All the research topics covered by the QMI are structured around the 3 main stages of the creation of a quantitative investment strategy. The first step is related to signal generation. Particular attention will be paid to the use of artificial intelligence in this production of signals. The second step involves portfolio construction and dynamic risk management. In particular, we are interested in the optimal use of techniques from the world of derivatives. Issues related to potential crowding due to joint use of related strategies by different funds will also be addressed. Finally, the last step covers all the challenges of real-world implementation of paper portfolios from the previous step.
Amongst the research areas of most interest to the QMI for 2018 we had:
Application of signal treatment to the estimation of factorial models, the detection of outliers, the filtering of trends and the robust estimation of Kalman models is an active research field of the IdR QMI. The robust Kalman filter is in particular used in a project aiming to filter the leverage of Real Estate Private Equity funds form reported NAV. These funds are reporting on a quarterly basis, and the use of classic Kalman filter produces in general poor results in this specific context.
Serge Darolles is working with his PhD Student Charles Chevalier on statistical approaches allowing to detect common trends in different times series. This topic is particularly relevant when the objective is to allocate money to a portfolio of different trends following systems. The risk of this strategy is then linked to the probability of observe simultaneously breaks in trends characterizing different markets. The project related to this research will be exposed in a research papers programmed in 2019.
Two working papers by D.E. Allen, M. McAleer, and A. Singh are focused on Big Data. E. Benhamou has started a PhD thesis on deep lerning applied to finance. He already has several working papers related to this area of research. Eric Benhamou, Serge Darolles and Gaëlle Le Fol are working on a project on “Illiquidity in Risk Analysis and Large dimensions: an application to Mutual Funds”. The idea is to propose risk measures that not only take into account the investment of a funds as well as the investments of other funds that use the same risky assets. They received a grant from Institut Europlace de Finance in 2018 to conduct that project. They also participated to a call for expression of interest by the French government (Appel à Manifestation d’intérêt) on data collection for artificial intelligence.
Finally, Serge Darolles, Gaëlle Le Fol, and her PhD Student Béatrice Sagna with another co-author are working on volume prediction (univariate and multivariate) models using machine learning method. Their first results show that machine learning technics outperform ARMA and SETAR specification both in and out of sample.
A « Quantitative Investing » session leaded by Gaëlle Le Fol and Serge Darolles, Members of the QMI has been organized at the Computational Financial Econometrics (CFE) conference in Pisa in December 2018 (see Other conferences). We organized a Hackathon on January 25-26 2019 to explore the areas of artificial intelligence and machine learning in the asset management industry.
Serge Darolles is working with his PhD Student Charles Chevalier on the characterization of a Multi-asset Trend Following Risk Premia that can be used to explain the cross-sectional dispersion observed in the CTA space. The corresponding risk factor can be used to improve the explanatory power of the linear factor models generally used to analyse hedge fund portfolios.
A change in the structure of a fund's client base affects the potential mismatch between the liquidity of its assets and liabilities. An asset/liability approach for liquidity management is therefore critical and requires a client behaviour model. Serge Darolles, Gaëlle Le Fol and Ran Sun are working on investor’s behaviour and the consequences on funding liquidity risk. This research has been presented at several seminars and international conferences (see Seminar and conference in the Annual Report 2018 in the Library).
Mardi Dungey and Eric Renault have received a funding of 10,000 euros by the QMI for their project on contagion modelling (See Recently Funded and Completed projects). Identifying contagion effects during periods of financial crisis is known to be complicated by the changing volatility of asset returns during periods of stress. To untangle this, they propose a GARCH (generalized autoregressive conditional heteroskedasticity) common features approach, where systemic risk emerges from a common factor source (or indeed multiple factor sources) with contagion evident through possible changes in the factor loadings relating to the common factor(s). This research has now been published in Journal of Applied Econometrics in 2018 (See Publications).Serge Darolles, Gaëlle Le Fol and her PhD Student Béatrice Sagna work with another co-author on some multivariate volume prediction methods applied to the circulation of liquidity within a portfolio. This research has been presented at the CFE 2018 meeting in December 2018.
Serge Darolles, Gaëlle Le Fol and Ran Sun work with another co-author on fund flows predictions, clustering effects and over-dispersion with implications on fund liquidity risk. This research has been presented at the International Finance Meeting (AFFI) as well as at the Financial Time Series workshop both in Paris in December 2018 (see Seminar and conference in the Annual Report 2018 in the Library).
Several methods are compared to jointly estimate the market risk of the returns of portfolios and evaluate the estimation risk. The comparison relies on asymptotic theory and numerical experiments. This research, jointly conducted by Christian Francq and Jean-Michel Zakoian, has been presented at several seminars and international conferences (see Seminar and conference in the Annual Report 2018 in the Library).
Several researches have been conducted by Christian Gourieroux to detect the systemic risks present in a portfolio, define rating for systemic risk, or construct scenario generators to measure the impact of systemic shocks.
Given the sharp increase of the number of alternative risk premia discovered by academics and practitioners, several issues need to be addressed: the factor construction methodologies, the consequences for portfolio diversification, the persistence of the alternative risk premia.
Regarding the first two issues, Marie Lambert et al. are working on construction rules of risk factors and the design of smart beta strategies. A proper methodology to stratify stock universe into style buckets is key for the design of persistent risk factors, asset allocation and performance attribution. The two working papers have been presented at academic and practitioner conferences and seminars (FMA – San Diego, Quant Vision Summit, AFFI, … see Seminar and conference in the Annual Report 2018 in the Library). Marie Lambert et al. also works on the design of alternative risk premia capturing non-linear payoffs. The working paper on the gamma trading of hedge funds have also been presented at several conferences and seminars.
Regarding the persistence of the alternative risk premia, Serge Darolles and Marie Lambert are working on the economic cycle of alternative risk premia and the change in business model from active to passive management for those investment strategies. Serge Darolles has presented the paper at Ecosta 2018 in Hong Kong and at the AFG in November 2018.
Looking at serial correlations, Serge Darolles, Gaëlle Le Fol and Ran Sun are working on hedge funds liquidity and managers’ skills(See Working Papers 2018).
Gaëlle Le Fol is leading a project that focuses on multivariate models to analyse the liquidity structure of a large panel of assets. Serge Darolles, Béatrice Sagna – PhD student under Gaëlle Le Fol’s supervision and Christian Brownlees from Pompeu Fabra are part of that project. Fabrice Riva is for his part, with two co-authors, working on ETF liquidity (See Working papers 2018).
In their project “Stock Market Liquidity and Trading Costs of Asset Pricing Anomalies”, Tamara Nefedova, with some co-authors, uses transaction-level data from Ancerno to investigate implicit cost dynamics and estimate transaction costs associated with trading asset-pricing anomalies. They find that the related costs are considerably lower than documented by previous studies.
Optimisation of the VWAP (Volume Weighted Average Price) replication algorithms, link between the speed of placing orders on the market and the arrival of information, liquidity trade-offs, maximum trading capacity are areas of research in which QMI is regularly investing.
Pas research showed that investors are acting strategically – by slicing their orders - to avoid being picked-off by HFTs. Doing so, they slow down the propagation of information in the prices. Again, this research has been presented several times in international conferences (see Annual report 2017).
Serge Darolles, Gaëlle Le Fol, and Béatrice Sagna with another co-author are working on basket VWAP strategies. This research was first presented at the 12th CSDA International Conference (CFE 2018) in Pisa in December 2018.
Albert Menkveld and Vincent van Kervel are working on HFT leaning with or against the wind of large institutional orders. They find that HFTs initially lean against these orders but eventually change direction and take position in the same direction for the most informed institutional orders. This research was initially funded by the 2013 QMI call for project and is forthcoming in the Journal of Finance (see Annual Report 2013, Articles)