Hierarchical Risk Parity

Various risk parity methodologies are a popular choice for the construction of better diversified and balanced portfolios. It is notoriously hard to predict the future performance of the majority of asset classes. Risk parity approach overcomes this shortcoming by building portfolios using only assets’ risk characteristics and correlation matrix. A new research paper written by Lohre, Rother and Schafer builds on the foundation of classical risk parity methods and presents hierarchical risk parity technique. Their method uses graph theory and machine learning to build a hierarchical structure of the investment universe. Such structure allows better division of assets into clusters with similar characteristics without relying on classical correlation analysis. These portfolios then offer better tail risk management, especially for skewed assets and style factor strategies.

Authors: Lohre, Rother and Schafer

Title: Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations

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Why Do Top Hedge Funds Outperform?

Every hedge fund manager and every trader wants to know what strategies are employed in a fund ran by his competition. The curiosity is even stronger if we want to see how strategies are mixed in the kitchen of the most successful hedge funds. Top performing funds are usually notoriously secretive about their portfolios. But we still can learn something from the history of their monthly returns. One such interesting methodology is described in a research paper written by Canepa, Gonzalez, and Skinner. Their analysis hints that the top-performing hedge funds are usually successful because they are able to manage their factor exposure better. They are not dependent so much on classical equity risk factors as average funds are. And if they are exposed to some risk factor, the top-performing hedge funds are able to close underperforming factor strategy sooner than average funds.

Authors: Canepa, Gonzales, Skinner

Title: Hedge Fund Strategies: A non-Parametric Analysis

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Pre-Election Drift in the Stock Market

There are many calendar / seasonal anomalies by which we can enhance our strategies to gain more return. One of the least frequent but still very interesting anomalies is for sure the Pre-Election Drift in the stock market in the United States. This year is the election year, and public discussion is getting more heated. The current president of the United States and candidate for re-election, Donald Trump, is a peculiar figure who split the population of the United States into two parts, ones who hate him and those who love him. We can probably expect volatile market moves as we will move closer to this year’s presidential election. But this post will not be about politics but about trading. In this post, we will try to uncover a pattern in historical data that shows significant market moves a few days before elections…

Authors: Vojtko, Cisar

Title: Pre-Election Drift in the Stock Market

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Why Did Trend-Following Underperform Last Decade?

Trend-following funds and strategies were extremely popular after the 2008/2009 crisis. They offered attractive performance, and diversification properties made them a nice addition to investor’s portfolios. Ten years later, “trend-following strategy” is not such a popular word. Strategies didn’t blow-up, but their performance was far from spectacular. What are the main reasons for that? Is it an increased correlation among markets? Are trend rules inefficient? An important recent academic study written by Babu, Hoffman, Levine, Ooi, Schroeder, and Stamelos (all from AQR Capital Management) analyzes trend-following performance for each decade in the last 140 years and uses three distinct factors: the magnitude of market moves, the efficacy of trend-following strategies at capturing profitability from market moves, and the degree of diversification across trends in a trend-following portfolio. They show that it’s the first factor (a lack of large risk-adjusted market moves, positive or negative) that had the biggest impact in the last decade. This suggests that trend-following strategies should be able to deliver better performance in the future if the size of the market moves reverts to levels more consistent with the long-term historical distribution of returns…

Authors: Babu, Hoffman, Levine, Ooi, Schroeder, and Stamelos

Title: You Can’t Always Trend When You Want

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Quantitative Easing Increases Connectedness of Equities and Commodities

Quantitative Easing policy in the US triggered a massive inflow of liquidity to financial markets. This liquidity, combined with the growing popularity of commodities as an asset class, is a cause for a higher inter-connectedness among equity and commodities markets. A recent academic study written by  Ordu-Akkaya and Soytas shows that commodities are not such a good diversifier as they used to be in the past. Moreover, commodity markets are also affected, as periods of higher equity volatility impact commodities significantly more …

Authors: Ordu-Akkaya, Soytas

Title: Unconventional Monetary Policy and Financialization of Commodities

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Impact of Currency Volatility on Momentum and Carry Factors

What is the impact of volatility (and changes in volatility) on popular Currency Momentum and Currency Carry strategies? That’s the topic of recent academic study written by Duc Hong Hoang, which decomposes foreign exchange volatility into two components, namely, secular (long-term) and transitory or mean-reverting (short-term) components. Long term component captures business cycle effects, while short term volatility usually represents funding tightness or shocks. Carry trade strategy is linked (and therefore partially predictable) to long-run volatility while momentum reacts mainly to short-run risks.

Author: Hoang

Title: Long Run and Short Run Risk Premium in Currency Market

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