Our paper on Meucci is in SSRN’s Top Ten on Optimisation! Hurray! :)

Today I open my mail and see the following:

 Your paper, “APPLICATION OF ENSEMBLE LEARNING FOR VIEWS GENERATION IN MEUCCI PORTFOLIO OPTIMIZATION FRAMEWORK”, was recently listed on SSRN’s Top Ten download list for: ERN: Optimization Techniques; Programming Models; Dynamic Analysis (Topic).

As of 22 September 2014, your paper has been downloaded 23 times. You may view the abstract and download statistics at: http://ssrn.com/abstract=2493362.

Top Ten Lists are updated on a daily basis. Click the following link(s) to view the Top Ten list for:

ERN: Optimization Techniques; Programming Models; Dynamic Analysis (Topic) Top Ten.

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Innovations as factor of absorptive capacity of FDI spillovers across regions of Russian Federation (A.Didenko, T.Egorova)

We study how innovations affect increase of regional TFP as a result of productivity spillovers from FDI, and confirm the presence of phenomenon in Russian data. We model TFP using DEA with the human capital, energy and and capital as inputs and the gross regional product as output. We develop innovations index for the regions of the RF, which proxies for regional absorptive capacity, based on 17 variables, characterizing economic, social and infrastructural aspects of regional development. FDI is measured as the the sum of ratios of sales of firms with FDI to the total sales in the region times squared distance to neighboring regions.

(Published in Review of Business and Economic Sciences, 2(3), September 2014)

Productivity Spillovers in the Russian Federation: The Case of the Chemical Market (A.Kuzyaeva, A.Didenko)

Foreign direct investment (FDI) and international trade are suggested to be major conduits of international technology transfer. The present paper aims to extend the current empirical literature by determining the effect and the source of productivity spillover in Russia in case of chemical industry. In order to find out the existence of FDI and trade productivity spillover we applied Ericson and Pakes (1995) and Olley and Pakes (1996) methodology. We estimated the model model for companies from chemical industry for the period 2007-2012. Our results confirm FDI and trade productivity spillovers in Russian chemical industry. The size of FDI spillovers is more important than imports-related spillovers. Based on the empirical results, we may predict that Russia’s accession to the World Trade Organization in 2012 should result in productivity growth. However, further research on this topic will be possible when the statistical data becomes available for several years after accession.

(Published in Review of Business and Economic Sciences, 2(3), September 2014)

Using constant volume scale for modeling fractal characteristics of financial time series (A.Didenko, M.Dubovkiov, B.Poutko)

We propose to use intrinsic time scale based on volume when measuring fractal dimension of financial time series. (Dubovikov, 2004) introduces a new method of measuring fractal dimension which is superior to other methods, including well-known Hurst index in terms of  speed of asymptotic. As a downside, estimates obtained with new method, are noisy and hard to predict, which in turn complicates its use in practice. We demonstrate that sampling time-series across volume scale, instead traditional physical time scale, could significantly improve predictability of fractal dimension.

(Published in Russian in “Science in Modern Information Society IV” , Fall 2014, ISBN 978-1-50232-179-4)

Scientific seminar on financial economics (Spring 2012 – Spring 2015, Financial University and MGIMO)

Course description: the course is intended to prepare 2nd-year master students for writing their master theses. It briefly overviews some of the most influential and/or controversial papers in financial economics, econometrics and macroeconomy. Papers are replicated in R using original datasets. During class students are obliged to take 2 individual projects, make 4 peer-reviews and one literature survey. All homeworks, R models and codes, datasets, reports, peer-reviews, and discussions are stored in internal knowledge base. 

Prerequisites: course is taught to master students of the final year, final semester.  It is assumed that student had successfully completed classes on financial markets, corporate governance, statistics, econometrics, financial mathematics, portfolio theory. Knowledge of R language is preferable, but not necessary. 

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What impacts efficiency of pension fund managers in Russia (E.Fedorova, A.Didenko, D.Sedykh.)

We use data envelopment analysis (DEA) to assess efficiency of 46 pension fund managers in Russia during 2004-2012. Our DEA model represents pension fund portfolio as decision making unit transforming risk, human and financial capital to active return and quality of diversification. We find that the highest impact to efficiency of pension funds have stock market returns, while interest rates, corporate debts, FX rate have lower impact, and energy prices have no impact at all. Bigger and more mature portfolios with higher share of equities and cash would have lower returns. Seasonal factor impact is also high with third quarter being the toughest for managers. We explain it by scale effects and constraints implied by funds’ investment declarations.

(Published in Russian in «Financial analytics: science and experience», ISSN 2073-4484, issue No. 33(219) – 2014 September)

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Algotrading in Bloomberg, R, and beyond (Financial University, Spring-Fall 2014). 

Class description: introductory course on developing trading algorithms and algotrading industry as a whole. Class is held in financial lab with 9 Bloomberg terminals; students are learning to fast-prototype algorithms using R language and real data from markets. Focus is on practice, but good understanding of underlying theory is a must. Knowledge of Bloomberg platform and R language is an advantage.

Prerequisites: basics of financial markets, technical and fundamental analysis, financial mathematics, modern portfolio theory, statistics and probability theory.

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