Research

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

  • A Case Study on the Tax-Aware Features in Axioma Portfolio

    We will explore the tax functionality of the Axioma Optimizer through the use of a fictional case study. The case study will discuss how loss harvesting or tax liability can be included in the objective function, as well as how gross and net tax constraints can be used when constructing a portfolio. We will also discuss how tax-sensitive optimizations can be backtested using the Axioma backtester or automated using the Axioma API.

    Walid Bandar, Director, Product Specialist Research Paper No. 82
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  • Risk, Trump and the Great Sector Rotation

    While the US equity market has been unusually strong since the US election, the tide has not lifted all boats. In the period immediately after the vote, some sectors that had been faring well reversed course and failed to participate in the rally. Others that had been underperforming surged. And, finally, some sectors' returns seemed wholly unrelated to the election. Overall, these changes led to higher dispersion across sectors than we have seen in some time.

    Melissa R. Brown, CFA, Senior Director, Applied Research Research Paper No. 81
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  • A Complete and Robust Set of Backtesting Tools: The First Step in Overcoming the Many Challenges of Backtesting

    Practitioners and academics test many investment strategies over various time horizons. This paper will discuss how to overcome a wide array of mechanical difficulties that can occur while completing a backtest. We’ll summarize the most frequent challenges that occur during backtesting and how Axioma solves for them, and we’ll look at a frontier backtest example that highlights Axioma’s comprehensive set of backtesting functionalities.

    Benjamin Lin, MBA Research Paper No. 80
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  • Market Volatility and a New President: Some Historical Perspectives

    With the election decided, the burning question now is, “What next?” And as “unprecedented” as the 2016 US Presidential election may have been, there are at least some precedents to which we can point for insights into what may now lie ahead. Granted, the economic impact of policies introduced by Donald Trump will not be seen for many months or years. Nevertheless, we can look to other market events to get an idea of what we might expect in equity and currency markets over the near term, while the markets are still absorbing the news.

    Melissa R. Brown, CFA, and Bill Morokoff, PhD Research Paper No. 79
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  • Best Practices in Factor-Based Analytics

    As a portfolio manager, have you ever been surprised by a bad return period? Or wondered if there is a better way to identify the risks in your portfolio? Have you wanted to look for sources of return beyond sector breakdowns? If so, this paper will provide an overview into how you can address these questions and more.

    Phil Martinelle Research Paper No. 78
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  • As Investors Flock to Emerging Markets, Whither Risk?

    Emerging Markets (EM) have been a big winner this year, turning in strong performance and seeing risk forecasts decline. Investors with a refreshed appetite for global risk have poured money into these markets. The enthusiasm could be facing headwinds, however.  In this study, we delve into the various components of risk and what investors might want to keep an eye on as they consider EM for their capital allocations.

    Diana R. Rudean, PhD and Melissa R. Brown, CFA Research Paper No. 77
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  • High Dividend Yield Portfolios: More Popular, Yes, But What About Risk?

    Several recent articles have cited the renewed popularity of funds composed of stocks with high dividends. These articles pointed out that as stock prices have risen, valuations have increased, and this type of strategy may be more risky because it has become overvalued. Our goal was to look at the "riskiness" of the strategy through the lens of our risk models and to test the thesis using some standard valuation measures.

    Melissa R. Brown, CFA Research Paper No. 76
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  • Real Estate and Financials Go Their Separate GICS Ways: An Opportunity for Those Seeking Lower Volatility?

    Having been extracted from the GICS Financials sector, Real Estate is now a GICS unto itself. A recent article on IPE.com noted that this change could lead to increased investment in REITs, as investors concerned about the volatility in public markets seek the relatively lower volatility and better risk-return tradeoff of REITs. The article added that the 10-year volatility of REITs is significantly lower than that of the overall Financials sector. We thus set out to look at a longer-term picture of expected volatility of the new Real Estate sector, Financials including Real Estate (the “old” Financials sector), and Financials without Real Estate (the “new” Financials). 

    Melissa R. Brown, CFA Research Paper No. 75
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  • What's in a Name? In The Case of Smart Beta, It's Hard to Tell

    Do ETF buyers, especially those seeking smart beta strategies, really know what they are getting? Is it alpha? In this paper, we focus on a few types of smart beta portfolios in order to highlight similarities and differences driven by methodology. Our results suggest a number of conclusions about how investors should be thinking about the proliferation of smart beta portfolios.

    Sebastian Ceria, PhD, Melissa Brown, CFA, Ian Webster, and Robert Stubbs, PhD Research Paper No. 74
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  • Executive Summary: A CVaR Scenario-based Framework For Minimizing Downside Risk In Multi-Asset Class Portfolios

    In this paper, which is an executive summary of an Axioma technical report, we show how to minimize downside risk in multi-asset class (MAC) portfolios. By comparing the scenario-based Conditional Value at Risk (CVaR) approach with parametric Mean-Variance Optimization (MVO) approaches that linearize all the instruments in the MAC portfolio, we show that (a) the CVaR approach generates MAC portfolios with better downside risk statistics, and that (b) the CVaR hedges return more attractive risk decompositions and stress-test numbers—tools commonly used by risk managers to evaluate the quality of hedges.

    Kartik Sivaramakrishnan, PhD, and Robert Stamicar, PhD Research Paper No. 73
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