Artificial Intelligence has become a buzzword lately. Artificial Intelligence has transformed many industries be it healthcare, banking, and finance, business operations etc. Artificial intelligence has a wide scope in terms of future applications.
Artificial intelligence is a separate branch of computer science that has many sub-branches. Machine Learning and Deep Learning are an example. For better understanding the article, let us first get to know about these terms.
Artificial intelligence:
Artificial Intelligence is a branch of computer science that empower machines to perform tasks that require human intelligence.Artificial Intelligence Services gives machines the power of thinking, reasoning, decision making and problem-solving.
Machine Learning:
Machine Learning is the subset of artificial intelligence. Machine Learning deals with training the machines by feeding them with huge datasets of real-life examples and instructions. On the basis of these examples, machine learning algorithm performs tasks autonomously without much human intervention.
Deep Learning:
Coming down to deep Learning, Deep Learning is the subset of machine learning. Deep learning is the simulation of the human brain in the form of artificial neural networks. Deep Learning machine algorithms consist of a large number of artificial neural networks that helps machines to become intelligent.
- Investment in financial market and stock market involves a huge amount of risk factors for the businesses. Large organizations want to invest carefully in the stock markets as stock market prediction is difficult. This is because the stock market is dependent upon several dependent and independent variables. Therefore, the degree of randomness in the stock market is high.
- In order to minimize the risks and maximize the profits, organizations are taking help of machine learning and deep learning. Stock markets are not completely random in nature. They also follow a certain pattern. By understanding the degree of randomness and understanding the pattern followed by the stock market, stock market analysis can be done.
Deep Learning Algorithms are helping organizations to understand the stock market behavior to up to 75 percent. Following steps are involved in building deep learning services that can help in analyzing the stock market:
1.Collecting the data
Data is the main building block in developing deep learning neural networks. Data is collected from various sources online and offline. Various web scrapping tools are used to collect the data from websites. The data we get from the internet is in an unstructured format. This data is to be converted into structured data that is understandable by machines.
Moreover, the data collected must be from reliable sources and good quality data. As poor quality data affects the outcomes to great extent.
2.Testing and training data
The next step is to categorize data into two parts. Namely, testing data and training data. Nearly 80 percent of data consists of 80 percent of the total data. Machines are trained using huge datasets. Various techniques used for training are time series analysis, cross-validation, bootstrap resampling etc.
3.Data Scaling
Deep Learning neural networks are needed to be scaled. This is because most of the functions are defined is a huge range of interval. Python can be used for scaling purpose that makes use of min-max scaler. One more thing to keep in mind before scaling is that scaling is to be done on the training data only.
4.Designing Network Architecture
The next step in designing neural network is to give weight and biasing to the variables. Also, hidden layers should also be transformed using activation functions. You can choose from a lot of activation functions. Most commonly used is Rectified Linear Unit.
5.Cost Functions
Cost function in the prediction system is defined as the deviation between network’s prediction and the actual training data. Because of regression, mean square value error function is used. MSE (Mean Square Error) is commonly used for the prediction of cost systems. Not only, MSE other functions that calculate the deviation between network’s prediction and the actual training data can also be used.
6.Optimizing the results
Optimizers are used to optimize the weight and biasing of training data. Developing fast and the accurate optimizer is a major concern in deep learning neural networks. There are many optimization algorithms but Adam Optimizer is one of the best optimizers in deep learning.
7.Training artificial neural network
After defining the placeholders, variables, initializers, cost functions and optimizers the model needs to be trained. Data is to be trained in the form of batches. A batch of data is fed into the artificial neural networks.
After training is completed, we can analyze the future stock market trend from past trends in the stock market.
Conclusion
Stock market prediction using traditional methods can be time consuming and inefficient. Using deep learning applications in High-Frequency Trading provides accurate results to an extent that too in the time frame. But implementation of deep learning in the stock market analysis is a difficult task. For that, you need extraordinary and talented people with good experience in data science and trading.
