RavenPack | March 20, 2018
Armando Gonzalez, RavenPack CEO, discusses the 5 key signs that the financial sector has entered a big data and machine learning revolution. Watch this short video from his presentation and read more insights below.
Harvard Business Review recently reported that 71% of surveyed financial services industry firms are exploring big data and predictive analytics while 70% report that big data is of critical importance to their firms.
As the amount of structured and unstructured data explodes, the financial sector is realizing the necessity of harnessing and analyzing that data in the fastest, most effective way possible in order to stay competitive.
A revolution can be defined as a fundamental change in an organizational structure that takes place in a relatively short period of time when people “revolt” against the current order. Currently the financial sector is making a massive shift towards
big data and machine learning technology
and applied solutions.
Here are five signs that this is the beginning of a revolution in finance:
There is an explicit discontent with traditional investment management, concerning management fees, lack of transparencies and a lack of trust. There is a change taking place where investors no longer trust the traditional investment process.
We are also seeing that a small group of companies control most of the data. Whether it’s the exchanges, market data providers, Bloomberg, Reuters, Google or Amazon. They have most of the data and they have full control of it.
Data is growing at a frantic pace and it’s becoming unmanageable without new technologies. It’s impossible to make sense of what information is being thrown at us without the right systems in place to manage it.
There has been a massive spike in the number of alternative data providers. The traditional provider would be your market data firm as well as your company, proving fundamental information. Most investment decisions are made using those two sources of data. Nowadays, we have hundreds of alternative data providers that claim that they can give you a way to measure micro variables before they have been released from the official means. Firms are claiming they have sources to predict stock returns or predict the company’s earnings, even before the information comes out. Some of them are valid and some of them are not, but the fact is, there are alternatives now.
Intuition instead of data driven decision making has been the norm, but we know better. We know that data science can help us make sense of the world of big data and yet the world of finance is still very traditional.
There is a massive shift towards big data and machine learning in the banking and financial sectors.
Big data and machine learning
companies such as RavenPack are responding to that need by introducing innovative solutions that help banking and finance professionals enhance returns, mitigate risk and improve their compliance processes.
There is no doubt that harnessing the power of big data and machine learning can enhance performance. Those in the financial sector that master this will stay ahead of their competitors.
The most successful hedge funds, banks and asset managers use
to enhance returns, reduce risk and increase efficiency by systematically incorporating data-driven insights on news, social media and proprietary textual content in their models and workflows.
Please use your business email. If you don't have one, please email us at firstname.lastname@example.org.
We will process your personal data with the purpose of managing your personal account on
RavenPack and offering our services. You can exercise your rights of access, rectification,
erasure, restriction of processing, data portability and objection by emailing us at email@example.com. For more information, you can
Your request has been recorded and a team member will be in touch soon.
High inflation has returned in developed markets after decades of lying low. In our latest paper, we show how to build an inflation-based asset allocation strategy using sentiment data and we illustrate that sentiment-based strategies outperform models that depend merely on past observed inflation values.
This year's RavenPack Research Symposium brought two intense days of knowledge sharing in London and New York, from 25 top experts in natural language processing, quantitative investing and machine learning. Together, we explored how firms can leverage new language models to generate alpha, better manage risk and respond to calls for more sustainable investment practices.
Human capital is at the heart of value creation. Our latest research demonstrates how unprecedented workforce insights, sourced from over 200 million job postings, can generate more alpha.