In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively until 2009. We demonstrate this using two different profitable machine learning-based trading strategies. However, the effectiveness of both approaches diminish over time, and neither of them are profitable after 2009.

this would be a massive sea change in american-style capitalism. though whether it's genuine and going to lead to actual differences in behaviour is quiet debatable. as the article concludes, quoting Andy Green from Center for American Progress, “We need to see the details to see if they put their money where their mouth is.”

"The change amounts to a call to reform capitalism in a time in which rising populism and concern about climate change have led politicians and shareholder activists to demand that companies consider their impact on the world beyond their balance sheets.

It is a significant departure from the bedrock belief that businesses serve the owners of their capital — a philosophy championed by Nobel Prize-winning economist Milton Friedman and which has driven corporate America for decades."

it's all about geopoliltics

“The recent escalation in US-China tensions reinforces our view that trade and geopolitical frictions have become the key driver of the global economy and markets,” BlackRock, the world’s biggest asset manager, said earlier this week.

moving beyond the profit motive

"The signs of this new post-supply side era are all around us. Witness the rise of the B-corporations, which balance purpose and profit, and the growth of investing based on environmental, social and governance factors."

AlphaStock fully exploits the interrelationship among stocks, and
opens a door for solving the “black box” problem of using deep learning models in financial markets. The back-testing and simulation experiments over U.S. and Chinese stock markets showed that

AlphaStock performed much better than other competing strategies. Interestingly, AlphaStock suggests buying stocks with high long-term growth, low volatility, high intrinsic value, and being
undervalued recently.

deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market.

sharpes 3-5

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

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.

“The potential amount of debt is an iceberg with titanic credit risks,” S&P credit analysts led by Gloria Lu wrote in a report Tuesday.

With the national economy slowing, and a Beijing-set quota for issuance of local-government bonds not being enough to fund infrastructure projects to support regional growth, authorities across the country have resorted to LGFVs to raise financing (Local Government Financing Vehicles)

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

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.

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.

In this article, the author revisits his seminal paper on tactical asset allocation published over 10 years ago in The Journal of Wealth Management. How well has this market strategy—a simple quantitative method that improves the risk-adjusted returns across various asset classes—held up since its 2007 publication? Overall, the author finds that the model has performed well in real time, achieving equity-like returns with bond-like volatility and drawdowns. The author also examines the effects of departures from the original system, including adding more asset classes, introducing various portfolio allocations, and implementing alternative cash management strategies.