Professional and academic papers using quantitative methods. All of the research in this section is either about the use of quant techniques, or uses quant methodology (mathematical formulae) within the text. The most viewed quant research in this section are papers focused on alpha strategies, particularly for tactical asset allocation. A key issue for quant analysts here is the robustness and validity of the quant model. One paper refers to the risk of "pseudo-mathematics" and "financial charlatanism", when data is... mined to conceive spurious relationships which don't survive out-of-sample. Therefore, quant papers providing a statistical framework for assessing the robustness of quantitative models (and reducing the risk of overfitting) have generated a lot of interest. Our most downloaded quant research in this section includes papers on risk estimation, performance attribution, big data and risk premia. Other popular reports and white papers cover risk factors and smart beta / scientific beta / alternative beta.
For years, institutions have turned to Intech® to complement their lineup of traditional managers. In 2016, one of our large European public clients approached us about building a bespoke investment solution to help them meet their absolute funding requirements within an absolute risk (total risk) budget. We worked closely…
Extending price momentum tests to the longest available histories of global financial assets, including country equities, government bonds, currencies, commodities, sectors and U.S. stocks, we create a 215-year history of cross-sectional multi-asset momentum, and confirm the significance of the momentum premium inside and…
This issue of Risk & Reward examines cryptotokens. Bitcoins and other cryptocurrencies have capti-vated a large segment of the retail investment world in ways not seen since the dotcom bubble. It is undoubtedly a speculative bubble – yet most speculative bubbles have something real and potentially transformative…
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.
This issue compares factor investing to the two other common investment approaches: traditional alpha strategies and market cap-weighted indexing. We argue that each of these three distinct investment approaches has its own merits and purposes and that, by understanding these differences, investors can make more informed…
By virtue of their lack of investment constraints relative to traditional equity and fixed income managers, hedge funds have produced positive, diversifying returns for more than 20 years. Investors have therefore used hedge funds to complement core equity and fixed income allocations with the expectation that this will…
The current approaches and products of quantitative equity investing stand on the shoulders of major theoretical and empirical contributions in financial economics. At the root of disciplined, modern investment processes are two intuitive concepts: risk and return. This report provides an introduction to these two concepts,…
In this paper, Cliff Asness of AQR Capital Management explains why hedge fund returns shouldn’t be compared to 100% long equities and how to do a more proper comparison.
The efficient market hypothesis (EMH) continues to be embraced as the preeminent model of financial markets. The rapid growth in assets managed by strategies this theory underpins – namely, index tracking, risk premia and smart beta − reflects the consensus it has achieved among investment professionals. Yet the EMH often…
The authors of this paper analyse the relative advocacy of competing approaches for the combination of investment signals in long/short portfolios. Specifically, they study if it is preferable to (1) calculate weights for individual signals and then combine those weights (Mixed); or (2) combine signals and then calculate…
Many Smart Beta ETFs are bought with the expectation of long-term market outperformance. The factors that many are based on have been proven both academically and empirically to produce excess returns. However, especially in the short-term, many of these funds may be vulnerable to different market, factor and economic events…
This is Robeco's Quarterly magazine for professional investors. The 39-page document focuses on a range of topics, including: quant investing, sustainability investing and research. This edition also includes articles on Brexit Norwegian Style, how to avoid the retail apocalypse, and a long-read on cybersecurity.
Pitched at an introductory level, this podcast centres around a series of in-depth conversations with experts from State Street Global Advisors with the aim of bringing investment processes to life.
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…
Berkshire Hathaway has realized a Sharpe ratio of 0.76, higher than any other stock or mutual fund with a history of more than 30 years, and Berkshire has a significant alpha to traditional risk factors. However, we find that the alpha becomes insignificant when controlling for exposures to Betting-Against-Beta and Quality-…
Using a novel dataset consisting of daily futures prices going back to 1877, we find that returns of commodity futures indexes have, on average, been positive over the long run. Although return premiums are associated with both carry and spot returns, commodity returns in different economic states (inflation up/down, expansion/…
The S&P GIVI (Global Intrinsic Value Index) Japan outperformed its benchmark index, the S&P Japan BMI, by 2 bps in Q1 2018.1 Since its launch in March 2012, the S&P GIVI Japan has outperformed its benchmark index by 0.65% per year, with a tracking error of 2.32%.
The advent of big data and artificial intelligence (AI) has emerged as a major game-changer for the financial industry. In the asset management sector, although the adoption of these innovations is still in its infancy, a growing number of players are examining how they can be used to design better investment strategies.
