What is Fundamental Analysis?
Fundamental Analysis is a method to evaluate the intrinsic value of a business through evaluating the financial, economic, qualitative and quantitative factors. It is usually applied when investors seek to invest in a business for a long-term. However, special situation investing, such as buy backs, rights issue, merger and acquisition, etc. also call for using knowledge of fundamental analysis to make profit in the short-term.
Just like groceries don’t come with a fixed price, equities are similar. Fundamental Analysis helps an investor to mute the short-term noises around the company and emphasise on the long-term prospects.
A person who is practising fundamental analysis has various sources of information, including historical data of the company, announcements released by the company, industry insights, economic projections related to the economy, in short both microeconomic and macroeconomic understating.
While daily information flows may affect the share prices, not everyone in the market is a trader because many believe in long term investing as well. The process of fundamental analysis requires understanding of accounting, business, and mathematics.
One should also understand the business models and how the business makes money and how the business is impacted by the underlying changes in the industry as well as in economy.
Consider you know onions are sold around $2 a kilogram, but when buying the seller asks you $4 for a kilogram. Either you will not buy from that seller, or there must be a reason behind the price hike like shortage of onion supply. Fundamental Analysis will help find such reasons with respect to share prices.
Since the fair value of bonds and equities is the discounted value of expected future cash flows, the cash flows of bonds are predetermined but not in equities, where cash flows fluctuate over the course of investment period.
To know more on bonds read: Fixed Income Securities – A look Into Bonds
Equity is a perpetual investment and non-redeemable, therefore, the emphasis of Fundamental Analysis remains on future earnings and estimates of growth in future earnings. There is a wide perception around fundamental investors that share price should reflect intrinsic value over the long-term despite mispricing in the short-term.
Moreover, returns on stock not only reaped from picking good stocks but also investing at the right price. Just like when buying groceries, you ask how much, it is also favourable to ask when investing in stocks. Readers should understand that, shares are a right to a part of business and not just a piece of paper. Fundamental Analysis contradicts the Efficient Market Hypothesis, which argues that share prices incorporate all relevant information.
More on buying cheap stocks: Investment Strategy Of Picking Undervalued Stocks
Top-down approach: It includes analysing macroeconomic indicators such as GDP growth rates, interest-rates, inflation, unemployment, currency exchange rates etc. One usually looks at industries that are in a favourable position based on macroeconomic indicators.
For more on this approach read: A lens over Top-Down Investing Strategy
Bottom-up approach: Industry analysis further requires research into the state of the industry like growth rates, competition, regulatory development, new entrants, disruptive technology, structural change. But more attention is given to individual businesses and potential effects as a result of underlying changes.
What is Economic, Industry and Company (EIC) analysis?
Economic Analysis: In this analysis, the investor seeks to evaluate microeconomic as well as macroeconomic factors. Within microeconomic factors, the emphasis is on behaviour of individuals and their spending decisions at given price levels. Consumer demand is also analysed while microeconomic also includes study of firm, including costs, profits, competition.
To Understand behaviour of individuals understanding Behaviourial economics is paramount.
Under macroeconomic study, the analyses of the trend in unemployment rates, growth rates, inflation, investment rates, savings rate, consumption, income and other major macroeconomic variables that could impact the potential investments is undertaken.
Here in this article, global energy consumption is discussed: EIA Anticipates 50 per cent Surge in Global Energy Consumption
It also includes study of fiscal policies developed by the Government as well as monetary policy by the Central Bank. And, the analysis also extends to international trade, exchange rates, trade deficits, geopolitical environment, global economy, especially nations with close relations.
More on this read: Monetary Policy vs. Fiscal Policy
Industry Analysis: Under this analysis, the investor emphasises on the trends in a particular industry and their impacts on individual companies. Industry analysis allows to gain valuable insights about the industry and understand the position of an individual company compared to its peers.
Michael Porter’s five forces model is a widely used template to study an industry. These five forces include: threat of new entrants, threat of established rivals, bargaining power of buyers, threat of substitutes, bargaining power of sellers.
Political, Economic, Socio-cultural, Technological, Legal and Environmental (PESTLE) Analysis is also widely used when evaluating an industry from an investing perspective. As the name suggests, this analysis is related to the study of all those areas that could affect the business.
Investors also monitor the regulatory developments in an industry because it is crucial to know the rules of the game. Also, there remains a possibility that change to regulatory environment can have a material impact on companies.
On the topic of regulation: Banking regulator tightens dividend paying capability, Fitch downgrades Banks
Company Analysis: In the analysis, the investor conducts research on an individual company and emphasises on specific details like business models, strength, competitive advantages, risks, opportunities etc.
It is important to know the cash-generation intensity of the business as well as how the company makes profit or what are margins of the business. What are the products or services of the company and who are the customers of the business.
Further, it also include research on the quantitative aspects of the company, including net income, operating expenses, assets, debt, liabilities, cash flows, earnings as well as growth history of the business.
Some basic valuation techniques
Dividend yield: Dividend yield is one of the basic valuation techniques to express, in percentages, the amount of dividends paid by a company in a given annual year relative to its current price. Dividends are paid by companies after meeting their necessary obligations, including taxes, interest costs, suppliers etc.
Earning yield: Expressed in percentages, it is the earning of a company in a given annual year in relative to its current price. Earnings or net income of the business is the amount of money the business has made as per accounting principles since actual cash realised could be otherwise.
Price-to-earnings ratio: P/E ratio is one of the widely used valuation techniques to indicate the premium given by the market to the company based on its earning potential. It is calculated as the current price of the stock/earnings per share.
Good read: Understanding Price-Earnings Ratio
Price-to-book ratio: P/B ratio is calculated by dividing the price of the stock by the book value per share of the company. It is primarily used to evaluated stocks based on their premium over the book value or discount over the book value.
Good fundamental analysis entails reading and synthesising lot of material and using a balanced approach to come to an investment decision. Seasoned investors read a lot of material including Annual reports, industry reports, newspapers, business journals, conference call transcripts, to name a few. Having a circle of competence is essential and for building a solid circle of competence one should also consider learning ideas from other domains to become a rounded thinker which is essential for good long-term fundamental analysis.
A Professional who conducts audits and Financial Statement Analysis. They have the required competency with educational or professional certifications to use titles related to accountant
Darvas Box system Every great trader/investor in the history of the markets had a specific method to approach the markets, which eventually led them to create a good fortune, Darvas Box system is one such method. It is a trend following strategy developed by Nicholas Darvas in the 1950s to identify stocks for good upside potential. This is one of the few methods to trade the markets which uses the combination of both the technical analysis and fundamental analysis for a much more refined decision. The fundamentals were used to identify the stocks, and technical analysis was used to time the entry and exits. Who was Nicholas Darvas? Nicholas Darvas was arguably one of the greatest stock traders/investors during 1950s – 1960s, but surprisingly he was a ball dancer by profession and not a professional stock trader. Even while trading and building his fortune, he was on a world tour for his performances in many countries and took up trading as a part-time job. In November 1952 he was invited to a Toronto Nightclub for which he received an unusual proposition of getting paid in shares by the club owners. At that time, all he knew was there is something called stocks which moves up and down in value, that’s it. He accepted the offer and received 6k shares of a Canadian mining company Brilund at 60 cents per share, with the condition that if the stock falls below this price within six months, then the owners would make up the difference. This was the introduction of a professional ball dancer to the stock market. Nicholas Darvas couldn’t perform at the club, so he bought those shares as a gesture. Within two months, Brilund touched $1.9, and his initial investment of $3000 turned to $11400, netting in almost three times of his investment. This triggered a curiosity into the stock markets, and he started to explore trading. Origin of the Darvas Box theory Initially, he was trading on his broker’s recommendation, tips from wealthy businessmen, he even approached some advisory services or any source that he could get his hands on for the tips, but all led him to losses. After losing a lot of money, he decided to develop his own theory, and after a lot of trial and error, his observations and continuous refinements he eventually invented his theory “The Box Theory”. So what exactly is the Box Theory? Fundamentals Analysis As stated earlier, the box theory uses a judicious bend of both the technical and fundamentals. Darvas believed that in order to spot a good stock or even a multibagger, there should be something brewing up in the respective sector as a whole or some major fundamental change in that specific company. Generally, the fundamentals that Darvas used to study were on a broader sector level, and not the company-specific fundamentals. Even for the specific company Darvas used to look from a general perspective like, is the company launching a new product which could be a blockbuster hit. He completely refrained from looking at numbers and financial statements as his initial experiment with ratios and financial statements didn’t yield any good result. To know more on the three financial statements read: Income Statement (P&L) Balance Sheet Cash Flow Statement Technical Analysis Darvas was a big believer in price action and volume of the stock. He believed if some major fundamental changes were to take place in a company, this soon shows up in the stock price and its volume of trading as more people get interested in buying or selling the stock. With his observations here realized by just observing the price action, he can participate in the rally which gets triggered by some major fundamental development without actually knowing about the change. Using the box theory, Darvas used to scan stocks based on rising volume as he needed mass participation in the rally. Also, he only picked up those stocks that were already rising. His theory is all about “buy high, sell higher” instead of the conventional belief of “buy low, sell high”. After the stock satisfies both the parameters of increasing price and volume with major underlying fundamental change, Darvas looks to enter the stock. Good read on momentum trading. How and where to enter? Major part of the box theory is based on entry and exit levels. To enter a stock, Darvas looked for a consolidation phase preceded by a rally. A consolidation phase is the price action wherein the price moves up and down in a tight range, that is, a non-directional move. He would then mark the high and low of the consolidation phase with the horizontal line, essentially making it a box-like structure, hence the name “Box Theory”. The high point is called the ceiling, and low is called the floor. Whenever the stocks break above the ceiling, Darvas would look to buy one tick above the ceiling with one tick below floor as a stop-loss point. Pyramiding Darvas discovered early on, in order to become successful in the market your winning bets should yield much more profit than the loss in the losing bets. This led him to do pyramiding in his winning trade, which is clearly defined in the box theory. Pyramiding means to increase the existing position if the stock is going in the favour, which leads to a much higher profit in the winning trades. According to the box theory, the repetition of the entry criterion is the new signal for adding onto the existing position. In other words, after a position, if the stocks stage the same setup, that is, a consolidation after a rally, then the break above the ceiling of this new box would signal to increase position with the revised stop loss of 1 tick below the new floor. In any case, whenever the stock falls below the current floor, the entire position would we sold off at once. This is the only exit condition in the box theory, and there is no method of booking profit upfront as Darvas believed in holding on to a rising stock. The only way to book profit is to let the stock to take out the revised stop loss.
What is Data Analytics? Data Analytics involves a set of quantitative and qualitative approaches and processes that can be used to determine useful information for business decision-making. The process involves various patterns and techniques, including: extracting a raw database, and categorising it to identify and analyse the behaviour, relation and connection of the results. The ultimate goal is to acquire valuable information in order to make decisions for businesses’ benefit and productivity. In today's competitive times, most companies chalk out their business plan with the help of data analytics. With organisations becoming customer-service oriented, data analytics has become a critical tool to reach the target audience in an effective manner while understanding their requirements. Once data is collected, it is analysed and stored according to organisations’ requirements. The data analysis process has multiple layers involved, and its diverse modules are not just used in businesses but also in science and social science fields. Rather than making decisions based on just available information, one can utilise data analytics in examining the data in standard ways and churning out the results from it. It has been observed that companies generally make decisions based on past references and future outcomes. Data analytics appears advantageous in providing useful information towards this end. Why do Businesses Need to Use Data Analytics? Many data analytics’ tools and softwares are readily available these days. These systems use resources, such as machine learning algorithms and automation. Data scientists and analysts are counted amongst the leading career options as well. These professionals use data analytics techniques while researching and presenting useful information for businesses to increase productivity and gain. The process helps companies understand their target audience and determine effective ways to cater to their needs. Data analytics can further be used to design strategies in marketing campaigns and promotions and also evaluate its results. Data analytics is primarily used in business-to-consumer (B2C) processes to boost business performance and improve the bottom line. There are data collection firms which gather consumer information and provide it to the businesses so that the companies can effectively influence the market. The collected data is not only used to understand and impact consumer behaviour but also determine market economics and its practical implementation. The data used in the process can be either be data collected in the past or newly updated data. There are various methods to manage consumer and market information. It may come directly from the customers or potential customers or can be purchased from the data collection vendors. The data primarily includes audience demographics, behavioural patterns and expense threshold. How Can Data Analytics be Effectively Used in Business Processes? Data analytics is an ever-evolving technique. Earlier, the data was collected manually, but with the rise of internet and technology, data is now collected online with the help of search engines and social media platforms. Subsequently, the information is analysed through available software. Here is a list of some key steps businesses can follow to leverage the benefits of data analytics: Set up crucial metrics: This step reduces the guesswork and provide data-based insights to the businesses. Before embarking on the data analytics process, it is vital to determine the goal for your business. Analysing customer data helps in understanding conversion rate, consumer spending ability, demographics etc. The results of the analysis can support the businesses while making decisions in launching an advertising or marketing campaign. Similarly, the unwanted data can be erased from the database so that the brands can focus on their right target audience. The relevant metrics will change the course of the company and push it in the right direction. Moreover, once your key metrics are set, even when the market conditions change in the future, you can adjust the metrics according to the requirement and achieve the results. Set a clear module: It is important to examine the data correctly by avoiding common mistakes. An ambiguous path can produce confusing insights while wasting time and energy of businesses. Therefore, it is recommended to draw a clear goal in order to achieve actionable insights. The data, when collected from different sources, need to be merged accurately in the analytics model. Businesses can modulate their data analytics systems either manually or through automation. There are various data modelling practices available in the market. The best use of these techniques can simplify the process of modelling complex data. Data visualisation: Once the relevant data is collected, and the modules are set to analysis, visualisation of that data will assist in understanding the information correctly. When the businesses have an acute knowledge of what their target audience wants, they can then focus on strategising advertisement and content, which matches the consumers' interest. It is the critical step in the data analytics process to distinguish insights from information. Not everyone is comfortable dealing with numbers. Hence, ensuring that key stakeholders understand essential points and information can be displayed in a visually appealing format seem crucial to capitalise on data effectively. Right tools to implement insights:Having access to data and insights can get overwhelming. However, the information is worthless if the businesses are unable to implement it successfully. While it is important to collect the data and set critical metrics and modules to analyse it, it is also imperative to translate the data into practical actions. The eventual goal is to improve sales or grow profits. It is ultimately in the marketers' hands to transform the gained insights into a successful implementation. The consumers' insights should be incorporated while establishing a marketing plan and at all decision-making steps.
What is Data Mining? Data mining is a process that facilitates the extraction of relevant information from a vast dataset. The process helps to discover a new, accurate and useful pattern in the data to derive helpful pattern in data and relevant information from the dataset for organization or individual who requires it. Key Features of data mining include: Based on the trend and behaviour analysis, data mining helps to predict pattern automatically. Predicts the possible outcome. Helps to create decision-oriented information. Focuses on large datasets and databases for analysis. Clustering based on findings and a visually documented group of facts that were earlier hidden. How does data mining work? The first step of the data mining process includes the collection of data and loading it into the data warehouse. In the next step, the data is stored and managed on cloud or in-house servers. Business analyst, data miners, IT professionals or the management team then extracts these data from the sources and accordingly access and determine the way they want to organize the data. The application software performs data sorting based on user’s result. In the last step, the user presents the data in the presentable format, which could be in the form of a graph or table. Image Source: © Kalkine Group 2020 What is the process of data mining? Multiple processes are involved in the implementation of data mining before mining happens. These processes include: Business Research: Before we begin the process of data mining, we must have a complete understanding of the business problem, business objectives, the resources available plus the existing scenario to meet these requirements. Having a fair knowledge of these topics would help to create a detailed data mining plan that meets the goals set up by the business. Data Quality Checks: Once we have all the data collected, we must check the data so that there are no blockages in the data integration process. The quality assurance helps to detect any core irregularities in the data like missing data interpolation. Data Cleaning: A vital process, data cleaning costumes a considerable amount of time in the selection, formatting, and anonymization of data. Data Transformation: Once data cleaning completes, the next process involves data transformation. It comprises of five stages comprising, data smoothing, data summary, data generalization, data normalization and data attribute construction. Data Modelling: In this process, several mathematical models are implemented in the dataset. What are the techniques of data mining? Association: Association (or the relation technique) is the most used data mining technique. In this technique, the transaction and the relationship between the items are used to discover a pattern. Association is used for market basket analysis which is done to identify all those products which customer buy together. An example of this is a department store, where we find those goods close to each other, which the customers generally buy together, like bread, butter, jam, eggs. Clustering: Clustering technique involves the creation of a meaningful object with common characteristics. An example of this is the placement of books in the library in a way that a similar category of books is there on the same shelf. Classification: As the name suggests, the classification technique helps the user to classify and variable in the dataset into pre-defined groups and classes. It uses linear programming, statistics, decision tree and artificial neural networks. Through the classification technique, we can develop software that can be modelled so that data can be classified into different classes. Prediction: Prediction techniques help to identify the dependent and the independent variables. Based on the past sales data, a business can use this technique to identify how the business would do in the future. It can help the user to determine whether the business would make a profit or not. Sequential Pattern: In this technique, the transaction data is used and though this data, the user identifies similar trends, pattern, and events over a period. An example is the historical sales data which a department store pulls out to identify the items in the store which customer purchases together at different times of the year. Applications of data mining Data mining techniques find their applications across a broad range of industries. Some of the applications are listed below: Healthcare Education Customer Relationship Management Manufacturing Market Basket Analysis Finance and Banking Insurance Fraud Detection Monitoring Pattern Classification Data Mining Tools Data mining aims to find out the hidden, valid and all possible patterns in a large dataset. In this process, there are several tools available in the market that helps in data mining. Below is a list of ten of the most widely used data mining tools: SAS Data mining Teradata R-Programing Board Dundas Inetsoft H3O Qlik RapidMiner Oracle BI