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.
The primary motivation behind our study stems from an observation of the Global Tactical Asset Allocation (GTAA) strategies throughout the existing papers – the majority of them require relatively frequent rebalancing from the point of view of the ordinary investor. Portfolio rebalancing is usually done on a weekly or monthly basis, and while this period may seem overly boring and slow for the majority of traders (who like to trade on intraday or daily basis), fans of GTAA strategies are not traders; they are investors. Of course, some like to follow the ebbs and flows of the market. But a lot of investors just want to have a life. The financial market is not their hobby. However, on the other hand, they also do not want to hold just the passive buy & hold portfolio. Recognizing the demand for the semi-active strategy, we introduce our novel Pragmatic Asset Allocation.
There are a few concepts in the world of finance that are taken for granted, and one of them is the free lunch of diversification. Investors like to mix stocks and bonds into a simple allocation portfolio and hope for better outcomes than investing in just one asset. But the favorable return-to-risk profile of those asset allocation strategies relies on the low correlation between those two asset classes, which, as we will see from today’s contribution, we can’t take for granted. We hope the recent study sheds more light on this topic.
You may remember our blog post from the end of March, where we tested the current state-of-the-art LLM chatbot. Time flies fast. More than six months have passed since our last article, and half a year in a fast-developing field like Artificial intelligence feels like ten times more. So, we are here to revisit our article and try some new hacks! Has the OpenAI chatbot made any significant improvement? Can ChatGPT be used as a backtesting engine? We retake our risk parity asset allocation and test the limits of current AI development again!
In recent years, the diversification potential of commodities has come under scrutiny. While the majority of studies examining the role of commodities in a portfolio typically focus on U.S. investors or those dealing primarily with U.S. dollar-denominated assets, Dequiedt et al. (2023) offer a unique perspective by considering the viewpoint of domestic investors in a sample of 38 developed and emerging countries. The study explores the relationship between diversification benefits of commodities for local investors and country’s level of commodity risk exposure. Findings reveal that incorporating commodities tends to enhance the Sharpe ratio of the optimal domestic asset portfolios in most countries with low commodity dependence but doesn’t benefit highly commodity-dependent ones.
The motivation for this study comes from the idea of simplifying the concept of relative valuation among the countries. There exist several ideas for relative value approaches that compare the “visible price” (or market capitalization) of the stock market to some unseen “intrinsic value” of the market. The ideas of what we can use to measure the unseen “intrinsic value” of each individual country/market are numerous – it may be a number derived from GDP (like in a Buffet Indicator), total earnings of listed companies in the selected country (Shiller’s CAPE ratio), or ratios derived from yields, demographic, etc., etc. We asked ourselves – can we create a relative valuation model and use just the price data?
Countries from the majority of the developed world face one challenge: Their population is steadily aging; the average age of individuals has been rising over recent periods. The United States is not different in this sense. Whenever there’s a never-inevitable reaching of higher ages, people reconsider their choices and often cut on riskier ones. So, is there a potential link between demographic changes associated with aging and aggregate financial market outcomes?
It’s always fun to push the boundaries of technology and see what it can do. The AI chatbots are the hot topic of current discussion in the quant blogosphere. So we have decided to test OpenAI’s ChatGPT abilities. Will we persuade it to become a data analyst for us? While we may not be there yet, it’s clear that AI language models like ChatGPT can soon revolutionize how we approach to finance and data analysis.
We’ve already analysed tens of thousands of financial research papers and identified more than 700 attractive trading systems together with hundreds of related academic papers.
Join our Facebook Group
