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.
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.
Our mission here at Quantpedia is to provide both retail and institutional investors with ideas for trading strategies that are easily understandable while based on and backed by quantitative academic research. Today, we present you with the results from a study that we came across. Although it’s not quantitative, but qualitative, it has really held our interest. The paper does not provide any images or figures; it is a study made from various types of surveys with answers from professionals concluded with an attention-grabbing summary table.
Passive index funds are believed to offer low fees, nearly limitless liquidity, very low trading costs and (most of the time) they beat most active managers. Although not all of the above are accurate, there are still many arguments in favour of passive indexing. However, what is often left forgotten are avoidable travails linked to index funds. In general, after an index rebalances, traditional cap-weighted index funds buy high and sell low. Their tendency to add recent highfliers and drop unloved value stocks is what causes investors to lose. Arnott et al. (2022) target the stock selection problem around index rebalancing and propose several ideas on how to adjust index strategies in order to earn above-market returns. They present simple ways to construct an index, thanks to which it is possible to reduce both negative effects of buy-high/sell-low dynamic and the turnover costs of cap-weighted indices.
