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Developing a Stock Price Prediction Model Using Machine Learning
Introduction
Predicting stock prices is an important problem in finance, and utilizing a company’s financial statement data to predict stock prices is valuable information for many investors. Using machine learning techniques to develop a stock price prediction model is one way to improve accuracy and efficiency.
Data Collection
The data required for stock price prediction is financial statement data and stock price data. Financial statement data includes income statements, balance sheets, and cash flow statements, while stock price data includes daily stock prices or quotes. It is necessary to collect, clean, and process these data.
Data preprocessing
It is necessary to process the collected data into a form that machine learning algorithms can learn. This process involves handling missing values in the data, detecting and removing outliers, and performing preprocessing tasks such as normalizing or standardizing the data to facilitate the training of the model.
Build the model
Once the data preprocessing is complete, the next step is to build the machine learning model. Some common algorithms used for stock price prediction include linear regression
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