119049 Moscow, Russia
Shabolovka 26, office 4415а
+7 (495) 916-89-00 *26146
Irina Ivashkovskaya —
Head of the School, Head of Corporate Finance Research Center, Dr., tenured professor
Alexey Yushkin —
The paper illustrates how a Bayesian approach to yield fitting can be implemented in a non-parametric framework with automatic smoothing inferred from the data. It also briefly illustrates the advantages of such an an approach using real data.
The paper uses an infinite dimensional (functional space) approach to inverse problems. Numerical computations are carried out using a Markov Chain Monte-Carlo algorithm with several tweaks to ensure good performance. The model explicitly uses bid-ask spreads to allow for observation errors and provides automatic smoothing based on them.
A non-parametric framework allows to capture complex shapes of zero-coupon yield curves typical for emerging markets. Bayesian approach allows to assess the precision of estimates, which is crucial for some applications. Examples of estimation results are reported for three different bond markets: liquid (German), medium liquidity (Chinese) and illiquid (Russian).
The result shows that infinite-dimensional Bayesian approach to term structure estimation is feasible. Market practitioners could use this approach to gain more insight into interest rates term structure. For example, they could now be able to complement their non-parametric term structure estimates with Bayesian confidence intervals, which would allow them to assess statistical significance of their results.
The model does not require parameter tuning during estimation. It has its own parameters, but they are to be selected during model setup.
Puzzling Premiums on FX Markets: Carry Trade, Momentum, and Value Alone and Strategy Diversification.
Technological development and digitalization plays a crucial role in financial sector by allowing firms to create value in a rapidly changing environment. The acquisitions of firms related to financial technologies are one of the ways to obtain vital knowledge. In order to identify the fintech companies we are looking at firms that are involved in business activities in both the IT and financial sectors. By examining the growing role of fintech firms in the recent mergers and acquisitions from an investor point of view, this paper contributes to the existing literature by investigating the post-acquisition performance of the acquirer firms measured by abnormal returns. We discovered significant positive average abnormal return after acquisition of fintech companies in the short-term and negative average abnormal return in the long-term using event studymethodology. The specifics of cross-border acquisitions, the level of the domestic market development of the acquirer, and other characteristics of M&A deals are considered in order to explain the reaction of investors to announcements of fintech firms’ acquisitions. The determinants of corresponding M&A deals in emerging and developed markets were revealed.
The textbook covers such important aspects of financial planning as methodological development of financial planning mechanisms, budgeting, forecasting the growth rate of the company and the company's value. Financial planning mechanisms in organizations should be adapted to the specific focus of the main business processes.When developing the optimal mechanism for planning financial resources and sources of their formation, developers of financial plans should be guided by the principles of scientific news and target orientation, the basic principles of financial planning in organizations. In this regard, the system knowledge gained from the study of this publication allows students to gain theoretical knowledge and practical skills in the competent preparation and implementation of financial plans and effective management of the company.
This article is devoted to a comprehensive study of toxic assets, which are on the balance sheets of professional securities market participants, in order to calculate the real value of capital. The authors have developed a method similar to a formalized algorithm for identifying toxic assets of liquidating or liquidated companies and assets in credit institutions with a revoked license, for subsequently adjusting the value of equity (net assets), and analyzing for compliance with the Bank of Russia supervisory requirements. The method is described by the authors in accordance with BPMN notation. The authors determined the information base of the method, which includes only mandatory reporting and open information resources, which makes it simple and transparent for users. Prospective users of the method may be the Bank of Russia, current and future counterparties of professional participants in the securities market, DBMS developers and scientific employees of economic specialties. The method was tested by the authors on the basis of a real-life brokerage company.
In this paper we are going to review both theoretical studies in the field of intellectual capital measurement and empirical research, devoted to analyses of intellectual capital influence on companies’ value and financial performance. As a result, potential areas for further investigations in this field were revealed.
Considering groups of intellectual capital measurement methods, we identified that direct intellectual capital methods and scorecard methods are the most appropriate for the purpose of IC components measurement. To obtain objective results of measurement it seems reasonable to develop system of proxy indicators for all intellectual capital components (human, structural and relational capitals) and subcomponents (process and innovation, client and network capitals). Basing on existing literature, we make an attempt to identify and systemize indicators, associated with intellectual capital and reveal that network capital metrics remain under-researched and deserve closer examination. It was also found that investigators should develop the system of intellectual capital indicators, taking into account industry specificity.
As for empirical studies, in order to investigate the influence of intellectual capital on corporate value and financial performance, it seems reasonable to elaborate models, which include factors, associated with all intellectual capital components and subcomponents and, what is just as important, their interrelations. Furthermore, it is vital to investigate the relationships between the values of IC components for companies. The models should be adopted for both developed and developing countries. It is also important to analyze the influence of intellectual capital in various industries separately, taking into consideration phase of economic cycle.
This article evaluates the benefits to merchants resulting from participation in the retail payments market. Using surveys to obtain a representative sample of 800 traditional (offline) Russian merchants, the article finds significant, robust evidence in favor of positive merchant's benefits. This study further separates the benefits into direct and opportunity: finding that the non-welfare improving regulatory initiatives might result from the failure to account for the opportunity benefits to merchants. This article also examines the factors affecting the level of merchants' benefits. Results show that factors affecting the value of benefits and the probability of accepting payment cards differ. Findings imply that unbalanced intervention may be detrimental to the agents' welfare, leading to a suggested mechanism for ex-ante evaluation of the effect of shocks and interventions.
The use of Big Data technology has been a modern trend in the travel industry over the last 10 years. At present, almost all travel companies that desire to stay profitable and be customeroriented use the Big Data technology. Therefore, we have several questions to answer: should we use Big Data in tourism or should we not? How to use it? What kind of risks we should consider in order to achieve effective results? These research problems were examined through a thorough analysis of Russian and world travel markets using statistical data on several sites, programs, and organizations that are associated with the tourism industry (e.g., Booking.com, Trivago). The main result of this study is to substantiate the importance of Big Data technology for the travel industry. Big Data technology helps to personally connect companies and clients of the sector so that their interaction would lead to their mutual benefits. The net result of this interaction is an increase in the economical aspect of the sector and thus the country’s growth.