*Conducted in term:*2020Z

*Erasmus code:*14.3

*ISCED code:*0311

*ECTS credits:*6

*Language:*English

*Organized by:*Faculty of Economic Sciences

*Related to study programmes:*

# Quantitative Strategies. High Frequency Data 2400-QFU2FEC

Lectures plan

Lecture 1: Organizational matters, introduction to R.

Lecture 2: Introduction to quantitative trading, evolution of high-frequency trading, types of high-frequency trading strategies.

Lecture 3: Characteristics of intra-day data, data sources, working with tick data, data adjustments.

Lecture 4: Review of the statistical and econometric foundations of the common types of high-frequency strategies: linear regressions.

Lecture 5: Review of the statistical and econometric foundations of the common types of high-frequency strategies: time series.

Lecture 6: Backtesting and evaluating performance of trading strategies.

Lecture 7: Strategies of the highest frequency, with position-holding periods of one minute or less.

Lecture 8: Market microstructure models.

Lecture 9: "Event arbitrage" strategies.

Lecture 10: Statistical arbitrage strategies.

Lecture 11: Portfolio construction, multistrategy portfolios.

Lecture 12: Factor models and factor based trading strategies.

Lecture 13: Execution systems.

Lecture 14-15: Students' presentations.

Lab sections

1. Introduction to R

2. Dealing with time series data of different frequency, frequency conversion, data aggregation, plotting the series.

3. Statistical and econometric analyses - correlation, regression, etc.

4. Rolling analyses, storing partial results of analyses, loops and own functions.

5. Backtesting of trading strategies, calculating evaluation statistics.

6. Portfolio construction and evaluation.

7. Students' presentations.

## Type of course

## Course coordinators

## Mode

## Learning outcomes

Students will be able to analyze and aggregate high-frequency time series data. They will know how to prepare and backtest trading strategies, calculate appropriate evaluation statistics and select best performing strategy. In addition, students will be able to indicate successful strategies for different data frequencies.

KW01, KW02, KU01, KU02

## Assessment criteria

Assessment of the lecture:

Written, open book exam, covering topics discussed during the lectures.

Assessment of the lab sections:

Trading strategies project prepared in groups of 2 students. Building and backtesting a trading strategy for 5 series (or groups of series – everyone exactly the same) of different frequencies.

100 points will be given for:

- presentation in class: 20 pts,

- written report: 40 pts,

- best results (ranking): 40 pts (max. 8 pts. per each series – 8 if returns in top quartile group, 6 if 2nd, 4 if 3rd and 2 if in lowest 25%).

## Bibliography

Literature

Aldridge, I. (2010), High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems, Wiley Trading.

Fabozzi, F.J., Focardi, S.M. and Kolm, P.N. (2010), Quantitative Equity Investing: Techniques and Strategies, John Wiley & Sons, Inc.

Chan, E. (2008), Quantitative Trading: How to Build Your Own Algorithmic Trading Business, Wiley Trading.

Narang, R.K. (2009), Inside the Black Box: The Simple Truth About Quantitative Trading, Wiley Finance.

Supplementary literature

Admati, A. and Pﬂeiderer, P. (1988), “A Theory of Intraday Patterns: Volume and Price Variability”, Review of Financial Studies 1, pp. 3–40.

Aldridge, I. (2009), “Measuring Accuracy of Trading Strategies”, Journal of Trading 4, Summer 2009, pp. 17–25.

Alexander, C. and Johnson, A. (1992), “Are Foreign Exchange Markets Really Efﬁcient?”, Economics Letters 40, pp. 449–453.

Alexander, C. (1999), “Optimal Hedging Using Cointegration”, Philosophical Transactions of the Royal Society, Vol. 357, No. 1758, pp. 2039–2058.

Avellaneda, M. and Stoikov, S. (2008), “High-Frequency Trading in a Limit Order Book”, Quantitative Finance, Vol. 8, No. 3., pp. 217–224.

Bernanke, B.S. and Kuttner, K.N. (2005), “What Explains the Stock Market’s Reaction to Federal Reserve Policy?”, Journal of Finance 60, pp. 1221–1257.

Brock, W.A., Lakonishok, J. and LeBaron, B. (1992), “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns”, Journal of Finance 47, pp. 1731–1764.

Burke, G. (1994), “A Sharper Sharpe Ratio”, Futures 23 (3), p. 56.

Campbell, J.Y., Lo, A.W. and MacKinlay, A.C. (1997), The Econometrics of Financial Markets, Princeton University Press.

Chaboud, A.P. and Wright, J.H. (2005), “Uncovered Interest Parity: It Works, but Not for Long”, Journal of International Economics 66, pp. 349–362.

Dacorogna, M.M. et all (2001), An Introduction to High-Frequency Finance, Academic Press: San Diego, CA.

Dowd, K. (2000), “Adjusting for Risk: An Improved Sharpe Ratio”, International Review of Economics and Finance 9 (3), pp. 209–222.

Eling, M. and Schuhmacher, F. (2007), “Does the Choice of Performance Measure Inﬂuence the Evaluation of Hedge Funds?”, Journal of Banking and Finance 31, pp. 2632–2647.

Engle, R.F. (2000), “The Econometrics of Ultra-High Frequency Data”, Econometrica 68, pp. 1–22.

Engle, R. and Ferstenberg, R. (2007), “Execution Risk”, Journal of Portfolio Management 33, pp. 34–45.

Engle, R.F. and Lunde, A. (2003), “Trades and Quotes: A Bivariate Point Process”, Journal of Financial Econometrics 1, pp. 159–188.

Engle, R.F. and Russell, J.R. (1998), “Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transactions Data”, Econometrica 66, pp. 1127–1162.

Fung, W. and Hsieh, D.A. (1997), “Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds”, Review of Financial Studies 10, pp. 275–302.

Goodhart, Ch.A.E. and O’Hara, M. (1997), “High Frequency Data in Fi-nancial Markets: Issues and Applications”, Journal of Empirical Finance 4, pp. 73–114.

Gourieroux, C., Jasiak, J. and Le Fol, G. (1999), “Intraday Trading Activity”, Journal of Financial Markets 2, pp. 193–226.

Hvidkjaer, S. (2006), “A Trade-Based Analysis of Momentum”, Review of Financial Studies 19, pp. 457–491.

Kissell, R. and Malamut, R. (2006), “Algorithmic Decision Making Framework”, Journal of Trading 1, pp. 12–21.

Ross, S.A. (1976), “The Arbitrage Theory of Capital Asset Pricing”, Journal of Economic Theory 13, pp. 341–360.

Sharpe, W. (1994), “The Sharpe Ratio”, Journal of Portfolio Management, Available at: www.stanford.edu/∼wfsharpe/art/sr/sr.htm.

Soroka, S. (2006), “Good News and Bad News: Asymmetric Responses to Economic Information”, The Journal of Politics 68, pp. 372–385.

Taleb, N. (2007), The Black Swan: The Impact of the Highly Improbable, Random House.

Tsay, R.S. (2002), Analysis of Financial Time Series, Hoboken, NJ: John Wiley & Sons.

Wagner, W. and Banks, M. (1992), “Increasing Portfolio Effectiveness via Transaction Cost Management”, Journal of Portfolio Management 19, pp. 6–11.

## Additional information

Information on *level* of this course, *year of study* and semester when the course
unit is delivered, types and amount of *class hours* - can be found in course structure
diagrams of apropriate study programmes. This course is related to
the following study programmes:

Additional information (*registration* calendar, class conductors,
*localization and schedules* of classes), might be available in the USOSweb system: