Don’t modern gadgets and mobile applications fascinate you? Looking at autonomous cars, voice assistants like Alexa, drones, the recommendation engine of Amazon, facial screen lock in smartphones, chatbots, and text predictions are some of the amazing things we witness in our day-to-day lives. The technology that powers all these applications is machine learning. An important subset of artificial intelligence, machine learning has penetrated almost every industrial sector and revolutionized the way people perform business operations.
There are various uses of machine learning to know about. It also comes under the data science lifecycle, where data scientists collect raw data and analyze it to draw meaningful conclusions so as to aid business decision-making. The power of machine learning to make accurate predictions can be realized in a number of applications like natural language processing, image recognition, speech recognition, fraud detection, computer vision, and so on. But did you know that machine learning can also be used in the finance industry, specifically in stock price prediction? Professionals engaged in the stock market will find machine learning quite beneficial for predicting the price of any stock in the near future.
This article makes you familiar with the applications of machine learning in the world of the stock market.
How is Machine Learning Used for Predicting Stock Price?
You already know that the stock market is a public market where people can buy or sell shares of publicly listed companies. It gives you a chance to invest money in companies that you think have high growth potential and earn profits over time. It has been a common challenge for investors to predict the stock price of a company with accuracy as there are multiple factors involved. The one who is able to predict the future value of stock accurately reaps the most profit. This is where machine learning can help.
Machine learning models have already been used to predict time series data such as election results, weather forecasting, house prices, and so on. Similarly, the stock prices can also be considered as time series, where the past stock prices and other factors can be used to estimate the values for the coming day or week. The machine learning models can be trained to assign specific weights to each market factor and identify how much historical data the model should look at to estimate future stock prices. Some of the popular machine learning models that can be used for this purpose are Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTM).
Consider Long Short Term Memory, for example. LSTM is a type of recurrent neural network that can learn order dependence in sequence prediction problems. This complex area of deep learning has revolutionized language comprehension, handwriting recognition, forecasting, and other applications of machine learning we see today. Contrary to the common feed-forward neural networks, LSTM networks are capable of recalling or erasing portions of the past data window actively.
You can use Python to implement an LSTM network and predict the stock price using machine learning. Price prediction can be considered a regression problem, and you can measure the accuracy of the prediction using Root Mean Squared Error or mean Absolute Percentage Error. You can use python libraries related to machine learning like TensorFlow, Keras, NumPy, SciPy, Pandas, and matplotlib. You need to load the training dataset with the price of your desired stock for a given time. Next, you will have to normalize the data, build the LSTM model, and use the test dataset to check whether the model is able to predict the stock price accurately. Continue the iterations until the model works satisfactorily.
How Accurate are Stock Price Predictions Using Machine Learning?
To predict the stock prices accurately, there is a long way to go when using machine learning. Though LSTMs is considered the best initial approach, more research is needed, and better algorithms can be used to predict stock prices. Nevertheless, the stock market varies according to hundreds of variables, and all of them can’t be taken into account when creating a machine learning model. For example, unforeseen events like the COVID-19 pandemic changed the stock market trends in a way that even financial experts failed to predict. But, this can be considered an important part of research to build robust models for stock price prediction in the future.
Overall, we can say that machine learning holds a good scope for predicting stock prices. But, it may fail to make valuable predictions in certain real-world scenarios. If you want to explore how machine learning is used in the finance industry, apart from just the stock market, you can take an online course. Various interesting machine learning courses are available to help you learn the technology from scratch. AI and machine learning skills are in high demand, and expertise in this field can lead you to high-paying career opportunities. Make the most of the learning options at hand and become familiar with machine learning.