Would you like to see the performance of your portfolio 100 years back in history? Do you want to analyze the risk of your strategy under 100 years of real historical scenarios? All of these, and much more, will be soon (in a few days) available for Quantpedia Pro subscribers. How? We will explain today how we can model a 100-year history of your portfolio.
Over the past several decades, economists and quantitative scientists found a very large number of asset pricing anomalies and published numerous research papers about their findings, and this is known in the financial jargon as “factor zoo.” However, one strong underlying force might drive the performance of many of those anomalies. What’s that force? The level and trend in the interest rates, as in almost all parts of the developed world, there was a long-term steady decline in rates and inflation for nearly 40 years. We use the past tense as it seems that the situation changed at the beginning of this year…
Van Binsbergen, Jules H. and Ma, Liang and Schwert, Michael (Sep 2022) touched on this subject and made a careful examination of both past factor research and found that a significant part of published papers and developed models are sometimes unknowingly exposed to fitting to low or even zero interest rates.
The recent surge in global inflation sent shock waves across financial markets and affected the complicated relationship between stocks and bonds. Today, we would like to present you with a review of two interesting papers, which provide both a deep and easy-to-understand examination of the correlation structure of those two main asset classes. The first paper reviews specifics in various parts of the world, and the second one summarizes known information about the macroeconomic drivers of the US stock-bond correlation.
If you follow Quantpedia’s blogs, you probably know that Quantpedia PRO already contains multiple risk management and portfolio construction tools for your quantitative investment strategies. The newest Quantpedia PRO tool (available in a few days) will analyze something completely different, though – how to manage multi-strategy portfolios. The newest Quantpedia PRO tool (available in a few days) will analyze something completely different, though – how to manage multi-strategy portfolios. You can easily apply these multi-strategy overlays to various types of underlying – ETFs, systematic strategies, multi-asset portfolios, or multi-strategy portfolios. This article again serves as a primer for the new report’s methodology.
Investing has been a reliable way to compound one’s inheritance over ages known throughout human history. But different monetary and fiscal situations, especially during times of uncertainty and extreme stress, force both individuals and institutions to adjust their financial habits. A recent research paper written by Guido Baltussen, Laurens Swinkels, and Pim van Vliet analyzed large samples of data starting from the 19th century and brought unique perspectives on how various asset classes perform during “quiet, good” periods and, on the other side, economic turmoil. Research summarized very actual topics of investing during those different cycles and what inflation does to returns across equities, bonds, and cash.
At the moment, there is a lot of attention surrounding overnight anomalies in various types of financial markets. While such effects have been well documented in research, especially in US equities and derivatives, there are other asset classes that are not as well addressed. A recent (2022) paper from Jiang, Luo, and Ye contributed appealing evidence in favor of validating these phenomena in the Chinese market. We highlight the finding that the market MKT factor beta premium is earned exclusively overnight and tend to reverse intraday (and in smaller potency also value HML and profitability RMW), which is the same finding as for the US equities. In contrast, the size SMB factor exhibit significantly opposite pattern: positive intraday premium and negative overnight premium (and the same for investment CMA factor).
