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

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.

accepted papers at:
BayesOpt 2017
NIPS Workshop on Bayesian Optimization
December 9, 2017
Long Beach, USA

We introduced ROBO, a flexible Bayesian optimization framework in python. For standard GP-based
blackbox optimization, its performance is on par with Spearmint while using the permissive BSD
license. Most importantly, to the best of our knowledge, ROBO is the first BO package that includes
Bayesian neural network models and that implements specialized BO methods that go beyond the
blackbox paradigm to allow orders of magnitude speedup.

Because foreign-law bonds are often priced in a foreign currency, we need to adjust the observed yields for the currency premium

Snoek 2012 paper
code here: http://www.cs.toronto.edu/˜jasper/software.html
TPA: https://github.com/jaberg/hyperopt/wiki

DeepMind's paper on bayesian optimization

overview of bayesian optimization by the author of Spearmint

analysis suggests that the Earth System may be approaching a planetary threshold that could lock in a continuing rapid pathway toward much hotter conditions—Hothouse Earth. This pathway would be propelled by strong, intrinsic, biogeophysical feedbacks difficult to influence by human actions, a pathway that could not be reversed, steered, or substantially slowed.

Where such a threshold might be is uncertain, but it could be only decades ahead at a temperature rise of ∼2.0 °C above preindustrial, and thus, it could be within the range of the Paris Accord temperature targets.

The impacts of a Hothouse Earth pathway on human societies would likely be massive, sometimes abrupt, and undoubtedly disruptive.

2 °C warming would translate to 1,119 (748–1,392) or 1,327 (1,123–1,516) cities committed under the baseline or triggered assumptions, respectively, and would affect land that is home to 19.0 (11.6–25.0) or 23.0 (16.8–28.1) million people today, respectively. Warming of 4 °C would increase central estimates to more than 1,745 cities and 30 million people under either assumption.

discusses the organization of the repurchaseagreement (repo) market in Canada. We define the repo contract, the market infrastructures that support repo trading and the composition of the market participants. We also describe repo trading practices in Canada, risks in the repo market and repo regulation. A repo is a financial contract that resembles a collateralized loan. It is used to support the funding needs of financial institutions and to procure on a temporary basis specific securities. The Canadian repo market is primarily composed of large banks and large investment institutions such as pension funds. A unique feature of the Canadian market is that Canadian investment institutions are net borrowers of cash via repo. Repo can transmit risks in the financial system because it can create levered interconnections among participants. Risks in the Canadian repo market are relatively smaller than in other jurisdictions.

Batch Normalization is quite effective at accelerating and improving the training of deep models. However, its effectiveness diminishes when the training minibatches are small, or do not consist of independent samples. We hypothesize that this is due to the dependence of model layer inputs on all the examples in the minibatch, and different activations being produced between training and inference. We propose Batch Renormalization, a simple and effective extension to ensure that the training and inference models generate the same outputs that depend on individual examples rather than the entire minibatch. Models trained with Batch Renormalization perform substantially better than batchnorm when training with small or non-i.i.d. minibatches. At the same time, Batch Renormalization retains the benefits of batchnorm such as insensitivity to initialization and training efficiency.

This article proposes a hierarchical clustering-based asset allocation method, which uses graph theory and machine learning techniques. Hierarchical clustering refers to the formation of a recursive clustering, suggested by the data, not defined a priori. Several hierarchical clustering methods are presented and tested. Once the assets are hierarchically clustered, the authors compute a simple and efficient capital allocation within and across clusters of assets, so that many correlated assets receive the same total allocation as a single uncorrelated one. The out-of-sample performances of hierarchical clustering-based portfolios and more traditional risk-based portfolios are evaluated across three disparate datasets, which differ in term of the number of assets and the assets’ composition. To avoid data snooping, the authors assess the comparison of profit measures using the bootstrap-based model confidence set procedure. Their empirical results indicate that hierarchical clustering-based portfolios are robust and truly diversified and achieve statistically better risk-adjusted performances than commonly used portfolio optimization techniques.