Research
Take a closer look at innovations and shifts in investment management and risk assessment.
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What’s in a Name? Part 2: Stress-Testing Smart Beta ETFs
In this paper, we show results of stress tests on a number of different Smart Beta ETFs. As we noted in our first paper on this topic, even portfolios with similar investment philosophies (and seemingly identical names) can have quite different exposures. We look at reactions to various types of stresses and show once again how results can differ substantially from one fund to the next. We also show how stress-test results can vary widely over time.
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A Tactical Asset Allocation Workflow
Long-term investors often passively track a strategic asset allocation benchmark whose weights across the various asset classes remains constant over a multi-year horizon (usually 10, 15 years, or longer). Specialist teams, internal or external, may be setup to add an active overlay portfolio in the form of a tactical asset allocation which deviates from the strategic benchmark over shorter time horizons. These programs are used to either de-risk the strategic portfolio in times of market stress, or to add alpha by aligning the portfolio with the economic cycle over shorter time periods (i.e. usually three to five years).
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Reverse Stress Testing Challenges: Toward a Systematic Framework
Regulators have strongly endorsed reverse stress testing programs within financial institutions since reverse stress tests can explicitly examine the solvency of a firm. Reverse stress tests are designed to identify economic scenarios that will threaten a firm's survival and potentially help managers hedge against hidden scenarios. These stress tests are attractive from a risk perspective, but implementing a reverse stress testing program that is independent of a manager’s bias is difficult. In this paper, we outline a systematic, quantitative framework to design and construct reverse stress tests. This paper is our third installment on stress testing.
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The Correlation See-Saw
The first five months of 2018 were characterized by dramatic shifts in multi-asset class relationships, with an unusual back and forth of asset prices and correlations, as themes dominating the investment landscape alternated. In this paper, Christoph Schon analyzes how the different correlation regimes present during this time affected the overall volatility and risk decomposition of Axioma’s global multi-asset class model portfolio.
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When Size Does Matter
In this paper, Olivier d’Assier evaluates the use of a custom small-cap risk model built for this segment of the Japanese market using the Risk Model Machine (RMM) module in Axioma Portfolio Analytics. Do risk and performance attributions differ from the standard Japan risk model built on the full universe of stocks? What are, if any, the advantages delivered by the custom model for strategies focused on this market segment? Given these results, should small-cap managers include a custom risk model in their investment process?
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What Are the Odds?
This paper, written by Olivier d'Assier, Axioma's Head of Applied Research for APAC, focuses on the risk and reward aspect of a risk analysis and how users can incorporate this information in their risk budgeting exercise.
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Q1 2018 Insights
Farewell Low Volatility…
Are we now closer to a trough or a peak?
After falling to historically low levels at the end of 2017 volatility surged in the first quarter, driven mainly, but not exclusively by market risk. Against this backdrop, interestingly, style factors remained quite well-behaved.
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How Brexit Changed Financials’ Spots
Since the UK’s decision to leave the European Union, investors have fretted over the impact of Brexit on the UK and European economies. At a high level, the UK and European financial sectors showed large negative returns before the vote and strong positive returns after. Risk peaked around the referendum date, only to decline abruptly afterwards. The question is, what were the main factors impacting this reversal in performance in each region and what were the contributors that led to the rise and decline in risk?
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The Many Faces of Japanese Style Portfolios
This is the second paper in a series of looking at portfolio construction methodologies for designing style factor portfolios in the Asia-Pacific region written by Olivier d'Assier, Axioma's Head of Applied Research for APAC. In this paper, Olivier focuses on the Japanese market to construct three variants of a long-only active factor portfolios on the following five style factors from Axioma’s Japan fundamental medium horizon risk model (AXJP4 – MH): Dividend Yield, Momentum, Growth, Profitability, and Value. He then compares each variant on the basis of the implicit costs of constraints on the portfolio’s ability to gain a pure exposure to the target factor.
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Multi-period portfolio optimization with alpha decay
This research paper appeared in The International Journal of Financial Engineering and Risk Management Special Issue on Applications of Optimization in Finance, Volume 2, Number 4, pp. 283-307.
The traditional Markowitz MVO approach is based on a single-period model. Single period models do not utilize any data or decisions beyond the rebalancing time horizon with the result that their policies are "myopic" in nature. For long-term investors, multi-period optimization offers the opportunity to make "wait-and-see" policy decisions by including approximate forecasts and long-term policy decisions beyond the rebalancing time horizon. We consider portfolio optimization with a composite alpha signal that is composed of a short-term signal that has a high IC but decays rapidly, and a long-term alpha signal that has lower IC but more persistence. We develop a simple two stage multi-period model that incorporates this alpha model to construct the optimal portfolio at the end of the rebalancing period. We compare this model with the traditional single-period MVO model and show that the multi-period model generates portfolios that are likely to have a better realized performance.
