Trending February 2024 # Stock Options Chain Analysis Using Excel # Suggested March 2024 # Top 8 Popular

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This article was published as a part of the Data Science Blogathon.

Introduction

Simple strategies for trend analysis in stock options data

Stock data analysis is one of the most endearing and exhaustive topics. Endearing because who does not want to earn profits in the stock market. Exhaustive because the length and breadth of this topic are infinite. You can easily get lost and overwhelmed with the amount of information that bounces at you when you explore this topic. So in this article, I will be focusing on one particular type of stock analysis i.e Options Chain analysis using Excel.

Option chain comprises data pertaining to option strikes of a particular stock or index in a single frame. It gives you all the specific data you need while trading in options. In this article, I will list out all the key concepts required to understand the option chain. I will show how to import option data to Excel and build custom reports based on option strategies. These reports will in turn help you to predict trends for options trading.

What is Option Chain Analysis?

Option chain analysis involves studying the available options contracts for an underlying asset, such as a stock, commodity, or index. An option chain is a comprehensive list that displays the options contracts for the underlying asset, including the strike price, expiration date, implied volatility, and bid/ask prices.

Traders can use option chain analysis to identify potential trading opportunities by examining the relationships between different options contracts and their corresponding prices. This analysis helps traders to determine the most favourable options to buy or sell based on their investment goals and risk tolerance.

Option chain analysis is also useful in evaluating potential profits or losses and identifying potential market trends or shifts in sentiment. By analyzing the option chain, traders can identify potential shifts in the demand for the underlying asset and make informed trading decisions based on this information.

Key Concepts for Stock Options Chain Analysis

Derivative – is an instrument that derives its value from a specified asset. It is a contract that takes place between two people.

Option Contract – is a type of Derivative. These are of two types, Call (CE) and Put (PE). Option contract takes place between a buyer and a seller (writer). An option contract gives the buyer the right but not the obligation to buy or sell an underlying asset at a specified strike price on a specified date.

Premium – is the amount paid to book a call or put option contract. This amount is decided by the seller.

Strike Price – is the price at which a specific derivative contract can be exercised.

Expiry Date – is the date at which the option contract expires. Normally every option contract expires on the last Thursday of every month. Based on expiry, the option contract is categorized into 3 groups, Running option contract (nearest expiry), Middle option contract (mid expiry), Far option contract (farther expiry). For example, if for a contract the nearest expiry is last Thursday of March, then mid expiry will be last Thursday of April, and far expiry will be last Thursday of May. Once the contract expires, a new contract for the next month is generated. As a buyer or seller, you can hold the contract till the expiry. Thereafter if you don’t buy or sell then the contract expires, and you will lose the premium amount.

Call option contract – is a contract that gives the buyer the right but not the obligation to buy an asset. A premium amount must be paid to the seller for booking the asset. For example, say the strike price for a contract is Rs.150 when the buyer booked it for a premium of Rs.20. Now, after one month if the price of the asset increases to Rs.200, then the buyer can go ahead and buy and book a profit of Rs.30 after deducting the premium. Suppose if the price decreases to Rs.100 then the buyer is not obligated to buy. Here the buyer only stands to lose the premium amount. This is known as a Call option contract (Right to buy).

Put option contract – is a contract that gives the buyer the right but not the obligation to sell an asset. A premium amount must be paid to the seller for booking the asset. For example, say the strike price for a contract is Rs.200 when the buyer booked it for a premium of Rs.20. Now, after one month if the price of the asset decreases to Rs.150, then the buyer can sell the asset and book a profit of Rs.30 after deducting the premium. Suppose if the price increases to Rs.300 then the buyer is not obligated to sell the asset as the price has risen. Here the buyer only stands to lose the premium amount. This is known as Put option contract (Right to sell).

ATM, ITM, OTM – based on the underlying price of the asset, options contracts can be categorized as In the Money (ITM), At the Money (ATM), and Out of the Money (OTM). If the strike price is less than the market price then it is ITM, if the strike price is equal to the market price then it is ATM, and if the strike price is greater than the market price then it is OTM.

In options trading, contracts are bought or sold in chunks/lots. For example, one contract will comprise 100 shares. So, you always buy or sell in terms of the number of contracts and not the number of shares that each contract has.

Option Chain Deconstructed

An options chain is a listing of all available options contracts for a given index/stock. It provides detailed quotes and price information. It shows all listed puts, calls, their expiration, strike prices, and volume for a single underlying asset within a given maturity period. The option chain is categorized by expiration date and segmented by calls and puts. Here is a screenshot of a portion of the option chain for Nifty taken from the NSE website.

Data in the option chain chart is grouped into 4 quadrants. Two for Calls (Yellow and White) and two for Puts (Yellow and White). The Yellow quadrant data is for In the Money contracts and the White quadrant data is for Out of the Money contracts. This is applicable for both Call and Put, but the meaning of ITM and OTM has reversed accordingly.

Some of the key columns that are required to understand the option chain chart/matrix are:

OI (Open Interest) – is the number of contracts that are traded but not exercised. It indicates the interest of traders for an option at the given strike price. Higher OI means more interest among traders, and hence indicates high liquidity for the buyer/seller to trade their options.

CHNG IN OI – is the change in OI within the expiration period. It indicates the number of contracts that are closed or exercised.

VOLUME – is the total number of contracts that are traded for a specific strike price in a given period. It is calculated on daily basis.

IV (Implied Volatility) – is the indication of how the market reacts to the price movement of an underlying asset.

LTP (Last Traded Price) – is the last traded price or premium price of an option.

CHNG – is the net change in LTP. It is indicated as a positive or negative value. Positive change means a rise in price (shown in green). A negative change means a decrease in price (shown in red).

BID QTY – is the number of orders for buying at a specific strike price. It indicates the current demand for the order.

BID PRICE – is the price for the latest buy order. If this price is higher than the LTP then it indicates higher demand for the option and vice versa.

ASK PRICE – is the price of the latest sell order.

ASK QTY – is the number of sell orders that are open. It indicates the option supply.

Importing Options Data in Excel

Now that you have an understanding of the option chain, I will show in this section how to import option chain data in Excel. Once the data is loaded you will learn various strategies to analyze this data and predict trends.

There are two options to get the data. One is the simple and straightforward method of downloading the CSV file for options data from the NSE website. The link to download the CSV file is given at the top of the option chain chart. Once you select the Options Contracts type or Symbol, Expiry Date, or Strike Price, download the CSV file.

Another option is to link to live data on the NSE website, to analyze options data in real-time. The data is in JSON format that has to be parsed from the NSE website. I will be explaining the process for this in the next part of this article along with different types of technical analysis.

For the options chain data analysis, I will use only some key columns and delete the remaining. The criteria for column selection will be explained when I discuss the strategy. For now, the columns that I will retain in both CALL and PUT sides are: OI, CHNG IN OI, VOLUME, LTP, CHNG, and STRIKE PRICE. Once the unwanted columns are deleted fill the empty cells with zero so that the computations are not affected by hyphens. These hyphens in the chart indicate no activity happening for the given period for the respective strike price.

Options Chain Data Analysis Strategy

The preprocessed data is now ready for analysis. Before diving into analyzing the data, you need to understand the strategy for this analysis. There are at least 100 different strategies based on which traders analyze the data. I will focus here on few commonly used strategies that will help you understand the market trend.

The key features of the options chart that is used for building the strategy are Change in price, Open interest, Change in open interest, and Volume. Few strategies omit Volume, few include other features like LTP and Implied volatility. As I mentioned earlier there are several combinations that can be used to understand the data and its movement. Buy is termed as Long and Sell as Short. The upward market trend is referred to as Bullish and the downward trend is Bearish. Based on these terms and features I have prepared a strategy table that will help in building the analysis.

Based on the strategy shown above, I have used conditional formatting and IF conditions in Excel to format my data. I have considered two conditions, less than zero and greater than zero to indicate the increase and decrease in a price change and change in open interest. Here “squaring” means a trader buys or sells a particular quantity of stock or option and later in the day reverses the transaction, hoping to earn a profit. Profit booking means exercising the options contract.

Options Chain Data Analysis

Now that the data is prepped up and strategy is in place it’s time to build the analysis. For this, insert the appropriate number of interpretation columns (four each) on both Call and Put side of the chart. Then use the following IF conditions to customize the outcome:

Interpretation — =IF(AND([@[OI Change]]=”UP”,[@[Price Change]]=”UP”),”Long Buildup”,IF(AND([@[OI Change]]=”UP”,[@[Price Change]]=”DOWN”),”Short Buildup”,IF(AND([@[OI Change]]=”DOWN”,[@[Price Change]]=”DOWN”),”Long Unwinding”,IF(AND([@[OI Change]]=”DOWN”,[@[Price Change]]=”UP”),”Short Covering”,””))))

Trend — =IF(OR([@Interpretation]=”Long Buildup”,[@Interpretation]=”Short Covering”),”Bullish”,IF(OR([@Interpretation]=”Short Buildup”,[@Interpretation]=”Long Unwinding”),”Bearish”,””))

Next use conditional formatting to enhance the visualization of the data interpretation. You can use a combination of formatting based on text and numbers. Use Icon sets, Data bars, and Color scales options in Conditional formatting for more varied analysis of different features in the chart (courtesy: Excelling Trade).

Now the chart is ready to be presented as a report for trend analysis in Options trading. You can make this chart dynamic by connecting it to live data. You can also import the data for different expiry dates and automatically refresh it. Based on strategies the analysis also varies. You can opt for technical analysis using line and bar graphs.

Frequently Asked Questions

Q1. How do you Analyse an option chain?

A. Analyzing an option chain involves examining the available options contracts for a particular underlying asset. Key aspects to consider include the strike prices, expiration dates, and associated premiums. Traders assess the implied volatility, open interest, and volume of options to gauge market sentiment. By evaluating these factors, traders can identify potential opportunities, determine risk/reward ratios, and make informed decisions when trading options.

Q2. What is the benefit of option chain analysis?

A. Option chain analysis offers several benefits for traders. It provides valuable insights into market sentiment and helps identify potential trading opportunities. By examining strike prices, expiration dates, and premiums, traders can evaluate risk/reward ratios and make informed decisions. Option chain analysis also assists in identifying potential support and resistance levels, understanding implied volatility, and formulating strategies to hedge or capitalize on market movements.

Conclusion

There is no end to the amount of information you can extract from different strategies in Options Chain analysis. I will roll out more articles in this series that will delve into connecting to real-time options data and technical analysis using Excel. Meanwhile, to learn more about options trading and stocks in general you can check out this comprehensive guide from Zerodha.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

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17 Options For Using Events Goals Reporting In Google Analytics

Track what turns your visitors on (or off) with Event Tracking in Google Analytics

Events Goals (Google help page) are a relatively unheralded feature which I thought would be useful to “flag up”, since it can helps marketers report on key marketing outcomes related to conversion and campaign tracking which are often missed in my experience.

I hope some of the 17 examples I cover in this post will help you report better on which features and messages site visitors are interacting with.

These interactions are measured using Event tracking (Google Help Page) that can then be connected to the Goals features in Google Analytics (read my post on Setting Goals in Google Analytics if you’re unfamiliar with this).

Event Goals are a relatively new feature (introduced in April 2011). Judging by many analytics setups I review where Event tracking or Event Goals aren’t used, the technique isn’t so well known by non analytics specialists. With Event Goals you can now easily report on and place a value against an action such as downloading a PDF to help with business reporting.

Before going into all the other examples, from a widget we used on a previous version of Smart Insights for encouraging email sign-up and growth of shares to social networks through the buttons at the bottom of this panel:

17 options for using Event Goals

I’ll group the different types of marketing outcomes based on user interactions you can record using Event Goals.

A. Events for rich media interactions

1. Video plays and duration. The original example used when Event tracking was first used in Google Analytics and found in the Event Tracking Help Page.

You can see that videos can be tracked using Events for different controls such as Play, Pause and Stop. The category video is used to group all videos played on the site which is useful if you have many videos.

A subtle benefit of creating events is that when you trigger an event within code the visit is no longer counted as a bounce, which is appropriate since the user has engaged.

For example, a video on a landing page with traffic on Adwords it’s important to know if someone engages with the ad.

This is the way I set it up with the designers.

1. Category event label for widget = “Widget Interactions” – we report on other widgets elsewhere on site

2. The action label in Google Analytics is the Location of widget = “Home page” (since we have some widgets in multiple locations).

B. Events for tracking interaction with navigation C. Events for tracking Response to call-to-action and promotional containers D. Events for tracking social sharing and user-generated content

6. Share button for page. The different networks an item is shared through a widget like chúng tôi can be recorded.

7. Join a social network button. Our example at the start of the article. 

9. Comments on a blog. Simple!

11. Mail To Links Not so important for most, I’ve grouped this here since often included in scripts to make it easier to track PDFs.

F. Events for tracking Ecommerce processes

Last, but not least, we have:

12. Add-to-basket Crucial for retail sites of course, this can now be analysed as an Event goal, so session Add-to-Basket conversion can be recorded more readily in the new version.

13. Product page interactions. For example selecting a size, colour or adding a rating.

14. Steps in a checkout when page addresses aren’t updated since the same pages is updated dynamically. As in the example above where there was an application quote with pages with the same address.

15. Login button This is particularly useful when linking to a domain tracked separately since this would be recorded as a bounce even though someone has engaged.

16. Form field abandonment You can trigger an event when a user interacts with each new field showing our far they have progressed through the form.

17. Form-field error messages These can be written to an Event with a label.

Wow, there were more than I thought when I started this post – I hope that’s given you some ideas to start using Event Goals or refine the ones you use.

Download 15 Years Of Nifty Index Options Data Using Nsepy Package

During the course of this article, I will also touch upon how to work with data objects and go back or move forward in time as well as optionally structure nested for loops

While there are many ways to achieve these objectives, the ones that interest me the most are equity options . Backtesting your options strategy is always a good idea before you start executing it. So I downloaded 15 years of index options data of the NSE Nifty. This is based on the very helpful nsepy package .

on fat tails in the NSE Nifty, we saw that fat-tailed events are more frequent than expected under a standard normal distribution. We also saw the impact on our returns if we are able to protect ourselves against the downside of negative black swans or

Note: This is an extremely simplistic view. Please read and watch some videos to understand this further. And don’t trade in options till you don’t understand the risks. It is better for you to donate to a charity.

Here’s a longish video on the basics of equity options that you should review before reading further if you have no idea about what options are. Below is a 2 minute Maggi noodle version

# lets try to download for a single option first; manually specify dates sample_opt1 = get_history(symbol = 'NIFTY', start = date(2004,11,1), end = date(2005,1,27), index = True, option_type = 'PE', strike_price = 2000, expiry_date = date(2005,1,27)) print(sample_opt1.shape) sample_opt1.head(n=3)

Let’s download the data for a single option contract so that we get an idea of which inputs are needed

For each options contract, we need to enter 5 variables- the start and end dates, option type, strike (contract) price, and its expiry date. The NSE site allows us to only download options data for 1 contract at a time. See the screenshot below.

Since our objective is to look at the impact of black swan events we need to get deep out of the money options for strategy. This means that for each month we need over 20 option prices for 90 months (15 years). We need to loop else we get loopy 🙂

Choosing the best option among the options

For each strike price, we have two types of options. Recall that an option is a contract that allows us to buy the underlying item (NSE Nifty for us) at a pre-agreed price on a future date irrespective of the market price on the expiry date. Call options are a right to buy the underlying and used when the market price increases while Put options are the right to sell and used to protect oneself when the market falls.

The NSE has weekly, monthly and yearly options listed on its website for strike prices that are sufficiently above and below the current market levels (aka at-the-money option price). See screenshot from the NSE site below

Of all these options, monthly options are the most liquid for the current month and next month. Liquidity shifts to the next month as the current month nears expiry. Weekly options were started a few years ago but they lack sufficient liquidity beyond the current week and next chúng tôi summary, we need to download data for monthly options with a start date 3 months prior to expiry for strike prices ~1000 points above and below the highest and lowest prices for the month for 90 months.

Sorting out dates

Monthly options are settled on the last Thursday of each month. The current date is defined as a “ object. We use the relativedelta from dateutils library to get the download start date by going back 2 months (see months = -2)

# current date - 3 months prior to the 1st option contract expiry current_date = date(2005, 1,1); print(current_date); print(type(current_date)) type(current_date) # price download start date start_date = current_date + relativedelta(months = -2); print(start_date); print(type(start_date)) start_month = current_date.month; print('Start Month:', start_month) start_yr = start_date.year; print('Start Year: ', start_yr)

NSE options expire on the last Thursday of the month for daily options and every Thursday for weekly options. For the last week of the month the monthly option doubles as the weekly option. We use the `get_expiry` function from the NSEPy library to get the list of data for all Thursdays for the month and put it inside a max function to get the date of the last Thursday or the monthly expiry.

# get expiry date end_month = current_date.month; print('End Month:', end_month) end_yr = current_date.year; print('End Year: ', end_yr) # Use the get expiry function to get a list of expiry dates - sample below # get_expiry_date returns a list of weekly expiries; use max to get the month end expiry date expiry_date = max(get_expiry_date(year = end_yr, month = end_month)) print('Expiry_date:', expiry_date, 'Type: ', type(expiry_date)) type(expiry_date) Let’s Loop

With a clear handle on start and end dates, we proceed to embed them into a loop to allow us to call the `get_expiry` function for each month over 15 years. We’ll use nested for-loops for this.

In order to identify the range of option strike prices we get the Nifty closing value for each month; define a range of strike prices that are 1000 points above the highest price for the month and 1000 points below the lowest closing for that month. For each option, we get daily prices that are 3 months prior to the expiry date.

Before we code, let’s do a recap and understand what exactly it is that we want to loop over. We want monthly option data for 15 years so 180 months. For each month assume that the average range between high and low values is 300 points. With over 1000 points above the high point and 1000 points below the option point, we have 23 option strike prices at each month and 2 types of options — Puts and Calls; which takes us to 46 discrete options per month. 46 options times 180 months gives us 8280 strike prices.

The nested loops are run as follows:

For each year in the range → For each month in the year → For each strike

# define and month year range to loop over month_list = np.arange(1, 13, step = 1); print(month_list) # break the year list into 2 parts - 2005 to 2012 and 2013 to 2023 yr_list = np.arange(2005, 2012, step = 1 ); print(yr_list) # create empty dataframe to store results nifty_data = pd.DataFrame() # to use in the loop option_data = pd.DataFrame() # to store output counter = 0 # break the loop into 2 parts to avoid querying errors for yr in yr_list: # loop through all the months and years print('Year: ', yr) for mnth in month_list: current_dt = date(yr, mnth, 1) start_dt = current_dt + relativedelta(months = -2) end_dt = max(get_expiry_date(year = yr, month = mnth)) # print('current: ', current_dt) # print('start: ', start_dt) # print('end: ', end_dt) # get nifty futures data nifty_fut = get_history(symbol = 'NIFTY', start = start_dt, end = end_dt, index = True, expiry_date = end_dt) nifty_data = nifty_data.append(nifty_fut) # calculate high and low values for each month; round off to get strike prices high = nifty_fut['Close'].max() high = int(round(high/100)*100) + 1000# ; print('High:', high) low = nifty_fut['Close'].min() low = int(round(low/100)*100) + 1000# ; print('Low :', low) for strike in range(low, high, 100): # start, stop, step """ get daily closing nifty index option prices for 3 months over the entire range """ #time.sleep(random.randint(10,25)) # pause for random interval so as to not overwhelm the site nifty_opt = get_history(symbol = 'NIFTY', start = start_dt, end = end_dt, index = True, option_type = 'PE', strike_price = strike, expiry_date = end_dt) option_data = option_data.append(nifty_opt) #time.sleep(random.randint(20,50)) # pause for random interval so as to not overwhelm the site nifty_opt = get_history(symbol = 'NIFTY', start = start_dt, end = end_dt, index = True, option_type = 'CE', strike_price = strike, expiry_date = end_dt) option_data = option_data.append(nifty_opt) counter+=1 print('Months: ', counter)

And voila! We have 15 years of option data for a range of strike prices that can be stored to csv; let’s verify before we store

# visually verify print(option_data.shape) option_data.tail()

All the above code is on this GitHub page.

Connect with me on LinkedIn, Twitter, or Medium to stay updated. That’s all folks!

Machine Learning Is Revolutionizing Stock Predictions

Stock predictions made by machine learning are being deployed by a select group of hedge funds that are betting that the technology used to make facial recognition systems can also beat human investors in the market.

Computers have been used in the stock market for decades to outrun human traders because of their ability to make thousands of trades a second. More recently, algorithmic trading has programmed computers to buy or sell stocks the instant certain criteria is met, such as when a stock suddenly becomes cheaper in one market than in another — a trade known as arbitrage.

Software That Learns to Improve Itself

Machine learning, an offshoot of studies into artificial intelligence, takes the stock trading process a giant step forward. Pouring over millions of data points from newspapers to TV shows, these AI programs actually learn and improve their stock predictions without human interaction.

According to Live Science, one recent academic study said it was now possible for computers to accurately predict whether stock prices will rise or fall based solely on whether there’s an increase in Google searches for financial terms such as “debt.” The idea is that investors get nervous before selling stocks and increase their Google searches of financial topics as a result.

These complex software packages, which were developed to help translate foreign languages and recognize faces in photographs, now are capable of searching for weather reports, car traffic in cities and tweets about pop music to help decide whether to buy or sell certain stocks.

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Mimicking Evolution and the Brain’s Neural Networks

A number of hedge funds have been set up that use only technology to make their trades. They include Sentient Technologies, a Silicon Valley-based fund headed by AI scientisk Babak Hodjat; Aidiya, a Hong Kong-based hedge fund headed by machine learning pioneer Ben Goertzel; and a fund still in “stealth mode” headed by Shaunak Khire, whose Emma computer system demonstrated that it could write financial news almost as well as seasoned journalists.

Although these funds closely guard their proprietary methods of trading, they involve two well-established facets of artificial intelligence: genetic programs and deep learning. Genetic software tries to mimic human evolution, but on a vastly faster scale, simulating millions of strategies using historic stock price data to test the theory, constantly refining the winner in a Darwinian competition for the best. While human evolution took two million years, these software giants accomplish the same evolutionary “mutations” in a matter of seconds.

Deep learning, on the other hand, is based on recent research into how the human brain works, employing many layers of neural networks to make connections with each other. A recent research study from the University of Freiburg, for example, found that deep learning could predict stock prices after a company issues a press release on financial information with about 5 percent more accuracy than the market.

Hurdles the Prediction Software Faces

None of the hedge funds using the new technology have released their results to the public, so it’s impossible to know whether these strategies work yet. One problem they face is that stock trading is not what economists call frictionless: There is a cost every time a stock is traded, and stocks don’t have one fixed price to buyers and sellers, but rather a spread between bid and offer, which can make multiple buy-and-sell orders expensive. Additionally, once it’s known that a particular program is successful, others would rush to duplicate it, rendering such trades unprofitable.

Another potential problem is the possible effects of so-called “black swan” events, or rare financial events that are completely unforeseen, such as the 2008 financial crisis. In the past, these types of events have derailed some leading hedge funds that relied heavily on algorithmic trading. Traders recall that the immensely profitable Long-Term Capital Management, which had two Nobel Prize-winning economists on its board, lost $4 billion in a matter of weeks in 1998 when Russia unexpectedly defaulted on its debt.

Some of the hedge funds say they have a human trader overseeing the computers who has the ability to halt trading if the programs go haywire, but others don’t.

The technology is still being refined and slowly integrated into the investing process at a number of firms. While the software can think for itself, humans still need to set the proper parameters to guide the machines toward a profitable outcome.

Technology and industry trends are shaping the next era of finance. Check out our complete line of finance industry solutions to stay ahead of the competition.

Iphone 4S Supply Chain Explained: The Winners And Losers

As you know by now, the handset is being assembled by Taiwan-based contract manufacturers Pegatron (an Asustek spin-off) and Hon Hai Precision Industry. The latter – also known under the Western moniker Foxconn – will be churning out iPhone 4 units this year, to be joined by Pegatron in 2012. Pegatron is reportedly tasked with building approximately one in seven iPhone 4S units. Tapping the economies of scale and long-term supply contracts, Apple is able to build iPhone 4S cheaper than its competitors while preserving traditionally high margins which are the envy of the industry.

Deutsche Bank analyst Chris Whitmore estimated in a note to clients Monday the iPhone 4S bill of materials in the $170-$220 range, depending on capacity. The figure translates to manufacturing margins between 71 and 73 percent, roughly in line with manufacturing margins for the previous-generation iPhone 4. Note that bill of materials excludes other costs associated with assembly, packaging, distribution, sales, marketing, licensing, research and development and more. As for sales potential and profitability, Asymco’s Horace Dediu praised the current iPhone family price matrix, seen right below.

The current iPhone family price matrix, courtesy of Asymco.

The analyst observed that “there is now an iPhone for every budget”, ranging from the free of charge iPhone 3GS to the $99 8GB iPhone 4 to the 16GB/32GB/64GB iPhone 4S costing $199/$299/$399 and all the way up to the unlocked 64GB iPhone 4S priced at $849. Estimating the price of a contract-free, unlocked iPhone 4S ($649/$749/$849 for the 16GB/32GB/64GB version), Dediu concludes it is “very nearly the price that operators themselves pay (excluding any volume discount)”. No surprises here, folks, the iPhone 4S remains a money-making machine. In fact, it’s more profitable than 4G Droids.

While dudes over at iPhoneItalia have taken a peek under the iPhone 4S’s hood, a thorough X-ray and teardown analysis by Chipworks and iFixit is needed to understand how Apple engineered the product. Early benchmarks confirm that iPhone 4S is twice as fast with seven times faster graphics, indicating a clock frequency of 800MHz (versus 1GHz in iPad 2). Meanwhile, UBS Research put together a list of potential key suppliers of components for the iPhone 4S (seen after the break).

Corning Glass, TPK Holdings and Wintek are being listed as touch screen suppliers. DIGITIMES thinks Apple shifted its touch panel orders among suppliers “due to a product flaw found at Wintek’s panels”. As a result, TPK Holdings’ September revenues spiked 53.7 sequentially and 139.7 percent annually while Wintek’s revenues declined 18.4 monthly and 4.5 percent annually “as Apple rejected a batch of defective touch panels for iPhone 4S”.

Sony supplies Apple with the eight-megapixel CMOS sensor for iPhone 4S, while Largan Precision is being credited with all-new optics.

Providers of the iPhone 4S’s improved camera system include CMOS supplier Sony (confirming a 9to5Mac report from April), camera modules from Sharp and LG Innotek and all-new optics with five lens instead of four, courtesy of Largan Precision and Genius Electronic Optical. It’s also possible that OmniVision joined Sony as a backup CMOS sensor supplier as they announced a thin 1080p camera sensor back in May. Most notably, however, Samsung has remained the manufacturer of Apple’s custom-designed A5 chip, arguably the iPhone’s most important hardware component…

Samsung’s declining semiconductor operations, which contributed to their weak second-quarter earnings, indicated Apple might have taken a significant portion of their business elsewhere in the face of the legal woes plaguing the long-standing partnership between the two companies. Samsung supplies Apple with A4/A5 processors, NAND flash chips and other components for their mobile devices. The Apple account was worth an estimated $5.7 billion last year, or four percent of Samsung’s total sales. Orders grew to a cool 5.8 percent in the first quarter of this year and Apple was projected to take $7.8 billion in parts from Samsung in 2011.

iPhone 4S bill of materials estimate by UBS Research

EETimes first reported back in March that Apple had shifted production of the then unreleased A5 chip for iPad 2 from Samsung to Taiwan Semiconductor Manufacturing Company (TSMC), which failed to materialize at the time. The report asserted the two companies had entered into a foundry relationship (here and here). Reuters followed up in July with the news that TSMC began a test production run of A6 chips on its newest 28-nanometer process and 3D stacking technologies, corroborated by the Taiwan Economic Times.

As yield rates improved, DIGITIMES wrote early August that TSMC received a “rushed order” from an unknown partner last minute. Two weeks later, the publication cited sources saying that Apple had recently signed a foundry partnership agreement for the next-generation CPUs on 28nm and 20nm process technologies. Although all of this seemingly points to a TSMC-manufactured A6 chip for next year’s iPhone and iPad, switching silicon providers usually takes months, if not years. Therefore, we’re not expecting TSMC to take over Apple’s chip biz from Samsung until late next year at the earnest.

In spite of its legal clash with Apple, Samsung manufactured the iPhone 4S’s A5 processor.

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Increase Your Supply Chain Productivity Through An Erp System

Maintaining a highly efficient and effective supply chain is the backbone of any successful organization and ensuring your distribution chain is well-managed and cost-effective is of crucial significance.

Surprisingly, however, many companies are not operating the series as easily as they can. If it comes to your logistical operations, you have to be considering several important things.

Total data visibility throughout your company?

Teams which could communicate readily?

Teams which may make informed decisions individually?

A system where issues are solved and caught quickly?

When the response to any one of these questions is ‘yes’, you might not be getting the best use from your own tools. Coordination and monitoring data between different sections and partners within the supply chain could be stressful. This, together with the fact that 60 per cent of internet consumers between 18-34 anticipate same-day shipping, means the pressure is increasing for companies today. In 2023, 81 per cent of companies were in the process of implementing an ERP or had already finished an implementation.

An ERP system basically streamlines management, consolidating all data and business processes from throughout the distribution chain, which includes a highly positive effect on productivity.

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Increased communication and cooperation

The best two reasons why folks employ an ERP system would be to boost performance and also make people’s jobs easier, based on the study from Panorama Consulting.

The issue with conventional working practices is that different sections do not necessarily communicate regularly or efficiently. This implies it is often hard for workers to acquire the information they require at the ideal moment. Information can be seen and shared readily –if on cellular, desktop or desktop meaning cooperation is simpler, copying of work is removed, and there is potential for greater client services.

An ERP system simplifies basic daily business tasks such as workflows and record-keeping. Repetitive activities that used to be accomplished by hand, like creating delivery notes and invoices, are considerably decreased, meaning supply chain employees can concentrate on more important jobs which add value and deliver results. By way of instance, contemporary ERP systems raise on-time deliveries by 21 per cent through automation, based on the study by the Aberdeen Group.

Offers insightful and accurate reporting

The way information is interconnected within an ERP system means that you may find a 360-degree perspective of the company at any certain time, then turn this raw information into actionable insight. Most programs have a dash view where you are able to view the larger image, drilling down into the information you want quickly and easily–if that is to resolve an issue and make procedures more effective, or discover a new business prospect.

As time passes, your supply chain information will collect and machine learning tools may be implemented. Applying machine learning and AI won’t just reduce mistakes, but also help companies make better decisions by creating predictions and forecasts based on the previous action within the distribution chain. Firms can finally price items more efficiently, have better stock monitoring, and program assets more correctly.

Allows for successful, minimally invasive safety(Safety checklist)

Many do not see the link between productivity and security, but both are often closely connected. By way of instance, if a company loses information, each has a massive influence on everyone. As data is centralized within an ERP system, making it simple for companies to establish automatic, scheduled backups to minimize downtime when things fail. Employees can become accidentally locked from programs, forget their passwords, or even get hung up in protracted confirmation procedures. Using an ERP system, users can access important files through one sign-on assistance, so that they simply need to recall details to get one system.

Machine learning programs in an ERP may also quickly block unauthorized access and alert administrators of any questionable activity. At precisely the exact same time, an ERP may also recognize routine behaviours and ensure, it is simple for people who have the right credentials to get the machine, fostering employee satisfaction and productivity within the procedure.

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