
No systemic risk from SVB failure, but watch out for areas o...
2023 will be a two-speed year, with plenty of risks to watch out for.
Read moreSince its introduction in 2017 (Vaswani et al., 2017), the Transformer model has excelled in a wide range of tasks involving natural language processing and computer vision. We investigate the Transformer model to address an important sequence learning problem in finance: time series forecasting.
More > 10 minutesWhat AI reveals about stock prices?- this piece outlines research by Amundi on the usefulness of using AI in stock analysis and valuation.
More > 10 minutesUsing textual data extracted by Causality Link platform from a large variety of news sources (news stories, call transcripts, broker research, etc.), we build aggregate news signals that take into account the tone, the tense and the prominence of various news statements about a given firm.
More > 10 minutesThis paper proposes a weighted group neural network model and reexamines whether treasury bond returns are predictable when real-time, instead of fully-revised, macro information is used.
More > 10 minutesThe most common models to assess asset returns are a linear combination of risk factors.
More > 10 minutesIn this research article, Amundi Quantitative Research explores the use of graph theory and neural networks in asset management.
More > 10 minutesWe study the introduction of robo-advising on a large representative sample of Employees Saving Plans. The robo-advisor proposes a portfolio allocation and alerts investors if their allocation gets too far from the target, while investors remain free to follow or ignore the advices.
More > 10 minutesWe start by considering some of the key reasons behind the academic and industry interest in robo-advisors. We discuss how robo-advice could potentially address some fundamental problems in investors’ decision making as well as in traditional financial advice.
More > 10 minutesIn this article, we explore generative models in order to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial time series, whose statistical properties are the same as those observed in the financial markets.
More > 10 minutesProfessional investors are Professional investors of the European Union, as defined as in European Directive 2004/39/EC dated 21 April 2004 on markets in financial instruments(MIFID) to investment services providers and any other professional of the financial industry,and as the case may be in each local regulation, and, as far as the offering in Switzerland is concerned, a Qualified Investor within the meaning of the provisions of the Swiss CollectiveInvestment Schemes Act of 23 June 2006 (CISA), the Swiss Collective Investment SchemesOrdinance of 22 November 2006 (CISO) and the FINMA’s Circular 08/8 on Public Advertising under the Collective Investment Schemes legislation of 20 November 2008
European Directive 2004/39/EC dated 21 April 2004 on markets in financial instruments (MIFID)
Private customers or retail investors, or to investors who do not comply with the definition of qualified investors as defined in the applicable legislation and regulation
as defined in the US Securities Act of 1933
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