Empirical Methods

U.S. Companies Have Outperformed Japanese Companies, or Have They?

While both the S&P 500 and the Nikkei indices have recently hit all-time highs, the valuation and balance sheet data we have reviewed indicate that the downside risks in Japanese stocks appear to be far less than the risks in U.S. stocks. Evidence such as this helps explain why legendary investor Warren Buffett has been buying Japanese stocks.

Smart rebalancing for factor strategies

Trading costs, discontinuous trading, missed trades, and other frictions, along with asset management fees can cause a shortfall between live and paper portfolios. The focus of this paper is to test an effective rebalancing method that prioritizes trades with the strongest signals to capture more of the factor premia while reducing turnover and trading costs.

Tracking Error is a Feature, Not a Bug

The benefits of diversification are well known. In fact, it’s been called the only free lunch in investing. Investors who seek to benefit from diversification of the sources of risk and return of their portfolios must accept that adding unique sources of risk means that their portfolio will inevitably experience what is called tracking error—a financial term used as a measure of the performance of a portfolio relative to the performance of a benchmark, such as the S&P 500.

Are stock returns predictable at different points in time?

For many benchmark predictor variables, short-horizon return predictability in the U.S. stock market is local in time as short periods with significant predictability (“pockets”) are interspersed with long periods with no return predictability.

Trend-Following Filters – Part 7

This article examines four digital filters commonly used for trend-following: moving average linear weighted moving average exponential smoothing time series momentum

Regression is a tool that can turn you into a fool

Running regressions on past returns is a great tool for academic researchers who understand this approach's nuance, assumptions, pitfalls, and limitations. However, when factor regressions become part of a sales effort and/or are put in the hands of investors/advisors/DIYers, "the tool can quickly turn you into a fool."

Submergence: A Tool to Assess Drawdowns and Recoveries

According to research by the authors, stocks and bonds have been submerged for about 75% of the time since 1980; and treasuries have been submerged 80% of the time. Submergences are therefore both commonplace and significant, which means that handling them is very important for investors and their investing strategies.

Does diversification always benefit investors? No.

This article examines the extent to which these assumptions hold and the extent to which investors should want them to hold.  The authors deliver a clever quote from Mark Twain (or maybe it was Robert Frost) that nails the issue in simple terms: “Diversification behaves like the banker who lends you his umbrella when the sun is shining but wants it back the minute it begins to rain”. Nicely expressed!

You Thought P-Hacking was Bad? Let’s talk about “Non-Standard Errors”

Most readers are familiar with p-hacking and the so-called replication crisis in financial research (see here, here, and here for differing views). Some claim that these research challenges are driven by a desire to find 'positive' results in the data because these results get published, whereas negative results do not get published (the evidence backs these claims).

But this research project identifies and quantifies another potential issue with research -- the researchers themselves! This "noise" created by differences in empirical techniques, programming language, data pre-processing, and so forth are deemed "non-standard-errors," which may contribute even more uncertainty in our quest to determine intellectual truth. Yikes!

Estimating the Stock-Bond Correlation

The Stock-Bond Correlation Megan Czasonis, Mark Kritzman, and David TurkingtonJournal of Portfolio ManagementA version of this paper can be found hereWant to read our summaries of [...]

Trend-Following Filters: Part 1/2

1. Introduction Many traders use strategies based on trends that occur in stock, bond, currency, commodity, and other financial asset price time series in order [...]

Predicting anomaly returns with politics, weather, global warming, sunspots, and the stars

Ferson, Sarkissian and Simin (2003) warn that persistence in expected returns generates spurious regression bias in predictive regressions of stock returns, even though stock returns are themselves only weakly auto correlated. Despite this fact a growing literature attempts to explain the performance of stock market anomalies with highly persistent investor sentiment. The data suggest, however, that the potential misspecification bias may be large. Predictive regressions of real returns on simulated regressors are too likely to reject the null of independence, and it is far too easy to find real variables that have “significant power” predicting returns. Standard OLS predictive regressions find that the party of the U.S. President, cold weather in Manhattan, global warming, the El Nino phenomenon, atmospheric pressure in the Arctic, the conjunctions of the planets, and sunspots, all have “significant power” predicting the performance of anomalies. These issues appear particularly acute for anomalies prominent in the sentiment literature, including those formed on the basis of size, distress, asset growth, investment, profitability, and idiosyncratic volatility. 

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