How Machine Learning Could Shape the Future of Finance

If you’ve ever accidentally wasted an hour of your time scrolling through your news feed on Facebook, chances are you’ve just experienced the power of machine learning (Forbes, 2017). Whether it be facial-recognition algorithms used on your new smartphone (Apple, 2017) or fraud-prevention algorithms used by major credit card companies (Lusis Payments, 2017), machine learning is proving itself to be a technology of the future. In addition to its use in these applications, it has the ability to reshape the financial world forever.

Supervised Learning Model

Modern machine-learning algorithms use a supervised learning model (see Figure 1). This model feeds training data, which is split into its identifying features, into the machine-learning algorithm and creates a series of triggers and outputs, called a neural network. The result is then manually paired with a label. Once the algorithm is trained with an adequate amount of data, it can be used to take feature vectors from new data and predict which label it falls under (AllProgrammingTutorials.com, 2015). Although this approach is extremely effective for picking out your face in a picture, or figuring out which song is playing in the background, it has yet to master more complex financial applications.
In the field of stock-market forecasting, the aim is for machine-learning algorithms to reach a stage that enables judgements to be made on stock values (i.e., in terms of direction and magnitude) with a success rate that significantly exceeds their running cost. This may sound like an unattainable and unachievable goal to many. However, with the prospect of a large financial reward, huge amounts of money are being poured into such research by many financial services companies, such as JP Morgan (Butcher, 2017) and Morgan Stanley (Harvard Business Review, 2017).
This doesn’t mean that you can simply create an algorithm and become rich within days. In an extensive investigation, André Anderson (Anderson, 2012) came to the conclusion that “No trading system was able to outperform the [average trader] when using transaction costs.” Further, Dr Yoshua Bengio, Head of the Montreal Institute for Machine Learning Algorithms, said, “Market inefficiencies tend to be localised in time and ‘space’ (particular markets, with a limited potential volume of profits). So it may well be that some firms have used and are using machine learning, but it’s not like [hitting the jackpot], rather like patiently pulling profits here and there, each time with a different specifically tuned model.” (Bengio, 2017) Dr Bengio, the author of the piece, goes on to say that companies are currently using a significant amount of human judgment to assess which trades to make.
Unfortunately, this is the underlying theme with current stock-prediction applications using machine learning: the algorithms just aren’t good enough to exceed the performance of an average speculative trader.
Past systems have used data from news articles to assess specific companies’ success. However, these approaches have failed to take into account the virtually random speculative investment that plays an important role in driving stock values. Speculative investment is the process of buying and selling stocks on a short-term basis with little to no evidence for an increase in value over that period (Hayes, 2017). Although counter-intuitive, this process—when performed by a significant number of people—can seriously affect a stock valuation (Roosevelt Institute, 2011). For this reason, incorporating some sort of detection of popular opinion on companies being traded is vital to accurately predicting their future values.
New, cutting-edge research performed by the Indian Institute of Technology (IIT) uses sentiment analysis (Pagolu, et al., 2016), a process of analysing language in sentences to assess opinion on specific matters. Complex sentiment analysis engines must be able to determine that, for example, “that horror movie we watched was so scary” is a positive subjective comment. This is something that is easy for humans to understand but much more challenging for computers, due to the use of contextual language (‘scary’ being positive in this case). The research uses sentiment analysis on Twitter to assess public opinion of particular companies which can later be fed into the machine-learning algorithm. Indeed, this technique is currently being employed by many research departments, including at Stanford (Mittal & Goel, 2017) and Cornell (Pang & Lee, 2017).
This, in conjunction with a computers’ ability to trawl through billions of words with ease, will enable machine-learning algorithms to detect a much larger spectrum of information—ranging from hints of speculative trading on social networks, to discussions on trading forums—in a way that humans never could. If successful, this research will mark a new era for the world of finance.

About the Author

Computer Science MEng, University College London

Computer Science MEng
University College London

Books

Jobs

Entry-Level Engineer at NC Department of Transportation
Expires: 02/03/2021 Employer: NC Department of Transportation
Registered Nurse ($3,200 Hiring Incentive)(Job Id 15544) at South Dakota State Government
Expires: 02/17/2021 Employer: South Dakota State Government - Department of Health
Registered Nurse ($3,200 Hiring Incentive)(Job Id 15541) at South Dakota State Government
Expires: 02/17/2021 Employer: South Dakota State Government - Department of Health
Engineering Technician III at Washington County, Oregon
Expires: 02/07/2021 Employer: Washington County, Oregon
Customer Accounts Specialist I at City of Portland Bureau of Human Resources
Expires: 01/30/2021 Employer: City of Portland Bureau of Human Resources
Social Worker IA&T - 2nd shift at Carteret County Government
Expires: 02/01/2021 Employer: Carteret County Government
Supervisory Interdisciplinary Scientist at US Food and Drug Administration (FDA)
Expires: 02/02/2021 Employer: US Food and Drug Administration (FDA)
Mental Health Professional at El Paso County
Expires: 02/01/2021 Employer: El Paso County
Maintenance Supervisor at CSL
Expires: 02/15/2021 Employer: CSL
Clean Utilities Engineer at CSL
Expires: 02/15/2021 Employer: CSL
BAS Engineer at CSL
Expires: 02/15/2021 Employer: CSL
Program Manager - Family Planning at El Paso County
Expires: 02/01/2021 Employer: El Paso County
Deputy Human Services Director - Health at Carteret County Government
Expires: 02/08/2021 Employer: Carteret County Government
Student Trainee for Engineers and Architects at U.S. Army Corps of Engineers, Baltimore District
Expires: 01/30/2021 Employer: U.S. Army Corps of Engineers, Baltimore District
Business Systems Analyst (Operations and Policy Analyst 2) at Oregon Department of Environmental Quality
Expires: 02/01/2021 Employer: Oregon Department of Environmental Quality
Management and Program Analyst Summer Student Trainee (GS-7/GS-9) at U.S. Government Accountability Office
Expires: 02/08/2021 Employer: U.S. Government Accountability Office
Management and Program Analyst Summer Student Trainee (GS-04) at U.S. Government Accountability Office
Expires: 02/08/2021 Employer: U.S. Government Accountability Office
Associate General Counsel at Office of the Massachusetts State Treasurer and Receiver General
Expires: 02/04/2021 Employer: Office of the Massachusetts State Treasurer and Receiver General
Fiscal Policy Analyst at Office of the City Auditor, City of Sacramento
Expires: 02/03/2021 Employer: Office of the City Auditor, City of Sacramento
Director Human Resources at Hillsborough County Government
Expires: 02/27/2021 Employer: Hillsborough County Government
Planning Director at County of Frederick, VA Local Government
Expires: 02/08/2021 Employer: County of Frederick, VA Local Government
Planner Coordinator at Maryland-National Capital Park and Planning Commission (Prince George's County, MD)
Expires: 02/16/2021 Employer: Maryland-National Capital Park and Planning Commission (Prince George's County, MD)
Consumer Safety Officer (Emergency Response Coordinator) at US Food and Drug Administration (FDA)
Expires: 01/25/2021 Employer: US Food and Drug Administration (FDA)
Public Affairs Specialist at Federal Emergency Management Agency (FEMA) Pathways Students and Recent Graduates
Expires: 01/29/2021 Employer: Federal Emergency Management Agency (FEMA) Pathways Students and Recent Graduates
Code Comp Investigator II at Fairfax County Government
Expires: 01/30/2021 Employer: Fairfax County Government - Fairfax County Human Resources
Behavioral Health Senior Clinician - Youth & Family at Fairfax County Government
Expires: 02/06/2021 Employer: Fairfax County Government - Fairfax County Human Resources
Social Services Specialist III at Fairfax County Government
Expires: 01/30/2021 Employer: Fairfax County Government - Fairfax County Human Resources
Public Health Nurse IV at Fairfax County Government
Expires: 01/30/2021 Employer: Fairfax County Government - Fairfax County Human Resources
Kinship Care Specialist (Social Services Specialist III) at Fairfax County Government
Expires: 01/30/2021 Employer: Fairfax County Government - Fairfax County Human Resources
Water Resources/Dam Safety Engineer (Senior Engineer III) at Fairfax County Government
Expires: 02/06/2021 Employer: Fairfax County Government - Fairfax County Human Resources
FOIA Analyst (Management Analyst II) at Fairfax County Government
Expires: 01/23/2021 Employer: Fairfax County Government - Fairfax County Human Resources
Administrative Assistant & Scheduler (Administrative Aide) at Fairfax County Government
Expires: 01/30/2021 Employer: Fairfax County Government - Fairfax County Human Resources