The mean-variance optimization (MVO) theory of Markowitz (1952) for portfolio selection is one of the most important methods used in quantitative finance. This portfolio allocation needs two input parameters, the vector of expected returns and the covariance matrix of asset returns. This process leads to estimation errors, which may have a large impact on portfolio weights. In this paper we review different methods which aim to stabilize the mean-variance allocation. In particular, we consider recent results from machine learning theory to obtain more robust allocation.
paper http://www.thierry-roncalli.com/download/Portfolio_Regularization.pdf
there are four principal exposures that explain up to 76% of corporate-debt returns, Israelov calculates: government obligations, equities, stock volatility and price swings in bonds. In his parlance, these are the most-rewarded risks out there for credit buyers.
In that spirit, investors can garner exposure to the asset class via a portfolio of fixed-income and equity-index futures, combined with selling options on a stock index and bond futures, according to the paper. All without holding cash bonds -- with smaller drawdowns and lower volatility compared with benchmarks.
paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3293357
One of Alexander’s Macedonian generals, who would go on to win an enormous kingdom stretching from Bulgaria to Afghanistan, introduced a new system for reckoning the passage of time. It is known, after him, as the Seleucid Era. This was the world’s first continuous and irreversible tally of counted years.
two-thirds of survey respondents who identified as atheist before the encounter no longer did (n=4285)
What does it mean that experiences that have formed a fundamental part of the world’s religions for millennia could, in the future, form a fundamental part of our treatment of depression?
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0214377
The back-propagation algorithm is the cornerstone of deep learning. Despite its importance, few variations of the algorithm have been attempted. This work presents an approach to discover new variations of the back-propagation equation. We use a domain specific lan- guage to describe update equations as a list of primitive functions. An evolution-based method is used to discover new propagation rules that maximize the generalization per- formance after a few epochs of training. We find several update equations that can train faster with short training times than standard back-propagation, and perform similar as standard back-propagation at convergence.
In our exercise, we built a database using only photos from public websites
by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets
A major attraction of the Black–Litterman approach for portfolio optimization is the potential for integrating subjective views on expected returns. In this article, the authors provide a new approach for deriving the views and their uncertainty using predictive regressions estimated in a Bayesian framework. The authors show that the Bayesian estimation of predictive regressions fits perfectly with the idea of Black–Litterman. The subjective element is introduced in terms of the investors’ belief about the degree of predictability of the regression. In this setup, the uncertainty of views is derived naturally from the Bayesian regression, rather than by using the covariance of returns. Finally, the authors show that this approach of integrating uncertainty about views is the main reason this method outperforms other strategies.
In this article, the author introduces the Hierarchical Risk Parity (HRP) approach to address three major concerns of quadratic optimizers, in general, and Markowitz’s critical line algorithm (CLA), in particular: instability, concentration, and underperformance. HRP applies modern mathematics (graph theory and machine-learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix—an impossible feat for quadratic optimizers. Monte Carlo experiments show that HRP delivers lower out-ofsample variance than CLA, even though minimum variance is CLA’s optimization objective. HRP also produces less risky portfolios out of sample compared to traditional risk parity methods.
Jupyter notebooks are a tool for exploration not for production
