The Wall Street School

Predicting Stock Prices with Machine Learning and Financial Modelling

Over the recent years, machine learning and stock markets have crossed ways, creating a versatile sub-field of stock market prediction. With more and more financial institutions embracing machine learning, trading decisions are taken with ease. It not only helps the investors gain a competitive edge but also helps them understand market dynamics. 

Stock market predictions depend on a range of complex factors like current market trends, company-specific data, economic indicators, and so on. Machine learning and financial modelling chalk out such complicated factors and help an analyst uncover the patterns that traditional models otherwise overlook. 

In this guide, we will give you a brief overview of how machine learning can help you predict stock movements. 

First, Get To Know the Stock Market

In a stock market, exchange takes place in stocks and other such securities. It is a platform where individuals and financial institutions buy and sell shares of publicly traded companies. Such a market helps expand a company’s capital and an investor’s wealth. 

Understanding basic concepts like demand, supply, and market indices provides a foundation for analysing stock markets. However, predicting future stock price movements is complex due to the unpredictability of factors involved. This is where machine learning comes into play. 

Understand LSTM

Although there are multiple machine learning algorithms for stock market prediction, like moving average, data normalisation, random forest, linear regression, etc, LSTMs are ideal. 

But, for a layman, the question is what is an LSTM, and what makes it so crucial for stock market prediction? 

LSTM, or Long Short-Term Memory, is a type of RNN or Recurrent Neural Network that helps overcome the loophole of a traditional RNN. A traditional RNN comes with the issue of a ‘vanishing gradient’ where gradients become lower as one moves further into the network, which LSTM solves.

Understanding LSTM and RNN is easy if we take the example of human thinking. When you think about reading a book, you won’t start from scratch. You’d use your previous vocabulary knowledge to read that book. Likewise, RNN uses past data to accomplish its work and gives an output. 

However, LSTMs struggle to understand patterns or relationships spread over a long period of time. They’re good at remembering information from recent steps but become less effective as the gap between relevant information grows. So, if the relationship between data points spans a longer sequence than the LSTM can handle, it struggles to capture that relationship accurately.

But what’s good about LSTMs is that they can remember data for longer durations, making them ideal for stock market predictions. It can use past variations in market indices to provide more accurate results. 

Predict Stock Market Price With Machine Learning

The stock market is dynamic and volatile, affected by multiple factors. Like looking for a needle in a haystack, researchers or analysts look for patterns within massive datasets. Thus, accurate predictions can become a burden, and machine learning seems like a boon to financial institutions.

Here’s a brief rundown of the steps involved in machine learning-aided stock market prediction:

  1. Importing libraries or initial data sets. For scientific computations, you must import numpy, matplotlib for graph plotting, etc.
  2. The next step is loading the data set with columns like date, open, close, high, low, and volume. 
    1. Start and close here means the price at which that particular stock was introduced and closed in the market.
    2. High and low here means the highest and lowest price of the particular stock during its trading span.
    3. Volume indicates the total trading volume during the trading time.
  3. The next step is data normalisation, which means simplifying or putting the data to a general scale.
  4. Next, the user has to incorporate time variations into the data set.
  5. The final step is to generate an LSTM model. 

Summing Up

Accurate stock market prediction is important for multiple reasons, like generating funds for a company’s expansion, making a company more accountable towards their shareholders, and diversifying an investor’s portfolio.

If your goal is to dive into the financial market, then you can join the Online Financial Modeling and Valuations Course or the Offline Financial Modeling and Valuations offered by The WallStreet School. Some course highlights are:

  • Real-world case studies
  • Industry expert-led training
  • A robust curriculum curated by industry leaders
  • Practical training-based learning

Enrol today with The WallStreet School!

You can contact us via email or call us at (+91-9953729651) for more information. 

FAQs

  • What are some ideal methods for stock market prediction?

Mostly all stock prediction methods can be categorised into three categories, which are:

Machine learning

Fundamental analysis

Technical analysis or charting

  • What is an ideal algorithm for stock market prediction?

You can opt for the following algorithms:

Linear regression

Moving average

LSTM

Auto ARIMA

KNN or k-nearest neighbour

  • What is the basic requirement for a Financial Modeling and Valuations course?

For any Financial Modeling and Valuations course, the basic requirements would be:

Minimal computer knowledge

Clear financial concepts

Knowledge of financial ratios

Some knowledge of Microsoft tools

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