Research

Take a closer look at innovations and shifts in investment management and risk assessment.

  • Toward Dynamic Stress Tests

    This note outlines an extension to Transitive Stress Tests. The main idea is to allow the stress test to “select” the proper period or regime for volatility/correlation estimations (using some common techniques from machine learning), i.e., a dynamic stress test. Typically, risk managers subjectively select periods of elevated correlation and volatilities, which are then used as inputs for transitive stress tests. Instead, we describe how the stress scenario itself determines the period when the shift was most likely to occur. Dynamic stress tests can be used alongside the traditional approach in which specific periods are explicitly specified.

    Iulian Cotoi and Robert Stamicar Research Paper No. 102
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  • Q3 2017 Insights

    Breaking Records… and Sounding Like a Broken Record
    Backed by robust economic data, global stocks continued on the upward trend started in the first half of the year. While some benchmark returns advanced at a slower pace this quarter, a few (such as the Russell 2000 and TSX Composite) accelerated. North Korea tensions weighed on equity markets in August, but by September major indices were hitting fresh records. The summer lulls brought relatively muted trading activity, and markets remained calm, despite high levels of political turmoil. Against this backdrop, benchmark risk in many cases hit historically low levels, driven by both lower volatility and lower correlations.

    Melissa R. Brown, CFA Research Paper No. 101
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  • To Hike or Not to Hike? Let us stress-test the question

    In this paper, we explore a number of historical scenarios of previous BoE rate hikes, analyzing the potential impact on a multi-asset class model portfolio.

    Christoph V. Schon, CFA, CIPM Research Paper No. 100
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  • Harvesting Factor Premia Down-Under

    In this paper, we use the medium horizon variant of Axioma’s newly released Australian fundamental factor model (AXAU4 – MH) to analyse and compare four Smart Beta ETFs listed on the Australian Stock Exchange (ASX). 

    Olivier d’Assier, Applied Research APAC Research Paper No. 99
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  • Identifying Risks and Revealing Opportunities Sector by Sector: China's Information Technology Sector

    This publication is part of the Applied Research content series in which we take a deep dive into individual sectors and analyze the nature of their investment opportunity through the lens of Axioma’s medium horizon fundamental risk model. In this note, we will focus on the Information Technology (IT) sector of the Chinese A-share market as represented by a market capitalization-weighted sector portfolio.

    Olivier d’Assier, Managing Director, APAC Applied Research Research Paper No. 98
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  • Identifying Risks and Revealing Opportunities Sector by Sector: Health Care

    Have the Political Debates Affected the Health Care Sector’s Fortunes and Risks?
    In this series of articles, we dig deep into the risk characteristics of—and investment opportunities provided by—a number of macroeconomic sectors. Our aim is to help risk model users better understand risk from a sector perspective. The information should be helpful to risk managers, especially at fundamental shops, as well as portfolio managers (PMs) with a sector focus. Even PMs who don’t build their portfolios sector by sector should find the analysis useful, as it shows how Health Care stocks interact with other parts of the portfolio and how returns in that sector may differ from those in other sectors.

    Health Care, for a long time a market “darling” rising more than 700% from 2008 to 2015, has seen its relative strength diminish over the past couple years. That said, the signals of whether the current debate is increasing Health Care’s volatility are decidedly mixed.

    Melissa R. Brown, CFA Research Paper No. 97
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  • Risk Resolution: A Framework for Generating Custom Risk Models

    At Axioma, we often debate what constitutes a standard multi-asset class (MAC) risk model. First, there is the choice of the risk factors. On the one hand, the standard model should consist of a parsimonious number of risk factors, but on the other hand, it should capture all relevant risk factors for a well diversified portfolio.

    Fabien Couderc and Robert Stamicar Research Paper No. 96
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  • Giving a Boost to Value Performance - with a Little Factor Awareness (and Luck)

    In a piece headlined “Hot-Stock Rally Tests the Patience of a Choosy Lot: Value Investors,” The Wall Street Journal on August 7 detailed the poor performance of Value funds and indices relative to their Growth counterparts. We thought it might be instructive to look at this issue through a risk and attribution lens, using the Russell 1000 Value and Russell 1000 Growth indices.

    Melissa R. Brown, CFA Research Paper No. 95
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  • Factor Correlations Revisited: How a recent shift in market focus affected major factor correlations and portfolio risk

    Since the middle of March this year, we have seen a shift in correlations between equity and foreign exchange risk factors. In this paper, we examine how changes in the correlations of major risk factor types, in particular the relationship between exchange rates and stock markets, affected a global, USD-denominated multi-asset class model portfolio.

    Christoph V. Schon, CFA, CIPM Research Paper No. 94
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  • Adding Alpha by Subtracting Beta: How Quantitative Tools Can Improve a Portfolio's Returns

    Fundamental (discretionary) portfolio managers typically build their portfolios from the bottom up. That is, they identify stocks they expect to beat the market and combine them to create a portfolio. However, fundamental managers can leverage quantitative tools to help identify and lessen potential issues in their portfolio, while still maintaining their investment views and goals. In this paper, we’ll use a “real world” portfolio to illustrate how quantitative tools can improve a portfolio’s realized returns.

    Chris Martin Research Paper No. 93
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