A market index could be defined as a representation of a security market, market segment, or asset class of freely tradable market instruments. A market index is primarily made up of constituent marketable securities and is re-calculated on a daily basis.
There are basically two forms or variations of the same market index, i.e., one version based upon the price return known as a price return index, and one version based upon total return know as a total return index.
Ideally, a large number of market participants including investors and institutional funds gather and analyse vast amounts of information about security markets; however, doing so could be a very troublesome and tiring task as the work is both time consuming and data-intensive.
Thus, a large number of market participants prefer to use a single measure that could represent and consolidate a plethora of information while reflecting the performance of an entire security market of interest.
This is where market indexes play a major role as they are often a simple measure to reflect the performance of any underlying market of interest. For example, S&P500, NASDAQ, are believed to reflect the true performance and picture of the U.S. stock market in particular and U.S. economy in general.
Likewise, many indexes such as S&P/ASX 200 is believed to reflect the performance of the Australian stock market and so on.
Constructing a market index is almost similar to constructing a portfolio of securities as the construction of an index requires:
Target Market and Security Selection
The first and the primary decision in constructing an index is to identify the target market and select financial instruments which reflect the true nature of the underlying market.
The target market, which determines the investment universe and securities available for inclusion, could be based on any asset class, i.e., equities, fixed income, commodities, real estates or on any geographic region.
Once the target market is identified, the next step is to select securities which represent the true nature of the target market and decide on the number of securities to be included in the index.
Ideally, a market index could be of all securities in the target market or a representative sample of the target market.
For example, some indexes such as FTSE 100, S&P 500, S&P/ASX 200, fix the number of stocks to be included in the index while indexes like Tokyo Stock Price Index (or TOPIX) select and represents all of the largest stocks, known as the First Selection.
For such indexes, the included securities must meet some basic parameters like pre-decided market capitalisation, the number of shares outstanding, to remain in the index.
The weight allocation varies considerably among indexes depending upon the method of weight allocation, and it basically decides on how much weight each security in an index carry.
The method of weight allocation is one of the most important parts that investors need to understand thoroughly as it has a substantial impact on the value of an index.
Some of the most widely-used weight allocation methods are as below:
This method was originally used by Charles Dow to construct the Dow Jones Industrial Average (or DJIA) and is one of the simplest methods.
The price weight method determines the weight of each individual security of an index by dividing the price of the security by the sum of prices of all securities. In simple terms, each security gets the weight of its price in proportional to the total price of the index.
The primary advantage of this method is its simplicity; however, the method leads to arbitrary weights for each security as the method is highly sensitive to some market actions such as stock split.
As the name suggests, this method assigns equal weight to all securities in an index.
Just like equal weighting, the major advantage of this method is its simplicity; however, this method tends to underrepresent the value of large securities and overrepresent the value of smaller securities.
Market-Capitalisation method weight each constituent by dividing its market capitalisation with the total market capitalisation of the index, i.e., the sum of the market capitalisation of each constituent.
The market capitalisation could be determined by multiplying the number of outstanding shares of the security with its market price per share.
Rebalancing and Reconstitution
Rebalancing of a market index could be defined as the adjustment to the weights of the constituent securities. Depending upon the method of weighting an index, the weight of each individual security tends to change due to market actions or price appreciation and deprecation, in similar fashion to a stock portfolio requires scheduled rebalancing.
A majority of market indexes are rebalanced on a daily basis as price tends to often change regularly.
On the other hand, reconstitution could be ideally defined as the process to change the constituent of a market index.
As suggested above, many market indexes such as TOPIX require each constituent security to meet some parameters for the inclusion; however, due to market dynamics, various securities tend to get added or removed from an index time to time.
Originally, market indexes were created to provide a sense to investors on how a security market performed on a given day. However, with the development of the modern finance theory and growing numbers of indexes in the market, uses of market indexes have been expanded significantly.
Some of the major uses of market indexes are as below:
A market index is usually a collection of the opinion of market participants; thus, they reflect the attitude and behaviour of the market participants, making them one of most widely used tool to gauge the market sentiment.
Market indexes could serve as a proxy for systematic risk in many popular models such as the Capital Asset Pricing Model (or CAPM). The market portfolio, which represents the systematic risk of the market often uses a market index, as a proxy of the market portfolio as including the whole population or all stocks in the model could lead to wrong output, and it could be very costly and cost consuming.
Difference between actual and an expected return. For example, if a stock increased by 7% because of some update, but the average market only increased by 3% and the stock has a beta of 1, then the abnormal return was 4% (7% - 3% = 4%)
Refers to most commonly the realty sector and indicates the rate of sale of homes in a certain market during a given period of time. It is calculated as the ratio of the average number of sales in a month by the total number of available homes.
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.