Alexander Barinov

Ph.D., Finance, Simon School of Business, University of Rochester, 2008, Beta Gamma Sigma

M.Sc., Finance, Simon School of Business, University of Rochester, 2006

M.A., Economics, New Economic School, 2003, Cum Laude

B.A., Economics, Lomonosov Moscow State University, 2002, Summa Cum Laude

2023 - present    Associate Professor of Finance (with tenure), School of Business, University of California Riverside

2015 - 2023    Assistant Professor of Finance, School of Business, University of California Riverside

2008 - 2015    Assistant Professor of Finance, Terry College of Business, University of Georgia

• Omnibus Travel Grant, UCR Senate, 2016-2023
• Regents' Faculty Development Grant, UCR Senate, 2021
• Casualty Actuarial Society Award, ARIA 2021, "Estimating the Cost of Equity Capital for Insurance Firms with Multi-period Asset Pricing Models" (joint with Jianren Xu and Steven Pottier)
• Regents' Junior Faculty Fellowship, UCR Senate, 2018
• Outstanding Paper in Investments Award, SFA 2013, "Stocks with Extreme Past Returns: Lotteries or Insurance?"
• Alpha Kappa Psi Teaching Award, 2013
• Finalist, Best Paper Award, AFFI 2013, "Institutional Ownership and Aggregate Volatility Risk"
• BB&T Summer Research Grant, 2013, 2014
• Runner-up for the Best Paper in Market Microstructure Award, FMA 2009 ("Turnover: Liquidity or Uncertainty?")
• Beta Gamma Sigma, University of Rochester, 2009
• Cum laude graduation, New Economic School, 2003
• Special Award of The Vedomosti (joint venture of The Wall Street Journal and The Financial Times in Russia), Lomonosov Student Conference, Lomonosov Moscow State University, 2003
• Summa cum laude graduation, Moscow State University, 2002
• Best Student Paper, Lomonosov Student Conference, Lomonosov Moscow State University, 2001
• Moscow Mayor Scholarship, Lomonosov Moscow State University, 2000, 2001

 A. Publications

1. On the Robustness of Idiosyncratic Volatility Effect

   [PDF]  [Online Appendix]

   Management Science, forthcoming

   The idiosyncratic volatility effect of Ang et al. (2006) is robust to restricting the sample to NYSE firms (once proper listing indicator is used) and to excluding from the sample small, illiquid, and low-price stocks. The IVol effect is also unlikely to stem from the short-run reversal of Jegadeesh (1990), as the IVol effect stays significant for about six months and seems stronger for high turnover firms, which, as Medhat and Schmeling (2022) find, do not exhibit short-term reversal. The IVol effect also does not seem to weaken post-publication.

2. Firm Complexity and Post-Earnings-Announcement Drift

   (with Shawn Park and Celim Yildizhan)

   Review of Accounting Studies, 2024, v. 29 (1), pp. 527-579

   [PDF]   [Slides]

   We show that the post earnings announcement drift (PEAD) is stronger for conglomerates than single-segment firms. Conglomerates, on average, are larger than single segment firms, so it is unlikely that limits-to-arbitrage drive the difference in PEAD. Rather, we hypothesize that market participants find it more costly and difficult to understand firm-specific earnings information regarding conglomerates as they have more complicated business models than single-segment firms. This in turn slows information processing about them. In support of our hypothesis, we find that, compared to single-segment firms with similar size, conglomerates have relatively low institutional ownership and short interest, are covered by fewer analysts, these analysts have less industry expertise and also make larger forecast errors. Finally, we find that an increase in firm complexity leads to larger PEAD and document that more complicated conglomerates have greater PEADs. Our results are robust to a long list of alternative explanations of PEAD as well as alternative measures of firm complexity.

3. Idiosyncratic Volatility, Growth Options, and the Cross-Section of Returns

   (with Georgy Chabakauri)

   Review of Asset Pricing Studies, 2023, v. 13 (4), pp. 653-690

   [PDF]   [Slides]   [Robustness]

   The paper shows that the value effect and the idiosyncratic volatility (IVol) discount (Ang et al., 2006) arise because growth firms and high IVol firms beat the CAPM during the periods of increasing aggregate volatility, which makes their risk low. All else equal, growth options' value increases with volatility, and this effect is stronger for high IVol firms, for which growth options take a larger fraction of the firm value and firm volatility responds more to aggregate volatility changes. The empirical volatility factor model with the market factor, the market volatility risk factor (FVIX) and the average IVol factor (FIVol) explains the value effect and the IVol discount and why those anomalies are stronger for firms with high short sale constraints.

4. Profitability Anomaly and Aggregate Volatility Risk

   Journal of Financial Markets, 2023, v. 64, Article 100782

   [PDF]   [Slides]   [Theory Appendix]   [Robustness]   [Data Appendix]

   Firms with lower profitability have lower expected returns because such firms perform better than expected when market volatility increases. The better-than-expected performance arises because unprofitable firms are distressed and volatile, their equity resembles a call option on the assets, and call options value increases with volatility, all else fixed. Consistent with this hypothesis, the profitability anomaly and its exposure to aggregate volatility risk are stronger for distressed and volatile firms; for such firms, aggregate volatility risk explains roughly half of the profitability anomaly, while in single sorts on profitability about 70% of the anomaly is explained.

5. Stock Liquidity and Issuing Activity

   Quarterly Journal of Finance, 2022, v. 12 (3), Article 2250010

   Top-5 most downloaded papers in QJF in 2022

   [PDF]   [Robustness]   [Slides]   [Data Appendix]

   The paper shows that issuing activity does not result in superior liquidity. Even the kinds of new issues that are supposed to be more liquid than others (IPOs backed by venture capital, new issues with high-prestige underwriters, severely underpriced IPOs) are just as liquid as their peer non-issuers or other similar issuing companies. The paper thus refutes the existing liquidity-based explanations of the new issues puzzle. The paper also shows that the low-minus-high turnover factor seems to explain the new issues puzzle and related anomalies only because it picks up volatility risk.

6. Estimating the Cost of Equity Capital for Insurance Firms with Multi-period Asset Pricing Models

   (with Jianren Xu and Steven Pottier)

   Journal of Risk and Insurance, 2020, v. 87 (1), pp. 213-245

   2021 Casualty Actuarial Society Award for the best paper on casualty actuarial science published in Journal of Risk and Insurance

   [PDF]    [Online Appendix]

   Previous research on insurer cost of equity (COE) focuses on single-period asset pricing models. In reality, however, investment and consumption decisions are made over multiple periods, exposing firms to time-varying risks related to economic cycles and market volatility. We extend the literature by examining two multi-period models—the conditional CAPM (CCAPM) and the intertemporal CAPM (ICAPM). Using 29 years of data, we find that macroeconomic factors significantly influence and explain insurer stock returns. Insurers have countercyclical beta implying that their market risk increases during recessions. Further, insurers are sensitive to volatility risk (the risk of losses when volatility goes up), but not to insurance-specific risks, financial industry risks, liquidity risk, or coskewness after controlling for other economy-wide factors.

7. Stocks with Extreme Past Returns: Lotteries or Insurance?

   Journal of Financial Economics, 2018, v. 129 (3), pp. 458-478

   Outstanding Paper in Investments Award, 2013 Southern Finance Association (SFA) meetings

   [PDF]   [Slides]

   The paper shows that lottery-like stocks are hedges against unexpected increases in market volatility. The loading on the aggregate volatility risk factor explains the majority of low abnormal returns to stocks with high maximum returns in the past month (Bali, Cakici, and Whitelaw, 2011) and high expected skewness (Boyer, Mitton, and Vorkink, 2010). Aggregate volatility risk also explains the new evidence that the maximum effect and the skewness effect are stronger for firms with high market-to-book or high expected probability of bankruptcy.

8. Institutional Ownership and Aggregate Volatility Risk

   Journal of Empirical Finance, 2017, v. 40, pp. 20-38

   Finalist, Best Paper Award, 2013 French Finance Association (AFFI) meetings

   [PDF]   [Slides]

   The paper shows that the difference in aggregate volatility risk can explain why several anomalies are stronger among the stocks with low institutional ownership (IO). Institutions tend to stay away from the stocks with extremely low and extremely high levels of firm-specific uncertainty because of their desire to hedge against aggregate volatility risk or exploit their competitive advantage in obtaining and processing information, coupled with the dislike of idiosyncratic risk. Thus, the spread in uncertainty measures is wider for low IO stocks, and the same is true about the differential in aggregate volatility risk.

9. Why Does Higher Variability of Trading Activity Predict Lower Expected Returns?

   Journal of Banking and Finance, 2015, v. 58, pp. 457-470

   [PDF]   [Slides]   [Data Appendix]   [Theory Appendix]   [Older version with Volume Variability]

   The paper shows that controlling for the aggregate volatility risk factor eliminates the puzzling negative relation between variability of trading activity and future abnormal returns. I also find that variability of other measures of liquidity and liquidity risk is largely unrelated to expected returns. Lastly, I show that the low returns to the firms with high variability of trading activity are not explained by liquidity risk and mispricing stories.

10. High Short Interest Effect and Aggregate Volatility Risk

    (with Julie Wu)

    Journal of Financial Markets, 2014, v. 21, pp. 98-122

    [PDF]   [Slides]   [Older Version with CAPM Benchmark]   [Theory Appendix]

    We propose a risk-based explanation on why stocks of firms with high relative short interest (RSI) have lower future returns. We argue that these firms have negative alphas because they are a hedge against expected aggregate volatility. Consistent with this argument, we show that these firms have high firm-specific uncertainty and real options, and the ICAPM with the aggregate volatility risk factor can explain the high RSI effect. The key mechanism is that high RSI firms have abundant growth options and, all else equal, growth options become less sensitive to the underlying asset value and more valuable as idiosyncratic volatility goes up. Idiosyncratic volatility usually increases together with aggregate volatility, i.e., in recessions.

11. Turnover: Liquidity or Uncertainty?

    Management Science, 2014, v. 60 (10), pp. 2478–2495

    Runner-up for the Best Paper in Market Microstructure Award, 2009 Financial Management Association (FMA) meetings

    [PDF]   [Slides]   [Theory Appendix]   [Data Appendix]   [Volatility Appendix]

    I show that turnover is unrelated to several alternative measures of liquidity and liquidity risk and that liquidity risk factors cannot explain why higher turnover predicts lower future returns. I find that the aggregate volatility risk factor explains why higher turnover predicts lower future returns. I also find that the negative relation between turnover and future returns is stronger for firms with high market-to-book or bad credit rating and these regularities are also explained by the aggregate volatility risk factor.

12. Analyst Disagreement and Aggregate Volatility Risk

    Journal of Financial and Quantitative Analysis, 2013, v. 48 (6), pp. 1877-1900

    [PDF]   [Slides]   [Theory Appendix]

    The paper explains why firms with high dispersion of analyst forecasts earn low future returns. These firms beat the CAPM in the periods of increasing aggregate volatility and thereby provide a hedge against aggregate volatility risk. The aggregate volatility risk factor can explain the abnormal return differential between high and low disagreement firms. This return differential is higher for the firms with abundant real options, and this fact can be explained by aggregate volatility risk. Aggregate volatility risk is also capable of explaining why the link between analyst disagreement and future returns is stronger for firms with high short-sale constraints.

13. Aggregate Volatility Risk: Explaining the Small Growth Anomaly and the New Issues Puzzle

    Journal of Corporate Finance, 2012, v. 18 (4), pp. 763-781

    [PDF]   [Slides]   [Theory Appendix]

    The paper shows that small growth firms earn low returns because they tend to beat the CAPM when expected aggregate volatility increases. Consistent with that, the ICAPM with the aggregate volatility risk factor can explain the small growth anomaly, as well as the new issues puzzle and the cumulative issuance puzzle. The key mechanism is that, all else equal, growth options become less sensitive to the underlying asset value and more valuable as idiosyncratic volatility goes up, which usually happens when aggregate volatility also increases, that is, in recessions. Small growth stocks, which have high idiosyncratic volatility and abundant growth options, are therefore a natural hedge against aggregate volatility risk.

 B. Working Papers

1. The Bright Side of Distress Risk (November 2022)

   [PDF]   [Slides]   [Theory Appendix]   [Robustness]   [Data Appendix]

   Revise and Resubmit at Journal of Banking and Finance, 2nd round

   The paper shows that distressed firms have positive abnormal returns when aggregate volatility unexpectedly increases. This hedging property of distressed firms explains the puzzling negative relation between firm-specific distress risk and future alphas from benchmark asset-pricing models. Controlling for aggregate volatility risk exposure also explains why the negative relation is stronger for volatile firms and growth firms.

2. Firm Complexity and Conglomerates Expected Returns (November 2021)

   [PDF]   [Slides]   [Robustness]   [Data Appendix]

   The paper discovers that firm complexity is negatively priced in cross-section. High/low-complexity conglomerates have 35-50/20-28 bp per month more negative five-factor Fama and French (2015) alphas than single-segment firms, and this effect is stronger in subsamples with low institutional ownership, higher idiosyncratic volatility, and around earnings announcements. The complexity effect is robust to controlling for a long list of pre-existing anomalies and seems to be generated by the interaction of higher uncertainty/disagreement about conglomerates (Barinov, Park, and Yildizhan, 2016) and short-sale constraints. The complexity effect seems to be contributing to the diversification discount by slowly eroding the valuations of conglomerates.

3. Firm Complexity and Limits to Arbitrage (July 2020)

   [PDF]   [Slides]

   Several important anomalies are stronger for more complex firms. Despite conglomerates being on average larger and more liquid than single-segment firms, anomalies are stronger for conglomerates. In the conglomerates-only subsample, anomalies are stronger for conglomerates with more between-segments difference in market-to-book and operating leverage.

4. Product Market Power and Technological Innovation (February 2022)

   (with Hyun 'Shana' Hong, Ji Woo 'Ian' Ryou, and Xiao-Jun Zhang)

   [PDF]  

   Product market power serves as a natural hedge against adverse shocks and competitive threats, thus increasing managerial risk tolerance of innovation investment. Consistent with that, we find that product market power is positively associated with firm innovation input and output. Additionally, consistent with learning from the leader’s market valuation, we find that firm innovation is positively and significantly sensitive to market valuation of its product market leader, especially if the stock price of the leader/followers is more/less informative. The follower firms alter their R\&D investments based on stock return around their leader’s patent grant dates. The followers mimic innovation investments of their product market leader and private information in leader’s prices is associated with improvement in their future profits. We find that liquidity shocks to leader’s stock price hamper the following firms’ learning. We conclude that product market power promotes innovation and firms learn from product market leader’s market valuation.

5. Why Is Asymmetric Timeliness of Earnings Priced? (September 2022)

   [PDF]   [Data Appendix]   [Slides]

   Asymmetric timeliness (AT) measure from Basu (1997) regression is priced. Sorting firms on AT produces a 40 bp per month spread in six-factor alphas. The AT effect is driven almost exclusively by the bottom AT quintile, populated by aggressive firms that recognize gains more timely than losses. Investors seem to misinterpret aggressive accounting numbers and are ill-prepared for future negative events. The AT effect in returns in concentrated around earnings announcements, writedowns, and downgrades. The AT effect is also stronger for high limits to arbitrage firms and seems unrelated to liquidity and the business cycle.

Idiosyncratic Volatility, Growth Options, and the Cross-Section of Returns

• University of Alberta, January 2008
• University of Washington, January 2008
• Tulane University, February 2008
• University of Georgia, February 2008
• The Second Risk Management Conference in Mont Tremblant, March 2008

Aggregate Volatility Risk: Explaining the Small Growth Anomaly and the New Issues Puzzle

• Northern Finance Association Meetings (NFA 2008), September 2008
• All-Georgia Finance Conference, Atlanta Fed, October 2008
• Southern Finance Association Meetings (SFA 2008), November 2008

Turnover: Liquidity or Uncertainty?

• University of Florida, October 2009
• Financial Management Association Meetings (FMA 2009), October 2009
• 20th Anniversary Conference on Financial Economics and Accounting (CFEA 2009), Rutgers University, November 2009
• Southern Finance Association Meetings (SFA 2009), November 2009
• Eastern Finance Association Meetings (EFA 2010), April 2010
• 6th Annual Central Bank Workshop on the Microstructure of Financial Markets, October 2010

Analyst Disagreement and Aggregate Volatility Risk

• 5th Mid-Atlantic Research Conference (MARC 2010), Villanova University, March 2010
• Eastern Finance Association Meetings (EFA 2010), April 2010
• Northern Finance Association Meetings (NFA 2010), September 2010
• Financial Management Association Meetings (FMA 2010), October 2010
• Southern Finance Association Meetings (SFA 2010), November 2010
• 7th Financial Intermediation Research Society Meetings (FIRS 2012), June 2012

Institutional Ownership and Aggregate Volatility Risk

• Financial Management Association Meetings in Europe (FMA Europe 2010), June 2010
• Southern Financial Association Meetings (SFA 2010), November 2010
• European Financial Management Association Meetings (EFMA 2011), June 2011
• II World Finance Conference (WFC 2011), June 2011
• 2nd Edwards Symposium on Financial Markets and Institutions, University of Saskatchewan, July 2011
• Midwest Finance Association Meetings (MFA 2012), February 2012
• Southwestern Finance Association Meetings (SWFA 2013), March 2013
• French Finance Association Meetings (AFFI), June 2013

Why Does Higher Variability of Trading Activity Predict Lower Expected Returns?

• Financial Management Association Meetings in Europe (FMA Europe 2011), June 2011
• II World Finance Conference (WFC 2011), June 2011
• Financial Management Association Meetings (FMA 2011), October 2011
• Southern Finance Association Meetings (SFA 2011), November 2011
• Southwestern Finance Association Meetings (SWFA 2013), March 2013
• Brenau University, March 2013

Short Interest and Aggregate Volatility Risk, (with Julie Wu)

• Southern Finance Association Meetings (SFA 2011), November 2011
• Midwest Finance Association Meetings (MFA 2012), February 2012
• III World Finance Conference (WFC 2012), July 2012
• Financial Management Association Meetings (FMA 2012), October 2012

Stocks with Extreme Past Returns: Lotteries or Insurance?

• Southwestern Finance Association Meetings (SWFA 2013), March 2013
• 8th Financial Intermediation Research Society Meetings (FIRS 2013), June 2013
• Financial Management Association Meetings in Europe (FMA Europe 2013), June 2013
• Northern Finance Association Meetings (NFA 2013), September 2013
• Financial Management Association Meetings (FMA 2013), October 2013
• Southern Finance Association Meetings (SFA 2013), November 2013
• European Financial Management Association Meetings (EFMA 2014), June 2014
• University of South Carolina, November 2014
• Fordham University, December 2014
• Rutgers University, December 2014
• Federal Reserve Board of Governors, February 2015
• Securities and Exchange Commission (SEC), February 2015
• U.S. Department of Treasury, Office of Financial Research, February 2015
• University of California Riverside, February 2015
• 75th Anniversary Conference at Economics Department, Lomonosov Moscow State University, September 2016

Firm Complexity and Post-Earnings-Announcement Drift, (with Shawn Park and Celim Yildizhan)

• Financial Management Association Meetings in Europe (FMA Europe 2014), June 2014
• V World Finance Conference (WFC 2014), July 2014
• American Accounting Association Meetings (AAA 2014), August 2014
• Southern Finance Association Meetings (SFA 2014), November 2014
• Southeastern American Accounting Association Meetings (SEAA 2015), April 2015
• Northern Finance Association Meetings (NFA 2015), September 2015
• 2016 Accounting Conference at Temple University, August 2016 (presented by coauthor)
• Georgia State University, September 2016 (presented by coauthor)
• Research in Behavioral Finance Conference 2016, September 2016 (presented by coauthor)
• Financial Management Association Meetings (FMA 2016), October 2016 (presented by coauthor)

Stock Liquidity and Issuing Activity

• Financial Management Association Meetings (FMA 2015), October 2015
• Southern Finance Association Meetings (SFA 2015), November 2015
• California State University, Fullerton, December 2016
• Southwestern Finance Association Meetings (SWFA 2019), March 2019
• International Banking, Economics and Finance Association Meetings (IBEFA 2022), June 2022

The Bright Side of Distress Risk

• International Banking, Economics and Finance Association Meetings (IBEFA 2016), July 2016
• Higher School of Economics, September 2016
• Northern Finance Association Meetings (NFA 2016), September 2016
• Law and Finance Conference, University of San Diego, October 2016
• Southern Finance Association Meetings (SFA 2016), November 2016
• 3rd Annual Citrus Finance Conference, April 2017
• University of Oklahoma, September 2017

Profitability Anomaly and Aggregate Volatility Risk

• American Accounting Association Meetings (AAA 2016), August 2016
• International Banking, Economics and Finance Association Meetings (IBEFA 2017), June 2017
• Northern Finance Association Meetings (NFA 2017), September 2017
• Financial Management Association Meetings (FMA 2017), October 2017
• Midwest Finance Association Meetings (MFA 2018), March 2018
• Southern Finance Association Meetings (SFA 2018), November 2018
• Eastern Finance Association Meetings (EFA 2022), April 2022

Firm Complexity and Conglomerates Expected Returns

• 4th Annual Citrus Finance Conference, May 2018
• Southwestern Finance Association Meetings (SWFA 2019), March 2019
• International Banking, Economics and Finance Association Meetings (IBEFA 2019), July 2019
• Financial Management Association Meetings (FMA 2019), October 2019
• Southern Finance Association Meetings (SFA 2019), November 2019
• 80th Anniversary Conference at the Economics Department, Lomonosov Moscow State University, December 2021

Idiosyncratic Volatility, Growth Options, and the Cross-Section of Returns (with Georgy Chabakauri)

• Northern Finance Association Meetings (NFA 2020), September 2020
• Southern Finance Association Meetings (SFA 2020), November 2020
• Southwestern Finance Association Meetings (SWFA 2021), March 2021

Firm Complexity and Limits to Arbitrage

• Financial Management Association Meetings (FMA 2021), October 2021
• Southern Finance Association Meetings (SFA 2021), November 2021
• Eastern Finance Association Meetings (EFA 2022), April 2022
• Southwestern Finance Association Meetings (SWFA 2023), March 2023

Why Is Asymmetric Timeliness of Earnings Priced?

• 30th Anniversary Conference (NES30), New Economic School, December 2022
• Southwestern American Accounting Association Meetings (SWAAA 2023), March 2023
• American Accounting Association Meetings (AAA 2023), August 2023
• Southwestern Finance Association Meetings (SWFA 2024), March 2024

On the Robustness of Idiosyncratic Volatility Effect

• Southwestern Finance Association Meetings (SWFA 2023), March 2023

Product Market Power and Technological Innovation

• American Accounting Association Meetings (AAA 2023), August 2023 (presented by coauthor)
• Hawaii Accounting Research Conference (HARC 2024), January 2024

• Empirical asset pricing
• Anomalies
• Idiosyncratic volatility
• Liquidity and market microstructure
• Aggregate volatility risk
• Capital markets research in accounting

Ad-hoc reviewer, Journal of Financial Economics (62), Review of Financial Studies (8), Management Science (7), Journal of Financial and Quantitative Analysis (4), Journal of Accounting and Economics (1), Review of Finance (1), Review of Asset Pricing Studies (2), Journal of Banking and Finance (20), Journal of Empirical Finance (7), Journal of Financial Markets (1), Journal of Corporate Finance (3), Financial Management (1), Journal of Econometrics (1), Quantitative Finance (1), Financial Review (7), Journal of Financial Research (1), International Review of Economics and Finance (4), Review of Financial Economics (1), Economic Systems (1), Economic Modelling (2), Financial Research Letters (2), Insurance: Mathematics and Economics (3), Voprosy Ekonomiki (3)

Program Committee, Southern Finance Association Meetings, 2012-2018, 2020, 2021

Program Committee, American Accounting Association Meetings, 2016, 2017

Program Committee, Northern Finance Association Meetings, 2017-2019

Program Committee, Southeastern American Accounting Association Meetings, 2016

School of Business, University of California Riverside

Empirical Methods in Finance, MFin Program, 2016-2024

Trading Strategies and Financial Models, MFin Program, 2016-2020, 2023

Investments and Portfolio Management, MFin Program, Spring 2017, Winter 2018, Winter 2020, Spring 2023, Winter 2024, Spring 2024

Trading Strategies, Undergraduate program, 2018-2022, 2024

Asset Pricing, PhD program, Winter 2019, Spring 2024

Terry College of Business, University of Georgia

Course Instructor and Developer, Trading Strategies and Financial Models, Undergraduate program, Spring 2012, Fall 2012, Spring 2013, Fall 2013, Spring 2014, Fall 2014, Spring 2015

Course Instructor, Survey of Investments, Undergraduate program, Spring 2009, Spring 2010, Spring 2011, Spring 2012

Course Instructor, Empirical Research in Investments, PhD program, Spring 2011, Spring 2013, Spring 2015

Course Instructor and Developer, Trading and Risks, Freshman Seminar, Spring 2013, Fall 2013, Fall 2014

Simon School of Business, University of Rochester

Course Instructor and Developer, Foundations of Economics, Ph.D. program, Summer 2007

MBA and undergraduate level:

• Investments
• Trading
• Derivatives
• Empirical Methods

PhD level:

• Empirical Asset Pricing
• Financial Econometrics