How to Use Data Science for Stock Market Analysis?

Share market or stock market can be explained as several exchanges where shares of different companies are traded. Trading includes the buying and selling of shares. These marketplaces are working under some regulations. So we hear in the news about the increase and decrease in points in different exchanges. And also we hear about the increase in the price of shares of companies or decrease in the value of shares. These all are happening in share markets. There are a lot of people related to stock markets. Stockbrokers are licensed professionals who can buy and sell shares for investors. There are portfolio managers who make decisions to invest in portfolios. They find the best portfolio that gives good returns. Also, there are investment bankers, custodians, etc who play a key role in the share market.

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Stock market or share market analysis is an important part of share trading. Keep tracking the movements of shares and buying or selling them to make a profit. In share market prediction plays a key role. For example, an investor who analyses the market and finds a particular company’s share is going up in recent times and is showing steady growth. Based on the recent performance of the share, he decided to buy the share and hope for more growth and sell it when it gains a good price. This is how prediction works in a share market.

Data Science in Stock Market Analysis

Data science is used to find unseen patterns. It uses modern-day tools and techniques to handle a huge volume of data and make meaningful decisions. Data science also helps with business decision-making. In data science, there is a life cycle, based on which data science works. It starts from the acquisition of data, then the processing of data, and then the classification of data after which the predictive analysis comes and at last reporting of data. In the last step, the communication is done. This cycle goes on and on.

Data science includes knowledge in maths, statistics, and programming languages. And it deals with a huge volume of data. In the share market, it needs to deal huge volume of data and also there are some calculations and coding processes involved. So if we use data science techniques in share market analysis, it is very easy to acquire data and process the data, and find solutions. Portfolio management is assisted by analysts to find out the best portfolios to invest in. With the use of data science, analysts can easily find out the best portfolios available.

Data Science Concepts for Share Market

Some important concepts are helpful for share market operations. Let us take a look at them.

• Algorithms

It can be defined as the group of rules used to perform a specific task. Algorithmic trading is where a set of rules is used for the buying and selling of shares in the share market.

• Training

This is different from the usual training concept. In this concept, data is selected and trained in a machine learning model. This happens with the help of data science. Machine learning models are used to predict the future price of shares. For this purpose, the model needs to be trained with previous data.

• Testing

In this process, the model is tested. It needs to work well. To find out the performance of the model, we will take the prediction of the model and then compare the data with the training set data.

• Features and Target

The data related to the stock market is always represented in an Excel sheet with a lot of data in it. In that sheet, the price of the share is the target and the rest of the columns will be the features.

• Modeling

In modeling, past behaviors are used to predict the feature. It is a mathematical approach in data science to predict the feature. The financial data in the share market will always be a time series model. There is another type of modeling called classification. In this type certain points of the data are classified.

• Overfitting and Underfitting

The performance of the data may be sometimes too much and sometimes it will be too low. Overfitting happens when the prediction of the model is too complex and underfitting happens when the predictions are too simple.

Uses of Data Science in Share Market

Let us look at how data science can be used in share markets.

• Real-time Market Insights

Stock market data must be at the distance of a single click. Real-time data is needed to make predictions and to make decisions quickly. With the help of data science, traders will get real-time data from share markets and can make decisions easily.

• Algorithmic Trading

We have discussed earlier the concept algorithm in the share market. Algorithmic trading means, the models are trained with data and these models help to predict the future of shares.

Data science helps to train the learning models with the algorithm.

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• Automated Risk Management

In the stock market, the risk factor is inevitable. Data science uses machine learning to tackle the risk problem. The identification, monitoring, and prioritizing of risk is automated, machine learning models will reduce the chance of human error and thereby reduce the risk factor.

• Fraud Detection

Data science plays a key role in fraud detection. In the banking sector also we had seen the help of data science in fraud detection. In almost all financial sectors fraud detection is done with the help of data science. With the help of machine learning and artificial intelligence, data scientists detect unusual behavior or anomalies and difference in patterns.

• Data Management

The data managed by the share market is very huge than we can’t imagine. Even if the data are digitized most of the data lack structure. So with the help of the data science life cycle, data scientists can manage the data and can make it effective and useful.

Conclusion

Data Science is a field that has a lot of importance in every business. Every field, especially the finance field is very much dependent on data science and its tools. That is why in share markets also data science plays a key role.