Terms Beginning With 'a'

Aggregate market Value (AMV)

  • January 11, 2020
  • Team Kalkine

AMV indicates the size of the company, usually referred to in terms of market capitalization. So, it measures the combined market value of all the outstanding shares of a company. For example, a company with 200 million outstanding stock, currently trading at a share price of $1 will have an Aggregate Market Value of $200 Million (200 million outstanding shares * Current share price).

The stock exchange often places the minimum required value that needs to be maintained by the traded companies to avoid delisting or meet other conditions.

AVM is a key part of the Debt to equity ratio. Debt to Equity Ratio measures the ratio of debt and equity contribution to the company's capital. Generally, a higher debt to equity ratio may signify a higher risk to its potential investors.

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. 

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