Dimitri Huwyler, Head of Quantitative Strategy and Aleksandar Pramov, Quantitative Researcher, Next Gate Capital
| May 21, 2018
View an extract of this session held at the London Big Data and Machine Learning Revolution event in April 2018.You can also access the full video and slides.
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.
It doesn't come as a surprise if I tell you that this revolution has pushed our Quants finance into a new era. We see two main reasons behind this. The first is something we have talked a lot about today, which is the access to a new type of data, which by construction and essence, captures very complementary and additional information compared to traditional datasets, such as fundamental data, analysis estimate or pricing to name a few. The second is the remarkable advancement in machine learning due to different reasons, algorithmic breakthrough, but also the ability for small firms today to access extremely computing power at very cheap costs.
Now our business will continue to be highly impacted by this revolution, but we don't see it as a negative disruptor or as a threat but an opportunity to revisit existing problems with new tools.
We talk about quantifying the investment process which is the ability to systemize more traditional, qualitative investment approach. We define that in five steps:
The context is a summary of the health and propitious aspect of the environment in which evolve the mapping financial markets.
Define market regimes by:
Context is broken down into two categories:
This specific indicator was built using RavenPack data. It shows how news can build an indicator, that can quantify and react to geopolitical events and intention.
Starting point is Time Series Momentum:
An illustration of a long-short TF strategy, our benchmark:
Motivation for enhancement:
Include Context Analysis to enhance the strategy, make it more reactive to changes in the context.
Here we have to establish the relationship goals within our universe. So the first options was to build individual models for each asset. The other options that we will more, is a sort of overlay which comes as a top down approach to our systematic investment strategy. This is done by clusters of instruments. A simple way to cluster them is to look at their historical correlations and also interpreting the end results. We end up with subcategories of asset classes.
RavenPack’s events have become global, with attendance exceeding 250 buy-side professionals at the London Big Data and Machine Learning Revolution in April 2018. RavenPack Research Symposium returns to New York on September 12th.
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We consider incorporating sentiment signals from news, earnings call transcripts, and insider transactions to
boost the risk-adjusted returns, and revive factor performance.
We find stronger, more predictable market reactions when the words of company executives agree with their actions.
We have gathered 12 insights from 2021 research that can be leveraged in 2022.