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.
Cryptocurrencies have attracted widespread interest among private investors and even some hedge funds as a vehicle for speculation. But what’s their role in a portfolio — growth engine or diversifier? State Street explores the potential role of bitcoin in multi-asset portfolios based on their track record for diversifying…
To account for asmmetric correlations, investors must measure them correctly. Many don't. Diversification is one of the core principles of investing. Unfortunately, it tends to disappear when it is needed most and turn up again when it is unwanted. To make matters worse, many investors and researchers measure these…
The 2018-2020 quant equity crisis posed an exceptional challenge to quantitative managers due to a rare combination of circumstances. There was basically only one way to outperform during this period, investing in the largest and most expensive growth stocks. Meanwhile, established factors were only effective to the extent…
Ray Dalio has seen and studied many market bubbles. This research piece shows how he has systemized his historical observations into a “bubble indicator”. His analysis shows the implications for US stocks.
QMA use a new approach to explore a link between the well-known macroeconomic exposures of traditional asset classes and those of value premia in a multi-asset context.
EDHEC-Risk Institute investigate the role of sectors on the performance of smart-beta products during the COVID-19 crisis. Cross sectional differences in excess returns (versus a market capitalized portfolio) are driven by strong exposures to a historically unique COVID-19 related industry rotation, rather than to long term…
Many well-known investment factors have proved durable in contributing to investment returns. Empirical research indicates that in short and intermediate time horizons different fundamental attributes (factors), styles and sectors work at different points in the US economic cycle. The following analysis on cycle dynamics may…
In this paper, the authors propose a theory in which each stock's environmental, social, and governance (ESG) score plays two roles: (1) providing information about firm fundamentals and (2) affecting investor preferences. The solution to the investor's portfolio problem is characterized by an ESG-efficient frontier, showing…
The authors present a numerically efficient approach for machine-learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. This approach can then be used to implement a stochastic implied volatility model by training a…
This is the third article in a series of three with the first two are linked in the article itself. The articles focus on examining from a digital signal processing (DSP) perspective various types of digital filters that are designed to model trends in time series, in order to illustrate their properties and limitations.…
For investors, the important takeaway is that despite the proliferation in the literature of a zoo of hundreds of factors, there are only a small number needed to explain the vast majority of differences in returns of diversified portfolios. And those factors hold up to replication tests. In “Your Complete Guide to Factor-…
Past industry returns predict the cross section of industry returns, and this predictability is at its strongest at the one-month horizon (Moskowitz and Grinblatt 1999). We show that the cross section of factor returns shares this property, and that industry momentum stems from factor momentum. Factor momentum is transmitted…
Applying emerging quantum technology to financial problems—particularly those dealing with uncertainty and constrained optimization—should also prove hugely advantageous for first movers. Imagine being able to make calculations that reveal dynamic arbitrage possibilities that competitors are unable to see. Beyond that,…
Contrasting with recent evidence that retail traders are informed, the authors of this report find that Robinhood ownership changes are unrelated with future returns, suggesting that zero-commission investors behave as noise traders. They exploit Robinhood platform outages to identify the causal effects of commission-free…
This excellent derivatives primer gives a great account of Monte Carlo, Black Scholes, discrete option hedging as well as many other principles in quant finance.
The massive growth in central bank balance sheets via quantitative easing, debt monetization, and firing of “big bazooka” stimulus packages gives the business cycle a renewed level of importance for understanding how changes in growth and inflation impact future shifts in global asset markets. We demonstrate the varying…
Financial markets have experienced several negative sigma events in recent years; these events occur with much more regularity than current risk models can predict. There is no guarantee that the training set's data generating process will be the same in the test set in finance. Mathematical models are designed to…
Financial systems rarely allow experimentation. For example, reproduction of the flash crash of 2010 while controlling for environmental conditions is not possible. As a result, much financial research relies on the statistical analysis of finite (historical) datasets, where: (a) Time series datasets are limited, and (b) The…
For quantitative research involving time series data, it is important to avoid relying on data available only at a later point in time (T2) when generating results for an earlier point in time (T1). The ability to time travel through versioned data sets is critical for accurate and reproducible results. Quansight Labs and…
