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Unlocking secrets in language helps investment process – expert comment

News Team, 03/04/2020

Natural Language Processing (NLP) is a branch of Artificial Intelligence which aims to enable machines to read, decipher, understand, and make sense of human language in a manner that is of value.

To explain this, and how it can be used to benefit investment process, thewealthnet spoke to Tian Guo, senior data scientist at RAM Active Investments.

Quant specialist RAM AI is a Geneva-headquartered alternative investment manager with asset under management of $2.7 billion.

“The NLP community’s current focus is on exploring several key areas of research, including: semantic representation, machine translation, textual inference, and text summarisation,” explained Mr Guo.

“Certainly, the recent advancements in Machine Learning techniques have enabled data scientists to advance these techniques hand in hand. Data is being generated and captured at an exponentially increasing rate, and NLP is an important tool in our box to enable us to better understand what is happening across global markets.”

Being able to leverage NLP across real-time voice transcriptions and chat can provide additional data that can be integrated into RAM’s learning models. In terms of finance, NLP can become “a powerful tool” for asset managers to discover actionable insight from the realms of unstructured data that is produced throughout markets.

“Ultimately, we believe that the implications of NLP are profound and extremely positive for augmenting our current data sets for us to better-capture signals which can help us understand the markets in which we invest.”

The challenge of applying NLP to quantitative investment mainly lies in both data and model aspects.

Financial textual data comes from diverse sources, e.g. financial news, earnings reports, and transcripts, etc and consequentially is diverse in terms of data formats and structures.

The challenge here is how to transform these symbolic text data into model-friendly quantitative representations, in order to enable quantitative models to discriminate the semantic meaning in the text.

"Moreover, given quantitative representations, it is still nontrivial to design quantitative models on them. The financial text is relatively sparse, for instance, financial news moves in-parallel with real-world events (i.e. at sporadic times) and different types of news possess distinct relations to the market.

"Thus, it requires combining expertise both in machine learning and quantitative investment to tailor models to identify and capture genuine patterns. This process involves specialised model architectures, training techniques and so on."

At RAM AI, current NLP and Deep Learning efforts are two fold. First, the team applies text mining and NLP techniques to extract information from finance text (news, transcripts, earnings reports, etc.) and transforms them into quantitative “model friendly” features. Then, in order to boost its existing market prediction models, the team develops specialised deep learning architectures and learning procedures across both textual features and fundamental factors....


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