{"id":3518,"date":"2024-03-01T07:00:09","date_gmt":"2024-03-01T01:30:09","guid":{"rendered":"https:\/\/www.thewallstreetschool.com\/blog\/?p=3518"},"modified":"2024-03-01T07:00:09","modified_gmt":"2024-03-01T01:30:09","slug":"predicting-stock-prices-with-machine-learning-and-financial-modelling","status":"publish","type":"post","link":"https:\/\/www.thewallstreetschool.com\/stg-new\/predicting-stock-prices-with-machine-learning-and-financial-modelling\/","title":{"rendered":"Predicting Stock Prices with Machine Learning and Financial Modelling"},"content":{"rendered":"

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.\u00a0<\/span><\/p>\n

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.\u00a0<\/span><\/p>\n

In this guide, we will give you a brief overview of how machine learning can help you predict stock movements.\u00a0<\/span><\/p>\n

First, Get To Know the Stock Market<\/b><\/h2>\n

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\u2019s capital and an investor\u2019s wealth.\u00a0<\/span><\/p>\n

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.\u00a0<\/span><\/p>\n

Understand LSTM<\/b><\/h2>\n

Although there are multiple machine learning algorithms for stock market prediction, like moving average, data normalisation, random forest, linear regression, etc, LSTMs are ideal.\u00a0<\/span><\/p>\n

But, for a layman, the question is what is an LSTM, and what makes it so crucial for stock market prediction?\u00a0<\/span><\/p>\n

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 \u2018vanishing gradient\u2019 where gradients become lower as one moves further into the network, which LSTM solves.<\/span><\/p>\n

Understanding LSTM and RNN is easy if we take the example of human thinking. When you think about reading a book, you won\u2019t start from scratch. You\u2019d use your previous vocabulary knowledge to read that book. Likewise, RNN uses past data to accomplish its work and gives an output.\u00a0<\/span><\/p>\n

However, LSTMs struggle to understand patterns or relationships spread over a long period of time.<\/span> 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.<\/span><\/p>\n

But what\u2019s 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.\u00a0<\/span><\/p>\n

Predict Stock Market Price With Machine Learning<\/b><\/h2>\n

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.<\/span><\/p>\n

Here\u2019s a brief rundown of the steps involved in machine learning-aided stock market prediction:<\/span><\/p>\n

    \n
  1. Importing libraries or initial data sets. For scientific computations, you must import numpy, matplotlib for graph plotting, etc.<\/span><\/li>\n
  2. The next step is loading the data set with columns like date, open, close, high, low, and volume.\u00a0<\/span>\n
      \n
    1. Start and close here means the price at which that particular stock was introduced and closed in the market.<\/span><\/li>\n
    2. High and low here means the highest and lowest price of the particular stock during its trading span.<\/span><\/li>\n
    3. Volume indicates the total trading volume during the trading time.<\/span><\/li>\n<\/ol>\n<\/li>\n
    4. The next step is data normalisation, which means simplifying or putting the data to a general scale.<\/span><\/li>\n
    5. Next, the user has to incorporate time variations into the data set.<\/span><\/li>\n
    6. The final step is to generate an LSTM model.\u00a0<\/span><\/li>\n<\/ol>\n

      Summing Up<\/b><\/h2>\n

      Accurate stock market prediction is important for multiple reasons, like generating funds for a company\u2019s expansion, making a company more accountable towards their shareholders, and diversifying an investor\u2019s portfolio.<\/span><\/p>\n

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