to the equity market. Individuals often use a passive approach by investing in broadly diversified index funds rather than spend time and energy trying to pick stocks. The passive approach is supported by efficient market arguments that active trading of stocks will not consistently beat the market index on a risk-adjusted basis. The efficient market hypothesis is based on the view that a large numbers of rational investors with access to the same information will generate security prices that are unbiased estimates of security values. As a result, there should be no consistent abnormal returns from active trading.1

Active investors seek opportunities for excess rates of return that are generally linked to behavioral explanations for departures from investor rationality. Behavioral finance theories maintain that predictable irrationality in security pricing, linked to human biases and heuristics, causes predictable deviations from efficient market pricing. These predictably irrational behaviors of investors have been tested and validated in behavioral finance studies reviewed by Nofsinger (2013) and have been translated into investment strategies by Pompian (2012). Ultimately, the ability to use information and metrics about stocks to earn an excess return is an empirical issue.

Even with market inefficiencies, small investors may remain passive because they feel that professionals with large research budgets have size, diversification, technology, transaction cost, and information advantages. Empirical evidence suggests that individual investors generally do not make good stock selection decisions. Barber and Odean (2000) found that returns on stocks sold by individuals subsequently turned out to be higher than returns on stocks they bought. In the same study Barber and Odean found that the average household with an account at a large discount brokerage firm underperformed by an average 15 basis points per month even without transaction cost adjustments. Korniotis and Kumara (2013) analyzed investment accounts of over 60,000 customers of a major discount broker over the period from 1991 to 1996. They found that retail investors (less informed) underperformed wholesale investors (well informed) by over 200 basis points per year on average.

A wide range of professional services are available to help individuals overcome disadvantages relative to well informed investors. One such service available to individual investors is provided by the American Association of Individual Investors (AAII). AAII is a nonprofit investment education organization offering individual investors a set of low cost services supporting active trading strategies. Investors have access to stock screens based on what AAII believes well known and highly regarded investors use to identify attractive stocks. AAII claims that 91% of their screened portfolios beat a passive S&P 500 index but there is no information given on performance after transactions costs, risk adjustments, and tests of statistical significance.2

In this article we present a range of performance measures addressing both practical and theoretical issues to test the investment performance of AAII strategies. Specifically, prior tests of investment performance from using mechanical screens are extended as follows:

1. We extend AAII performance analysis in prior studies to more recent years that include the period since the 2008 market crash. Our results help establish whether or not prior findings are robust over more recent holdout periods.

2. We introduce an analysis of both the mean and median monthly returns to identify potential skewing of returns.

3. We conduct a more rigorous analysis of screened portfolio performance by using factor models. In this way we test for screened portfolios that offered significant excess returns even after more extreme risk factor hypotheses are considered. …

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