Fundamental Analysis and Technical Analysis

Published at 1710901619.284457

Fundamental analysis and technical analysis are two popular methods used in stock investment. They are to evaluate the potential of financial instruments and make investment decisions. However, these two methods have key differences in their approaches and objectives.

Fundamental Analysis

Fundamental analysis (FA) involves evaluating publicly available information and building pricing models to estimate the intrinsic value of an asset.

In the context of stock investment, fundamental analysis includes a detailed evaluation of a company’s business model, financial conditions, and impacting macroeconomic factors. The main objective is to identify companies with better growth prospects than what the market price reflects. Also, analysts or investment managers create asset pricing models to estimate the intrinsic value. If the current price is significantly lower than the estimated intrinsic value, portfolio managers may consider increasing the allocation to the stock in their investment portfolio.

Fundamental analysis includes part or all of the following process:

  • Business analysis

    • Determine the business model: understanding how a company generates revenue, profit, and competitive advantages.

    • Analyze financial reports: using financial ratios to evaluate operational efficiency, financial conditions, and potential profit of a company.

    • Analyze business leadership: assessing the experience and capabilities of the business leaders.

  • Sector and competitor analysis

    • Identify the main competitors: identify companies that have similar products or services and assess their competitiveness.

    • Identify industry trends: identify growth trends, changes in the industry, and their impacts on the company.

  • Forecasts. Use financial models and historical data to forecast future revenues, expenses, and cash flows.

  • Stock valuation. Use valuation models such as discounted cash flow, dividend discount, price-to-earnings ratio model to determine the intrinsic value of stocks.

Technical Analysis

Technical analysis (TA) is a method to predict the future price trend of an asset based on its historical data on prices and trading volumes. Technical analysts use various types of charts, graphs, indicators, and patterns to identify potential buying and selling points.

Technical analysis is based on the underlying logic:

  • Supply and demand determine prices.

  • Changes in supply and demand - both in volumes and price levels - can cause price changes.

  • Past action can be used to predict future price trends.

With this logic, this approach can be used for any financial instruments (such as stocks, bonds, commodities, currencies, and futures) as long as it is traded in the free market system. A free market is defined as a system where buyers and sellers engage in voluntary trade without external interference or hindrance. Also prices are set based on the interaction between supply and demand in real time.

In practice, however, technical analysis is mostly effective in highly liquid markets. These markets have a large trading volume, providing ample data for analysis and enhancing prediction accuracy. Additionally, in this environment, small transactions have less impact on prices. This makes technical signals more recognizable, minimizes noise, and increases the reliability of analysis.

The main tools in technical analysis are as follows:

  • Charts: These visual representations show price and trading volume data over time. Common chart types include line charts, candlestick charts, and bar charts. They help traders visualize historical price movements and identify patterns like head and shoulders pattern, diamond pattern, cup and handle pattern.

  • Indicators: Indicators are technical analysis tools built on math formulas. They use price and volume data on charts to offer investors insights into market trends, momentum, and potential reversal signals. Some popular technical indicators include Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Bollinger Bands.

Differences Between Fundamental and Technical Analysis

Fundamental analysis and technical analysis differ primarily in the data that analysts use. Technical analysts rely on price and trading volume data, which are considered objective because they reflect market transactions. In contrast, fundamental analysts rely on business financial statement data. These are entirely objective because they contain accounting estimates and assumptions during aggregation. Moreover, financial statements are potentially subject to fraud, distorting the analytical significance of the data. For instance, businesses may hide losses and exaggerate profits in their financial reports.

Regardless of the data or strategy, analysts must rely on historical information to make informed judgments and predictions. Both fundamental and technical analyses involve a subjective aspect from the user. Technical analysts interpret price charts and technical indicators. It means they need to be able to recognize patterns and make predictions about future market trends. On the other hand, fundamental analysts assess a business’s financial conditions, growth prospects, and risk factors based on information from financial statements and other sources.

Technical analysis requires less in-depth knowledge of financial instruments compared to fundamental analysis. In stock investing, technical analysis focuses on price charts, trading volumes, and indicators to identify movement trends, support/resistance levels, and buy/sell signals. While they may not research deeply into a company’s business, it’s essential to recognize that market volatility and external factors persist. Therefore, when using technical analysis, understanding the company’s business model, financial conditions, and risk factors remains important for informed investment decisions.

Summary of the key differences between fundamental and technical analysis:

 

 

 

 

 

 

 

 

 

 

 

Fundamental and Technical Analysis in Algorithmic Trading

Both fundamental and technical analysis can help algorithmic traders in formulating algorithmic hypotheses. Fundamental analysis guides decisions on which stocks to buy, helping to build stock selection strategies and determine each stock’s weight in the portfolio. On the other hand, technical analysis informs strategies for opening positions, taking profit and stopping losses. These factors are important components in developing a trading algorithm. Algorithmic traders can flexibly use fundamental and/or technical analysis depending on their goals in long-term investment or short-term scalping.

Also, it’s essential to recognize that while a pricing model derived from fundamental or technical analysis may be useful in a specific market, it’s not universally applicable to every market at all times. Regardless of the method used, algorithmic traders should conduct comprehensive testing and optimization to establish a solid scientific foundation and confidence level before applying algorithms to real trading.

With fundamental analysis, it can be a challenge for algorithmic traders to collect and process data. Fundamental analysis mainly uses financial statement data. However, this data often has the following problems:

  • Data quality:

    • Accuracy: financial statements can be manipulated or erroneous for different reasons.

    • Completeness: some businesses may not disclose sufficient financial information, or even disclose inaccurate information.

    • Comparability: businesses may use different account methods and standards that have changed over different periods, making data comparison more challenging.

  • Accessibility:

    • Financial data can be published in various formats, making data collection and analysis difficult.

    • Collecting financial data can be expensive, especially for historical data or data from unlisted businesses.

    • Financial statements may not be fully explained, making it difficult to understand and use the data. Financial data analysis requires an algorithmic trader with specialized knowledge of accounting and finance.

Compared to fundamental analysis, technical analysis data is easier to collect and process. However, tick price and volume data still require integrity. In the Vietnamese stock market, integrity can be achieved in the derivatives market but there may still be limitations in the equity market.

In algorithmic trading, the contrasting nature of fundamental and technical analysis lies in their underlying assumptions. For instance, growth in after-tax earnings is usually a good sign. However, whether the stock price rising beyond its peak is a good sign or not is still difficult to explain scientifically. The answer can only rely on statistics and data models. Therefore, algorithmic traders should build a habit of understanding underlying assumptions before forming algorithm trading hypotheses.