The Big Data & Machine Learning Revolution Comes to London

Armando sets the stage and highlights why we have entered a big data and machine learning revolution.

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Nitish focused on the similarities and differences between quantitative and discretionary investing with an emphasis on the importance of data. He will also host a demonstration of quantitative alpha using a web based simulator.

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In order to maintain an edge in the marketplace, asset managers are to a larger extend turning to unstructured content for alpha creation, using NLP and text analysis techniques. In addition, more and more managers are expanding their mandate, trading global portfolios, to ensure more scalable strategies. As part of his presentation, Peter showcases how news sentiment can be a valuable input to such process.

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Artificial Intelligence (AI) is rapidly moving from a mesmeric technology to a powerful teammate and a foundation for consumer and business decision making. However, AI is a young field full of amazing potential. It’s mystery and lack of understanding is also allowing for hype to grow unchecked. Unrealistic claims of an "AI singularity" and portrayals of an "AI apocalypse" are creating a hype machine that is unparalleled in recent history. The reality is somewhere in between these two extreme scenarios. This session focuses on some of the good practices around practical and high value applications of data and AI across healthcare, insurance, financial services, and digital commerce.

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Asger covers forecasting of Chinese macroeconomic time series using a large number of prediction variables. He investigates what is the extent of improvement of forecasts when news sentiment indexes are included among the predictors. The results suggest that forecasts obtained with this method outperform univariate autoregressions and in shorter prediction horizon news indexes improve the forecasts.

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Successful market timing is a tantalizing holy grail for investors. On both side, investors and researchers have discovered that the market timing is harder than it might seems. At Next Gate Capital they think that this is a perfect research playground for new machine intelligence techniques and new alternative dataset. They use classic variables to build economic climate and global sentiment indicators, enhanced with news sentiments, particularly on politics and monetary policy (two fields very difficult to handle with classic dataset) and economy. They cover a practical example of enhancement of a trend following strategy.

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The financial sector is making a massive shift towards big data and machine learning technologies. Panelists will share their experience in using data science and domain expertise in understanding data context.They will address how machine learning can be useful in creating new alpha signals, as well as in the data generation/preparation process, in portfolio construction or risk management.

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J.P. Morgan use two examples to demonstrate how big data and machine learning can add value to systematic strategies. The first strategy is on equity mean-reversion in Japan, where they find that news volume and news sentiment can be a useful overlay due to behavioral bias. The second example covers a Global value strategy based on Machine Learning algorithms. They show that Machine Learning models can outperform simple linear benchmarks, and news sentiment could further enhance the strategy.
For compliance reason, slides and videos are not available, but Ada referred to the two white papers below:

Enhancing Reversals with News and Neutralization With Tradable Systematic Strategies in Japan >
Value Strategies based on Machine Learning >

Practitioner’s point of view on what in big data and machine learning investing is challenging and what to do about it. Demystifying the “magic box”, sharing best practices and real-life examples of machine learning application to investing including NLP with RavenPack.

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This talk reviews recent academic literature that attempts to ensure causation rather than correlation in the use of machine learning. Applications of ML in Genome/Cancer research have recognized this critical issue for some time and the case is obviously equally strong in Finance where money may be allocated on the basis of completely spurious data driven models. We look at developments in "Post Model Selective Inference" and "Counter-factual Causal Prediction" with examples. We also discuss recent statistical literature that questions the notion of "Big" data where the value of incremental data may tend to zero and how inference should be adapted.

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Combining alternative news and sentiment data with traditional signals can provide increased risk-adjusted returns in long/short equity portfolios. In this presentation we consider the application of Machine Learning techniques to capture these effects and explore non-linear approaches to alternative data.

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Do factors perform differently under news driven sentiment? Using Style Research and RavenPack sentiment data, Louis constructs regional factors and sentiment indices in the spirit of Hafez and Xie 2016. Results show strong differences under periods of high and low sentiment. The design of a quarterly moving average is distinct from most findings that reveal intra-day to a few days efficacy for sentiment. In particular, a strong difference in underlying distribution of factor returns is revealed and the Sortino ratios are distinct under sentiment regimes.

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Lightning talk: Facebook has recently come under significant scrutiny from customers, the press, and US Congress about how it allowed firms to siphon personal information from millions of user accounts. While this case is interesting in its own right, it highlights a number of lessons for vendors and users of alternative data about the risks of selling or using data that could provide personally identifiable information.This presentation discusses these risks and the steps firms should take to mitigate these risks.

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Lightning talk: RavenPack automatically detects thousands of different types of market moving events in unstructured text documents. An enriched event captures more context from the document to provide more color about what the event means. We take a quick look at how events are detected now and what innovations are happening to help enrich the events the system can detect moving forward.

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Lightning talk: Dan discusses alternative data integration on the buy-side, with an emphasis on quantifying strategy. He's highlighting how nearly 50 fundamental investors are actively building these efforts and are approaching alternative data from a talent, infrastructure and resourcing perspective. He also covers the data science landscape across investment managers.

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This panel addresses what key areas financial institutions should have in mind when looking at alternative data to avoid wasting resources on alternative datasets doomed not to provide value. Panelists will share their experience highlighting what pitfalls they should try to avoid as quant or fundamental investors, and how to be successful with alternative data. They will also discuss what attributes are required of a potentially performing alternative dataset.

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