Alpha Architect

Life Insurance Instruments May Help Improve After-Tax Wealth

Fee-only fiduciary advisors often summarily dismiss the use of life insurance solutions as financial planning tools—perhaps due to past experiences trying to get clients out of poorly structured, high expense policies. In this post, Colva Actuarial Services and Colva Capital principal Rajiv Rebello explains how fiduciary advisors can properly structure life insurance products and utilize low-expense/no-commission products to provide better after-tax diversification and returns for the fixed income portion of their clients’ portfolios as opposed to investing in bonds directly.

An Introduction to Investing in Carbon Markets

Carbon markets are quickly making their way to the forefront of Environmental, Social, and Governance (ESG) investing, as well as the finance community as a whole. The Kraneshares Global Carbon ETF, (Ticker: KRBN) (whose holdings I’ll dive into shortly) was one of the top 5 performing ETFs in 2021 on a % return basis (Ferringer, Best performing ETFs of the Year - etf.com). However, it doesn’t appear that 2021 was a one-hit-wonder for Carbon Markets, but instead, the beginning of a new and very real trend.

ETF Tax Efficiency isn’t Always Efficient

Compared to mutual funds or separately managed accounts, ANY benefit from redeeming in-kind is a bonus. That being said, not all ETFs and situations are created equal when it comes to tax efficiency, and the "golden rule" always applies - when given the option, the IRS wants to create scenarios where they receive tax dollars now instead of later. Here are some big-ticket items that cause inefficiencies (read as taxes…), many related to the “golden rule” above.

Trend-Following Filters – Part 5

There are two general types of Kalman filter models: steady-state and adaptive. A steady-state filter assumes that the statistics of the process under consideration are constant over time, resulting in fixed, time-invariant filter gains. The gains of an adaptive filter, on the other hand, are able to adjust to processes that have time-varying dynamics, such as financial time series which typically display volatility and non-stationarity.

Machine Learning: The Recovery of Missing Firm Characteristics

Firm characteristics are often missing, which forces both researchers and practitioners to come up with workarounds when handling missing data. Previous approaches resorted to either dropping observations with missing entries or simply imputing the cross-sectional mean of a given characteristic. As both procedures accompany serious drawbacks (see below), there is a need for more advanced methods. The authors set up an attention-based machine learning model, motivated by recent advances in natural language to find some answers

Trend-Following Filters – Part 4

This article considers a different type of filter called the Kalman filter. The Kalman filter is a statistics-based algorithm used to perform the estimation of random processes. Our research will explain what Kalman Filters are and utilize them with financial time series data for trend following purposes.

Smarter in 10 Minutes with Matt Topley

Time to get smarter in less than 10 minutes.

Welcome to our weekly series, "Smarter in 10 Minutes." This weekly series is aligned with our mission to empower investors through education and is curated by Matt Topley, a 25yr+ vet in the business who currently runs Lansing Street Advisors.

Matt wakes up "Jocko style" every morning and shoots out a daily newsletter (I'm a loyal reader!). The weekly "Smarter in 10 Minutes" is a lower frequency version of his daily newsletter.

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