Asset class picking

Stocks Not For the Long Run?

21.May 2020

There are very few observations of the attributes of financial markets that are considered by most of the investors as nearly permanent facts. One of the most often cited examples is that over the long interval stocks outperform bonds. But is it really such truth? Over how long interval? 10 years, 20 years, 30 years? As the new and better historical data are becoming available for analysis, they show interesting findings. Let’s show one example. There exist one very long interval during which the return of stocks was nearly equal to bonds. What do you think is the length of such an interval in the case of the US? It’s 150 years! Yes, that’s correct, there was a one-and-half-century long period in the history of the United States when the performance of stocks and bonds was nearly identical. We do not imply that it will be the case in the 21st century. But an important research paper written by Edward McQuarrie shows that investors must prepare for even the most unexpected possibilities when they are making their asset allocation decisions.

Author: McQuarrie

Title: The US Bond Market before 1926: Investor Total Return from 1793, Comparing Federal, Municipal and Corporate Bonds Part II: 1857 to 1926

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Hierarchical Risk Parity

21.February 2020

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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Alternative Fair-Value Models for Currency Value Strategy

17.January 2020

The idea of buying an investment asset for a lower price than a fair-value is the cornerstone of value factor strategies. Various value strategies were popularized by famous investor Benjamin Graham (and his successors like Warren Buffett) and were firstly employed in the stock market. This idea of looking for investment opportunities that can be bought cheaply can also be applied in currency markets – Currency Value Factor strategy. There is, however, one catch – an investor must know the fair-value exchange rate for currencies. The most popular equilibrium exchange rate model used for this purpose is based on PPP (purchasing power parity). A new research paper written by Ca’ Zorzi, Cap, Mijakovic, and Rubaszek analyzes two additional models – Behavioral Equilibrium Exchange Rate (BEER) and the Macroeconomic Balance (MB) approach to assess which model has the best forecasting power.

Authors: Ca’ Zorzi, Cap, Mijakovic, Rubaszek

Title: The Predictive Power of Equilibrium Exchange Rate Models

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The CAPE Ratio and Machine Learning

10.January 2020

Professor Robert Shiller’s work and his famous CAPE (cyclically-adjusted price-to-earnings) ratio is well known among the investment community. His methodology for assessing a valuation of the U.S. equity market is not the first one but is surely the most cited and the most discussed. There are numerous papers that tweak or adjust Shiller’s methodology to assess better if U.S. equities are under- or over-valued. We recommend the work of Wang, Ahluwalia, Aliaga-Diaz, and Davis (all from The Vanguard Group ) in which they use a combination of machine learning and a regression-based approach to obtain forecasted CAPE ratio, and subsequently, U.S. stock market returns, more accurately.

Authors: Wang, Ahluwalia, Aliaga-Diaz, Davis

Title: The Best of Both Worlds: Forecasting US Equity Market Returns using a Hybrid Machine Learning – Time Series Approach

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Top Ten Blog Posts on Quantpedia in 2019

29.December 2019

The end of the year is a good time for a short recapitulation. Apart from other things we do (which we will summarize in our next blog in a few days), we have published around 50 short blog posts / recherches of academic papers on this blog during the last year. We want to use this opportunity to summarize 10 of them, which were the most popular (based on Google Analytics tool). Maybe you will be able to find something you have not read yet …

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How to Choose the Best Period for Indicators

3.December 2019

Academic literature recognizes a large set of indicators or factors that are connected with the various assets. These indicators can be utilized in a variety of trading strategies, which means that such indicators are popular among practitioners who seek to invest their funds. Usually, the indicators are connected with some evaluation period.

This paper aims to show some possible approaches to find the optimal evaluation periods of indicators. This is a key question among practitioners and therefore we see it as crucial to shed a light on this topic. Although we are focused on momentum strategies, the information in this paper is widely applicable also in the construction of any other trading strategy where the investor has to decide indicator’s period…

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