2015 - Present Assistant Professor of Finance, San Jose State University
2004-2006 Head of Commercial Loan Department, Federal Deposit Bank, Russia
2002-2004 Credit analyst, Federal Deposit Bank, Russia
2001-2002 Credit/Debit card specialist, Bank Vozrojdenie, Russia
2000-2001 Teller, Payment processor, Russian Southern Bank, Russia
PhD, Finance, Texas Tech University, TX
MBA, Marshall University, WV
BS, Economics, Volgograd State Technical University, Russia
Can non-local traders capture the local information advantage and profit, Journal of Financial Research, 2019, vol.XLII(1), pp.41-69.
Abstract: Market makers located in geographic proximity (local) to companies possess a local information advantage that comes from access to soft information. We study whether a non-local trader can capture the local information advantage and profit without relocating. We develop a trading strategy for the non-local trader that generates “buy” and “sell” signals for stocks based on quotes of local market makers. Our findings suggest it is possible albeit difficult for non-local traders to extract local information from local market makers’ quotes. Using limit orders from buy signals we generate up to 7.6 basis points of abnormal return per day.
Using online search querries in Real estate researchwith empirical example of arson forecast, Journal of Real Estate Literature, 2018, vol.26(2), pp. 331-361.
Abstract: In this article, we introduce a user’s guide to Google Trends, a service created by Google to make statistics about online searches available to everyone at no cost. We thoroughly review the service’s advantages over conventional sources of data from a researcher’s point of view. We also cover the most important stages of a real estate study that employs online search statistics from Google in a step-by-step user’s guide. In the guide, we discuss how to compose and refine a list of search terms and how to access, download, process, and apply online search data in real estate research. We illustrate each step of an empirical real estate study. In the study, we test if intensity of online searches for specific key words in metropolitan statistical areas (MSAs) can help to forecast future arson incidents in those areas. We find that lagged searches for “foreclosure” are significantly positively associated with the number of arson incidents in the same MSA where online searches have been conducted. We also demonstrate that lagged searches for “arson,” “restructuring,” and “strategic default” are negatively related to the number of intentional property fires.
Investor's sentiment in predicting the Effective Federal Funds Rate (with Stoyu Ivanov) Economics Bulletin, 2017, vol. 37(4), pp.2767-2796.
Abstract: In this article we study if investor's sentiment measured by an intensity of Google searches may be used to predict future changes of the Effective Federal Funds rate. We find that online searches for “fed funds rate”, “fed interest rate”, “fed reserve”, “fed reserve rate” and “federal interest rate” are associated with next week decrease of the Effective Federal Funds Rate. Google searches for “fed rate hike” and “fed raise rates” are associated with next week increase of the Effective Federal Funds Rate even after we control for a number of macroeconomic indicators. We also find that intensity of Google searches is associated with the future decrease of volatility of the Effective Federal Funds rate. This finding can be explained by the reduction of information asymmetry about future changes that leads to a reduced volatility.
Price Discovery of one security traded in several markets around the World (with Stiyu Ivanov), International Journal of Financial Service Management, 2018, vol.9(1), pp.14-21.
Abstract: In this paper, we analyse the price discovery for SPDR Gold Trust, which is traded on five different markets across the world: the USA, Mexico, Hong Kong, Japan, and Singapore. We find that all prices in the five markets are identical until 23 January 2013 when the Hong Kong prices start deviating from the rest of the group. On 1 July 2013 Mexico joins Hong Kong and starts differing from the rest. We hypothesise that until 23 January 2013 price discovery occurs in the USA and the rest of the markets become price takers. We find that even after 23 January 2013 the US gold market of the SPDR Gold Trust ETF still dominates other markets with more than 90% of the price discovery when using the Hasbrouck information share methodology.
Dynamic correltion structure and security risk, Journal of Economics and Business, 2014, vol.73, pp.48-64.
Abstract: We investigate the relationship between changing correlation structure of returns, security risk, and mean return. According to our results, securities that were highly correlated with the market-wide risk factors in the past are likely to have high systematic and idiosyncratic risk at present. Correlations with the risk factors, however, are not directly related to the mean return of securities, nor can they consistently explain the puzzling relationship between idiosyncratic risk and return. We demonstrate further that the effect of past correlations on security risk is more likely among less transparent securities.