Fundamental vs Technical Analysis

21 Mar, 2025 posted by Himanshu

So, WHAT is the difference?

In the simplest of terms, Fundamental analysis tells us WHAT to buy (or sell) and Technical analysis tells us when to buy (or sell).

Now that we got the definition out of the way, let’s dive a little deeper. In this post we will focus mainly on the Fundamental part ….

Fundamental Analysis

Fundamental analysis involves evaluating a company’s intrinsic value based on its financial statements, competitive position, and various economic factors. Investors who use this method look at both quantitative measures (like revenue, earnings, and growth rates) and qualitative aspects (such as management quality and industry conditions).

Key Components:

Financial Statements

Analysts do a deep dive into the balance sheet, income statement, and cash flow statement. They consider ratios such as price-to-earnings (P/E), price-to-book (P/B), debt-to-equity, and dividend yield to gauge financial health of the stock. Fun suff. This is why they make lots of money.

Economic and Industry Factors

In addition to a company’s internal metrics, fundamental analysis extends
to industry trends, economic conditions, regulatory impacts, and market competition.

Intrinsic Valuation

The main goal is to determine a “fair value” for the stock. When the market price is lower than the intrinsic value, the stock may be considered undervalued, making it a potential buying opportunity.

Top-Down vs. Bottom-Up

Some analysts start with the overall economic outlook (top-down), then narrow down to sectors and finally individual stocks. Others begin by analyzing individual companies in detail (bottom-up), regardless of broader economic trends. Neither way is better than the other, it’s just a matter of personal preference or corporate guidelines.

Fundamental analysis is well suited for long-term investing. It requires patience, as the market takes time to reflect the underlying value of a company.

Technical Analysis

Technical analysis is based on the idea that all known information is already reflected in a stock’s price and volume history. It involves studying charts and using statistical indicators to predict future price movements.

Key Components:

Price and Volume Data:

Analysts use historical data to identify trends, support and resistance levels, and patterns such as head-and-shoulders, flags, and triangles. All this is visual. Analysts look at price charts of stocks and futures or forex. Some look at daily charts, some at weekly charts and some at 5-minute charts.

Indicators and Oscillators

These are mainly derived from the price itself but are visual in nature. Some of the more widely used ones are Moving averages, Relative Strength Index (RSI), MACD, Bollinger Bands, and a few others. These technical tools help determine momentum and potential turning points in the underlying stock and or futures.

Chart Patterns

These are patterns that show up in charts, whatever the timeframe of the chart.  Patterns like double tops, double bottoms, and cup-and-handle formations are used to forecast price movements based on recurring price behavior. Over time you will see all these in some posts.

Market Psychology

Technical analysis seeks to capture and quantify market sentiment. The idea is that price patterns often repeat because market participants tend to behave in predictable, emotional ways. This is something that none of us can argue against. We see this everyday not just in the markets but in politics, in sports, in movies, books and general life.

This method (technical analysis) is popular among short-term traders, swing traders, and day traders who are looking to capitalize on market movements that can occur over days or even minutes.

Quantitative Analysis

Quantitative analysis employs mathematical models, statistical techniques, and computer algorithms to evaluate securities and forecast market behavior. It relies heavily on data and often incorporates complex calculations that can sift through enormous datasets. This also requires a lot of CPU power and is thus relatively new.

Key Components

Mathematical Models

These can range from simple regression models to sophisticated algorithms that incorporate hundreds of variables. This is relatively new in the investment arena. Back in the day, this was mainly used by pharmaceutical firms during clinical trials where the amount of data generated is insane and by actuaries working for insurance firms.

Algorithmic Trading

Many quantitative analysts design automated trading systems that execute trades based on predefined criteria.

Back testing

Large amounts of historical data is used to test how a model or strategy would have performed in the past.

Risk Models

Quantitative methods often integrate risk management parameters, such as Value at Risk (VaR) and Monte Carlo simulations, to quantify potential losses.

Quantitative analysis is the choice for hedge funds, institutional investors, and high-frequency trading firms that depend on technical infrastructure and statistical analysis to make rapid decisions across multiple markets and asset classes. Regular folk usually stay away from this cause of the extremely large technology demands.

Sentiment Analysis

Sentiment analysis seeks to measure the mood or attitudes of investors by analyzing qualitative data from news sources, social media, analyst reports, and other public communications. It addresses the idea that markets are driven as much by emotion as by fundamentals or technical trends.

Key Components

Data Sources

Analysts aggregate data from tweets, news articles, financial reports, and investor sentiment surveys.

Natural Language Processing (NLP)

Sophisticated algorithms parse written text to gauge whether the
prevailing sentiment is positive, negative, or neutral.

Contrarian Signals

Extremely bullish or bearish sentiments can sometimes indicate that a market reversal is near, prompting contrarian investment strategies.

Market Psychology

Sentiment analysis attempts to predict when investor emotions might lead to overbought or oversold conditions. And this is done by quantifying mood.

In general, Sentiment analysis is useful for both short- and long-term investors who wish to complement traditional analysis techniques. It is especially valuable during periods of high volatility or uncertainty when emotions can drive market moves. Of course, we know that emotions are the main reason for market movements but in this case, we are talking about emotions running amok!