Data science in predicting the stock market

Can data science predict the stock market? Understanding how the stock market works, can make you an overnight millionaire but it is anything but simple. Predicting the stock market is very difficult and even the most sophisticated of systems, struggle to achieve this.

In this blog post, we will take a look at the possibility of predicting the stock market, using data science. The blog post will explore the phases needed to develop a model that can possibly predict the stock market and advise you on the best time to invest in the stock market and when to keep your money in your pocket.

Data science in predicting the stock market

For starters, we need to acknowledge the fact that large data sets are analyzed to identify patterns and trends in various areas. It is therefore possible to analyze data from the stock market and use it to make predictions on the future trends of the stock market.

Although the stock market is not consistent and is affected by a number of external factors, it is possible to pick up certain trends by analyzing the stock market data. These trends can then be used to influence the trading decisions of those in the stock market.

If ever there was an ideal stock market predictor, here is how it would work and the phases that would be involved in predicting the future trends in the stock market.

Phases of predicting the stock market through data science

Preparation of data

The first step would be to get as much information as possible to train the model. In our case, we’ll need information on the factors that affect the stock market, the stock market performance at various periods of the year and so forth.

This information can be found on various sites such as Yahoo Finance have massive information on the performance of various companies on the stock market every year.

Split the data into subsets

Once you have the data you need, you will need to divide the data into various categories. Part of the data should be for training purposes, some of it meant for validation and the final set for testing. Each of the data sets should be independent from the others.

Convert and manipulate the data

The next step is to reform the data in a way so that the data can be used to train the model and test things out. The data will need to be normalized first where the data is scaled to a given range. In most cases the binary range of between 0 and 1 is used.

Display the normalized data

Once the data has been normalized, the next step is to display the data. The displaying of the data doesn’t influence the working of the model in any way but it is very important in debugging your code and identifying any mistakes in the code. It is also a good practice to visualize any data set and it helps with understanding the working of the code and troubleshooting for errors.

Train the model

Training the model involves setting parameters which directly reflect the performance of the model. The training will involve rigorous fine tuning of the right combinations and evaluation of the model based on the choices you make.

You will need to feed the model with as many variables as possible with predetermined outcomes. Feed in as many variations of the parameters as possible and in each case, declare the most appropriate outcome from the data collected in step 1.

The training could take anything from hours to weeks depending on the hyper parameters that were selected when starting to train the model.

Testing the model

The penultimate step is to try and test the model using a set of data whose outcome you already have. Feed in variables to the model and compare the outcome of the model to the actual data that you have from step 1.

If there are too many inconsistencies, you will need to retrain your model by choosing different parameters and hyper parameters. Repeat this process over and over again until you can see some consistency in your model’s prediction.

Try predicting the stock market in real time

Once your model has started exhibiting some signs of accurate forecasting, it is time to put it to test by trying to predict the stock market in real time. While predicting the stock market, remember to keep using the daily data to continue training the model and making it even smarter.

Remember, no system has so far been able to accurately predict stock market trends to date. All the available systems have their own shortcomings and the stock market is never consistent- it is affected by a number of external factors. In an attempt to make a model to predict the stock market therefore, you should be careful not to lose any money.

Talk to Runrex on data science use in predictions

Want more information on how you can create models that are able to make accurate predictions? Give us a call here at Runrex and we will be service.