AI in trading and stock selection

And though most of us can be in denial, sophisticated algorithms already dominate our everyday lives more than we can imagine. Right from how the traffic lights are moderated to your social media feeds, everything is run on intelligent and smart algorithms that we otherwise call artificial intelligence. This extends to all industries and functions including the financial trading industry.

Artificially intelligent systems are able to draw profits from day-trading within short timeframes. In fact, automated accounts make up for over 75% of the total market volume. And no doubt, that number is only increasing. These algorithms that power up the trading and stock market often go unnoticed though they are reshaping trading that being done on Wall Street. Investors are leveraging the power of AI to drive more efficiency to the financial markets. It is also enabling investors to explore unchartered territories in the financial trading industry.

How do these complex algorithms work like a charm?

“The most powerful thing in your world now is an algorithm about which you know nothing about.”

Kelly B. McBride, an American writer

The truth about artificially intelligent algorithm trading is if you stay persistent enough you will be able to reap the benefits (or otherwise).

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Source: http://mangmaoclub.com

In definition, algorithmic trading is the process of purchasing and/or selling stocks or security based on a set of pre-defined rules that have been designed based on historical data. These rules could be based on numerous factors or datapoints. The key factor that is responsible for the efficient performance of these algorithmic trading is resource allocation.

Benefits of using AI in stock selection or trading

When AI is implemented to analyze and predict the right stocks to invest in, what investors are essentially doing is eliminating human emotions that drive investors to sometimes hold on to losses for a longer time in the hope that the market scenario will eventually change. On the flip side, investors tend to sell profitable securities way too early, instead of waiting it out and reap the maximum benefits from the investment. These kinds of decisions are not data driven. Instead, they are driven by emotions and bad instincts. What AI does is to test various trade ideas using historical data so that such bad decisions on the part of the investors can be eliminated.

Automation of the financial markets

One of the common misconceptions that retail traders have is that, they do not trust the algorithmic approach to trading; they are reluctant to adopt artificial intelligence as part of their trading strategy. This is probably because artificial intelligence as a technology is often misunderstood and believed to be only for large enterprises. But this isn’t the case. If you can grasp the basics of how the algorithm works, it is easy to implement. It is also a reliable source of insightful information to make smart trading decisions.

The financial market is now witnessing a high-level of automation. The algorithms that are being implemented by various AI platforms have become more sophisticated leading to better stock selection and decisions. That being said, these algorithms have to be closely monitored so that adjustments can be made to the algorithms and AI models to market changes.

The financial industry is now investing in machine learning and automation intelligence. Most of the algorithms work on a process that allow you to make a profit irrespective of how the market shifts or the direction the value of stocks go.

One of the important reasons for such a tremendous growth of AI technology is its capability to process millions of data in one millionth of a second. Another key reason for the trading industry to adopt this technology in their trading approach is to deprive other companies from imitating their methodology and keep their approach unique. This way they can stay ahead of their competition and draw maximum profit from the trading.

AI-driven stock selection models

There are numerous approaches in which a stock is valued. This can be done in a holistic way by analyzing the balance sheets of various companies. Or limit the analysis to only a set of stocks that you want to target and focus on. Depending on where the focus is, the portfolio can be automatically built. The AI-driven system then makes decisions based on the parameters that have been set to decide the value of a stock.

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Source: www.towardsdatascience.com

To the framework, new additional inputs can be added to further determine the value of the stock. These new input layers are added according to the value investing principles the broker or investor sees fit. These can be done depending on the complexity of the output that is desired. Alternatively, investors step away to allow the platform to automatically define an ideal strategy.

The future of algorithm trading

The financial trading industry has a bright future with AI technology growing exponentially. For the last two decades, large trading firms, financial organizations, and banks have used made use of algorithmic trading and have seen a significant success. However, AI shouldn’t be restricted for use by large financial enterprises alone. The free and more widespread accessibility to data, and the progress made in the AI industry has enabled smaller financial firms and retailers to jump onto the bandwagon of algorithmic trading. And these organizations have not been disappointed by the results they have witnessed. However, there is more scope for AI-powered trading solutions for financial traders and stockbrokers. This will only grow as investors are exposed to the nuances of this technology.

Closing thoughts

We are already aware that there are plenty of AI-enabled strategies out in the market for various niche use cases within the trading and stock selection industry. It’s not too long before these strategies can be simplified and offered in a plug-to-play manner for the end user. At Brainalyzed, we are crafting artificial swarm intelligence models and simulating their performance for various sectors within the financial industry. Our aim is to bring this powerful technology to everyone, removing dependency on data scientists. If you’d like to explore how Brainalyzed can help you make better trading decisions, leave a comment.

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