Understanding Balance Sheet

A balance sheet, also known as a "statement of financial position", reveals a company's assets, liabilities and owners' equity (net worth). The balance sheet, together with the income statement and cash flow statement, make up the cornerstone of any company's financial statements. If you are a shareholder of a company, it is important that you understand how the balance sheet is structured, how to analyze it and how to read it.

How the Balance Sheet Works

The balance sheet is divided into two parts that, based on the following equation, must equal (or balance out) each other. The main formula behind balance sheets is:

assets = liabilities + shareholders' equity

This means that assets, or the means used to operate the company, are balanced by a company's financial obligations along with the equity investment brought into the company and its retained earnings.

Assets are what a company uses to operate its business, while its liabilities and equity are two sources that support these assets. Owners' equity, referred to as shareholders' equity in a publicly traded company, is the amount of money initially invested into the company plus any retained earnings, and it represents a source of funding for the business.

It is important to note, that a balance sheet is a snapshot of the company’s financial position at a single point in time.

Know the Types of Assets

Current Assets

Current assets have a life span of one year or less, meaning they can be converted easily into cash. Such assets classes are: cash and cash equivalents, accounts receivable and inventory. Cash, the most fundamental of current assets, also includes non-restricted bank accounts and checks. Cash equivalents are very safe assets that can be are readily converted into cash such as Treasuries. Accounts receivable consists of the short-term obligations owed to the company by its clients. Companies often sell products or services to customers on credit, which then are held in this account until they are paid off by the clients. Lastly, inventory represents the raw materials, work-in-progress goods and the company’s finished goods. Depending on the company, the exact makeup of the inventory account will differ. For example, a manufacturing firm will carry a large amount of raw materials, while a retail firm caries none. The makeup of a retailers inventory typically consists of goods purchased from manufacturers and wholesalers.

Non-Current Assets

Non-current assets, are those assets that are not turned into cash easily, expected to be turned into cash within a year and/or have a life-span of over a year. They can refer to tangible assets such as machinery, computers, buildings and land. Non-current assets also can be intangible assets, such as goodwill, patents or copyright. While these assets are not physical in nature, they are often the resources that can make or break a company - the value of a brand name, for instance, should not be underestimated.

Depreciation is calculated and deducted from most of these assets, which represents the economic cost of the asset over its useful life.

Learn the Different Liabilities

On the other side of the balance sheet are the liabilities. These are the financial obligations a company owes to outside parties. Like assets, they can be both current and long-term. Long-term liabilities are debts and other non-debt financial obligations, which are due after a period of at least one year from the date of the balance sheet. Current liabilities are the company’s liabilities which will come due, or must be paid, within one year. This is comprised of both shorter term borrowings, such as accounts payables, along with the current portion of longer term borrowing, such as the latest interest payment on a 10-year loan.

Shareholders' Equity

Shareholders' equity is the initial amount of money invested into a business. If, at the end of the fiscal year, a company decides to reinvest its net earnings into the company (after taxes), these retained earnings will be transferred from the income statement onto the balance sheet into the shareholder’s equity account. This account represents a company's total net worth. In order for the balance sheet to balance, total assets on one side have to equal total liabilities plus shareholders' equity on the other.

Another interesting aspect of the balance sheet is how it is organized. The assets and liabilities sections of the balance sheet are organized by how current the account is. So for the asset side, the accounts are classified typically from most liquid to least liquid. For the liabilities side, the accounts are organized from short to long-term borrowings and other obligations.

Analyze the Balance Sheet with Ratios

With a greater understanding of the balance sheet and how it is constructed, we can look now at some techniques used to analyze the information contained within the balance sheet. The main way this is done is through financial ratio analysis.

Financial ratio analysis uses formulas to gain insight into the company and its operations. For the balance sheet, using financial ratios (like the debt-to-equity ratio) can show you a better idea of the company’s financial condition along with its operational efficiency. It is important to note that some ratios will need information from more than one financial statement, such as from the balance sheet and the income statement.

ADR Basics

ADR Basics:

Globalization is the dissolution of barriers to trade and the tendency of the world's businesses to integrate customs and values. Globalization is making it increasingly easy to travel, correspond and even invest in other countries.

Investing money in your own country's stock market is relatively simple. You call your broker or login to your online account and place a buy or sell order. Investing in a company that is listed on a foreign exchange is much more difficult. Would you even know where to start? Does your broker provide services in other countries? For example, imagine the commission and foreign exchange costs on an investment in Russia or Indonesia.

However, now there is an easy way around this through American depository receipts (ADRs). More than 2,000 foreign companies provide this option for U.S. and Canadian investors interested in buying shares.

What Is An ADR?
Introduced to the financial markets in 1927, an American depository receipt (ADR) is a stock that trades in the United States but represents a specified number of shares in a foreign corporation. ADRs are bought and sold on American markets just like regular stocks, and are issued/sponsored in the U.S. by a bank or brokerage.

ADRs were introduced as a result of the complexities involved in buying shares in foreign countries and the difficulties associated with trading at different prices and currency values. For this reason, U.S. banks simply purchase a bulk lot of shares from the company, bundle the shares into groups, and reissues them on either the New York Stock Exchange (NYSE), American Stock Exchange (AMEX) or the Nasdaq. In return, the foreign company must provide detailed financial information to the sponsor bank. The depository bank sets the ratio of U.S. ADRs per home-country share. This ratio can be anything less than or greater than 1. This is done because the banks wish to price an ADR high enough to show substantial value, yet low enough to make it affordable for individual investors. Most investors try to avoid investing in penny stocks, and many would shy away from a company trading for 50 Russian roubles per share, which equates to US$1.50 per share. As a result, the majority of ADRs range between $10 and $100 per share. If, in the home country, the shares were worth considerably less, then each ADR would represent several real shares.

There are three different types of ADR issues:
Level 1 - This is the most basic type of ADR where foreign companies either don't qualify or don't wish to have their ADR listed on an exchange. Level 1 ADRs are found on the over-the-counter market and are an easy and inexpensive way to gauge interest for its securities in North America. Level 1 ADRs also have the loosest requirements from the Securities and Exchange Commission (SEC).
Level 2 - This type of ADR is listed on an exchange or quoted on Nasdaq. Level 2 ADRs have slightly more requirements from the SEC, but they also get higher visibility trading volume.
Level 3 - The most prestigious of the three, this is when an issuer floats a public offering of ADRs on a U.S. exchange. Level 3 ADRs are able to raise capital and gain substantial visibility in the U.S. financial markets.
The advantages of ADRs are twofold. For individuals, ADRs are an easy and cost-effective way to buy shares in a foreign company. They save money by reducing administration costs and avoiding foreign taxes on each transaction. Foreign entities like ADRs because they get more U.S. exposure, allowing them to tap into the wealthy North American equities markets.

Risks:

There are several factors that determine the value of the ADR beyond the performance of the company. Analyzing these foreign companies involves further scrutiny than merely looking at the fundamentals. Here are some other risks that investors should consider:
Political Risk - Ask yourself if you think the government in the home country of the ADR is stable? For example, you might be wary of Russian Vodka Inc. because of the characteristic instability of the Russian government.
Exchange Rate Risk - Is the currency of the home country stable? Remember the ADR shares track the shares in the home country. If a country's currency is devalued, it will trickle down to your ADR. This can result in a big loss, even if the company had been performing well.
Inflationary Risk - This is an extension of the exchange rate risk. Inflation is the rate at which the general level of prices for goods and services is rising and, subsequently, purchasing power is falling. Inflation can be a big blow to business because the currency of a country with high inflation becomes less and less valuable each day.
With globalization dissolving borders, it only makes sense that we have the ability to invest in foreign entities. Many nations who are striving to become industrialized are undervalued compared to the levels they will eventually reach.

ADR
An American Depository Receipt (or ADR) represents ownership in the shares of a foreign company trading on US financial markets. The stock of many non-US companies trades on US exchanges through the use of ADRs. ADRs enable US investors to buy shares in foreign companies without undertaking cross-border transactions. ADRs carry prices in US dollars, pay dividends in US dollars, and can be traded like the shares of US-based companies.

Each ADR is issued by a US depository bank and can represent a fraction of a share, a single share, or multiple shares of foreign stock. An owner of an ADR has the right to obtain the foreign stock it represents, but US investors usually find it more convenient simply to own the ADR. The price of an ADR is often close to the price of the foreign stock in its home market, adjusted for the ratio of ADRs to foreign company shares. In the case of companies incorporated in the United Kingdom, creation of ADRs attracts a 1.5% stamp duty reserve tax (SDRT) charge by the UK government.

Depository banks have numerous responsibilities to an ADR holder and to the non-US company the ADR represents. The first ADR was introduced by JPMorgan in 1927, for the British retailer Selfridges&Co. The largest depository bank is the Bank of New York Mellon.
Individual shares of a foreign corporation represented by an ADR are called American Depository Shares (ADS).

Algorithmic trading

In electronic financial markets, algorithmic trading or automated trading, also known as algo trading, black-box trading or robo trading, is the use of computer programs for entering trading orders with the computer algorithm deciding on aspects of the order such as the timing, price, or quantity of the order, or in many cases initiating the order without human intervention. Algorithmic Trading is widely used by pension funds, mutual funds, and other buy side (investor driven) institutional traders, to divide large trades into several smaller trades in order to manage market impact, and risk.[1][2] Sell side traders, such as market makers and some hedge funds, provide liquidity to the market, generating and executing orders automatically. In this "high frequency trading" (HFT) computers make the decision to initiate orders based on information that is received electronically, before human traders are even aware of the information.

Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically ("on auto-pilot").

A third of all EU and US stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based financial services industry research and consulting firm Aite Group.[3] As of 2009, high frequency trading firms account for 73% of all US equity trading volume.[4]

In 2006 at the London Stock Exchange, over 40% of all orders were entered by algo traders, with 60% predicted for 2007. American markets and equity markets generally have a higher proportion of algo trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algo trading (about 25% of orders in 2006).[5] Futures and options markets are considered to be fairly easily integrated into algorithmic trading,[6] with about 20% of options volume expected to be computer generated by 2010.[7] Bond markets are moving toward more access to algorithmic traders.[8]

One of the main issues regarding high frequency trading is the difficulty in determining just how profitable it is. A report released in August 2009 by the TABB Group, a financial services industry research firm, estimated that the 300 securities firms and hedge funds that specialize in rapid fire algorithmic trading took in roughly $21 billion in profits in 2008[9].

History

Computerization of the order flow in financial markets began in the early 1970s with some landmarks being the introduction of the New York Stock Exchange’s “designated order turnaround” system (DOT, and later SuperDOT) which routed orders electronically to the proper trading post to be executed manually, and the "opening automated reporting system" (OARS) which aided the specialist in determining the market clearing opening price (SOR; Smart Order Routing).

Program trading is defined by the New York Stock Exchange as an order to buy or sell 15 or more stocks valued at over $1 million total. In practice this means that all program trades are entered with the aid of a computer. In the 1980s program trading became widely used in trading between the S&P500 equity and futures markets.

In stock index arbitrage a trader buys (or sells) a stock index futures contract such as the S&P 500 futures and sells (or buys) a portfolio of up to 500 stocks (can be a much smaller representative subset) at the NYSE matched against the futures trade. The program trade at the NYSE would be pre-programmed into a computer to enter the order automatically into the NYSE’s electronic order routing system at a time when the futures price and the stock index were far enough apart to make a profit.

At about the same time portfolio insurance was designed to create a synthetic put option on a stock portfolio by dynamically trading stock index futures according to a computer model based on the Black-Scholes option pricing model.

Both strategies, often simply lumped together as “program trading,” were blamed by many people (for example by the Brady report) for exacerbating or even starting the 1987 stock market crash. Yet the impact of computer driven trading on stock market crashes is unclear and widely discussed in the academic community.[10]

Financial markets with fully electronic execution and similar electronic communication networks developed in the late 1980s and 1990s. In the U.S., decimalization, which changed the minimum tick size from 1/16th of a dollar ($0.0625) to $0.01 per share, may have encouraged algorithmic trading as it changed the market microstructure by permitting smaller differences between the bid and offer prices, decreasing the market-makers' trading advantage, thus increasing market liquidity.

This increased market liquidity led to institutional traders splitting up orders according to computer algorithms in order to execute their orders at a better average price. These average price benchmarks are measured and calculated by computers by applying the time weighted (i.e unweighted) average price TWAP or more usually by the volume weighted average price VWAP.

As more electronic markets opened, other algorithmic trading strategies were introduced. These strategies are more easily implemented by computers because machines can react more rapidly to temporary mispricing and examine prices from several markets simultaneously. For example Stealth (developed by Deutsche Bank), Sniper and Guerilla (developed by Credit Suisse[11]), arbitrage, statistical arbitrage, trend following, and mean reversion.

This type of trading is what is driving the new demand for Low Latency Proximity Hosting and Global Exchange Connectivity. It is imperative to understand what is latency when putting together a strategy for electronic trading. Latency refers to the 'delay' between the transmission of information from a source and the reception of the information at a destination. Latency has as a lower bound the speed of light; this corresponds to a few microseconds per kilometer of optical fibre. Any signal regenerating or routing equipment will introduce greater latency than this speed-of-light baseline.

Strategies

Many different algorithms have been developed to implement different trading strategies. Much early algo trading was developed for the buy side in order to reduce transactions costs. Recently, high frequency trading, which is generally a type of market making by sell side traders, has become more prominent and controversial.[12] These algorithms or techniques are commonly given names such as "Stealth", "Iceberg", "Dagger", "Guerrilla", "Sniper" and "Sniffer".[13]

[edit] Transaction cost reduction
Large orders are broken down into several smaller orders and entered into the market over time. This basic strategy is called "iceberging". The success of this strategy may be measured by the average purchase price against the VWAP for the market over that time period. One algorithm designed to find hidden orders or icebergs is called "Stealth".

[edit] Market making and high frequency trading
Market making involves placing a limit order to sell (or offer) above the current market price or a buy limit order (or bid) below the current price in order to benefit from the bid-ask spread. Automated Trading Desk, which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both NASDAQ and the New York Stock Exchange.[14]

High frequency traders use computers that execute trades within milliseconds, or "with extremely low latency" in the jargon of the trade. In the U.S., high-frequency trading firms represent 2.0% of the approximately 20,000 firms operating today, but account for 73.0% of all equity trading volume.[15] As of the first quarter in 2009, total assets under management for hedge funds with high frequency trading strategies were $141 billion, down about 21% from their high.[16] The high frequency strategy was first made successful by Renaissance Technologies.[17] High frequency funds started to become especially popular in 2007 and 2008.[16] Many high frequency firms say they are market makers and that the liquidity they add to the market has lowered volatility and helped narrow spreads, but unlike traditional market makers, such as specialists on the New York Stock Exchange, they have few or no regulatory requirements.[18]

These funds are highly dependent on ultra-low latency networks. They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors.[4] The revolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform in order to benefit from implementing high frequency strategies.[4] Strategies are constantly altered to reflect the subtle changes in the market as well as to combat the threat of the strategy being reverse engineered by competitors. There is also a very strong pressure to continuously add features or improvements to a particular algorithm, such as client specific modifications and various performance enhancing changes (regarding benchmark trading performance, cost reduction for the trading firm or a range of other implementations). This is due to the evolutionary nature of algorithmic trading strategies - they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios. As a result, a significant proportion of net revenue from firms is spent on the R&D of these autonomous trading systems.[4]

[edit] Arbitrage
A classical arbitrage strategy might involve three or four securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency. If the market prices are sufficiently different from those implied in the model to cover transactions cost then four transactions can be made to guarantee a risk-free profit. Algorithmic trading allows similar arbitrages using models of greater complexity involving many more than 4 securities. The TABB Group estimates that annual aggregate profits of low latency arbitrage strategies currently exceed US$21 billion.[4]

[edit] More complicated strategies
A "benchmarking" algorithm is used by traders attempting to mimic an index's return.

Any type of algo trading which depends on the programming skills of other algo traders is called "gaming". Dark pools are alternative electronic stock exchanges where trading takes place anonymously, with most orders hidden or "iceberged."[19] Gamers or "sharks" sniff out large orders by "pinging" small market orders to buy and sell. When several small orders are filled the sharks may have discovered the presence of a large iceberged order.

Any sort of pattern recognition or predictive model can be used to initiate algo trading. Neural networks and genetic programming have been used to create these models.

“Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”[20]

The arms race has allegedly included stealing computer code. UBS has sued three of its former traders and Jefferies & Company for stealing algorithmic trading programs.[21]

Yield curve

In finance, the yield curve is the relation between the interest rate (or cost of borrowing) and the time to maturity of the debt for a given borrower in a given currency. For example, the U.S. dollar interest rates paid on U.S. Treasury securities for various maturities are closely watched by many traders, and are commonly plotted on a graph such as the one on the right which is informally called "the yield curve." More formal mathematical descriptions of this relation are often called the term structure of interest rates.


The yield of a debt instrument is the overall rate of return available on the investment. For instance, a bank account that pays an interest rate of 4% per year has a 4% yield. In general the percentage per year that can be earned is dependent on the length of time that the money is invested. For example, a bank may offer a "savings rate" higher than the normal checking account rate if the customer is prepared to leave money untouched for five years. Investing for a period of time t gives a yield Y(t).
This function Y is called the yield curve, and it is often, but not always, an increasing function of t. Yield curves are used by fixed income analysts, who analyze bonds and related securities, to understand conditions in financial markets and to seek trading opportunities. Economists use the curves to understand economic conditions.

The typical shape of the yield curve




Yield curves are usually upward sloping asymptotically: the longer the maturity, the higher the yield, with diminishing marginal increases (that is, as one moves to the right, the curve flattens out). There are two common explanations for upward sloping yield curves. First, it may be that the market is anticipating a rise in the risk-free rate. If investors hold off investing now, they may receive a better rate in the future. Therefore, under the arbitrage pricing theory, investors who are willing to lock their money in now need to be compensated for the anticipated rise in rates—thus the higher interest rate on long-term investments.
However, interest rates can fall just as they can rise. Another explanation is that longer maturities entail greater risks for the investor (i.e. the lender). A risk premium is needed by the market, since at longer durations there is more uncertainty and a greater chance of catastrophic events that impact the investment. This explanation depends on the notion that the economy faces more uncertainties in the distant future than in the near term. This effect is referred to as the liquidity spread. If the market expects more volatility in the future, even if interest rates are anticipated to decline, the increase in the risk premium can influence the spread and cause an increasing yield.
The opposite position (short-term interest rates higher than long-term) can also occur. For instance, in November 2004, the yield curve for UK Government bonds was partially inverted. The yield for the 10 year bond stood at 4.68%, but was only 4.45% for the 30 year bond. The market's anticipation of falling interest rates causes such incidents. Negative liquidity premiums can exist if long-term investors dominate the market, but the prevailing view is that a positive liquidity premium dominates, so only the anticipation of falling interest rates will cause an inverted yield curve. Strongly inverted yield curves have historically preceded economic depressions.
The shape of the yield curve is influenced by supply and demand: for instance if there is a large demand for long bonds, for instance from pension funds to match their fixed liabilities to pensioners, and not enough bonds in existence to meet this demand, then the yields on long bonds can be expected to be low, irrespective of market participants' views about future events.
The yield curve may also be flat or hump-shaped, due to anticipated interest rates being steady, or short-term volatility outweighing long-term volatility.
Yield curves continually move all the time that the markets are open, reflecting the market's reaction to news. A further "stylized fact" is that yield curves tend to move in parallel (i.e., the yield curve shifts up and down as interest rate levels rise and fall).

Types of yield curve
There is no single yield curve describing the cost of money for everybody. The most important factor in determining a yield curve is the currency in which the securities are denominated. The economic position of the countries and companies using each currency is a primary factor in determining the yield curve. Different institutions borrow money at different rates, depending on their creditworthiness. The yield curves corresponding to the bonds issued by governments in their own currency are called the government bond yield curve (government curve). Banks with high credit ratings (Aa/AA or above) borrow money from each other at the LIBOR rates. These yield curves are typically a little higher than government curves. They are the most important and widely used in the financial markets, and are known variously as the LIBOR curve or the swap curve. The construction of the swap curve is described below.

Besides the government curve and the LIBOR curve, there are corporate (company) curves. These are constructed from the yields of bonds issued by corporations. Since corporations have less creditworthiness than most governments and most large banks, these yields are typically higher. Corporate yield curves are often quoted in terms of a "credit spread" over the relevant swap curve. For instance the five-year yield curve point for Vodafone might be quoted as LIBOR +0.25%, where 0.25% (often written as 25 basis points or 25bps) is the credit spread.

Normal yield curve
From the post-Great Depression era to the present, the yield curve has usually been "normal" meaning that yields rise as maturity lengthens (i.e., the slope of the yield curve is positive). This positive slope reflects investor expectations for the economy to grow in the future and, importantly, for this growth to be associated with a greater expectation that inflation will rise in the future rather than fall. This expectation of higher inflation leads to expectations that the central bank will tighten monetary policy by raising short term interest rates in the future to slow economic growth and dampen inflationary pressure. It also creates a need for a risk premium associated with the uncertainty about the future rate of inflation and the risk this poses to the future value of cash flows. Investors price these risks into the yield curve by demanding higher yields for maturities further into the future.

However, a positively sloped yield curve has not always been the norm. Through much of the 19th century and early 20th century the US economy experienced trend growth with persistent deflation, not inflation. During this period the yield curve was typically inverted, reflecting the fact that deflation made current cash flows less valuable than future cash flows. During this period of persistent deflation, a 'normal' yield curve was negatively sloped.

Steep yield curve
Historically, the 20-year Treasury bond yield has averaged approximately two percentage points above that of three-month Treasury bills. In situations when this gap increases (e.g. 20-year Treasury yield rises higher than the three-month Treasury yield), the economy is expected to improve quickly in the future. This type of curve can be seen at the beginning of an economic expansion (or after the end of a recession). Here, economic stagnation will have depressed short-term interest rates; however, rates begin to rise once the demand for capital is re-established by growing economic activity.

In January 2010, the gap between yields on two-year Treasury notes and 10-year notes widened to 2.90 percentage points, its highest ever.

Flat or humped yield curve
A flat yield curve is observed when all maturities have similar yields, whereas a humped curve results when short-term and long-term yields are equal and medium-term yields are higher than those of the short-term and long-term. A flat curve sends signals of uncertainty in the economy. This mixed signal can revert to a normal curve or could later result into an inverted curve. It cannot be explained by the Segmented Market theory discussed below.

Inverted yield curve
An inverted yield curve occurs when long-term yields fall below short-term yields. Under unusual circumstances, long-term investors will settle for lower yields now if they think the economy will slow or even decline in the future. An inverted curve has indicated a worsening economic situation in the future 6 out of 7 times since 1970.[citation needed] The New York Federal Reserve regards it as a valuable forecasting tool in predicting recessions two to six quarters ahead. In addition to potentially signaling an economic decline, inverted yield curves also imply that the market believes inflation will remain low. This is because, even if there is a recession, a low bond yield will still be offset by low inflation. However, technical factors, such as a flight to quality or global economic or currency situations, may cause an increase in demand for bonds on the long end of the yield curve, causing long-term rates to fall. This was seen in 1998 during the Long Term Capital Management failure when there was a slight inversion on part of the curve.