To implement this test, we compute the average return via lm() and then employ the coeftest() function. Newey and West ( 1994) and available in the sandwich package. This automatic selection is advocated by Whitney K. While it seems that researchers often default on choosing a pre-specified lag length of 6 months, we instead recommend a data-driven approach. One necessary input for Newey-West standard errors is a chosen bandwidth based on the number of lags employed for the estimation. Newey and West ( 1987) \(t\)-statistics to test the null hypothesis that average portfolio excess returns are equal to zero. In the asset pricing literature, one typically adjusts for autocorrelation by using Whitney K. ![]() We compute the average return and the corresponding standard error to test whether the long-short portfolio yields on average positive or negative excess returns. The current chapter relies on the following set of packages.īeta_longshort pivot_wider( id_cols = month, names_from = portfolio, values_from = ret) |> mutate( long_short = high - low) To illustrate how portfolio sorts work, we use estimates for market betas from the previous chapter as our sorting variable. ![]() We start by introducing univariate portfolio sorts (which sort based on only one characteristic) and tackle bivariate sorting in Chapter 9.Ī univariate portfolio sort considers only one sorting variable \(x_\) at time \(t\) as the outcome variable. You can then attribute the differences in the return distribution to the impact of the sorting variable. The different portfolios then represent well-diversified investments that differ in the level of the sorting variable. In general, the idea is to sort individual stocks into portfolios, where the stocks within each portfolio are similar with respect to a sorting variable, such as firm size. The key application of portfolio sorts is to examine whether one or more variables can predict future excess returns. In this chapter, we dive into portfolio sorts, one of the most widely used statistical methodologies in empirical asset pricing (e.g., Bali, Engle, and Murray 2016).
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