An Introduction to Investing in Carbon Markets

By |March 10th, 2022|ESG, Research Insights, Guest Posts, Other Insights|

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

By |February 25th, 2022|Research Insights, Guest Posts, Tax Efficient Investing, ETF Investing|

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

By |February 15th, 2022|Research Insights, Trend Following, Trend-Following Course, Guest Posts|

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

By |February 10th, 2022|Research Insights, Guest Posts, Academic Research Insight, Machine Learning|

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

Smarter in 10 Minutes with Matt Topley

By |December 3rd, 2021|Smarter in 10 Minutes, Guest Posts|

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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