The empirical research findings demonstrate that the return premium generated by being long low-distress risk stocks and short high-distress risk stocks is persistent and that the capital asset pricing model (CAPM) and the Fama-French three-factor models cannot explain it. Hence, we have the distress puzzle, or anomaly.
As pundits wrestle over the cause, implications, and sustainability of the recent massive moves in interest rates, I’ll instead delve into the two terms most often blamed for these shifts in rates: R-star and the Term Structure Premium. Unfortunately, what most want—a measure of them—is unknowable. But we can benefit from understanding the theories and models behind these terms. We can glean guidance on what we need, namely a better understanding of the risks and rewards of buying longer-maturing bonds at current rate levels. I contend that now is a good time to secure future cash flows by buying bonds, although determining the precise amount to invest remains a challenge.
The timelier adoption of new technology and the higher likelihood of large-scale technology adoption make the risk associated with technological innovation more systematic, which in turn increases returns required by investors for technology spillover recipients.
This article examines four digital filters commonly used for trend-following: moving average linear weighted moving average exponential smoothing time series momentum
As discussed in Part 1 of this blog series, the Certified Financial Planner (CFPⓇ) exam can be a stressful and intimidating experience. With eight areas of content to cover – both as siloed financial knowledge and also as an integrated approach to building a comprehensive financial plan – it's important to be organized and intentional in your study efforts.
Over the very long term, while value stocks have been less profitable and have had slower growth in earnings than growth stocks, they have provided higher returns.
While ESG investors can express their values through their investments, they should expect lower returns from their portfolios—though they also will be taking less investment risk.
The higher returns to high R&D stocks represent compensation for heightened systematic risk not captured in standard asset pricing models.
Let’s talk about the right approach(es) and the proper study techniques you need to pass the CFPⓇ exam with confidence and get the certification you need to advance your career in finance and investing.
This piece outlines the high-level benefits of the ETF structure, which boils down to market access, tax efficiency, and transparency. It covers the considerations for a 351 tax-free conversion and the mechanics and tax consequences of a 351 conversion.
By using a novel measure of investor attention, generated from InvestingChannel’s clickstream data on online financial news consumption, we can identify broad groups of stocks which are less efficiently priced and therefore where anomalies such as Value and Momentum are likely to produce greater cross sectional differentiation in returns. We also apply these groupings to proprietary ExtractAlpha stock selection signals.
The “Intangible Value Factor” (IHML) can play an additive role in factor portfolios alongside the established market, size, value, quality, and momentum factors. This Six-Factor Model avoids the problematic “anti-innovation” bias of traditional factor portfolios and can be easily implemented using ETFs.
There are several significant, well-documented benefits of index funds. In addition to outperforming a large majority of actively managed funds, they tend to have low fees, low turnover (resulting in low trading costs and high tax efficiency), broad diversification, high liquidity, and near-zero tracking error (generally assumed to mean that they incur negligible trading costs).
I’ve received calls from clients inquiring about moving assets to gold. When I asked them why, three reasons dominated.
No surprise: reddit message boards don't lead to alpha generation.
In this paper, we propose a cross-sectional option momentum strategy that is based on the risk component of delta-hedged option returns. We find strong evidence of risk continuation in option returns.
This article analyzes six trend-following indicators from a digital signal processing (DSP) frequency domain perspective in which the indicators are considered as digital filters and their frequency response characteristics are determined.
The paper documents that return forecasts from machine learning methods lead to superior out-of-sample returns in emerging markets.
We examine trend-following rules when the stock returns follow a two-state process that randomly switches between bull and bear markets.
In this article, the author examines several important questions related to asynchronous trading, or the variation in trading frequency that occurs when trading stocks or other assets.
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