Five new strategies have been added. Six new related research papers have been included into existing strategy reviews and two short free blog posts has been published during last few weeks. Plus, four trading strategies have been backtested in QuantConnect in the previous two weeks.
Factor investing and detailed allocation according to different sets of factors are lively researched topics with many unanswered and open questions. Many views are often conflicting and from both radical sides — on one, that only a few factors should be necessary to explain the cross-section of mean returns, which is attractive, especially because of its simplicity; on the other, that you can use complex (authors examine the 161 “clear predictors” and 44 “likely predictors”) combinations of factors from less known and unorthodox models, but falling into dangerous and often unexamined “factor zoo” with many undesirable, unexamined and non-controllable outcomes. A huge gap is often seen in finance between the theory of academia and practical applications (by PMs [portfolio managers]), and so is especially present in this one. Let’s take a look at what the complexity of factors does for various equities pricing models.
The problem of missing financial data is widespread yet often overlooked. An interesting insight into the structure of missing financial data provides a novel research paper by authors Bryzgalova et al. (2022). Firstly, examining the dataset of the 45 most popular characteristics in asset pricing, the authors found that missing data is frequent among almost any characteristic and affects all kinds of firms – small, large, young, mature, profitable, or in financial distress. The requirement of multiple characteristics simultaneously makes the problem even worse. Moreover, the data is not missing randomly; missing values clusters both cross-sectionally and over time. This may lead to a selection bias, making most famous ad-hoc approaches like the median invalid. Considering the abovementioned findings, the authors propose a novel imputation method based on Principal Component Analysis (PCA).
Five new strategies have been added. Five new related research papers have been included into existing strategy reviews and three short free blog posts has been published during last few weeks. Plus, three trading strategies have been backtested in QuantConnect in the previous two weeks.
Cryptocurrencies are an asset class that isn’t easy to ignore in the modern world. In February 2023, the crypto market capitalization was at around $1.1 trillion, which is roughly half of the value of all U.S. notes and coins in circulation. With their properties quite different from other investment options, it might prove useful to an investor to understand and navigate this market well. The authors Campbell R. Harvey, Tarek Abou Zeid, Teun Draaisma, Martin Luk, Henry Neville, Andre Rzym, and Otto Van Hemert in their paper An Investor’s Guide to Crypto (July, 2022), offer an overview surely interesting for anyone willing to enter this market.
Cryptocurrencies are a high-risk and very speculative asset class that, from being used only by tech geeks worldwide, spread from small retail craziness of early adopters to institutional adoption and mainstream. Some claim it to be a world-changing concept with the utilization of blockchain (databases) and smart contracts that open a wide range of opportunities, from decentralizing finance to self-governing algorithms; some others point to unnecessary scams, money laundering, and bubbles. We have been covering the concepts and topics relating to crypto extensively. This article will continue our investigation of this interesting field. We would like to test how the price action looks around some of the events unique to the cryptocurrency world – namely the Bitcoin reward halvings and hard and soft forks in Bitcoin and Ethereum networks.
