The technology behind Google’s AlphaGo has been strangely overlooked by quants
A combination of machine learning techniques provides multi-period portfolio optimisation
With margin requirements a potential drain on financial resources, delivering healthy returns while meeting regulatory obligations is paramount. To help participants optimise more of their risk, Varqa Abyaneh, chief product officer, Quantile, discusses…
Michael Hollingsworth, head of financial risk analytics in the Data and Access Solutions division at Cboe Global Markets, reveals how trading firms are calculating margin in real time to manage pre- and post-trade risk and end-of-day clearing-house…
Hitachi ABB Power Grids’ dominant position in Energy Risk’s 2021 Software Rankings reflects its deep understanding of current market challenges
Crif-plus will capture risk exposures for all instruments, boosting optimisation potential
Flush with new cash, vendors ready rebalancing services ahead of risk-sensitive leverage framework
In this paper, the authors investigate the optimization of systemic risk based on DebtRank by considering two contagion channels: interbank lending and common asset holdings.
Long-awaited proposal must be replicated by US and UK to be effective, participants say
Smaller drawdowns, higher average and risk-adjusted returns for equity portfolios, using options and power-log optimization based on a behavioral model of investor preferences
The authors use a power-log utility optimization algorithm based on a behavioral model of investor preferences, along with either a call or a put option overlay, to reverse the negative skewness of monthly Standard & Poor’s 500 (S&P 500) index returns…
ESG risks will become part of investment and risk management processes across all funds at the firm
Machine learning shows promise in grouping assets better, predicting regime shifts
Matthew Beddall’s Havelock restyles value investing for the big data age
This paper derives an alternative fast Fourier transform-based computational approach for calculating the target capital of the SST that is more than 600 times faster than a Monte Carlo simulation.
This paper is devoted to the question of optimal portfolio construction for equity factor investing. The authors discuss the question of multifactor portfolio construction and show that the simplistic approaches often used by practitioners tend to be…
After a difficult 2018, investors are increasingly wary of risk premia, concerned that factors leading to underperformance might be a recurring problem. Imene Moussa, executive director at UBS, clarifies this issue
In this paper, the authors propose a modification of expected shortfall that does not treat all losses equally. We do this in order to represent the worries surrounding big drops that are typical of multiperiod investors.
There is a lot to learn before quantum computers can be applied to specific financial problems
Skewed target range strategy for multiperiod portfolio optimization using a two-stage least squares Monte Carlo method
In this paper, the authors propose a novel investment strategy for portfolio optimization problems.
In this paper, the authors construct strategies for an American option portfolio by exercising options at optimal timings with optimal weights determined concurrently.
Modular tech and micro-services – plus new risk and regulatory needs – are creating openings for insurgents and incumbents
This paper demonstrates how to directly incorporate common value-investing idea into the portfolio optimization process.
This paper investigates the distributional characteristics of stock market returns and analyzes the significance of higher moments.
This paper examines how the Kelly criterion can be implemented into a portfolio optimization model that combines risk and return into a single objective function using a risk parameter.