Natural Language Processing
Natural language processing (NLP) is an element of computer science and machine learning whereby NLP uses artificial intelligence to “teach” machines how to interpret human language.
There are many examples of areas where natural language processing is used such as chatbots on websites for customer service enquiries, speech recognition and even platforms such as Google Translate where machine learning is applied to deliver translation results in real time. We will see the process of NLP used in order to teach computers how to decipher and understand bodies of text used by humans. The result is that the computer can then quickly and effectively select appropriate responses having been “trained” to understand the complexities of human linguistics. Due to the computer based and automated nature, it has proven to be a popular tool used by companies and sectors across the world as it saves on time and of course allows for a constant company presence. For example, even when companies are technically closed for business, chatbots can still provide answers to questions and even log inquiries and store potential customer details ready for when a human representative is next available. Not only is this efficient, it can help business retain any potential leads which normally would not have been received.
Natural Language Processing in Financial Markets
There are several ways in which NLP can also be effectively utilized in financial markets such as using it to assess market sentiment, fine tuning an investment portfolio as well as also stock market predictions. This applies to both indices as well as single stocks.
From the viewpoint of financial sentiment, NLP can be used to assess market news and data to create a market sentiment model. In “normal” market analysis the general aim is to determine whether market news is positive or negative, whereas using NLP the goal is to go one step further and not simply assume that “negative” news will result in a decrease in the stock price. In other words, what could be deemed as “negative” such as a company chief executive officer selling a large volume of shares will be assessed by NLP, as it could actually indicate that they are seeking to create more liquidity in the company and does not mean they are simply selling for a negative reason.
Deep learning is a component of natural language processing, which consists of machine learning using multiple neural networks. In many cases over the last several years deep learning has proven to be more effective than human judgment and analysis. By applying this deep learning component to stock market examples, the accuracy of a stock’s potential future is increased therefore showing NLP to be an effective tool in this domain.
In line with predicting stock market behavior, NLP can also be used to refine an existing stock portfolio, or build a strong portfolio from scratch. Again, due to the machine learning aspect of NLP, past results and historical data can be used to predict potential outcomes and in the case of a portfolio, create one whereby de-risking and a balanced portfolio are a prime goal.
The process of Natural Language Processing consists of several steps: segmentation, tokenization, stop words, stemming, lemmatization, speech tagging and finally named entity tagging.
This is the method of breaking down a document of text into separate sentences to a computer can better understand the text body.
Here, sentences are further broken into individual words, and each word is known as a token.
Stop Words are popularly used articles and words such as “a” and “the” which are removed from any text analysis due to them not serving any purpose from an analytics point of view.
This is the process of explaining the document to the machine in more detail, and grouping different versions of a particular word together. For example, the word drive, drives and driving are rooted in the action of driving.
Lemmatization is the action of specifying different tenses, emotions and genders in a given body of text.
For each token, its grammatical representation is assigned so that the machine can understand this in more detail. For example in a typical sentence there will most likely be verbs, nouns and prepositions. By clarifying which words would fall under a specific category the machine is effectively taught how to identify and spot these.
Named Entity Tagging
In this part of the process, particular geographical locations and even people’s names can be tagged so the machine becomes more familiar with them. This could also include popular figures as well as companies such as Google and Amazon.
As mentioned earlier, NLP is very effective in the world of finance and financial markets. By applying NLP and the process described above we can use the example of deciding whether to invest in a particular company. NLP can be used to assess financial reports, annual general meeting (AGM) reports as well as letters to investors issued by company chairmen and CEOs. By applying the concepts of NLP, any potential “red flags” such as recurring patterns of uncertainty or negative language could help an investor decide whether the investment is right for them from a risk point of view. Conversely, regular positive news and “green flags” regarding potential stock buybacks, stock splits and strong earnings reports could also be interpreted using NLP and help an investor make an informed decision.
As we have seen, Natural Language Processing is a highly effective and progressive methodology, and by implementing machine learning in order to teach computers to interpret human language it can also be a helpful tool in the world of financial markets. Whether trying to optimize a stock portfolio, assess market sentiment or even predicting the future of individual stocks and index funds, NLP can be a beneficial ally. The detailed process beginning with segmentation to named entity tagging covers all angles and ensures the computer being “taught” has a fully comprehensive understanding of all aspects of the subject matter in question.