Analysis of Stock Market Prediction
Abstract : Stock marketplace is a complicated and demanding system in which people make more money or lose their entire savings. The stock market prediction having high accuracy yields more profit for stock investors.
Stock market data is generated in a very large amount and it varies quickly every second. The decision making in stock marketplace is a very challenging and strenuous task of financial stock market. The development of efficient models for prediction decisions is very difficult because of the convolution of stock market financial data and should have high accuracy.
This study attempts to compare existing models for the stock market. Various Machine learning methods like Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and Convolution Neural Networks – Long Term Short Memory (CNN-LSTM) have been used for the comparison.
The models are estimated using conventional strategic measure: MAE (Mean Absolute Error). The measured low values indicates that the models are effective in predicting stock prices.
The intrinsic value of a company’s stock is the value determined by estimating the expected future cash flows of a stock and discounting them to the present, which is known as the book value. This is distinct from the market value of the stock, that is determined by the company’s stock price.
This market value of a stock can deviate from the intrinsic value due to reasons unrelated to the company’s fundamental operations, such as market sentiment. The fluctuation of stock market is violent and there are many complicated financial indicators.
Only few people with extensive experience and knowledge can understand the meaning of the indicators and use them to make good prediction to get fortune. Most people have to rely solely on luck to earn money from stock trading.
However, the advancement in technology, provides an opportunity to gain steady fortune from stock market and also can help experts to find out the most informative indicators to make better prediction.
Uncertainty and Volatility: The stock market is inherently volatile and influenced by numerous unpredictable factors, including economic data, geopolitical events, and market sentiment. This makes accurate prediction extremely challenging.
Overreliance on Historical Data: Many prediction models rely heavily on historical data. While past performance can provide insights, it does not guarantee future results. Market conditions and behaviors can change, making historical data less reliable.
Model Risk: Predictive models are based on certain assumptions and methodologies. If these assumptions are flawed or if the models are not updated to reflect new information, predictions can be misleading or incorrect.
Model Risk: Predictive models are based on certain assumptions and methodologies. If these assumptions are flawed or if the models are not updated to reflect new information, predictions can be misleading or incorrect.
In recent times stock market predictions is gaining more attention, maybe due to the fact that if the trend of the market is successfully predicted the investors may be better guided.
The profits gained by investing and trading in the stock market greatly depends on the predictability.
If there is a system that can consistently predict the direction of the dynamic stock market will enable the users of the system to make informed decisions.
More over the predicted trends of the market will help the regulators of the market in taking corrective measures.
Informed Decision-Making: Accurate predictions can help investors make more informed decisions by providing insights into potential future market movements. This can lead to better investment strategies and portfolio management.
Risk Management: Predictive models can help in identifying potential risks and opportunities. By forecasting market trends, investors can implement strategies to mitigate risks and capitalize on potential gains.
Optimized Trading Strategies: Algorithmic trading systems that rely on predictive models can optimize trading strategies by executing trades at the most favorable times, potentially increasing profitability and efficiency.
Market Efficiency: Predictions can contribute to market efficiency by integrating new information into asset prices. This helps ensure that prices reflect available information and that markets function smoothly.
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