new concepts in quantitative research
by David Varadi, MBA, CFA When inflation shifts from low to high, a traditional 60/40 equity-and-bond allocation breaks down because both fall together. You need real assets to act as the ballast. — Ray Dalio Inflation is one of the most powerful forces in asset allocation—and one of the hardest to measure in real time.Everyone [ ]
“big forces to worry about: growth and inflation. Each could either be rising or falling, so I saw that by finding four different investment strategies—each one of which would do well in a particular environment (rising growth with rising inflation, rising growth with falling inflation, and so on)—I could construct an asset-allocation mix that was [ ]
**UPDATE: it was recently brought to my attention by Roman Rubsamen who does an excellent job of curating the large body of research in optimization and mathematics and of Portfolio Optimizer that the same general methodology was created and extensively tested by Higham in 2016 (the shrinkage targets in IPS and correlation thresholds are different). [ ]
In the previous post I introduced a Drawdown Implied Correlation (DIC) that is a joint time-series measurement which converts maximum drawdowns into a correlation coefficient using a simple formula derived from portfolio math. The DIC had some unique features such as a point-in-time reference to the exact point of maximum drawdown, and a triple reference [ ]
Diversification is a concept that is critical to most asset managers and traders. The foundation of this body of research is built upon the Pearson correlation coefficient, which is the most popular metric to determine whether adding an asset to a portfolio might enhance diversification. Despite its widespread use, most investment practitioners recognize its limitations. [ ]
The business cycle is a pattern that captures changes in economic activity over time. The changes in the business cycle occur in a sequential or serial manner, moving through a predictable sequence of phases. These cycles are consistent but vary in both duration and intensity. The phases of the business cycle are: Notice that the [ ]
In the last post on Adaptive Momentum I presented a backtest on the S P500 via SPY. Since this was an exploratory post, I had not tried the methodology on other asset classes. I wasn t sure how effective it would be on markets such as commodities since the leverage effect was less likely to be present. [ ]
The premise of using either time-series momentum or trend-following using moving averages is the same only the math differs very slightly (see Which Trend Is Your Friend? by AQR): using some fixed lookback you can time market cycles and capture more upside than downside and therefore improve performance vs buy and hold OR at the [ ]
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In the last post I reviewed the Momentum Trading Strategies Course by Quantra (a division of QuantInsti) which I reviewed as part of a recent educational journey to improve my quantitative skill set. The next course that I will be reviewing is Mean-Reversion Strategies in Python which is taught by Dr. Ernest Chan. I have [ ]