Artificial Intelligence (AI) in Stock Trading

Warren Buffet is one of the most successful investors of all time. Buffett carefully evaluates a company by looking at every aspect of its business. People without Buffett’s business and trading knowledge often rely on hedge funds to make bets to beat the market on their behalf. However, the market’s unpredictability and traders’ biases make it difficult for any person to beat the market. According to an analysis of the S&P 500 by 

, 88.4% of hedge funds underperformed over the past 15 years. One proposed solution to improve this underwhelming statistic is using software. According to 

, over 80% of the stock market is powered by bots. A growing part of the hedge funds technology fleet is AI powered software.

• Ways AI is implemented

• Pattern recognition

• Sentiment analysis

• Roadblocks

• Future of AI trading

A skill that traders fine tune throughout their career is pattern recognition. Traders look for patterns between a company’s earning reports and their stock reaction. This shows traders what factors to look for and when it’s best to invest in a company or shed your shares.

With thousands of companies putting out earning reports, combing through all the data becomes extremely time consuming and nearly impossible to do alone. AI-powered pattern recognition is used heavily by hedge funds to identify relevant patterns in the market to make better and faster predictions off of.

Pattern recognition works similar to machine learning’s image recognition. Hedge funds put market data into an algorithm which categorizes data points using a neural network. The more data the network is given, the better it becomes at recognizing patterns. The algorithm is trained to identify patterns of when it’s most appropriate to buy and sell stocks, and use those patterns to make future predictions on live stock trading.

A shortcoming of AI’s ability to predict the market is it’s lack of awareness of human emotion. The performance of a company’s stock is heavily rooted in consumer confidence. Hedge funds are hoping to automate a way to gauge consumer confidence by using sentiment analysis.

Sentiment analysis is the process of computationally identifying whether an attitude is positive, negative, or neutral. Hedge fund companies are using sentiment analysis in tandem with tweets about the market to gauge consumer confidence for a company.

Sentiment analysis works in four steps. The first is tokenization in which a statement is divided into its individual words.

1
2
3
4
5
6
7
8
- it’s
- nice
- and
- warm
- today
- .
- JJ
- agrees

Next, the tweet will be broken down, removing any special characters and words which don’t add value to the statement.

1
2
3
4
- nice
- warm
- today
- agrees

A classifier is used to give each word a rating based on whether they are positive (+1), negative (-1), or neutral (0).

• nice (+1)

• warm (0)

• today (0)

• agrees (+1)

The numbers are then added together and if the statement receives a score greater than 0, the statement shows positive favor. If the score is less than 0, the statement shows a negative opinion.

Python libraries such as

and

are making it increasingly easier for companies of any size to use sentiment analysis. While machines can’t yet understand human emotion, they have become able to unpack what we’re saying on social media. As more people get and share news from Twitter, sentiment analysis and its use in the stock market will become a more and more relevant tool to gage consumer confidence.

Thus far, we haven’t identified a definitive observation that AI trading is better than human trading. Though already quite impressive, AI trading is still in its early stages as a technology and lacks certain abilities such as intuition in making its own decision in regards to external factors; such as economics, politics or even possible manipulation.

There are also numerous ways to trade, multiple markets geographically distributed, different metrics for success and prices can fluctuate within milliseconds. Much of current AI trading technology today incorporates simplistic analysis and may not account for the highly unpredictable market leading to homogenous investing environments and limiting the potential for greater returns.

AI is already among the best forecasting tools available for investing in stocks, particularly involving risk-management. AI trading tools are currently being utilized to reduce the time, gaps in expertise, and knowledge while removing the “emotion” out of the equation to develop a solid investment portfolio. It won’t be long before AI technology is widely adopted and eventually dominates the stock markets in the near future