Tag: University of Mumbai BMS Notes

When a customer is considering a purchase that is more expensive or requires some kind of monthly commitment they will usually spend more time thinking about it. They may want to research different options, talk to a friend or family member about it, and weigh the pros and cons of going through with the sale.

In business, this process is often portrayed as a sales funnel with more and more people dropping off as they move further into the funnel.

At each point during this process, the customer will go through a specific thought pattern. To help your customer follow through with the sale, you must understand what their needs are at each point.

Let’s look at the six stages of the buying process below:

Stage 1: Problem Recognition

This is the most important step in the decision process because your customer has to realize they need your product before a purchase can take ever place. This presents you with both the opportunity and the challenge of identifying with your customer. The best strategy is to articulate their problem in your marketing efforts.

With traditional marketing or PR, this can be done through advertising: having an ad that explains what the customer’s problem is, and how the product or service can solve it.

With any online business, on the other hand, the best way to influence the “problem recognition” stage is through content marketing. With the right content, you could identify with your audience, articulate their needs, and offer helpful resources and tools.

Stage 2: Information Search

Now the customer will begin searching for information to help them find the best solution to their problem. Most people will immediately turn to friends, family members, and colleagues for recommendations.

While you can’t really talk the above-mentioned friends or family members into endorsing your product, there are several things you could do.

• Focusing on the Product: If your product is really good, people are going to start being your brand advocates, and you won’t even have to pay them.
• Build Authority: This one’s pretty generic, and translates into regular marketing. It could mean working on your company web presence, for example, so that it’s easy for your customers to find you and learn more about your product.
• Reviews & Partnerships: Other than friends and family, there’s something else that’s extremely helpful in influencing decision-making: the influencers. Establishing connections with experts in your field (or bloggers, review websites, etc.) will help you stand out.

Stage 3: Evaluation of Alternatives

Although some people will come to a quick decision, most customers will not settle for the first solution they find. They will evaluate several different options and the possible benefits or drawbacks to each. And even if your company has the best product to meet their needs, they still may decide to go with someone else.

So, the one thing you could do at this stage is to offer a lot more value than your competition & communicate that with your customers. This can be easier in some industries (software, for example, where you can add more powerful features), but hard in others (consumer goods. Who looks at the brand of their toilet paper, anyway?)

Stage 4: Purchase Decision

Once the customer has explored their options they will make a decision about whether or not to move forward with the purchase. Yes, even though they have reached the middle of the buying process they could still choose to walk away.

At this point, customers need a sense of security. They also needed to be reminded of the problem that brought them here in the first place.

And if a customer does decide to walk away this is the best point in the process to bring them back. Depending on your industry, this could be a simple email reminder, for example (“hey, you were interested in out software!”).

Stage 5: Purchase

At this stage, you want to make it as easy as possible for your customers to buy from you. Does your website load too slowly? Can they order from their phone just as easily as on a desktop? These are questions you should consider.

The customer already decided that they want to do business with you, you don’t want to make it hard for them. Let’s say if your payment processing software is being laggy, they might just decide to ditch and go to your competitor!

Stage 6: Post-Purchase Evaluation

You may think you are in the clear now but your work doesn’t end after the customer makes their purchase! Customers will evaluate their purchase based on previous expectations and decide whether or not they are satisfied. If they’re not happy with your product, they’ll just never use it again and everyone knows that recurring customers are much better than those buying just once.

Or it could end up going even worse, with the customer asking for their money back.

Depending on how you handle this situation, the customer will react differently. If you put their concerns at ease & even make them feel better, they’re much more likely to come back or even refer their friends. Or, if you treat them wrong, you’re never going to see them (or their friends) again.

There are a couple of ways to work with this stage…

• Good Customer Service: Being able to talk to your customers & help them use their product can take you a long way.
• Follow-Up Emails, Survey: Showing the customer that you care about their experience is a pleasant experience on its own.
• Fair Treatment: Sometimes, the product might just end up not being what the customer is looking for. If you treat them with respect & offer a refund, they’re more likely to come back for a different purchase. If you shut them down, they’re lost forever.

Hopefully, these six steps have given you a better understanding of the thought process that goes into making a purchase. They can be extremely helpful if used as a framework to analyze your customer’s thinking, and then use what you learn in combination with other marketing efforts.

The service marketing triangle or the Service triangle as it is commonly called, underlines the relationships between the various providers of services, and the customers who consume these services.

As we know, relationships are most important in the services sector. The service triangle outlines all the relationships that exist between the company, the employees and the customers. Furthermore, it also outlines the importance of systems in a services industry and how these systems help achieve customer satisfaction.

As the name suggests, the service marketing triangle can also be used to market the service to consumers. The marketing completely depends on the interaction going on between the customer and the service provider. We will look at each of these interactions in detail, and also read on how to market to your customer based on the interaction.

There are 6 main relationships in the Service triangle. And based on these relationships, there are three ways to apply marketing tactics.

Let us first go through the 6 relationships in the Service marketing triangle

1. Company to Customers

One of the critical thing is to communicate the service strategy to the customers. Most of the E-commerce companies are nowadays employed in convincing the customers to buy from their portal only. For this buying, they are communicating various service advantages which the customers have.

Communication of the service strategy to customers is important to build the trust of customers and hence to convert the customers to be loyal to the company.

2. Company to employees

Another important relationship in the service triangle is that between the company and the employees. Imagine an Airline where the flight attendants themselves are frustrated with the company. You, as a customer, will land up with the poorest services.

Hence, training employees, building value and trust, and empowering employees are some of the ways that the company can make their employees a positive influencing force for the customers.

3. Company to systems

To keep customers happy, efficient and productive systems need to be developed. Imagine your bank in the 1960’s where everything was done by paper. If you wanted to transfer money, you will have to fill many forms, and the recipient had to fill many forms. Ultimately it was a tedious process.

However, due to advanced systems, nowadays you can not only transfer money to others sitting at home, you can practically do 80% of the banking work sitting at home from your laptop. That’s the importance of systems in a service marketing triangle.

4. Customers to systems

Although building systems are important, these systems should be most useful to customers. Taking the same example of banking systems above, it is surprising that even today when you go to a bank, there is a queue. Look at retail stores. There’s always a big line to check out.

The interaction between customer and system is critical to build the service brand. Taking the example of E-commerce systems, when the customer is promised various service advantages, and when he fails to return a product due to system errors or logistics errors, he becomes dissatisfied with the service.

For a company, it is important not only to build systems, but ensure that the systems comply to the customers and give excellent experience to customers.

5. Employees to system

Not only do systems leave customers frustrated, they also leave the employees frustrated. Imagine a McDonald’s where orders taken at the front desk are not reaching the kitchen. Or imagine a service center, where although you have entered a grievance, the employee is not getting your complaint and hence not calling you. Ultimately it is the employee on whom you are going to get angry!!

In one of the consumer durable companies i know, the systems were top of the line, but they had so many processes with regards to outstanding and inventory, that a simple order processing took 20 minutes. This same company had at least 1 lakh dealers and distributors. So imagine the continuous delay in order processing and the pressure on employees due to this system issue. The system was working excellently, but it was creating friction between the employees and the system.

Both, Employee motivation, and the empowerment of employees depends on the type of system you hand over to your employees. If the systems are very good and your employees are able to make good use of it, you will get very happy and satisfied customers.

6. The most important relationship in the service triangle: Employee to Customers

The employee to customer interaction is also known as the “moment of truth” or “critical incidents”. A single customer can become dissatisfied with the way the employee treated him. Or that single customer can buy a lot of material from the same store, because the employee treated him or her like a king or queen.

That’s the difference your employees can create when they interact with customers. There are companies which are high in the customer satisfaction index, just because their employees are well-trained and are empowered to take their own decisions. More importantly, these employees are ingrained with the habit that “Customer is king”.

Once your employees starts treating the customer as if they are really king, the whole service triangle gets completed, and you will get the best results from all processes employed.

The capital market line (CML) represents portfolios that optimally combine risk and return. Capital asset pricing model (CAPM), depicts the trade-off between risk and return for efficient portfolios. It is a theoretical concept that represents all the portfolios that optimally combine the risk-free rate of return and the market portfolio of risky assets. Under CAPM, all investors will choose a position on the capital market line, in equilibrium, by borrowing or lending at the risk-free rate, since this maximizes return for a given level of risk.

Portfolios that fall on the capital market line (CML), in theory, optimize the risk/return relationship, thereby maximizing performance. The capital allocation line (CAL) makes up the allotment of risk-free assets and risky portfolio for an investor. CML is a special case of the CAL where the risk portfolio is the market portfolio. Thus, the slope of the CML is the sharpe ratio of the market portfolio. As a generalization, buy assets if the sharpe ratio is above the CML and sell if the sharpe ratio is below the CML.

CML differs from the more popular efficient frontier in that it includes risk-free investments. The intercept point of CML and efficient frontier would result in the most efficient portfolio, called the tangency portfolio.

The CAPM, is the line that connects the risk-free rate of return with the tangency point on the efficient frontier of optimal portfolios that offer the highest expected return for a defined level of risk, or the lowest risk for a given level of expected return. The portfolios with the best trade-off between expected returns and variance (risk) lie on this line. The tangency point is the optimal portfolio of risky assets, known as the market portfolio. Under the assumptions of mean-variance analysis that investors seek to maximize their expected return for a given amount of variance risk, and that there is a risk-free rate of return all investors will select portfolios which lie on the CML.

According to Tobin’s separation theorem, finding the market portfolio and the best combination of that market portfolio and the risk-free asset are separate problems. Individual investors will either hold just the risk-free asset or some combination of the risk-free asset and the market portfolio, depending on their risk-aversion. As an investor moves up the CML, the overall portfolio risk and return increases. Risk averse investors will select portfolios close to the risk-free asset, preferring low variance to higher returns. Less risk averse investors will prefer portfolios higher up on the CML, with a higher expected return, but more variance. By borrowing funds at the risk-free rate, they can also invest more than 100% of their investable funds in the risky market portfolio, increasing both the expected return and the risk beyond that offered by the market portfolio.

• The capital market line (CML) represents portfolios that optimally combine risk and return.
• CML is a special case of the CAL where the risk portfolio is the market portfolio. Thus, the slope of the CML is the sharpe ratio of the market portfolio.
• The intercept point of CML and efficient frontier would result in the most efficient portfolio called the tangency portfolio.

As a generalization, buy assets if sharpe ratio is above CML and sell if sharpe ratio is below CML.

The Capital Market Line Equation:

Where:

Rp = Portfolio return

rf = Risk free rate

RT = Market return

σp = Standard deviation of portfolio returns

σT = Standard deviation of market returns

The Capital Market Line and the Security Market Line

The CML is sometimes confused with the security market line (SML). The SML is derived from the CML. While the CML shows the rates of return for a specific portfolio, the SML represents the market’s risk and return at a given time, and shows the expected returns of individual assets. And while the measure of risk in the CML is the standard deviation of returns (total risk), the risk measure in the SML is systematic risk, or beta. Securities that are fairly priced will plot on the CML and the SML. Securities that plot above the CML or the SML are generating returns that are too high for the given risk and are underpriced. Securities that plot below CML or the SML are generating returns that are too low for the given risk and are overpriced.

History of the Capital Market Line

Mean-variance analysis was pioneered by Harry Markowitz and James Tobin. The efficient frontier of optimal portfolios was identified by Markowitz in 1952, and James Tobin included the risk-free rate to modern portfolio theory in 1958. William Sharpe then developed the CAPM in the 1960s, and won a Nobel prize for his work in 1990, along with Markowitz and Merton Miller.

Dow Theory (Dow Jones Theory) is a trading approach developed by Charles Dow. Theory is the basis of technical analysis of financial markets. The basic idea of Dow Theory is that market price action reflects all available information and the market price movement is comprised of three main trends.

The Averages Discount Everything.

Every knowable factor that may possibly affect both demand and supply is reflected in the market price.

The Market Has Three Trends.

According to Dow an uptrend is consistently rising peaks and troughs. And a downtrend is consistently rising lowering peaks and troughs.

Dow believed that laws of action and reaction apply to the markets just as they do to the physical universe, meaning that each significant movement is followed by a certain pullback.

Dow considered a trend to have three parts:

1. Primary (compared to tide, reaching further and further inland until the ultimate point is reached).
2. Secondary (compared to waves and representing corrections in the primary trend, normally retracing between one-third and two-thirds of the previous trend movement and most frequently about half of the previous move)
3. Minor (ripples) (fluctuations in the secondary trend).

Major Trends Have Three Phases.

Dow mainly paid attention to the primary (major) trends in which he distinguished three phases:

• Accumulation phase: The most astute investors are entering the market feeling the change in the current market direction.
• Public participation phase:A majority of technicians begin to join in as the price is rapidly advancing.
• Distribution phase:A new direction is now commonly recognized and well hiked; economic news are all confirming which all ends up in increasing speculative volume and wide public’s participation.

The Averages Must Confirm Each Other.

Dow used to say that unless both Industrial and Rail Averages exceed a previous peak, there is no confirmation of inception or continuation of a bull market. Signals did no have to occur simultaneously, but the quicker one followed another the stronger the confirmation was.

Volume Must Confirm the Trend. 

Volume increases or diminishes according to whether the price is moving in direction of a trend or in reverse. Dow considered volume a secondary indicator. His buy or sell signals were based on closing prices.

A Trend Is Assumed to Be Contiunous Until Definite Signals of Its Reversal.

The overall technical approach in market analysis is based upon the idea that trends continue in motion until there is an external force causing it to change its direction just like any other physical objects. And of course there are reversal signals to be looking for.

Dow Theory Principles

• The Averages Discount Everything.
  Every knowable factor that may possibly affect both demand and supply is reflected in the market price.
• The Market Has Three Trends.
  According to Dow an uptrend is consistently rising peaks and troughs. And a downtrend is consistently rising lowering peaks and troughs. 
  Dow believed that laws of action and reaction apply to the markets just as they do to the physical universe, meaning that each significant movement is followed by a certain pullback.Dow considered a trend to have three parts:

Primary (compared to tide, reaching further and further inland until the ultimate point is reached).

Secondary (compared to waves and representing corrections in the primary trend, normally retracing between one-third and two-thirds of the previous trend movement and most frequently about half of the previous move)

Minor (ripples) (fluctuations in the secondary trend).

• Major Trends Have Three Phases.

  Dow mainly paid attention to the primary (major) trends in which he distinguished three phases:

• Accumulation phase:The most astute investors are entering the market feeling the change in the current market direction.
• Public participation phase:A majority of technicians begin to join in as the price is rapidly advancing.
• Distribution phase:A new direction is now commonly recognized and well hiked; economic news are all confirming which all ends up in increasing speculative volume and wide public’s participation.

The Averages Must Confirm Each Other.
Dow used to say that unless both Industrial and Rail Averages exceed a previous peak, there is no confirmation of inception or continuation of a bull market. Signals did no have to occur simultaneously, but the quicker one followed another the stronger the confirmation was.

Volume Must Confirm the Trend. 
Volume increases or diminishes according to whether the price is moving in direction of a trend or in reverse. Dow considered volume a secondary indicator. His buy or sell signals were based on closing prices.

A Trend Is Assumed to Be Contiunous Until Definite Signals of Its Reversal.
The overall technical approach in market analysis is based upon the idea that trends continue in motion until there is an external force causing it to change its direction just like any other physical objects. And of course there are reversal signals to be looking for.

Failure Swing.

The failure of the peak at C to overcome A, followed by the violation of the low at B, constitutes a “sell” signal at S.

Nonfailure Swing.

Notice that C exceeds A before D falling below B. Some Dow theorists would see a “sell” signal at S1, while others would need to see a lower high at E before turning bearish at S2.

Dow only took in consideration closing prices. Averages had to close higher than a previous peak or lower than a previous trough to be significant. Intraday penetrations did not count.

Failure Swing Bottom. 

The “buy” signal takes place when point B is exceeded (at Bl).

Nonfailure Swing Bottom.

“Buy” signals occur at points B1 or B2.

The efficient market hypothesis (EMH), alternatively known as the efficient market theory, is a hypothesis that states that share prices reflect all information and consistent alpha generation is impossible. According to the EMH, stocks always trade at their fair value on exchanges, making it impossible for investors to purchase undervalued stocks or sell stocks for inflated prices. Therefore, it should be impossible to outperform the overall market through expert stock selection or market timing, and the only way an investor can obtain higher returns is by purchasing riskier investments.

The Efficient Market Hypothesis (EMH) is a controversial theory that states that security prices reflect all available information, making it fruitless to pick stocks (this is, to analyze stock in an attempt to select some that may return more than the rest).

Stock picking takes, in the best of cases, a lot of work to be just feebly fruitful, so there are probably better things to do with our resources

The rationale behind this is that the plentiful well-informed motivated professionals that work in the financial markets allegedly form an efficient system for assigning each security the most adequate price, given the available information. Therefore, no individuals can outsmart this fabulous group and beat the marketby regularly buying securities at prices that are lower than what they should be.

Put in other words, the hypothesis is saying that no stock trades too cheaply or too expensively; hence, it would be useless to select which ones to buy or sell. According to the EMH, the reason for this perfect pricing is that, if one stock happens to be trading even just a bit too cheaply (or too costly), then its demand increases (or decreases), rapidly moving the price to its most reasonable value.

This sounds against ordinary wisdom, as we have all heard stories of successful stock picking by keen traders. Sometimes, these traders justify their accomplishments, explaining how they anticipated certain news that produced a change of price, which was unseen for most of the other stock traders. Nevertheless, these cases don’t necessarily contradict the EMH. When some news triggers a change of value, the previous price may have reflected the amount of probability of the news really happening and the price shift it would produce. There was a probability of the news not happening, and if that had been the case, the price would have shifted in opposite direction. If the EMH happens to be right, those who were lucky to select the right outcome this time, may be unlucky the next.

If we are hiring professionals to do stock picking for us, their fees shouldn’t be too high, because the potential benefits aren’t

To decide if investors can beat the market or not, what we need to know is if their predictions are more often right than wrong (actually, that “more often” should be a weighted average that considers the amount of possible profits and losses). On the one hand, people tend to remember and communicate their success stories more than their failures, especially if they are trying to sell a service. Moreover, among the veteran traders in the markets, there are more who won in the past, because those who lost money were more inclined to finding something else to do with their time and remaining assets. So you will hear a lot of success stories about traders supposedly using their knowledge to beat the market, but that doesn’t necessarily prove the EMH to be wrong.

Forms: Weak, Semi-Strong and Strong EMH

There is scientific evidence to support the EMH. According to some it is conclusive (and so they talk about an Efficient Market Theory) and according to others it is not. In part, it depends on the flavor of EMH being under study, as there are three versions of it, which differ in their definition of available information. We said that the hypothesis states that this fabulous team called the market assigns the more adequate price given a certain information. It is key to know what kind of information that is, because if we had more than that data then the EMH wouldn’t say anything about our chances to beat the market.

The EMH version that most interests us (semi-strong) has strong factual support, although it is arguable to say that it is conclusive

The weak version of EMH says that this information is past prices and trading volumes. This type has the strongest support but it is the least significant, as everyone has access to more information than past trading data. For example, company earnings, indebtment, product profile, among other facts (that are called fundamentals). Therefore not much is said about the possibility of investors beating the market or not. Nevertheless, it has an interesting consequence: it would be of no use to perform technical analysis (which is stock price prediction based exclusively on past trading data, in contrast to fundamental analysis, which studies the financial performance of the corporation).

A stronger flavor of EMH, called semi-strong, says that the information in question is all which is publicly available. This version is the most interesting for our case because, as investors, that is exactly the information that we have access to, so if semi-strong EMH is true, then it is useless for us to analyze stock in an attempt to separate winners from losers.

There is a stronger version, or strong EMH, which is based on all information, public or private. This one has evidence against. Therefore, it is illegal to use insider information for trading, as it would mean insiders taking profits from the general public and thus pushing them away from stock trading, something that society doesn’t want. Corporate officers can buy their corporations’ stock, but when they do they have to inform the government, and that information is made public so that their purchase becomes a publicly-known fact.

Implications

The EMH version that most interests us (semi-strong) has strong factual support, although it is arguable to say that it is conclusive. Personally I take it to be not totally true but to a high degree, and that level of acceptance is enough for inferring some important practical conclusions:

• Stock picking takes, in the best of cases, a lot of work to be just feebly fruitful, so there are probably better things to do with our resources.
• Instead of picking stocks, it makes sense to buy passively-managed funds with low commissions, such as various ETFs, to obtain the market’s average returns.
• If we are hiring professionals to do stock picking for us (which happens, for example, when we purchase shares of an actively-managed fund) their fees shouldn’t be too high, because the potential benefits aren’t.
• Whenever we attempt to beat the market, by performing security picking ourselves or through a professional (fund manager), lets consider the rationale behind the EMH, to identify potential sources of market inefficiency. For example, we better not try to beat the market by analyzing large-cap companies, because lots of people are doing it, with the same information that is available to us. Instead, coming to know a small company and a niche market could put us (or our fund manager) in an advantageous position compared to the rest of the market. Therefore, active management sounds like a better idea for small-cap funds than for large.
• Don’t feel too bad if you bought a security and then its price fell, you only were as silly (or intelligent) as that fabulous team called the market. There are other better criteria for judging your portfolio-building skills.

Instead of picking stocks, it makes sense to buy passively-managed funds with low commissions, to obtain the market’s average returns

EMH shouldn’t be misinterpreted into thinking that there is no such thing as investment-portfolio design. There are still important decisions to make in order to obtain a portfolio with a risk that suits you; a good (expected) reward for that risk, and the lowest possible costs, meaning commissions and other fees. Modern Portfolio Theory is a set of theories that provide the basis for doing it, with EMH as one of its pillars, and will be treated in subsequent articles. Just as the Efficient-Market Hypothesis, much of the rest of Modern Portfolio Theory is easy to grasp and has immediate practical consequences, even for small investors.

The Elliott Wave Theory was developed by Ralph Nelson Elliott to describe price movements in financial markets, in which he observed and identified recurring, fractal wave patterns.

How Elliott Waves Work

The Elliott Wave principle consists of impulse and corrective waves at its core:

Impulse Waves: Impulse waves consist of five sub-waves that make net movement in the same direction as the trend of the next-largest degree.

Corrective Waves: Corrective waves consist of three, or a combination of three, sub-waves that make net movement in the direction opposite to the trend of the next-largest degree.

These impulse and corrective waves are nested in a self-similar fractal to create larger patterns. For example, a one-year chart may be in the midst of a corrective wave, but a 30-day chart may show a developing impulse wave. A trader with this Elliott wave interpretation might therefore have a long-term bearish outlook with a short-term bullish outlook.

Elliott recognized that the Fibonacci sequence denotes the number of waves in impulses and corrections. Wave relationships in price and time also commonly exhibit Fibonacci ratios, such as ~38% and 62%.

Other analysts have developed indicators inspired by the Elliott Wave principle, including the Elliott Wave Oscillator, which is pictured in the image above. The oscillator provides a computerized method of predicting future price direction based on the difference between a five-period and 34-period moving average. Elliott Wave International’s artificial intelligence system, EWAVES, applies all Elliott wave rules and guidelines to data to generate automated Elliott wave analysis.

Elliott’s waves

Elliott saw that there is typically an impulsive wave which moves with the trend, followed by a corrective wave which is counter-trend. He saw that there is typically five waves that make up one larger impulsive wave, before a three-wave corrective phase. The ability to see the first five waves as one impulsive move highlights the fractal nature, given that you are expected to see the same patterns on a smaller and larger timeframe.

The theory

Elliott believed that every action is followed by a reaction. Thus, for every impulsive move, there will be a corrective one.

The first five waves form the impulsive move, moving in the direction of the main trend. The subsequent three waves provide the corrective waves. In total we will have seen one five-wave impulse move, followed by a three-wave corrective move (a 5-3 move). We label the waves within the impulsive wave as 1-5, while the three corrective waves are titled A, B and C.

Once the 5-3 move is complete, we have completed a single cycle.

However, those two moves (5 and 3) can then be taken to form the part of a wider 5-3 wave.

Taking the moves in isolation, the first impulsive move includes 5 waves: 3 with the trend and 2 against it. Meanwhile, the corrective move includes three waves: 2 against the trend and 1 with the trend.

Interestingly, the fact that the corrective wave has three legs can have implications for the wider use of highs and lows for the perception of trends. Thus, while the creation of higher highs and higher lows will typically signal an uptrend, Elliott Wave theory highlights that you can often see the creation of a lower high and lower low as a short-term correction from that trend. This does not necessarily negate the trend, but instead highlights a period of retracement that is stronger than the previous corrections seen within the impulsive move.

Rules

Wave 2 never retraces more than 100% of wave 1.

The image above shows a break below the start point of the wave sequence, thus negating the notion that it is wave1.

Wave 3 cannot be the shortest of the three impulse waves.

The image above highlights the instance when we see a third wave that is too short, thus negating the possibility that this is a correct wave count. Therefore, the subsequent waves remain part of the third wave rather than forming 4 and 5.

Wave 4 does not cross the final point of wave 1.

The break below the wave 1 point clearly negates the classification of the fourth wave, instead remaining within wave 3.

Cycles

Elliott assigned a series of categories to the waves, which highlight the fact that you will see the same patterns within both long-term and shorter-term charts. The categories are as follows.

• Grand supercycle: multi-century
  • Supercycle: multi-decade (about 40 to 70 years)
  • Cycle: one year to several years (or even several decades under an Elliott Extension)
  • Primary: a few months to a couple of years
  • Intermediate: weeks to months
  • Minor: weeks
  • Minute: days
  • Minuette: hours
  • Sub-minuette: minutes

Security market line (SML) is the representation of the capital asset pricing model. It displays the expected rate of return of an individual security as a function of systematic, non-diversifiable risk. The risk of an individual risky security reflects the volatility of the return from security rather than the return of the market portfolio. The risk in these individual risky securities reflects the systematic risk.

Formula

The Y-intercept of the SML is equal to the risk-free interest rate. The slope of the SML is equal to the market risk premium and reflects the risk return tradeoff at a given time:

E(R_i) = R_F + β_i × (E(R_M) – R_F)

Where:

E(Ri) is an expected return on security

E(RM) is an expected return on market portfolio M

β is a nondiversifiable or systematic risk

RM is a market rate of return

Rf is a risk-free rate

When used in portfolio management, the SML represents the investment’s opportunity cost (investing in a combination of the market portfolio and the risk-free asset). All the correctly priced securities are plotted on the SML. The assets above the line are undervalued because for a given amount of risk (beta), they yield a higher return. The assets below the line are overvalued because for a given amount of risk, they yield a lower return. In a market in perfect equilibrium, all securities would fall on the SML.

There is a question about what the SML looks like when beta is negative. A rational investor will accept these assets even though they yield sub-risk-free returns, because they will provide “recession insurance” as part of a well-diversified portfolio. Therefore, the SML continues in a straight line whether beta is positive or negative. A different way of thinking about this is that the absolute value of beta represents the amount of risk associated with the asset, while the sign explains when the risk occur.

SML Graph

The x-axis of the SML graph is represented by the beta, and the y-axis is represented by the expected return. The value of the risk-free rate is the beginning of the line.

• The zero-beta security will have the expected return equal to the risk-free rate. The expected return of zero-beta portfolio also equals the risk-free rate.
• The slope of the security market line is determined by the market risk premium (RPM), which is the difference between the expected market return and the risk-free rate. The higher the market risk premium, the steeper the slope and vice versa.
• The SML is not fixed and can change the slope and y-axis intersection over time. It depends on changes in interest rates, risk-return trade-off.
• If the beta coefficient of the given security changes over time, its position on the line will also change.

The shift of SML can also occur when key economical fundamental factors change, such as a change in the expected inflation rate, GDP, or unemployment rate.

Basic Principles of the portfolio investment process are given below:

1. It is the portfolio that matters:

Individual securities are important only to the extent that they affect the aggregate portfolio. For example, a security’s risk should not be based on the uncertainty of a single security’s return but, instead, on its contribution to the uncertainty of the total portfolio’s return.

In addition, assets such as a person’s career or home should be considered together with the security portfolio. In short, all decisions should focus on the impact the decision will have on the aggregate portfolio of all assets held.

2. Larger expected portfolio returns come only with larger portfolio risk:

The most important portfolio decision is the amount of risk which is acceptable, which is determined by the asset allocation within the security portfolio.

This is not an easy decision, since it requires that we have some idea of the risks and expected returns available on many different classes of assets. Nonetheless, the risk/return level of the aggregate portfolio should be the first decision any investor makes.

3. The risk associated with a security type depends on when the investment will be liquidated:

A person who plans to sell in one year will find equity returns to be more risky than a person who plans to sell in 10 years.

Alternatively, the person who plans to sell in 10 years will find one year maturity bonds to be more risky than the person who plans to sell in one year. Risk is reduced by selecting securities with a payoff close to when the portfolio is to be liquidated.

4. Diversification works:

Diversification across various securities will reduce a portfolio’s risk. If such broad diversification results in an expected portfolio return or risk level which is lower (or higher) than desired, then borrowing (or lending) can be used to achieve the desired level.

5. Each portfolio should be tailored to the particular needs of its owner:

People have varying tax rates, knowledge, transaction costs, etc. Individuals who are in a high marginal tax bracket should stress portfolio strategies which increase after-tax returns. Individuals who lack strong knowledge of investment alternatives should hire professionals to provide needed counseling.

Large pension portfolios should pursue strategies which will reduce brokerage fees associated with moving capital between equity and non-equity managers (for example, by using options on futures). In short, portfolio strategy should be molded to the unique needs and characteristics of the portfolio’s owner.

6. Competition for abnormal returns is extensive:

A large number of people are continuously using a large variety of techniques in an attempt to obtain abnormal returns larger than should be expected given a security’s risk.

Securities which are believed to be undervalued are bought until the price rises to a proper level, and securities which are believed to be overvalued are sold until the price falls to a proper level.

If the actions of these speculators are truly effective, security prices will adjust instantaneously to new information the efficient market theory (EMT) will be correct.

The extent to which EMT is correct as well as the extent to which one has unique information determiners whether a passive “investment” strategy or an active “speculative” strategy should be used.

In finance, technical analysis is an analysis methodology for forecasting the direction of prices through the study of past market data, primarily price and volume. Behavioral economics and quantitative analysis use many of the same tools of technical analysis, which, being an aspect of active management, stands in contradiction to much of modern portfolio theory. The efficacy of both technical and fundamental analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, and research on technical analysis has produced mixed results.

Fundamental analysts examine earnings, dividends, assets, quality, ratio, new products, research and the like. Technicians employ many methods, tools and techniques as well, one of which is the use of charts. Using charts, technical analysts seek to identify price patterns and market trends in financial markets and attempt to exploit those patterns.

Technicians using charts search for archetypal price chart patterns, such as the well-known head and shoulder or double top/bottom reversal patterns, study technical indicators, moving averages and look for forms such as lines of support, resistance, channels and more obscure formations such as flags, pennants, balance days and cup and handle patterns.

Technical analysts also widely use market indicators of many sorts, some of which are mathematical transformations of price, often including up and down volume, advance/decline data and other inputs. These indicators are used to help assess whether an asset is trending, and if it is, the probability of its direction and of continuation. Technicians also look for relationships between price/volume indices and market indicators. Examples include the moving average, relative strength index and MACD. Other avenues of study include correlations between changes in Options (implied volatility) and put/call ratios with price. Also important are sentiment indicators such as Put/Call ratios, bull/bear ratios, short interest, Implied Volatility, etc.

There are many techniques in technical analysis. Adherents of different techniques (for example: Candlestick analysis, the oldest form of technical analysis developed by a Japanese grain trader; Harmonics; Dow theory; and Elliott wave theory) may ignore the other approaches, yet many traders combine elements from more than one technique. Some technical analysts use subjective judgment to decide which pattern(s) a particular instrument reflects at a given time and what the interpretation of that pattern should be. Others employ a strictly mechanical or systematic approach to pattern identification and interpretation.

Contrasting with technical analysis is fundamental analysis, the study of economic factors that influence the way investors price financial markets. Technical analysis holds that prices already reflect all the underlying fundamental factors. Uncovering the trends is what technical indicators are designed to do, although neither technical nor fundamental indicators are perfect. Some traders use technical or fundamental analysis exclusively, while others use both types to make trading decisions.

Principles

A core principle of technical analysis is that a market’s price reflects all relevant information impacting that market. A technical analyst therefore looks at the history of a security or commodity’s trading pattern rather than external drivers such as economic, fundamental and news events. It is believed that price action tends to repeat itself due to the collective, patterned behavior of investors. Hence technical analysis focuses on identifiable price trends and conditions.

Characteristics

Technical analysis employs models and trading rules based on price and volume transformations, such as the relative strength index, moving averages, regressions, inter-market and intra-market price correlations, business cycles, stock market cycles or, classically, through recognition of chart patterns.

Technical analysis stands in contrast to the fundamental analysis approach to security and stock analysis. In the fundamental equation M = P/E technical analysis is the examination of M (multiple). Multiple encompasses the psychology generally abounding, i.e. the extent of willingness to buy/sell. Also in M is the ability to pay as, for instance, a spent-out bull can’t make the market go higher and a well-heeled bear won’t. Technical analysis analyzes price, volume, psychology, money flow and other market information, whereas fundamental analysis looks at the facts of the company, market, currency or commodity. Most large brokerages, trading groups, or financial institutions will typically have both a technical analysis and fundamental analysis team.

In the 1960s and 1970s it was widely dismissed by academics. In a recent review, Irwin and Park reported that 56 of 95 modern studies found that it produces positive results but noted that many of the positive results were rendered dubious by issues such as data snooping, so that the evidence in support of technical analysis was inconclusive; it is still considered by many academics to be pseudoscience. Academics such as Eugene Fama say the evidence for technical analysis is sparse and is inconsistent with the weak form of the efficient-market hypothesis. Users hold that even if technical analysis cannot predict the future, it helps to identify trends, tendencies, and trading opportunities.

While some isolated studies have indicated that technical trading rules might lead to consistent returns in the period prior to 1987, most academic work has focused on the nature of the anomalous position of the foreign exchange market. It is speculated that this anomaly is due to central bank intervention, which obviously technical analysis is not designed to predict. Recent research suggests that combining various trading signals into a Combined Signal Approach may be able to increase profitability and reduce dependence on any single rule.

In security selection process, a traditional approach of Economic Industry Company analysis is employed. EIC analysis is the abbreviation of economic, industry and company. The person conducting EIC analysis examines the conditions in the entire economy and then ascertains the most attractive industries in the light of the economic conditions. At last the most attractive companies within the attractive industries are pointed out by the analyst.

EIC Analysis of a Company

Below are the further details of the components of EIC analysis, which analyst always consider before choosing or reaching any decision about any business.

• Economic Analysis
• Industry Analysis
• Company Analysis

Economic Analysis:

Every common stock is susceptible to the market risk. This feature of almost all types of common stock indicates their combined movement with the fluctuations in the economic conditions towards the improvement or deterioration.

Stock prices react favorably to the low inflation, earnings growth, a better balance of trade, increasing gross national product and other positive macroeconomic news. Indications that unemployment is rising, inflation is picking up or earnings estimates are being revised downward will negatively affect the stock prices. This relationship is reasonably reliable that the US economy is better represented by the Standard & Poor 500 stock index, which is famous market indicator. The stock market will forecast an economic boom or recession properly from the signs in front of average citizen. The Federal bank of New York has conducted a research that describes that the slope of the yield curve is the perfect indicator of the economic growth more than three months out. Recession is indicated by negative slope while positive slope is considered as good one.

The implications of market risk should be clear to the investor. When there is recession in the economy, the prices of stocks moves downward. All the companies suffer the effects of recession despite of the fact that these are high performing companies or low performing ones. Similarly the stock prices are positively affected by the boom period of the economy.

Industry Analysis:

It is clear there is certain level of market risk faced by every stock and the stock price decline during recession in the economy. Another point to be remembered is that the defensive kind of stock is affected less by the recession as compared to the cyclical category of stock. In the industry analysis, such industries are highlighted that can stand well in front of adverse economic conditions.

In 1980, Michael Porter proposed a standard approach to industry analysis which is referred to as competitive analysis frame work. Threats of new entrants evaluate the expected reaction of current competitors to new competitors and obstacles to entry into the industry. In certain industries it is quite difficult for new company to compete successfully.

For example new producers in the automobile industry face difficulty in competing the established companies, like General Motors and Ford etc. There are certain other industries where the entry of new company is easier like financial planning industry. No extraordinary efforts are required in such kind of industries to establish any new company. The growth in the industry is slowed down through the rivalry among the current competitors. Profits of the company are reduced when it tries to cover more market share because under existing rivalry the company has to invest a large portion of its earnings in this enhancing market share. The industry where the rivalry is friendly or modest among competitors provides greater opportunity for product differentiation & increased profits. The intense competition is favorable for the customer but not good for the producer of the product. In case of airline industry there are common fare price wars among the competitors. When one airline company reduces its price then the other must also adjust its price accordingly in order to retain the existing customers.

Another threat faced by company in industry is the treat of substitutes which prevents the companies to enhance the price of their products. When there is much increase in the price of particular product, then the consumer simply switches to other alternative product which has lower price. For example there are two different video games named Sega and Nintendo. These games competes each other directly in the market. If the price of Nintendo is enhanced then the new video game customers are switch toward the Sage which has relatively lower price. The investor conducting industry analysis should focus the level of risk of product substitution which seriously affects the future growth of company.

Another aspect of the industry analysis is the bargaining power of buyers which can greatly influence the large percentage of sales of seller. In this condition the profit margins are lower. Concessions are necessary to be offered by the seller because it is not affordable for him to lose customer. For example there is ship building company and the US Navy is its main customer. Only two to three ships are produced by the company every year and so it is very harmful for the firm to lose the Navy contract. On the other hand in case of departmental store, there is large number of customers and so the bargaining power of customers is low. In this business, losing one or two customers will not much affect the sales or profitability of the retail store.

The only capital intensive industry should not be focused. There are other industries that are not capital intensive like consultants required in retail computer store. There is need that is present which force the computer technician to solve the problems of the computer systems of people. In recent year, consumers are usually more sophisticated in area of personal computers. So they are better guided and they try to make their own decisions in the needs of software and hardware aspects. In fact they possess high power when they contact the sales staff.    

The bargaining power of suppliers has also substantial influence over the profitability of the company. The supplies for manufacturing products are required by the company and it does not have sufficient control over the costs. It is not possible for the company to increase the price of its finished products in order to cover the increased costs due to the presence of powerful buyer groups in market of substitute products. So while conducing industry analysis, the presence of powerful suppliers should be considered as negative for the company.

The above considerations of industry structure should be analyzed by the investor in order to make an estimate about the future trends of the industry in the light of the economic conditions. When potential industry is identified then comes the final step of EIC analysis which is narrower relating to companies only.

Company Analysis:

In company analysis different companies are considered and evaluated from the selected industry so that most attractive company can be identified. Company analysis is also referred to as security analysis in which stock picking activity is done. Different analysts have different approaches of conducting company analysis like

• Value Approach to Investing
• Growth Approach to Investing

Additionally in company analysis, the financial ratios of the companies are analyzed in order to ascertain the category of stock as value stock or growth stock. These ratios include price to book ratio and price-earnings ratio. Other ratios like return on equity etc. can also be analyzed to ascertain the potential company for making investment.

Advantages:

Fundamental analysis is good for long-term investments based on long-term trends, very long-term. The ability to identify and predict long-term economic, demographic, technological or consumer trends can benefit patient investors who pick the right industry groups or companies.

Sound fundamental analysis will help identify companies that represent a good value. Some of the most legendary investors think long-term and value. Graham and Dodd, Warren Buffett and John Neff are seen as the champions of value investing. Fundamental analysis can help uncover companies with valuable assets, a strong balance sheet, stable earnings, and staying power.

One of the most obvious, but less tangible, rewards of fundamental analysis is the development of a thorough understanding of the business. After such painstaking research and analysis, an investor will be familiar with the key revenue and profit drivers behind a company. Earnings and earnings expectations can be potent drivers of equity prices. Even some technicians will agree to that. A good understanding can help investors avoid companies that are prone to shortfalls and identify those that continue to deliver. In addition to understanding the business, fundamental analysis allows investors to develop an understanding of the key value drivers and companies within an industry. A stock’s price is heavily influenced by its industry group. By studying these groups, investors can better position themselves to identify opportunities that are high-risk (tech), low- risk (utilities), growth oriented (computer), value driven (oil), non-cyclical (consumer staples), cyclical (transportation) or income-oriented (high yield).

Stocks move as a group. By understanding a company’s business, investors can better position themselves to categorize stocks within their relevant industry group. Business can change rapidly and with it the revenue mix of a company. This happened to many of the pure Internet retailers, which were not really Internet companies, but plain retailers. Knowing a company’s business and being able to place it in a group can make a huge difference in relative valuations.

Disadvantages:

The main disadvantage for me is that if used on its own, fundamental analysis (FA) doesn’t take into consideration the “herd mentality” phenomenon. In the long run, the price per share (PPS) of companies is driven by their earnings, i.e., the profit they’re yielding. In the short term, the momentum can be quite influential on the PPS; I’m sure you’ve noticed that some stock are considered market darlings and, to a certain degree, it doesn’t matter what their quarterly results are; people keep on buying. The same applies for companies that, all of a sudden, fall out of favor for whatever reason, genuine or not. They keep getting hammered regardless of the results the company pumps out, until one day it reverses. FA doesn’t consider this irrational behavior.

Fundamental analysis may offer excellent insights, but it can be extraordinarily time-consuming. Time-consuming models often produce valuations that are contradictory to the current price prevailing on Wall Street. When this happens, the analyst basically claims that the whole street has got it wrong. This is not to say that there are not misunderstood companies out there, but it is quite brash to imply that the market price, and hence Wall Street, is wrong.

Valuation techniques vary depending on the industry group and specifics of each company. For this reason, a different technique and model is required for different industries and different companies. This can get quite time-consuming, which can limit the amount of research that can be performed.

Fair value is based on assumptions. Any changes to growth or multiplier assumptions can greatly alter the ultimate valuation. Fundamental analysts are generally aware of this and use sensitivity analysis to present a base-case valuation, a best-case valuation and a worst-case valuation. However, even on a worst-case valuation, most models are almost always bullish, the only question is how much so.

The majority of the information that goes into the analysis comes from the company itself. Companies employ investor relations managers specifically to handle the analyst community and release information. As Mark Twain said, “there are lies, damn lies, and statistics.” When it comes to massaging the data or spinning the announcement, CFOs and investor relations managers are professionals. Only buy-side analysts tend to venture past the company statistics. Buy-side analysts work for mutual funds and money managers. They read the reports written by the sell-side analysts who work for the big brokers (CIBC, Merrill Lynch, Robertson Stephens, CS First Boston, Paine Weber, DLJ to name a few). These brokers are also involved in underwriting and investment banking for the companies. Even though there are restrictions in place to prevent a conflict of interest, brokers have an ongoing relationship with the company under analysis. When reading these reports, it is important to take into consideration any biases a sell-side analyst may have. The buy-side analyst, on the other hand, is analyzing the company purely from an investment standpoint for a portfolio manager. If there is a relationship with the company, it is usually on different terms. In some cases this may be as a large shareholder.

When market valuations extend beyond historical norms, there is pressure to adjust growth and multiplier assumptions to compensate. If Wall Street values a stock at 50 times earnings and the current assumption is 30 times, the analyst would be pressured to revise this assumption higher. There is an old Wall Street adage: the value of any asset (stock) is only what someone is willing to pay for it (current price). Just as stock prices fluctuate, so too do growth and multiplier assumptions. Are we to believe Wall Street and the stock price or the analyst and market assumptions? It used to be that free cash flow or earnings were used with a multiplier to arrive at a fair value. In 1999, the S&P 500 typically sold for 28 times free cash flow. However, because so many companies were and are losing money, it has become popular to value a business as a multiple of its revenues. This would seem to be OK, except that the multiple was higher than the PE of many stocks! Some companies were considered bargains at 30 times revenues.

To conclude, fundamental analysis can be valuable, but it should be approached with caution. If you are reading research written by a sell-side analyst, it is important to be familiar with the analyst behind the report. We all have personal biases, and every analyst has some sort of bias. There is nothing wrong with this, and the research can still be of great value. Learn what the ratings mean and the track record of an analyst before jumping off the deep end. Corporate statements and press releases offer good information, but they should be read with a healthy degree of skepticism to separate the facts from the spin. Press releases don’t happen by accident; they are an important PR tool for companies. Investors should become skilled readers to weed out the important information and ignore the hype.