Equal-weighted benchmark portfolios are sometimes overshadowed by the more popular market capitalization benchmarks but are still popular and often used in practice. One of the advantages of equal-weighted portfolios is that academic research shows that in the long term, they tend to outperform their market-cap-weighted peers, mainly due to positive loadings on well-known factor premiums like size and value. So, if equal weighting outperforms market-cap weighting (in the long term), what options do we have if we want to outperform equal weighting? A recent paper by Cirulli and Walker comes to our aid with an interesting proposal …

Does the investment in sophisticated machine learning algorithm research and development pay off? It is an important question, especially in light of the increasing costs related to the R&D of such algorithms and the possibility of decreasing returns for some methods developed in the more distant past. A recent paper by Azevedo, Hoegner, and Velikov (2023) evaluates the expected returns of machine learning-based trading strategies by considering transaction costs, post-publication decay, and the current high liquidity environment. The obstacles are not low, but research suggests that despite high turnover rates, some machine learning strategies continue to yield positive net returns.

There exist two most common techniques for constructing multifactor portfolios. The mixing approach creates single-factor portfolios and then invests proportionally in each to build a multifactor portfolio. The integrated approach combines single-factor signals into a multifactor signal and then constructs a multifactor portfolio based on that multifactor signal. Which methodology is better? It is hard to tell, and numerous papers show each method’s pros and cons. The recent paper from Joris Blonk and Philip Messow explores this question from the standpoint of the credit fixed-income portfolio manager and offers their analysis, which shows that an integrated approach is probably better in this particular asset class.

One of the main reasons why the Pragmatic Asset Allocation Model was designed is to give investors a tax-efficient possibility to invest in a global equity portfolio with a lower risk than the passive buy&hold approach. Therefore, the PAA model is not the “absolute return” model but rather the tactical model that prefers to invest in the equity risk premium and move to the hedging portfolio (gold, treasuries, or cash), only for short periods and only when it’s absolutely necessary. We use price trend+momentum indicators and yield curve inversion as signals for such situations when (based on the past data) there is a higher probability of recessions and equity bear markets. What is unusual in the current situation is the length of time that the YC is inverted (19 months at the moment), which makes it the 2nd longest YC inversion in the last 100 years, and we are analyzing the implications for the PAA model.

Our new study aims to investigate the lead-lag effect between prominent, widely recognized stocks and smaller, less-known stocks with similar ticker symbols (for example, TSLA / TLSA), a phenomenon that has received limited attention in financial literature. The motivation behind this exploration stems from the hypothesis that investors, especially retail investors, may inadvertently trade on less-known stocks due to ticker symbol confusion, thereby impacting their price movements in a manner that correlates with the leading stocks. By examining this potential misidentification effect, our research seeks to shed some light on this interesting factor.

In one of our older posts titled ‘Case Study: Quantpedia’s Composite Seasonal / Calendar Strategy,’ we offer insights into seasonal trading strategies such as the Turn of the Month, FOMC Meeting Effect, and Option-Expiration Week Effect. These strategies, freely available in our database, are not only examined one by one, but are also combined and explored as a cohesive composite strategy. In partnership with Deltaray, using MesoSim — an options strategy simulator known for its unique flexibility and performance — we decided to explore and quantify how our Seasonal Strategy performs when applied to options trading. Our motivation is to investigate whether this strategy can be improved in terms of risk and return. We aim to systematically harvest the VRP (volatility risk premium) timing the entries using calendar strategy to avoid historically negative trading days.