Six new strategies have been added. Four new related research papers have been included into existing strategy reviews and two short free blog posts have been published during last few weeks. Plus, four trading strategies have been backtested in QuantConnect in the previous two weeks.
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.
Five new strategies have been added. Four new related research papers have been included into existing strategy reviews and three short free blog posts have been published during last few weeks. Plus, three trading strategies have been backtested in QuantConnect in the previous two weeks.
The Internet (and especially the part related to finance, trading, and cryptocurrencies) can be dangerous and full of offers of guaranteed returns, pictures of forever-growing bank accounts, and guys with golden rings swimming in the bathtub filled with cash. The truth is usually less rosy. Lucrative frauds, so-called white color crimes, have always been there, but with new technologies, they can spread faster and hide under a colorful disguise. One of the oldest concepts, from the beginnings of conceptualizing probability and statistics branches of mathematics, is Martingale betting, and this method is very often exploited to lure inexperienced new traders, who are then eaten alive by marketing sharks, selling them seemingly non-losing signals. How? An interesting paper by Carlo Zarattini and Andrew Aziz sheds some some light on these schemes.
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.
