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Machine Learning Will Shape the Future of the Stock Market

In 2018, there was over $3.5 trillion under management in the hedge fund industry. It doesn’t seem over-ambitious to seek a higher degree of precision in stock prediction since that could reduce the number of risks and increase profits. To achieve this, the stock markets use machine learning algorithms to forecast stock prices to some degree of accuracy. The challenges remain the volatility of the market and the vast amount of data needed to make forecasts covering a long period of time.

Machine Learning Revolutionizing The Stock Market

The stock market is influenced by precise analysis and accurate predictions, making it a great concern for analysts and investors. Due to the volatile nature of the sector, experts are leveraging Machine Learning (ML) algorithms to unsettle some of the challenges facing the sector. This does not come as a surprise since ML has revolutionized other sectors like healthcare, agriculture, communication, and tech.

Every ML venture is aimed at tackling complex problems that would have been otherwise difficult or impossible for humans to solve. ML tools work by studying trends over a long period of time and picking gave commands from millions of data supplied to them. Stock prediction bots also use mathematical and computational skills to predict the movement of financial markets. But this has not been without concerns of experts.

Will ML And AI Be Able To Analyze Stocks Accurately?

Over time, there have been traditionally held opinions that ML and AI machines will never be able to predict or analyze stock trends accurately. In a report by Market Watch, it argued that ML will not be able to “crack the code” of financial markets because “financial markets are not stationary. They change all the time, driven by political, social, economic, or natural events. The data are limited by how often and how much into the future we want to predict.”

The report further stated that the data required for the machine learning model to make a one-day prediction in the stock market is insufficient. And for a trustable prediction model, vast amounts of data spanning over a long period of time is the essential ingredient for determining a reliable prediction or analysis, the report added. As true as this sounds, researchers are not giving up. They are engaged in making the best of the available technology while pursuing that holy grail.

Bots Have Been Better Than Humans In Predicting The Stocks

Bots have been successful in predicting and even beating humans in the financial market table. In 2001, BBC reported that an IBM bot was pitted against unprofessional human investors in trading commodities like gold and pork bellies. IBM’s bot was able to make 7% more money than its human counterparts.

Ever after this experiment, other robust architectural ML models have been introduced to help analysts predict accurate stock values. Recurrent Neural Network (RNN) is one model that has proven to be useful in processing a series of data that are effective in the stock exchange market. RNN is a collection of networks with loops that tackles long term dependency problems in analyzing historical and current information for stock prediction.

Long Short Term Memory (LSTM) a special type of RNN is designed with memory cells that are used in computation outperforming the traditional artificial neuron. LSTM’s memory cells “are able to effectively associate memories and input memories in time and can predict with high precision capacity,” according to edureka! video.

Data Sources and Stages of Analysis in Predicting Stock Using Machine Learning

The volatile nature of the stock market makes a prediction an uphill task. This volatility pushes experts to focus on the roles of many other external factors in influencing trends in the market.

In an article by Wasiat Khan et al, published by Springer Link, “Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior.” Khan et al also noted that some stocks are more difficult to predict and some others are easier to predict because they are influenced by data from social media or financial news.

Some Stocks Are More Difficult To Predict Than Others

Their result showed that some stocks are more difficult to predict like New York and Red Heart stocks. New York and IBM stocks being easier to forecast are more influenced by social media, while London and Microsoft stocks are influenced by financial news, the report concluded. 80.53% accuracy was the highest recorded for social media influenced stock while 75.16 was for financial news.

But the input data has to be of high quality (like the foundation of financial technical analysis and capital management) and accurate otherwise the algorithm it will generate would equally be wrong. Unsurprisingly, feeding the machine with all the data is not enough. A command would also be issued that will expect the robot to detect a sell or buy opportunity from a signal including other quantitative financial features that can help the machine make accurate predictions.

The Process Of Stock Prediction

The additional stages beyond supplying the data follow rigors that ensure a high degree of precision of stock prediction. Once the data has been sorted and fed to the machine, it undergoes a pre-processing stage which involves data discretization. Feature extraction, training a neural network, and visualization are the other steps incorporated in the process.

Journey into the use of ML in successful stock prediction may not have seen it heyday yet but has advanced successfully. It has been able to defy the traditional beliefs that the volatility of the market would downplay the successful use of the technology but experts appear to be finding their ways around it with some recorded level of success

Disclaimer: This article is not a guide to stock investment and should not be taken as such. It was only written to explore the impact of ML on the market.

Veronica Ugwu
Veronica Ugwu
Veronica Ugwu is a writer for RegTech Global, with her enthusiasm for tech and business.


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