This article explains how researchers studied small investors' trading habits by looking at tiny price differences, called subpennies, in stock trades. They found that the current method to identify these trades isn't very accurate. By using a new approach, they improved the accuracy, helping to better understand how small investors buy and sell stocks.
The propagation of factors actually reflect valid characteristics of the markets and market fluctuations.
This article describes digital filters derived from time series regression models that can be used as technical analysis tools. The filters are analyzed from a digital signal processing (DSP) frequency domain perspective to illustrate their properties. Example charts of the filters applied to the S&P 500 index are also included.
We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems.
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.
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.
Simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations.
New research reveals that the performance of the hedge fund industry has not been as bad as the results from studies that relied on hedge fund data providers.
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.
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.
This article examines four digital filters commonly used for trend-following: moving average linear weighted moving average exponential smoothing time series momentum
The article explores the limitations of traditional country-level stock market indexes that are constructed based on the domicile of issuing firms.
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."
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.
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!
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!
Optimal Strategies for ESG Portfolios Fabio Alessandrini and Eric JondeauJournal of Portfolio ManagementA version of this paper can be found hereWant to read our summaries of [...]
This time is different. --John Templeton "This time is different," is a sentiment that leads many investors to stray from using data analysis in their [...]
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 [...]
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 [...]
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