stock analysis subjects
Value investing
An investment strategy focused on buying securities undervalued relative to their intrinsic worth. Investors analyze financial metrics to find bargains, emphasizing margin of safety and long-term holding. It contrasts with momentum or speculative investing by prioritizing fundamental value over short-term market trends.Deep value investing
A more aggressive subset of value investing targeting stocks priced far below their intrinsic value. Deep value investors seek assets with significant discounts, often in distressed or unpopular companies, expecting the market to eventually recognize their true worth and generate substantial returns.Contrarian investing
An investment approach that goes against prevailing market trends by buying assets when others sell and selling when others buy. Contrarians believe markets overreact, creating opportunities by investing in undervalued or unpopular securities before the market corrects.Margin of safety (investment principle)
The practice of purchasing securities at a price significantly below their intrinsic value to reduce investment risk. This buffer protects investors from errors in analysis or market downturns, forming a key tenet of value investing.Fundamental analysis
Evaluating a security's value by examining related economic, financial, and other qualitative and quantitative factors. Analysts study company financial statements, market conditions, and economic indicators to determine fair value and make investment decisions.Dividend investing
A strategy focusing on purchasing stocks that pay regular dividends, providing income and potential capital appreciation. Dividend investors often seek companies with stable earnings and strong cash flows, valuing income generation alongside growth.Special situations investing
Investment in companies undergoing events like mergers, restructurings, or bankruptcies that create unique valuation opportunities. These situations often involve mispriced securities due to market inefficiencies around corporate changes.Stock market—Forecasting
The practice of predicting future stock market behavior using technical, quantitative, or qualitative methods. Forecasting aims to anticipate price movements, trends, or volatility to guide trading or investment timing.Technical analysis
Analyzing past market data, primarily price and volume, to forecast future price movements. It uses charts, patterns, and indicators, differing from fundamental analysis by focusing on market behavior rather than intrinsic value.Econometric modeling
Applying statistical models to economic data to test hypotheses or forecast trends. In finance, econometric models predict stock prices, volatility, or market risk based on historical data and economic indicators.Sentiment analysis
Using computational techniques to determine market sentiment from text data like news, social media, or reports. It helps predict market movements driven by investor psychology and collective mood.Market timing
The strategy of making buy or sell decisions by attempting to predict market highs and lows. Successful market timing requires accurate forecasts to maximize returns, but it is considered risky due to market unpredictability.Price trend analysis
Examining stock price movements over time to identify persistent directions or reversals. Trend analysis is foundational in technical analysis and helps investors decide entry and exit points.Predictive analytics in finance
Using data mining, machine learning, and statistical algorithms to forecast financial outcomes like stock prices or risk. This approach leverages big data and technology to enhance investment decision-making.Quantitative analysis
The use of mathematical and statistical methods to analyze financial markets and securities. Quantitative analysts develop models for pricing, risk management, and algorithmic trading, emphasizing data-driven decisions over qualitative judgments.Mathematical finance
An interdisciplinary field applying mathematical methods to solve problems in finance, such as option pricing and risk modeling. It underpins quantitative finance and supports the development of complex financial models.Statistical analysis
Techniques for collecting, reviewing, and interpreting data to uncover patterns and trends. In finance, it is used to test hypotheses, assess risk, and inform quantitative models.Algorithmic trading
The use of computer algorithms to automate trading decisions, often based on quantitative models. It enables high-speed execution and systematic strategies, minimizing human emotion in trading.Risk modeling
Quantitative methods to identify, assess, and prioritize financial risks. Models estimate potential losses, volatility, or exposure, guiding portfolio management and regulatory compliance.Factor investing
An investment strategy targeting specific drivers of returns, such as value, momentum, or size. Factor investing combines quantitative analysis to build portfolios with enhanced risk-adjusted performance.Data mining in finance
The process of extracting meaningful patterns and knowledge from large financial datasets. It supports predictive analytics, fraud detection, and automated trading strategy development.Computational finance
The application of numerical methods and algorithms to solve complex financial problems. It involves simulation, optimization, and modeling for pricing derivatives, risk analysis, and portfolio management.