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.
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.
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.
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
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.
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.
Option Return Predictability with Machine Learning and Big Data Bali, Beckmeyer, Moerke, WeigertA version of this paper can be found hereWant to read our summaries of academic finance papers? Check out our Academic Research Insight category [...]
Market Returns and A Tale of Two Types of Attentions Da, Hua, Hung, and PengA version of this paper can be found hereWant to read our summaries of academic finance papers? Check out our Academic [...]
Sorry for the clickbait, but Hoover Institute fellow and “Grumpy Economist" John Cochrane's answers to the seemingly benign question, "How should long-term investors form portfolios," is too important to both advisors and academics to overlook. [...]
I’m constantly hearing from financial advisors who are getting nowhere on LinkedIn, despite having invested considerable time and money. In this post, I'll provide some guidance on how to avoid sounding like the typical (boring) [...]
When an owner sells their business, the IRS and state taxing authorities will be there to take as much of it as they lawfully can. This one sale can lead to the largest tax payment [...]
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 investment decision process and more towards discretionary judgment. The logic [...]
I examine the performance records of performance of Ben Graham's well-known disciples: Walter Schloss, Tom Knapp, Warren Buffett, Bill Ruane, Charlie Munger, Rick Guerin, and Stan Perlmeter. The research question I seek to address is [...]
I spent some time on Google, but could not ascertain the origins of the term fixed income. Presumably, it was created to describe investments where cash flows are dictated via a contractual obligation. A simple [...]
Part 2: From understanding factors to solving investor problems In Part 1, we defined fixed income factors. But factors alone will not solve each investor’s problem. Below, we extend the discussion by walking through a [...]
Part 1: The End of Accounting This is the first part of a series of guest posts by Kai Wu, the CIO & Founder of Sparkline Capital. The Factor Zoo As readers of Alpha Architect’s [...]
When it comes to predicting long-term equity returns, several well-known indicators come to mind—for example, the CAPE ratio, Tobin’s Q, and Market Cap to GDP, to name a few. Yet there is another indicator without [...]
A few years ago, before launching our freedom-weighted emerging markets index, I was in a coffee shop where I overheard a group of students talking at the next table. They were anxious about an upcoming [...]
Introduction This is the third article in a series of three, the first two are available here and here. Those articles focus on examining from a digital signal processing (DSP) perspective[ref]For background information on DSP, [...]