Stock price movement prediction based on the historical data using machine learning
ABSTARCT :
predicting stock market is one of the challenging tasks in the field of computation. Physical vs. physiological elements, rational vs. illogical conduct, investor emotions, market rumors, and other factors all play a role in the prediction.
All of these factors combine to make stock values very fluctuating and difficult to forecast accurately. We look towards data analysis as a potential game-changer in this field.
When all information about a company and stock market events is promptly available to all stakeholders/market participants, according to efficient market theory, the impacts of those occurrences are already incorporated in the stock price..
As a result, it is stated that only the historical spot price accurately represents all other market events and may be used to predict future movements. As a consequence, we infer future trends using Machine Learning (ML) techniques on historical stock price data, using the previous stock price as the final representation of all influencing factors.
Machine learning techniques can reveal previously undiscovered patterns and insights, which can subsequently be used to make accurate predictions. Using the LSTM (Long Short-Term Memory) model and the company's net growth calculation approach, we create a system for assessing and projecting a company's future development
EXISTING SYSTEM :
The stock market constantly fluctuating in the fast-paced financial sector, finding it challenging to formulate reliable stock predictions. Investors and traders feel compelled to seek out creative approaches to reduce investment risks and maximize profits because of the possibility of generating substantial profits.
Trading stocks on the stock market is one of the major investment activities. In the past, investors developed a number of stock analysis method that could help them predict the direction of stock price movement. Modelling and predicting of equity future price, based on the current financial information and news, is of enormous use to the investors .
DISADVANTAGE :
s. Machine learning (ML) is becoming a strong tool for intelligent investment management that can help investors do better at buying and selling stocks. It can also be used to make decisions and handle portfolios in new ways.
This research investigation investigates three distinct models: Facebook Prophet, which is intended for time series forecasting and focuses on weekly, yearly, and seasonal trends; Long Short-Term Memory (LSTM), a type of Recurrent Neural Network that can forecast values in the future by learning patterns from sequential data; and the Random Forest Regressor model from Ensemble Learning, that develops multiple decision trees during training to improve overall accuracy and generalization
PROPOSED SYSTEM :
s. Machine learning (ML) is becoming a strong tool for intelligent investment management that can help investors do better at buying and selling stocks. It can also be used to make decisions and handle portfolios in new ways.
This research investigation investigates three distinct models: Facebook Prophet, which is intended for time series forecasting and focuses on weekly, yearly, and seasonal trends; Long Short-Term Memory (LSTM), a type of Recurrent Neural Network that can forecast values in the future by learning patterns from sequential data; and the Random Forest Regressor model from Ensemble Learning, that develops multiple decision trees during training to improve overall accuracy and generalization
ADVANTAGE :
The Long Short-Term Memory (LSTM) model is a sophisticated data mining technique designed to identify fundamental trends and analyse complex patterns in unstructured data. LSTM model is implemented as a neural network composed of LSTM units, each consisting of a cell state, input gate, forget gate and output gate. These essential components collaboratively interact with the input layer, incorporating features such as historical stock prices, date/time information, and relevant market indicators.
The input gate determines the information to be stored in the long-term memory cell, the forget gate decides what to retain or discard from the past, and the output gate shapes the final output based on the current input and stored information.
This dynamic interaction enables LSTMs to effectively capture and retain crucial patterns and dependencies in sequential data, establishing their suitability for predicting stock market prices. The specific number of LSTM units is subject to variation based on task complexity and dataset characteristics, often determined through iterative experimentation and turning in the model development process
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