Average Selling Price (ASP): Definition, Calculation and Examples

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Average Selling Price (ASP): Definition, Calculation and Examples

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What Is Average Selling Price (ASP)?

The term average selling price (ASP) refers to the price at which a certain class of good or service is typically sold. The average selling price is affected by the type of product and the product life cycle. The ASP is the average selling price of the product across multiple distribution channels, across a product category within a company, or even across the market as a whole.

Key Takeaways

  • The term average selling price refers to the price at which a certain class of good or service is typically sold.
  • ASPs can serve as a benchmark for entities who want to set a price for their product or service.
  • Computers, cameras, televisions, and jewelry tend to have higher ASPs, while books and DVDs have a low average selling price.
  • Average selling price is affected by the type of product and the product life cycle.
  • Average selling price is usually reported during quarterly financial results.

Understanding Average Selling Price (ASP)

The average selling price is the price for a product or service in various markets, and is normally used in the retail and technology industries. The established ASP for a particular good can act as a benchmark price, helping other manufacturers, producers, or retailers set the prices for their own products.

Marketers who try to set a price for a product must also consider where they want their product to be positioned. If they want their product image to be part of a high-quality choice, they have to set a higher ASP.

Products like computers, cameras, televisions, and jewelry tend to have higher average selling prices while products like books and DVDs will have a low average selling price. When a product is the latter part of its product life cycle, the market is most likely saturated with competitors, therefore, driving down the ASP.

In order to calculate the ASP, divide the total revenue earned from the product by the total number of units sold. This average selling price is usually reported during quarterly financial results and can be considered as accurate as possible given regulation on fraudulent reporting.

Special Considerations

The smartphone market is a big industry which uses average selling prices. In the smartphone market, the average selling price indicates how much money a handset manufacturer is receiving on average for the phones that it sells.

In the smartphone market, advertised selling prices can differ drastically from average selling prices.

For product-driven companies like Apple, calculations for average selling price provide pivotal information about its financial performance and, by extension, the performance of its stock price. In fact, there’s a clear relationship between Apple’s iPhone ASP and its stock price movements.

The iPhone’s ASP matters even more when considering how each device drives overall profitability for Apple. Apple consolidates its operations under a single profit-and-loss statement (P&L), meaning investors can’t tell how costs, such as marketing and research and development (R&D) are spread among the company’s various products.

Since the iPhone has the highest gross margin in Apple’s device family, the device generates the lion’s share of Apple’s profits. That makes the iPhone crucial in determining Apple’s overall financial performance each quarter.

Examples of Average Selling Price

The term average selling price has a place in the housing market. When the average selling price of a home within a particular region rises, this may be a signal of a booming market. Conversely, when the average price drops, so does the perception of the market in that particular area.

Some industries use ASP in a slightly different way. The hospitality industry—especially hotels and other lodging companies—commonly refers to it as the average room or average daily rate. These average rates tend to be higher during peak seasons, while rates normally drop when travel seems to be low or during off-seasons.

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Affiliate: Definition in Corporate, Securities, and Markets

Written by admin. Posted in A, Financial Terms Dictionary

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What Is an Affiliate?

Affiliate is used primarily to describe a business relationship wherein one company owns less than a majority stake in the other company’s stock. Affiliations can also describe a type of relationship in which at least two different companies are subsidiaries of the same larger parent company.

Affiliate is also commonly used in the retail sector. In this case, one company becomes affiliated with another in order to sell its products or services, earning a commission for doing so. This term is now used widely in partnerships among online companies in which the affiliate supports another company by channeling internet traffic and e-sales.

Key Takeaways

  • An affiliate is a company in which a minority stake is held by a larger company.
  • In retail, one company becomes affiliated with another to sell its products or services for a fee.
  • Affiliate relationships exist in many different types of configurations across all sorts of industries.

Understanding Affiliates

There are several definitions of the term affiliate in the corporate, securities, and capital markets.

Corporate Affiliates

In the first, an affiliate is a company that is related to another. The affiliate is generally subordinate to the other and has a minority stake (i.e. less than 50%) in the affiliate. In some cases, an affiliate may be owned by a third company. An affiliate is thus determined by the degree of ownership a parent company holds in another.

For example, if BIG Corporation owns 40% of MID Corporation’s common stock and 75% of TINY Corporation, then MID and BIG are affiliates, while TINY is a subsidiary of BIG. MID and TINY may also refer to one another as affiliates.

Note that for the purposes of filing consolidated tax returns, IRS regulations state a parent company must possess at least 80% of a company’s voting stock to be considered affiliated.

Retail Affiliates

In retail, and particularly in e-commerce, a company that sells other merchants’ products for a commission is an affiliate company. Merchandise is ordered from the primary company, but the sale is transacted at the affiliate’s site. Amazon and eBay are examples of e-commerce affiliates.

International Affiliates

A multinational company may set up affiliates to break into international markets while protecting the parent company’s name in case the affiliate fails or the parent company is not viewed favorably due to its foreign origin. Understanding the differences between affiliates and other company arrangements is important in covering debts and other legal obligations.

Companies can become affiliated through mergers, takeovers, or spinoffs.

Other Types of Affiliates

Affiliates can be found all around the business world. In the corporate securities and capital markets, executive officers, directors, large stockholders, subsidiaries, parent entities, and sister companies are affiliates of other companies. Two entities may be affiliates if one owns less than a majority of voting stock in the other. For instance, Bank of America has a number of different affiliates around the world including Merrill Lynch.

Affiliation is defined in finance in a loan agreement as an entity other than a subsidiary directly or indirectly controlling, being controlled by or under common control with an entity.

In commerce, two parties are affiliated if either can control the other, or if a third party controls both. Affiliates have more legal requirements and prohibitions than other company arrangements to safeguard against insider trading.

An affiliate network is a group of associated companies that offer compatible or complementary products and will often pass leads to each other. They may offer cross-promotional deals, encouraging clients who have utilized their services to look into the services offered by an affiliate.

In banking, affiliate banks are popular for underwriting securities and entering foreign markets where other banks do not have direct access.

Affiliates vs. Subsidiaries

Unlike an affiliate, a subsidiary’s majority shareholder is the parent company. As the majority shareholder, the parent company owns more than 50% of the subsidiary and has a controlling stake. The parent thus has a great deal of control over the subsidiary and is allowed to make important decisions such as the hiring and firing of executives, and the appointment of directors on the board.

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Autocorrelation: What It Is, How It Works, Tests

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Autocorrelation: What It Is, How It Works, Tests

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What Is Autocorrelation?

Autocorrelation is a mathematical representation of the degree of similarity between a given time series and a lagged version of itself over successive time intervals. It’s conceptually similar to the correlation between two different time series, but autocorrelation uses the same time series twice: once in its original form and once lagged one or more time periods. 

For example, if it’s rainy today, the data suggests that it’s more likely to rain tomorrow than if it’s clear today. When it comes to investing, a stock might have a strong positive autocorrelation of returns, suggesting that if it’s “up” today, it’s more likely to be up tomorrow, too.

Naturally, autocorrelation can be a useful tool for traders to utilize; particularly for technical analysts.

Key Takeaways

  • Autocorrelation represents the degree of similarity between a given time series and a lagged version of itself over successive time intervals.
  • Autocorrelation measures the relationship between a variable’s current value and its past values.
  • An autocorrelation of +1 represents a perfect positive correlation, while an autocorrelation of -1 represents a perfect negative correlation.
  • Technical analysts can use autocorrelation to measure how much influence past prices for a security have on its future price.

Understanding Autocorrelation

Autocorrelation can also be referred to as lagged correlation or serial correlation, as it measures the relationship between a variable’s current value and its past values.

As a very simple example, take a look at the five percentage values in the chart below. We are comparing them to the column on the right, which contains the same set of values, just moved up one row.

 Day  % Gain or Loss Next Day’s % Gain or Loss
 Monday  10%  5%
 Tuesday  5%  -2%
 Wednesday  -2%  -8%
 Thursday  -8%  -5%
 Friday  -5%  

When calculating autocorrelation, the result can range from -1 to +1.

An autocorrelation of +1 represents a perfect positive correlation (an increase seen in one time series leads to a proportionate increase in the other time series).

On the other hand, an autocorrelation of -1 represents a perfect negative correlation (an increase seen in one time series results in a proportionate decrease in the other time series).

Autocorrelation measures linear relationships. Even if the autocorrelation is minuscule, there can still be a nonlinear relationship between a time series and a lagged version of itself.

Autocorrelation Tests

The most common method of test autocorrelation is the Durbin-Watson test. Without getting too technical, the Durbin-Watson is a statistic that detects autocorrelation from a regression analysis.

The Durbin-Watson always produces a test number range from 0 to 4. Values closer to 0 indicate a greater degree of positive correlation, values closer to 4 indicate a greater degree of negative autocorrelation, while values closer to the middle suggest less autocorrelation.

Correlation vs. Autocorrelation

Correlation measures the relationship between two variables, whereas autocorrelation measures the relationship of a variable with lagged values of itself.

So why is autocorrelation important in financial markets? Simple. Autocorrelation can be applied to thoroughly analyze historical price movements, which investors can then use to predict future price movements. Specifically, autocorrelation can be used to determine if a momentum trading strategy makes sense.

Autocorrelation in Technical Analysis

Autocorrelation can be useful for technical analysis, That’s because technical analysis is most concerned with the trends of, and relationships between, security prices using charting techniques. This is in contrast with fundamental analysis, which focuses instead on a company’s financial health or management.

Technical analysts can use autocorrelation to figure out how much of an impact past prices for a security have on its future price.

Autocorrelation can help determine if there is a momentum factor at play with a given stock. If a stock with a high positive autocorrelation posts two straight days of big gains, for example, it might be reasonable to expect the stock to rise over the next two days, as well.

Example of Autocorrelation

Let’s assume Rain is looking to determine if a stock’s returns in their portfolio exhibit autocorrelation; that is, the stock’s returns relate to its returns in previous trading sessions.

If the returns exhibit autocorrelation, Rain could characterize it as a momentum stock because past returns seem to influence future returns. Rain runs a regression with the prior trading session’s return as the independent variable and the current return as the dependent variable. They find that returns one day prior have a positive autocorrelation of 0.8.

Since 0.8 is close to +1, past returns seem to be a very good positive predictor of future returns for this particular stock.

Therefore, Rain can adjust their portfolio to take advantage of the autocorrelation, or momentum, by continuing to hold their position or accumulating more shares.

What Is the Difference Between Autocorrelation and Multicollinearity?

Autocorrelation is the degree of correlation of a variable’s values over time. Multicollinearity occurs when independent variables are correlated and one can be predicted from the other. An example of autocorrelation includes measuring the weather for a city on June 1 and the weather for the same city on June 5. Multicollinearity measures the correlation of two independent variables, such as a person’s height and weight.

Why Is Autocorrelation Problematic?

Most statistical tests assume the independence of observations. In other words, the occurrence of one tells nothing about the occurrence of the other. Autocorrelation is problematic for most statistical tests because it refers to the lack of independence between values.

What Is Autocorrelation Used for?

Autocorrelation can be used in many disciplines but is often seen in technical analysis. Technical analysts evaluate securities to identify trends and make predictions about their future performance based on those trends.

The Bottom Line

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Asset Swapped Convertible Option Transaction (ASCOT)

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Asset Swapped Convertible Option Transaction (ASCOT)

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What Is an Asset Swapped Convertible Option Transaction (ASCOT)?

An asset swapped convertible option transaction (ASCOT) is a structured investment strategy in which an option on a convertible bond is used to separate a convertible bond into its two components: a fixed income piece and an equity piece. More specifically, the components being separated are the corporate bond with its regular coupon payments and the equity option that functions as a call option.

The ASCOT structure allows an investor to gain exposure to the option within the convertible without taking on the credit risk represented by the bond part of the asset. It is also used by convertible arbitrage traders seeking to profit from apparent mis-pricings between these two components.

Key Takeaways

  • An asset swapped convertible option transaction, or ASCOT, is a way to separate the fixed-income and equity components from a convertible bond.
  • An ASCOT is constructed by selling an American call option on the stock of the convertible bond issuer at a strike price that accounts for the cost of unwinding the strategy.
  • ASCOTs let investors remove the credit risk from convertibles and provides opportunities for convertible arbitrage strategies.

Understanding Asset Swapped Convertible Option Transactions

ASCOTs are complex instruments that allow parties to take the role of equity investor and credit risk buyer/bond investor in what was initially sold as a combined instrument — the convertible bond itself.

An asset swapped convertible option transaction is done by writing (selling) an American option on the convertible bond. This essentially creates a compound option, as the convertible bond already comes with an embedded equity call option itself due to the conversion feature. The American option can be exercised by the holder at any time, but the strike price paid must include all the costs of unwinding the asset swap.

How an ASCOT Works

Convertible bond traders are exposed to two types of risk. One is the credit risk inherent in the bond portion of the investment. The other is the market volatility on the share price of the underlying, as it impacts whether or not the conversion option has any value.

For our purposes, let’s assume the convertible bond trader wants to focus on the equity angle of their convertible bond portfolio. To do this, the trader sells the convertible bond to an investment bank, which will be the intermediary in the transaction.

The investment bank structures the ASCOT by writing a call option on the convertible portion of the bond and selling it back to the convertible bond trader. The bond portion of the convertible bond with its payments is then sold to a different party who is prepared to take on the credit risk in return for the fixed returns. The bond component may be broken down into smaller denomination bonds and sold to multiple investors.

ACOTS and Convertible Arbitrage

When a convertible bond is stripped of its credit risk through an asset swap, the option holder is left with a volatile — but potentially very valuable — option. ASCOTs, specifically the equity portion, are bought and sold by hedge funds employing convertible arbitrage strategies. Hedge funds are able to easily increase their portfolios’ leverage because of the nature of the compound option within an ASCOT, leaving the less lucrative bond side and its credit risk out of the equation.

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