The Theory of Portfolio Management sub-page presents a selected list of Quantpedia’s research articles related to essential portfolio management topics like factor analysis, theory of asset allocation, risk management and position sizing.
One of the most popular reports in the Portfolio Analysis section of our Quantpedia Pro tool is “Volatility Targeting”. In this article, we will explain some theory behind this portfolio management method. And then, we will go more in-depth, pick several examples and explain some common volatility targeting variants.
This blog post aims to introduce the multi-factor regression model we use in the Quantpedia Pro tool, its logic and the method we have decided to use.
The industry standard for backtesting futures strategies is to construct one data sequence from a stream of contracts. Our short article shows the importance of choosing the correct methodology for building continuous futures contracts data series…
In this article, we will look deeper on whether the market anomalies can be arbitraged away, if the profits are lower for the specific strategy once the strategy becomes well-known, and even if the strategies can be timed. Quantpedia‘s readers are often interested in these common topics, and we will try to shed some light on them.
Our own research paper ESG Scores and Price Momentum Are More Than Compatible utilized the Knapsack problem to make the ESG strategies more profitable or Momentum strategies significantly less risky. The implementation of the Knapsack problem was created in R, using slightly modified Simulated annealing optimization algorithm. Recently, we have been asked about our implementation and the code.
This article is a primer into the methodology we use for the Portfolio Risk Parity report, which is a part of our Quantpedia Pro offering. We explain three risk parity methodologies – Naive Risk Parity (inverse volatility weighted), Equal Risk Contribution and Maximum Diversification.
This report helps calculate the efficient frontier portfolios based on the various constraints and during different predefined historical periods. Furthermore, it aims to create the most return-to-risk efficient portfolio by analyzing various portfolio combinations based on expected returns (mean) and standard deviations (variance) of the assets.
Constant Proportion Portfolio Insurance (CPPI) is a strategy that enables an investor to keep exposure to a risky asset’s upside potential while providing a guarantee against the downside risk by dynamically scaling the direction. It’s a popular strategy often used to build protected funds or as a part of various synthetic derivative products.
We choose the most popular clustering methods such as Partitioning Around Medoids (PAM), Hierarchical Clustering, and Gaussian Mixture Model to introduce them and show examples of their usage in portfolio management. Complete methodology for all three methods is available in the following article and its 2nd and 3rd continuation.
Systematic tactical FX hedging uses currency factor strategies such as currency carry, currency momentum, and currency value to protect a current or predicted position from an unwanted move in an exchange rate. Therefore, tactical FX hedging strategies serve as a good starting point for investors who need to decide when to hedge and when not to hedge their FX risk.
Value at Risk (VaR) is the maximum loss with a given probability, in an established period, with an assumed probability distribution, and under standard market conditions. It helps to understand what level of risk we can expect during a crisis to better prepare for it.
This blog will analyze and combine different US stock momentum strategies using five pre-coded asset and strategy allocation methods from Quantpedia Pro’s toolbox. As follows, we proved that it’s possible to combine strategies more sophisticatedly and improve risk-adjusted returns even in real-life implementations.