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 330B
140 Commonwealth Avenue
Chestnut Hill, MA 02467

Phone: (217) 721-3772 or (617) 552-0883

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., 2014
    Internet Appendix,
    Excel example of how option returns are computed in the paper.
    Revision requested by the Journal of Finance

  • The paper presents three pieces of evidence that the inventory risk faced by market-makers has a primary effect on option prices. 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. Both price impact components are significant for option trades, but the inventory-risk component is larger. Second, using the full panel of option daily returns an instrumental variable estimation finds that option order imbalances attributable to inventory risk have five times larger impact on option prices than previously thought. Finally, past order imbalances have more predictive power than a set of fifty other plausible predictors of future option returns.
  • Option Trading Costs Are Lower Than You Think , 2014, (with Neil Pearson),

  • Conventionally measured bid-ask spreads of liquid equity options are large. This presents a puzzle, which we resolve. At high frequency, changes in option prices can be predicted using recent changes in stock prices. A large proportion of option trades exploit this predictability to take liquidity at low cost, buying and selling immediately before option prices are expected to change. Conventional measures of effective spreads and price impact do not account for this execution timing but can be adjusted to do so. For the average trade, effective spreads that take account of trade timing ability are one-third smaller than the conventionally measured effective spreads; for trades that reflect execution timing, they are five times smaller. These findings have striking implications for the profitability of options trading strategies that involve taking liquidity. In addition, conventional measures of price impact overstate it by a factor of more than two. Our results also indicate that most option trades originate from investors who time executions, for example proprietary traders and institutional investors who have access to execution algorithms.
  • Does Trade Clustering Reduce Trading Costs? Evidence from Periodicity in Algorithmic Trading , 2014, (with Joerg Picard),

  • We document how sub-second and sub-minute periodicities in trading activity affect volatility and liquidity. Many more trades and quote updates arrive within the first 100 milliseconds (first second) than during the rest of a second (minute). These periodicities originate from algorithms that trade predictably by repeating instructions in loops with round start times and time increments. This seemingly irrational behavior serves as a synchronization mechanism for other investors. We find that during the beginning of a second, when trading activity is high, volatility increases but liquidity remains unaffected. These results are consistent with the Back and Pedersen (1998) model of strategic trading but are hard to reconcile with the predictions of Admati and Pfleiderer (1988). Overall, perhaps surprisingly, trade clustering does not reduce trading costs.
  • 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.