Dmitriy Muravyev
Carroll School of Management
Boston College
Dmitriy Muravyev

Dmitriy Muravyev, Assistant Professor of Finance

Curriculum Vitae   

Research Interests
Empirical Asset Pricing, Derivatives, Market Microstructure, Information Processing

Contact Information
Carroll School of Management, Boston College
Finance Department, Fulton Hall 346
140 Commonwealth Avenue
Chestnut Hill, MA 02467

Email: muravyev@bc.edu
Phone: (217) 721-3772


Research Papers
  • Is There Price Discovery in Equity Options?, (with Neil Pearson and John Broussard ), Internet Appendix.
    Journal of Financial Economics, 2013, Volume 107, Issue 2, Pages 259-283
  • Option price quotes do not contain any information about future stock prices beyond what is already reflected in current stock prices. We focus on events when the two markets disagree about the stock price. In these disagreement events the options market adjusts bid and ask prices to eliminate the disagreement, while the stock market behaves normally, as if there were no disagreement. These results are consistent with the hypothesis that option price quotes do not participate in the price discovery process for the underlying stock price.
  • Order Flow and Expected Option Returns., 2013
    Internet Appendix, Excel example of how option returns are computed in the paper.
    Revision requested by the Journal of Finance

  • The paper presents four pieces of evidence that the inventory risk faced by market makers is the main determinant of expected option returns. First, I introduce a simple method for decomposing the price impact of trades into inventory-risk and asymmetric-information components. The components are inferred from the difference between price responses of the market-maker who receives a trade and those who do not. An average option trade has inventory-risk impact of 0.4% which is twice as large as the information component. Second, at the daily level, I suggest several solutions to endogeneity between order flow and prices. Option expiration triggers exogenous variation in order imbalance as investors roll over their positions to non-expiring options. The selling pressure around expiration dates reduces option prices by 5.7% in three days, while the underlying price and volatility remain unchanged. Finally, the inventory-related order imbalance predicts future option returns substantially better than fifty commonly-used predictors. If it increases by one standard deviation, the next-day return is 1.2% higher.
  • What Does Text Sentiment Really Measure? Evidence from Earnings Calls, 2013, (with Tatiana Chebonenko),

  • Text sentiment extracts textís attitude by counting negative words and has proved extremely useful in a variety of contexts. The literature interprets it in three ways: quantitative information, soft news, and psychological sentiment. We use a quasi-natural experiment to show that text sentiment reflects primarily omitted quantitative information and does not capture soft news or sentiment. We first extract text sentiment from earnings call transcripts with dictionary and supervised-learning methods, and then compare how it predicts returns during overnight and intraday calls; specifically, whether text sentiment explains a larger portion of stock returns for overnight calls. The overnight and intraday cases differ only in the timing of a quarterly report. Overnight calls are dominated by quantitative news from a quarterly report, while the intraday cases contain mostly soft news and sentiment. Text sentiment explains overnight returns well but fails to predict returns or volatility during intraday calls. Thus, text sentiment reflects news only during periods dominated by quantitative information.

  • Negative Externality of Algorithmic Trading: Evidence from the Option Market , 2014, (with Neil Pearson),

  • High frequency and other algorithmic traders both provide and take liquidity. Theoretical models that incorporate algorithmic liquidity taking predict that it exacerbates the adverse selection problem faced by liquidity providers. Algorithmic trading technologies can also potentially mitigate adverse selection because they allow liquidity providers to quickly cancel quotes, making the net impact of algorithmic trading on adverse selection unclear. Using a novel method to identify algorithmic liquidity taking trades, we show that non-algorithmic tradersí costs of taking liquidity in the option markets increased substantially relative to average costs during the period when algorithmic trading became important. Algorithmic liquidity takersí costs declined substantially and their share of trading volume increased. Overall average costs of taking liquidity declined, driven by the reduced costs of algorithmic liquidity provision. Due to the ability of algorithmic traders to time their trades, the quoted and effective bid-ask spreads overstate average costs of taking liquidity by a factor of about two, and we show how to correct measures of the bid-ask spread and price impact for this bias. The execution timing ability of algorithmic liquidity takers also explains why option bid-ask spreads increase in the absolute value of option delta.