Quantitative Asset Management

Meet the team
who created
our Robo-Advisor

Innovation at all levels

 

Swissquote’s Quantitative Asset Management department (QAM) was created in 2008 to explore quantitative research in finance. One of the team’s landmark achievements is the launch of the Robo-Advisor, a sophisticated automated asset manager that uses powerful and fully in-house developed algorithms.

The team
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Bringing together an alchemy of skills, the QAM department is made up of seven physicists and mathematicians. These talented professionals boast a wide range of expertise and experience, and a passion for research and analysis.

Purpose
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QAM’s goal is to provide clients with the latest science in finance, all in a simple and intuitive format, while developing new ideas and opportunities. 

QAM has three main activities

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Quantitative
finance

This field seeks to optimise investment portfolios, order execution, dynamic management of portfolio limits, and quant strategies. Our Robo-Advisor provides quantitative finance services.

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Artificial intelligence
and big data

QAM leverages data and artificial intelligence to serve several Swissquote departments. For example, it helps the Legal department detect insider trading and the Marketing department with the creation of personas.

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Quantitative risk
analysis

Quantitative risk analysis is derived from quantitative finance and is particularly important for the Controlling department. Its purpose is to anticipate market risk, especially for options and futures.

Timeline and key achievements

2008

Creation of the QAM department.

2009

Creation of Quant Funds, Long Only Equity Swiss Regulated Funds, in CHF and EUR. 2016 Lipper Fund award winner.

2010

Launch of the first customisable automated asset manager in Europe – ePrivate Banking – now known as the Robo-Advisor.

2017

AI Volatility Surface: first use of artificial intelligence to calculate volatility surface and optimise internal processes (client segmentation and detection of insider trading).

2021

Launch of a new widget – Investment Inspiration – which makes daily equity investment recommendations based on trading activity.

The Swissquote EPFL chair
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The Swissquote Chair in Quantitative Finance promotes research, teaching and the sharing of knowledge in order to improve expertise and understanding of financial engineering among the academic community, the financial industry and policy makers.

Housed at the Swiss Finance Institute @ EPFL, the Swissquote Chair plays an important role in leading research and teaching initiatives in financial engineering at EPFL.

Learn more

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Source: epfl.ch

QAM publications

Statistically validated leadlag networks and inventory prediction in the foreign exchange market

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Pricing Tokens on Industrial Production

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Managing Inventory with Proportional Transaction Costs

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The market nanostructure origin of asset price time reversal asymmetry

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Liquidity provision in the foreign exchange market

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Improving stock selection using a momentum-based algorithm

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A Monte Carlo Approach to Price American-Bermudan-Style Derivatives

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Eigenvalue Distribution of Sample Covariance Matrices with Arbitrary Variance Profile

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Efficient Reduction of the Sample Covariance Matrix of Returns with Application to Portfolio Allocation

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The Spectral Coarse Graining of Matrices with Application to Graph Theory

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Spectral and dynamical properties of weighted covariance matrices: old and new memory profiles

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Patience and limit order book dynamics in the Swiss stock market

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Traders’ collective portfolio optimization with transaction costs : towards microscopic validation of agent-based models

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Fast and realistic European ARCH option pricing and hedging

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Option pricing with realistic ARCH processes

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Realistic Processes for Stocks from one Day to one Year

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Contact the QAM

Need information or clarifications?
Feel free to write to the QAM team at qam@swissquote.ch