Research

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

  • Q4 2017 Insights

    If “The Market Climbs a Wall of Worry” What Happens When the Market Stops Worrying?
    2017 was a remarkable year, with stocks around the globe extending steep gains, while volatilities plunged to levels at or near historic lows, even in the face of a long list of geopolitical and economic events worldwide. We identified last year’s winners and losers and analyzed the factors driving this large drop in risk worldwide. Finally, we highlighted some positives, negatives and confounding data that may provide insights into the direction and volatility of markets in 2018.

    Melissa R. Brown, CFA Research Paper No. 107
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  • An Aussie Sense of Style

    In this paper, Olivier d'Assier, Axioma's Head of Applied Research for APAC, takes a close look at the compromises involved in constructing a viable smart beta product. In his analysis, Olivier focuses on the key portfolio construction issue of how to balance a desire for target-factor purity with a goal of achieving a high exposure to the target factor. Can we have both? 

    Olivier d'Assier Research Paper No. 106
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  • A Cvar Scenario-Based Framework For Minimizing Downside Risk In Multi-Asset Class Portfolio

    In this research paper published in The Journal of Portfolio Management, we consider a scenario-based conditional value at risk (CVaR) approach for minimizing the downside risk of an existing portfolio with multi-asset class (MAC) overlays. MAC portfolios can be composed of investments in equities, fixed income, commodities, foreign exchange, credit, derivatives, and alternatives such as real estate and private equity. The return for such nonlinear portfolios is asymmetric with significant tail risk. The traditional Markowitz mean–variance optimization (MVO) framework, which linearizes all the assets in the portfolio and uses the standard deviation of return as a measure of risk, does not always accurately measure the risk for such portfolios. We compare the CVaR approach with parametric MVO approaches that linearize all the instruments in the MAC portfolio on two examples involving the hedging of an equity portfolio with index puts and the hedging of a callable bond portfolio with interest rate caps, and show that the CVaR approach generates portfolios with better downside risk statistics; and further, it selects hedges that produce more attractive risk decompositions and stress test numbers—tools commonly used by risk managers to evaluate the quality of hedges.

    Kartik Sivaramakrishnan and Robert Stamicar Research Paper No. 105
  • When It Comes to Momentum, Don't Cramp My Style

    This paper looks at the impact of common constraints on a long-only Momentum-based strategy. The idea is to show how these constraints drive differences in portfolio characteristics, risk and return. The analysis is geared toward helping factor-based managers gain more insight into the potentially harmful impact of portfolio constraints, which may also prove to be unnecessary. The paper also looks into the risk characteristics (other than momentum)  of relatively unconstrained portfolios, to help managers understand past and future performance drivers of a momentum-tilted portfolio.

    Melissa R. Brown, CFA Research Paper No. 104
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  • For Style Factors, One Size Does Not Fit All

    In this article published in The Journal of Investing, Melissa R. Brown challenges the notion of “one size fits all” in regards to style factor performance. She explains how using style factors as measures of both risk and return are commonly incorporated into an alpha-generating process, but that any factor will come with associated volatility. The purpose of this article is to highlight how use of style factors as either alpha generators or for risk management should vary based on investor objectives.

    Melissa R. Brown, CFA Research Paper No. 103
  • 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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