Predicting Stock Market Using Deep Learning: Is It Possible?

The financial markets are considered to be primary platforms that allow investors to buy and sell shares of publicly traded companies. It is a well-known fact that stock markets have a huge financial impact on the economy of the whole world, which was brightly shown during the market crashes in 2008 and 2020.

Stock market’s complex nature makes it really difficult to predict the whole situation. Thus, forecasting has been one of the main interests of many professionals around the globe. Over the years, different technologies have been used with statistical models. Well, among those technologies, Artificial Intelligence field, and Deep Learning algorithms in particular, have shown the most promising results in stock market prediction as such tools for identifying patterns and predicting future movements in the stock market.

How Artificial Intelligence, Machine Learning and Deep Learning, in particular can be used to predict Stock Market?

Well, Machine Learning algorithms can spot patterns in large volumes of data. Such algorithms are commonly used to parse data, learn from that data, and make informed decisions based on this knowledge. And for the stock market prediction, Machine Learning algorithms can find associations in historical data that can then be applied to algorithmic trading strategies.

And, Deep Learning as a subset of Machine Learning simulates and analyzes complex patterns in unstructured data. Deep learning models are well-known for their capacities to solve image recognition, speech recognition, natural language processing (NLP) problems. The application of such tools in stock market prediction is rapidly gaining popularity and attention because of its ability to handle large datasets and data mapping with accurate prediction.

The algorithms used for forecasting can be split into 2 categories:

  • linear models, which are used to predict the observation based on the historical data of previous time spots recorded for the same observation. The bright examples of such models are: AR, MA, ARIMA, ARMA
  • non-linear models such as ARCH, GARCH, and Neural Network describe nonlinear relationships in experimental data.

Most professionals highly recommend using the following types of deep learning architectures for predicting the stock prices based on the historical prices available:

  • Autoencoders are neural networks that are trained to reconstruct the input data. They can be used for time series prediction by training the autoencoder to reconstruct the input data and then using the learned representation for prediction
  • Recurrent Neural Network (RNN) is commonly used for speech recognition and natural language processing (NLP). RNNs can use patterns to determine the next most likely scenario by recognizing data's sequential characteristics.
  • Long Short-Term Memory (LSTM) is able to learn long-term dependencies, especially in sequence prediction problems. LSTM has feedback connections, and can handle not only single data points (for example, pictures), but also complete data streams (like speech or videos).

Talking about the studies, we would like to provide you with an example: the “Financial time series forecasting with deep learning” is focused on the various financial time series forecasting implementation areas using Deep Learning: stock, index, trend, commodity, volatility, foreign exchange, and cryptocurrency forecasting. This study shows the effectiveness of using Deep Learning for stock market forecasting on various examples.

Deep Learning for stock market prediction: results it can deliver.

As we already know, patterns and trends are one of the most important parts of the trading industry. So, let us show you what are the main tasks of Deep Learning in the sector of the stock market and what results it can deliver to you.

Sentiment Analysis

Sentiment Analysis of the market can be really helpful for investors to determine the movement of stock prices and make decisions accordingly. For example, whether the price of a brand will increase or decrease. Here the data can be collected from multiple sources, for example, social media, different websites, forums, blogs, news platforms, and so on.

Also, it is important to mention that Natural Language Processing (NLP) can be used here to understand the context of the data and, thus, determine the “mood of the market”. So, traders and investors can use this knowledge to adjust their investments and decide their plan based on it.

Pattern Detection

Usually, manual stock market predictions take hours of work and years of experience. But, Artificial Intelligence and its subsets can reduce the need for hard work by automating the analysis process. Nevertheless, the expertise of a real person is still vital to deriving insights because investors and traders should know where to look to identify the patterns. Please, remember that you can not absolutely rely on AI. Even if it can detect patterns, the experience of humans, intuition and knowledge is necessary too for good performance. Technologies can take control over the time-consuming tasks of collecting and processing information, but the final decision is made by people who can use the insights.

Real-Time Data Forecasting

Deep Learning can receive the data in real-time. The models are trained to learn and improve the predictions and, as a result- to increase accuracy. For example, there are many aspects that should be taken into account like: the issues in the real world, politics, the news about particular companies, climate change, pandemic situation, etc., that directly impact stock market sectors. The algorithms can predict the results of such aspects and, therefore, provide investors with information on what could happen in the stock market subsequently. Using a combination of forecasts can help traders and investors reach better results, as multiple factors can influence each other.

The advantages and disadvantages of using Deep Learning to predict the stock market. Prior to using any tool or technology, you need to identify its main advantages and disadvantages of it.

Here you can see the positive aspects of using Deep Learning for stock market forecasting. 01.png

Let’s discuss them in more detail:

  • Self-learning abilities mean the capacity to learn from data, and become more “intelligent” over time.
  • Pattern detection ability: means that deep learning can detect the patterns that can be too complicated or subtle to be detected by humans or other technologies.
  • Deep Learning can work with inaccurate or even insufficient data as one or more corrupted neurons do not sufficiently impact the final result.
  • The multitasking ability allows running multiple tasks at the same time.
  • Offer new opportunities that can improve the whole trading performance.

Now, let’s talk about the disadvantages of using Deep Learning to predict movements on the stock market.


  • As with any technology, Deep Learning is not absolutely failsafe.
  • It cannot fully replace the analysis made by humans. Therefore, investors still need to obtain relevant knowledge and understand the impact of Deep Learning on the trading strategy, possible ways of improvement, etc.
  • Deep Learning is highly dependable on the training data. For example, when the pandemic started, due to a lack of relevant data, some systems predicted the economical factors incorrectly.
  • Keep in mind that training of Deep Learning models can be really time-consuming and expensive.

Taking this into account, you should be ready not only for the great results that artificial intelligence can deliver to you but also face some risks if your system is not implemented properly.

Bottom Line

At Sciforce, we believe that deep learning can be extremely useful for stock market prediction if trained and implemented properly. This technology can help investors with pattern detection, real-time data forecasting, sentiment analysis and many other tasks. Keep in mind that before using Artificial Intelligence for these functions, you will need to consider the advantages and the risks.

Also, discussed in the article things are just a few examples of the many deep learning architectures that can be used for time series prediction. The choice of architecture will depend on the specifics of the problem, the input data, the quality of the data, etc. So, it may be necessary to try multiple architectures to find the one that works best for a given data.

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