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.
The credit markets are finally embracing quantitative investing and this paper provides an in-depth primer to quant credit. Readers will learn about the dynamics of systematic trading, the factors employed in quant models, and the future paths this rapidly-evolving strategy is likely to take.
CFA Institute Research Foundation introduces the first edition of the International Guide to Cost of Capital Summary Edition (IGCC) from Duff and Phelps/Kroll. This publication, which examines the important difference in risk characteristics of investing in various countries, is now a valuable part of the Research Foundation…
For institutional asset owners, the vast majority of potential investment returns and risk will derive from the strategic portfolio allocation decision. This raises the important challenge of how best to construct and evaluate different strategic allocations, acknowledging that risk can be defined beyond a simple metric of…
Robeco examines factors in a novel dataset on the cross-section of emerging market hard currency corporate bonds. They find that the size, low-risk, value, and momentum factors predict future excess returns. Single-factor and multi-factor portfolios obtain economically and statistically significant premiums. Further, alphas…
Tenure-seeking researchers publish thousands of academic articles that promote dubious investment strategies, without controlling for multiple testing. Some of those articles are written for, funded, or promoted by investment firms with a commercial interest. As a consequence, today’s academic finance exhibits some…
Intangible assets have always been part of the economic landscape. In this study Dimensional Fund Advisors examines the impact of internally developed intangibles on the ability to identify differences in expected stock returns in U.S., developed ex U.S., and emerging markets. Their research does not find compelling evidence…
The Nasdaq-100 Volatility Index (VOLQ) offers traders a consistent measure of expected volatility for the Nasdaq-100 Index (NDX). This measure extrapolates how concerned market participants are about future volatility in the near term. Understanding VOLQ and applying this knowledge to trading any instrument tied to NDX is an…
In this paper, Amundi examines the materiality of ESG on country creditworthiness from a credit risk analysis viewpoint. To address this, they consider a granular set of 269 indicators within the three ESG pillars to determine what the sovereign bond market is pricing in. From this set of ESG metrics covering the 2015-2020…
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers the modeling of the liability liquidity risk (or funding liquidity), the second dimension is dedicated to the modeling of the asset liquidity risk (or market…
A major crisis is threatening the sustainability of pension systems across the globe. The first pillar of pension systems, which is made of public social security benefits and aims at providing a universal core of pension coverage to address basic consumption needs in retirement, is strongly impacted by rising demographic…
The authors employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, they achieve state-of-the-art predictive accuracy by training simpler…
Large market drawdowns are a feature of historic market crises. Whether the Flash Crash, VIX Volmageddon, or the GFC, all are etched on investors' memories. This paper looks at several theories as to why these events took place and the lessons that can be learnt in future.
In this edition of Risk & Reward, Invesco presents two examples of how their quantitative analysts helps achieve two goals: striving to deliver strong performance to clients while supporting the ongoing transition to sustainable energy.
In this edition of Risk & Reward, Invesco presents two examples of how their quantitative analysts help achieve two goals: striving to deliver strong performance to clients while supporting the ongoing transition to sustainable energy.
Quantitative equity investing is having a bit of a midlife crisis. Will the same approach that has worked for many of us for several decades continue to work in the future?
The purpose of this paper is to appraise recent ESG trends in global equity markets. It contributes to a broader research project started at Amundi in 2014 on the relevance of ESG. Since the latest update in 2019, Amundi have lived through a global pandemic that profoundly reshaped the global economy and society, and an…
Equity factors follow their own sentiment-driven cycle that cannot be explained by traditional business cycle indicators. This implies that factor premiums are a behavioural phenomenon, rather than a reward for macroeconomic risks.
This is the third and final article from the clustering series. This section examines trading strategies based on previously introduced clustering methods with a focus on clustering with risk parity weightings.
This section demonstrates the Partitioning Around Medoids (PAM) – a centroid-based clustering method, Hierarchical Clustering, which uses machine learning and Gaussian Mixture Model based on probability distribution and applies all three methods to an investment portfolio that consists of eight liquid ETFs.
