Despite the significant challenges in accurately forecasting market trends and economic conditions over such lengthy periods, this task remains critically important for investors, portfolio managers, and policy makers. They depend on these forecasts to develop effective investment strategies and policy frameworks.
After years of waiting, the recent launch of spot Bitcoin ETFs marked a significant milestone in the cryptocurrency market, making Bitcoin even more accessible for investors. Spot ETFs provide a convenient and regulated way to gain exposure to Bitcoin without the need to hold the digital asset directly, potentially attracting a broader range of market participants. Many investors are waiting to see this change’s long-term impact on the cryptocurrency’s price while putting their faith in the potentially significant returns from Bitcoin within their investment portfolios. These events are taking place after two significant milestones in Bitcoin’s history – the introduction of BTC futures in 2017 and the launch of the BTC futures ETF (BITO) in 2021. While examining the whole history of Bitcoin may give the impression of a new super asset, we need to set realistic expectations. What have all these historical changes brought, and what lessons can we learn from similar occurrences involving other assets throughout history?
The efficacy of ETF momentum strategies, while robust until around 2010, began to show signs of waning in subsequent years. This observation raises questions about the sustainability and adaptability of these strategies in varying market cycles. Central to this research is exploring how various factors/parameters—such as the ranking period, the selection quantity of assets, and the liquidity of ETFs—impact the performance of ETF momentum strategies. The aim is to uncover whether these strategies can deliver sustainable alpha in the complex and ever-evolving market landscape of the 2020s.
Investing truly is an intense intellectual undertaking. For a Portfolio Manager (PM) to execute an investment, they must first convince themselves, then others, that the rationale behind the investment is sound. The variables they utilize in developing their rationale are of the upmost importance; These variables inevitably serve as a foundation in the evaluation of a given Asset, and therefore possess the power to influence a PM’s level of confidence in the investment. If a variable is weak, it can lead to a poor diagnosis of the asset in question, which can lead to unfavorable results on a given investment. If a variable is strong, then it will indeed provide insight into asset and therefore help paint a clear picture into the future of the asset. To be on the right side of this sword, it is imperative that portfolio managers correctly implement quantitative reasoning if not within their decision-making process, then definitely around it. This article introduces the theory of mutual information as a tool for asset managers to gauge the predictive efficiency of their selected variables.
More and more, a few mega-cap companies dominate the US stock market performance. Financial journals come up with different names for those stocks every few years. They are now called the “Magnificent Seven”, but we all remember FAANG, right? Naturally, several questions arise – Is the current status quo, when the stock market capitalization is highly concentrated among the few extremely large companies, an exception or rule over history? And what’s the impact of this concentration on the performance of the one particular factor – the Size premium? We present the research paper written by Emery and Koëter that tries to answer those questions.
Hello all,
What have we accomplished in the last month?
– A new Savings Plan Analysis report
– 10% discount code for those who help us and fill out our survey
– an invitation to the webinar – ETFs Uncovered: Strategies, Data, and Innovations
– a new Quantpedia Awards 2024 competition with a $15.000 prize pool
– 11 new Quantpedia Premium strategies have been added to our database
– 12 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 8 new backtests written in QuantConnect code
– 6 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month
