"Trading Strategies That Are Designed Not Fitted" by Robert Carver,
Independent Systematic Futures Trader, Writer, and Research Consultant
Engineers design stuff. Why do Quants prefer to fit? In this talk, Robert will explain
what designing a trading system actually involves, explore why designing might
be better than fitting, and introduce some of the tools you could use. He will also
take you through the design process for some example trading ideas. Finally, he will
think about how we can have the best of both worlds: strategies that
are well designed and also fitted to the data.
"Enhancing Statistical Significance of Backtests" by Dr. Ernest Chan,
Managing Member of QTS Capital Management, LLC.
Insufficient historical data is a major hurdle in building a trading model free
from data snooping bias. Dr. Chan's talk will discuss several techniques,
some borrowed from machine learning, that can alleviate overfitting and
enhance the statistical significance of a backtest.
"Snake Oil, Swamp Land, and Factor-Based Investing"
By Gary Antonacci, author of Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk
BlackRock forecasts smart beta investing oriented toward size, value, quality, momentum, and low volatility to reach $1 trillion by 2020 and $2.4 trillion by 2025. Gary’s talk will show that this growth may not be justified due to these factors' lack of robustness, consistency, persistence, intuitiveness, and investability. Gary will also show that the success attributed to these factors would be better directed toward macro momentum and the short interest ratio.
"Quantum Hierarchical Risk Parity - A Quantum-Inspired Approach to
Portfolio Risk Minimization" by Maxwell Rounds, Finance Specialist, 1QBit
Maxwell will present the methodologies and results behind the algorithm that has been developed by 1QBit, named Quantum Hierarchical Risk Parity, or QHRP.
This is an extension of the work done by Marcos Lopez de Prado on
Hierarchical Risk Parity in his paper "Building Diversified Portfolios that Outperform Out-of-Sample."
QHRP tackles the problem of minimizing the risk of a portfolio of assets using a quantum-inspired approach. Although the ideas surrounding this go back to Markowitz’s mean-variance portfolio optimization of 1952’s Portfolio Selection, we have applied recent quantum-ready machine learning tools to the problem to demonstrate strong performance in terms of a variety of risk measures and lower susceptibility to inaccuracies in the input data.
The quantum-ready approach to portfolio optimization is based on an optimization problem that can be solved using a quantum annealer. The algorithm utilizes a hierarchical clustering tree that is based on the covariance matrix of the asset returns. The results of real market data used to benchmark this approach against other common portfolio optimization methods will be shared in this presentation.
View the White Paper: http://bit.ly/2k5xTxW.
"Developing FX Trading Strategies" by Saeed Amen, Founder of Cuemacro
In this presentation, Saeed will discuss his general approach to modelling trading strategies for FX cash and vol markets, and how it can differ from other asset classes. He will give some practical examples, in Python, of a basic FX trading trend following strategy and examples of analysis on FX spot and vol markets, using the open source library finmarketpy, to assess the impact of major economic events like FOMC.
Lastly, he will discuss some tips and tricks for speeding up
your Python code when backtesting.
"Finding a DeLorean: Building an Investment Strategy that Explains Asset Returns
in the Past and Makes Money in the Future" by Christopher Covington,
Portfolio Manager and Researcher at AJO Partners
Academics and practitioners alike often repeat several missteps in
factor and strategy research that render their work unprofitable out of sample.
This talk will discuss several of these missteps including the assessment of implementation costs and back test over-fitting. The hope is to provide the audience with experience and tools to avoid these pitfalls and produce work that
not only explains the past, but also forecasts the future.
"Bayesian Global Optimization: Using Optimal Learning to Tune Trading Models"
by Scott Clark, Co-founder and CEO of SigOpt, Inc.
Many trading strategies require fine tuning of various configuration parameters to reach their full potential. We'll show how Bayesian Global Optimization can be used as an efficient way to optimize model parameters, especially when evaluating or backtesting different configurations is time consuming or expensive.
We'll also show several examples of how these techniques can help unlock the potential of these sophisticated models faster, better, and cheaper than standard techniques - including some joint work with the Quantopian team.
"Using Partial Correlations for Increasing Diversity of Mean-variance Portfolio"
by Dr. Alec Schmidt, Lead Research Scientist at Kensho
It is found that partial correlations between 12 major US equity sector ETFs conditioned on the state of economy (mimicked here by the S&P 500 index) are significantly lower than the Pearson’s correlations. The Markowitz mean-variance portfolio theory is modified in terms of partial covariance. The maximum Sharpe portfolios formed by 12 equity sector ETFs in 2007 – 2015 are examined. With the exclusion of the bear market of 2008, the partial correlation based portfolios (PaCP) are much more diversified than the Pearson’s correlation based portfolios (PeCP).
Out-of-sample performance of the maximum Sharpe PeCP and PaCP, and the
equal-weight portfolio (EWP) are compared. The results are very sensitive to the model parameters (portfolio calibration window and frequency of portfolio rebalancing). While the PeCP weights change significantly from month to month,
the PaCP weights outside the bear market effects are almost constant. PaCP outperforms both EWP and PeCP when the 36-month calibration window and
one-month rebalancing frequency are used. We conclude that partial covariance is
a promising concept for constructing optimal portfolios.
More talks will be announced soon.