How to Use Machine Learning Methods for Stock Market Predictions?

6 min readDec 21, 2020

Machine Learning Methods can Prove Vital in Stock Market Predictions

Stock Market is synonymous to Volatility. Ancient poets from India have always attributed unpredictability to women’s minds!! Stock Markets are no different either! In spite of a lot of analysis going into predicting stock market behavior, still predicting the market behavior is the biggest challenge for the human mind to comprehend. In this context utilizing the innovations that have taken place in the field of Machine Learning seems promising to predict the patterns in the Stock Market Trading.

Existing Stock Market Analysis Methods

No matter which domain you are dealing with a systematic workflow will be in place.

In Stock Market Trading too experts have been using three time-tested types of analysis to predict the market. The 3 types of analysis are:

Technical Analysis
Sentimental Analysis
Fundamental Analysis

Technical Analysis

The historical stock market patterns are analyzed by the experts and based on those patterns futuristic patterns are determined. In this analysis investors hope certain positive trends of the past for a particular stock will get repeated.

Sentimental Analysis

This analysis is completely dependent on the public opinion about a market situation or political situation of a country or merger of a big company with a smaller one. Basically the public mood is the detrimental factor here.

Fundamental Analysis

Fundamental Analysis is witnessed as the most effective analysis type. It takes into consideration the ‘Financial Well-Being’ of a company like Revenue, PAT, EBITDA and takes a call on how well the stocks of a company under scanner performs. Interestingly it is known that Warren Buffet follows the principles of Fundamental Analysis before investing should guide all newbies to embrace this analysis method over its counterparts.

What is the Learning Algorithm for future stock prices?

Different Machine Learning algorithms are popular for suggesting solutions to stock market predictions. Three important techniques are discussed here:

Classification Based Prediction
Reinforcement Learning for Prediction
Time Series based Prediction

Classification Based Prediction

Classification based Machine Learning algorithms are used in many fields like Healthcare, internet technology solutions, Banking, Automobiles, Pharma etc. Today there exists a wide array of prediction applications like Loan payment willingness predictors in Banks, Cancer Tumor detection, Self-driving car navigators, Drugs classification, E-mail Spam segregator etc. Let us understand a few basic features and terminologies present in Classification Based Prediction techniques.

Classifier is an algorithm employed to map the input data to a particular category, say for example Buy, Sell or Hold categories from Stock Trading perspective.

Classification Models helps users to draw a conclusion by employing the input data. It helps to predict the class labels or class category for the inputs. (* More about labels and categories in the following pointers.)

Multi-Class classification can be explained with an example: A Stock can be Unlisted or Listed but not both at the same time. Here Stock belongs to a Multi-class category.

Multi-Label classification this is just opposite to Multi-class classification. For example an Investment means it can be Direct Equity, Indirect Equity, Debt, Fixed Deposits, ETF etc. So here investment is a Multi-labelled value.

Binary Classification can be explained from Stock Trading point of view in terms of Market Trends. Market trends can have only two values either Bullish (Up-Trend) or Bearish (Down-Trend).

According to Marco Santos, a data scientist in Artificial Intelligence and Machine Learning, Fundamental analysis of Stock Market can be achieved using classification algorithms in three simple steps. Here is an illustration of the same:

The fundamental analysis relies on three values: 1) Past Quarterly Performance 2) Present Quarterly Performance 3)Future Quarterly Performance.

The Steps involved in BUY, HOLD or SELL decision making through Classification Model is depicted here:

Steps in Classification Based Prediction of Stock Market

Reinforcement Learning for Prediction

The classification algorithm and classifier models relies on historical data and employs supervised learning or unsupervised learning. But Reinforcement Learning or (RL) uses minimal historical data. Instead it learns from the environment through actions.

In layman’s terminology consider how a kid learns to play a game on the smartphone. The kid doesn’t expect any inputs from us. It does a series of trial and error actions.Using such actions it learns to operate the phone and in turn the game. The Kid doesn’t need any user manual or instruction set to operate a game. This is Reinforcement Learning.

Various entities used in RL are:

An Agent — The Investor in form of machine
Environment — Stock Market (Learning Environment)
Action — The decisions Agent takes by looking at the environment
Rewards — Profit/Loss that occurs based on the actions taken (Reward indicates whether the action taken was beneficial or not.)
States — Based on Rewards the agent moves to the next State.

This Humanoid (Robot) Learns to ‘RUN’ by taking different actions in Reinforcement Learning.

Time Series based Prediction

In common man’s language a Time Series based Prediction method can be explained by considering weather forecasting of a city. There exists two types of forecasting:

Single-Step or One-Step Forecasting
Multi-Step Forecasting

In One-Step Forecasting, Assume if the temperature of Bengaluru City is known from Monday to Wednesday. Then the System can predict temperature for Thursday (only one day.)

Multi-Step Forecasting, Assume if the temperature of Bengaluru City is known from Monday to Wednesday. Then the System can predict temperature for next two days or three days i.e. Thursday, Friday and Saturday.

So, Same principle can be applied to predict the stock market behaviour of forthcoming days!!

Conclusion

Predicting Gene mutation or Stock Market Trends are equally complex. When advances in Machine Learning have been successfully employed by genetic engineers and healthcare specialists why not Stock Markets speculation be systematically managed using Machine Learning techniques. Stock Markets in today’s world determine the well being of a country’s economy and all citizens are directly or indirectly connected to stock markets. Also, Many algorithms have been developed by Machine Learning and Data Science engineers to simulate the behaviour of Stock Markets. In this context Zeva Astras team thought of educating our readers and investors on the various possibilities Machine Learning has opened up in the arena of Investment Management and Trading.

Frequently Asked Questions

How is machine learning used in the stock market?

Machine Learning Models can be employed to automate various tasks in Stock Trading. For instance to Predict Future Risks and to Predict Stock Prices it is used predominantly.

What is the learning algorithm for future stock prices?

With advances in Machine Learning techniques Stock Market predictions are done Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are widely used to predict Stock Market trends. Every algorithm has its own ways of learning and predicting methods.

Can data science predict the stock market?

Recent research at Massachusetts Institute of Technology says financial data can be effectively predicted by Machines. In fact in the study Machines outperformed Humans by 57 percent.