This Illegally Collected Data Set Produced More Alpha for Hedge Funds by Leigh Drogen at QuantCon 2016

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"Portfolio Optimisation When You Don’t Know the Future (or the Past)" by Rob...

It takes hard work, skill and time to develop robust trading models, but that is just the beginning of the journey. The question then is what you can do with it, and how to go about building a career in quant finance. If your plan is to move beyond hobby trading and build a career in in the professional quant trading field, the work is not over once you have a great model. This presentation will discuss how to leverage your trading models into building a successful career in quant trading. We will look at the various options available, and their respective merits and faults. Whether you want to trade your own money for a living, find a job in the industry or build your own business, your model design will have to be adapted to your aim. We will discuss what type of models and results there is a market for, how to go about finding investors for your trading, and how the real economics of the business look.

"Quant Trading for a Living – Lessons from a Life in the Trenches" by Andreas...

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From QuantCon Singapore 2017: The vast proliferation of data related to the financial industry introduces both new opportunities and challenges to quantitative investors. These challenges are often due to the nature of big data and include: volume, variety, and velocity. In this talk, Dr. Cheng will take the audience on a tour of the “big-data production line” in InfoTrie and show how the financial news collected from various and customizable sources are transformed into quantitative signals in a real-time manner. The talk will touch on various kind of topics like sentiment analysis, entity detection, topic classification, and big-data tools.

“Real Time Machine Learning Architecture and Sentiment Analysis Applied to Fi...

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“Market Insights Through the Lens of a Risk Model” by Olivier d'Assier, Head ...

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Factor modeling and style premia are historically well documented and extensively researched in generating abnormal returns. Despite the large amount of research around factors, there is less clarity around effectively capturing and extracting this alpha from a given universe. In this presentation, Cheng will demonstrate different techniques for combining multiple factors, and the rationale behind maximizing alpha while maintaining scalability.

"Maximize Alpha with Systematic Factor Testing" by Cheng Peng, Software Engin...

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From QuantCon 2017: Running a quantitative trading business in China used to be very difficult and require strong IT skills, however it's getting much easier nowadays, when traders with no professional IT training can also do all the tasks in quantitative trading using Python. In this sharing session, Xiaoyou will share his experience in using Python for data collection, strategy development and automated trading. He will also introduce some related open source projects including TuShare, quantOS, vn.py and so on.

"How to Run a Quantitative Trading Business in China with Python" by Xiaoyou ...

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From QuantCon 2017: Fundamental and quantitative stock selection research has long focused on creating accurate forecasts of company fundamentals such as earnings and revenues. In this talk we examine why fundamental forecasts are powerful and survey some classic methods for generating these forecasts. Next we explore some newer methodologies which can be effective in generating more accurate fundamental forecasts, including new uses of traditional data as well as novel crowdsourced and online behavior databases. Finally, we present new research examining the temporal variation in efficacy of these forecasts with an eye towards understanding the market conditions in which an accurate fundamental forecast can be more or less profitable.

"Fundamental Forecasts: Methods and Timing" by Vinesh Jha, CEO of ExtractAlpha

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From QuantCon 2017: Implementation is the efficient translation of alpha research into portfolios. It includes portfolio construction and trading. It is a vital step in the quant equity workflow, as poor implementation can ruin even the best alpha ideas. Two crucial challenges must be solved: how to construct a portfolio that most efficiently captures a given alpha signal; and, in the presence of multiple signals, how to optimally combine them into a single composite alpha factor. This talk addresses these challenges, examines common pitfalls in the implementation of quantitative strategies and good practices to avoid them. A common theme is striking the right balance between factor signal purity and investability. We look at how factor models and optimisation techniques help professional investors answer three key questions: · What risks should your risk model be cognisant of? · What objective function should you use? · What effect do investability constraints have on your portfolio?

"From Alpha Discovery to Portfolio Construction: Pitfalls and Solutions" by D...

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From QuantCon 2017: Financial trading is essentially a search problem. The buy-side agent needs to find a counterpart sell-side agent willing to trade the financial asset at the set quantity and price. Ilija will present a deep reinforcement learning algorithm for optimizing the execution of limit-order actions to find an optimal order placement. The reinforcement learning agent utilizes historical limit-order data to learn an optimal compromise between fast order completion but with higher costs and slow, riskier order completion but with lower costs. The talk will continue with the challenges of applying reinforcement learning to optimal trading and their potential solutions. Finally, Ilija will share the system architecture and discuss future work.

"Deep Reinforcement Learning for Optimal Order Placement in a Limit Order Boo...

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"Making the Grade: A Look Inside the Algorithm Evaluation Process" by Dr. Jes...

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Hierarchical Risk Parity (HRP) portfolios address three major concerns of quadratic optimizers in general and Markowitz’s CLA in particular: Instability, concentration and underperformance. HRP applies modern mathematics (graph theory and machine learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix, an impossible feat for quadratic optimizers. Monte Carlo experiments show that HRP delivers lower out-of-sample variance than CLA, even though minimum-variance is CLA’s optimization objective. HRP also produces less risky portfolios out-of-sample compared to traditional risk parity methods. Read the corresponding white paper here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2713516

"Building Diversified Portfolios that Outperform Out-of-Sample" by Dr. Marcos...

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Over the last 10 years, the world of economics has been playing a catching up game and many economists have been struggling to explain their theories. The world has adopted technology in nearly every aspect of life, from phones to cars; however, good, reliable and quality data in economics is still elusive. There is over reliance on macroeconomic principles in comparison to the quality of data available. Macro-economic figures move markets, only to get revised one, two or three times in the following months. Some fields of economic study are exceptions, such as analysing trade data. Trade data, with the support of technology, has become readily available and can now be analysed in depth, providing actual numbers indicating the health and state of economies. Trade data, which is export and import information of all the goods and services from one country to another, can be seen as an inseparable marker of real economic activity. It can be used to predict various market indicators exhibiting high correlations, from currencies to commodities to equities to macroeconomic data, with varying degree of certainties. Trade data, at an in-depth level, acts like a compilation of millions of real life mathematical functions. This presentation explores this new economic area of trade data as a quantitative tool, its intense big data analysis and its applications in trading markets.

"From Insufficient Economic data to Economic Big Data – How Trade Data is red...

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"Machine Learning Approaches to Regime-aware Portfolio Management" by Michael...

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"A Framework-Based Approach to Building Quantitative Trading Systems" by Dr. ...

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"Don't Lose Your Shirt Trading Mean-Reversion" by Edith Mandel, Principal at ...

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Reinforcement Learning (RL) has been around for a long time, but it has not attracted much attention over the last decade. Until, that is, a group of Google researchers showed how RL can be used to train a computer to play video games at far above human capabilities. Besides video games, the RL problem is also well aligned to solve trading problems as well (e.g., work by Dr. Michael Kearns). In this talk, Tucker will provide a gentle introduction to Q-Learning, one of the leading RL methods. He will also show how Q-Learning can be integrated with artificial neural network learners and how such a system can be used to learn and execute a trading strategy. This is joint work with David Byrd at Georgia Tech.

"Deep Q-Learning for Trading" by Dr. Tucker Balch, Professor of Interactive C...

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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: https://bit.ly/2k5xTxW.

"Quantum Hierarchical Risk Parity - A Quantum-Inspired Approach to Portfolio ...

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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.

"Snake Oil, Swamp Land, and Factor-Based Investing" by Gary Antonacci, author...

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You have created a great trading strategy, backtested, traded it and now you want to take it to the next level. You may find that developing the strategy was just the first of many difficult steps. With the increased availability of low cost, high quality quant modelling platforms, the field is much more open than it once was. The interest for algorithmic trading his higher than ever and anyone has the potential develop a great trading model. But having a great trading model is not enough. The work is not done yet. This presentation will discuss turning your algorithmic trading strategy into a business or a great job, and becoming a professional trader. We’re going to talk about what it takes to move to the next level and where the common pitfalls lay. What kind of strategies are marketable are which are not. The pros and cons of trading your own money and how to go about finding external capital and gaining traction in the business. Are you ready to take the step?

"From Trading Strategy to Becoming an Industry Professional – How to Break in...

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"Enhancing Statistical Significance of Backtests" by Dr. Ernest Chan, Managin...

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