Predicting the stock market with absolute certainty is an elusive goal because it's influenced by numerous unpredictable factors such as economic indicators, geopolitical events, investor sentiment, and even natural disasters. However, many analysts and researchers attempt to forecast stock market movements using various methods and tools.
I believe statistical models such as linear regression, ARIMA, GARCH are some of the most popular ones in analyzing historical price data and identify patterns or trends. There are few other typical analyses such as
(a) quantitative (maths (Black-Scholes) + statistics = pricing or Var for loses),
(b) fundamentals (company's financial health, quality, trends, and macroeconomic = intrinsic value and predict future stock prices),
(c) technical - past market data, primarily price and volume = identify patterns and trends = predict future price movements. Tools : moving averages, chart patterns, and technical indicators such as RSI and MACD and,
(d) sentiment - alternative news from social media, news, anything of public sentiment to gauge investor mood and predict market movements. NLP should be useful to extract insights from textual data.
And now here comes Artificial Intelligence (my gen used to call it machine learning - we don't have sophisticated tools like AI then) - the principles are much the same except the approach is far more advanced. Algorithms such as neural network, SVM, random forests (data mining) and make predictions. AI can handle non-linear relationships and capture complex patterns better than traditional statistical methods (although I may disagree with that :-) )
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