Impact of artificial intelligence on investment decisions
The deployment of artificial intelligence and machine learning in the financial sector in recent times has been met with much success. This could be attributed to the exponential strides in technology, with innovations being discovered every day, most importantly in computing, data analysis, processing and storage. Thus, machine learning, artificial intelligence, big data and computerised trading have increasingly become favourite tools for market investors and participants.
Input sources for artificial intelligence in finance
For machine learning and artificial intelligence to function effectively in finance and provide informed advice on investment, they draw data from an array of sources and in many different ways. Some of the common sources are:
- Search Engines – Includes data generated by search engines like Google could provide a demographic insight in decision making
- Individuals – Includes activities by individuals (both Singular and Collective) such as articles, reports, social media posts, etc.
- Sensory machines and gadgets – This includes data from satellite imagery, weather machines, etc.
- Business Processes – Such as customer information and preferences, credit and/or debit card data, etc.
- Time series – Includes all stocks and indices etc.
- Social media
From the foregoing, it could be surmised that quite a lot of data goes into machine learning and artificial intelligence. This behemoth of data is referred to as ‘Big Data’.
Big data is the application of advanced data analytics methods such as descriptive and exploratory analysis, predictive analysis, user behaviour analysis, etc., to extract value from large data sets.
Ingress of machine learning and big data into investment
Traditionally, an investor relies on information to make wise investment choices. However, getting this information within the ambit of the legal system (and avoiding insider trading) requires great skill and experience gathered through years of practice. Even when such information is obtained, it takes further skill to analyse and apply it. This necessitates the need to find alternatives.
Although computing has always existed in finance, the expenses involved in getting superior computing power as well as large storage capacity has always limited its usage in the analysis and storage of data sets. However, thanks to continuous advancement in technology, the development of new data analytics methods, new data sets, etc., more investors (including amateur and expert investors) are turning to big data, machine learning and artificial intelligence to make precise and well-informed investment choices.
Other factors encouraging the use of big data are:
- Development of faster, more powerful processors at cheaper prices
- Continued progress in the development of storage devices that are smaller in size, yet larger in capacity
- Development of powerful data analytics methods that are easy to understand for the user
- Access to more data sets
- The precision of computers greatly reduces human error.
Artificial intelligence/machine learning techniques
There are three broad types of machine learning which can be used to solve a variety of problems. It is pertinent to note that for any machine learning problem, there are possibly quite a number of algorithms that can be used to solve it. Thus, identifying the right algorithm to apply is usually the first step. These various algorithms are however classified under the three categories which are:
- Supervised Learning: This is the most popular form of machine learning. It involves an input set submitted to the system as an example during training. Each input is tagged with the output desired from the system. The system analyses the training data and generates an inferred function which can be used to create new examples.
- Unsupervised Learning: This involves the system drawing an inferred function(s) from unlabelled input (i.e. input that isn’t tagged with the desired output).
- Deep Learning: This is modelled after the human brain and how it works. It involves a model comprised of levels of learning representations where each level provides information for the next level above it to use to learn deeply, thus forming a neural structure which progresses from simple to complex.
Possible effects of machine learning and big data on investment
Machine learning and big data individually, are forces to reckon with. Put together, they have the potential to transform the entirety of the investment landscape. Investment managers who embraced these technologies early have pulled an unarguable advantage over their counterparts. As it stands, the investment industry is at the precipice where it is either you go with the flow or stand the risk of being swept away by the tide.
Given the quantitative, and numerical nature of investment, as well as the need for precision, machine learning, artificial intelligence and big data fit right in and are a boost on returns. As such, more investment houses are making the switch to dynamic data sets and big data. This further has the effect of making the investment market as a whole more responsive to input.
Investors making a switch to these systems however have to do so with care as not all data sets are profitable, and a distinction has to be drawn first before employing these methods.
The applications for machine learning, artificial intelligence and big data in investment are no doubt numerous and pervasive. Access to data and the resources to manage it is fast changing how investment is done and the 21st century investment manager needs to leverage on these technologies to stand a shoulder above his traditional counterparts.
While there are reservations that the widespread usage of artificial intelligence will fast render redundant the need for human investment managers, these fears are however unfounded as there is still a place for traditional data analysis and management even in machine learning as not all decisions can be left in the hands of a computer.
Thus, employing machine learning and big data in investment decisions remains the classic scenario of ‘look before you leap’ at this time and for the foreseeable future.