::StockHome::

      Personal Online Notedesk

Portfolio And Valuation

Achieving Better Returns In Your Portfolio

If you had the ability to surpass market returns over extended periods of time, wouldn't you do it? Achieving better returns in your portfolio may be easier than you think. It has nothing to do with luck, skill or Wall Street insight. Instead, it has everything to do with portfolio construction. So, put away the darts, ignore the egocentric cocktail-party chatter and forget your hunches. The truth about exerting better control of your investment results lies just ahead.
Research, Not Intuition

Successful portfolios are based on research and reasonable expectations, not intuition. Illogical investors attempt to guess which manager, stock or asset class will have tomorrow's best performance. That's why so many have consistently failed. Successful, rational investors excel because of a clear methodology, and, of course, discipline. What type of investor are you? What type of investor do you want to be?
See: Stock-Picking Strategies

Asset Allocation
Asset allocation deals with how investors divide their portfolio among three major asset categories: cash, bonds and stocks. The asset-allocation decision, otherwise known as investment policy, is arguably the most important determinant of a portfolio's long-term return. A study by landmark Brinson, Hood and Beebower, "Determinants of Portfolio Performance" (1986, 1991) argues that investment policy accounts for 94% of the variation in returns in a portfolio, leaving market timing and stock selection to account for only 6%. (For more information, see Five Things To Know About Asset Allocation and 6 Asset Allocation Strategies That Work.).

An overwhelming amount of evidence shows that there is little advantage in attempting either to time markets or select individual equities. Such efforts instead result in additional cost, additional risk and lower returns over time.

Risk and Reward
Risk and reward are related. An investor must be induced by a potential investment return in order to give up a risk-free alternative like a Treasury bill. The same holds true for small company stock. Wouldn't you demand a greater potential return to buy a fledgling, unknown company instead of Microsoft? It's a no-brainer. There's more risk involved in the transaction, so you should demand to get paid more, right?
Investing in the equity markets can be a risky proposition. For decades, investors were only concerned with one factor, beta, in their portfolio selection. Thus, beta, the relative volatility of an asset (or portfolio) to market movements was believed to explain most of a portfolio's return. This one-factor model, otherwise known as the capital asset pricing model (CAPM), implies that there is a linear relationship between an asset's expected return and its corresponding beta. In truth, however, beta is not the only determinate of portfolio returns. Thus, CAPM has been expanded to include two other key risk factors that together better explain portfolio performance: market capitalization and book value/market value. (For more information, see Market Volatility, Weak Economy Delay Major IPOs.)

Three-Factor Model
A pioneering study by renowned academics, Eugene Fama and Ken French, suggests that three risk factors: market (beta), size (market capitalization) and price (book/market value) dimensions explain 96% of historical equity performance. This model goes further than CAPM to include the fact that two particular types of stocks outperform markets on a regular basis: value stocks (high book/market value) and small caps. Although it is not clear as to why this is so, this pattern nonetheless persists in multiple time frames and every global market where we can assemble data. Below is historical data showing that between 1960 and 2004, small value (small-cap, high book/market value) stocks outperformed both the market (S&P 500) and large-value stocks. What this implies is that historically, small-value "just happens" to deliver higher returns and higher volatility than the stock market as a whole. (To learn more, see How is the value of the S&P 500 calculated?)


The Fama-French three-factor model allows investors to calculate the way portfolios take different types of risk and to calculate their expected returns. The following exhibit shows how portfolios are plotted using their risk exposures. The vertical and horizontal axes represent the exposures to value and small-cap stocks. Portfolios exposed to size risk (small-cap stocks) plot along the vertical (size) axis; and those that take risk on distressed companies (value stocks) plot on the horizontal (value) axis. Since all equity portfolios assume a market risk (beta), no additional axis is needed. Market risk is represented just below the intersection of the axes, and anything above the line is excess return above the market.


Fama and French aren't particular about why book/market value measures risk, although they and others have suggested some possible reasons. For example, high book/market values could mean a stock is distressed, temporarily selling low because future earnings look doubtful, which implies that value stocks are more risky than average - exactly the opposite of what a traditional business analyst would tell you. The business analyst would say high book/market values indicate a buying opportunity since the stock looks cheap relative to its intrinsic value. Regardless of the reasoning, there is one thing that you should take away from the three-risk-factor model - history has shown that small value stocks tend to have higher returns and higher volatility than the stock market as a whole. Thus, by carefully managing the amount of some small value stocks in your portfolio, you can achieve above-average returns. (To learn more, see How To Avoid Closing Options Below Intrinsic Value.)


Market Cycles
Asset classes have unique cycles. When growth is doing well, value may not do as well and vice versa. In some years, small and value stocks may outperform the market; in others they may underperform. It takes resilience and psychological preparedness to endure the times they underperform. Remember, investing in small and value stocks should augment the bottom line in the long run, but investors should understand that their portfolio will not identically track the market every single year (and that's OK).

Deciding on the degree to which your portfolio should be based on the three risk factors is the challenge for the investor. Tilting toward small and value stocks will help you reach above global market returns, but portfolio risk must be tempered by adding other assets with low correlations (e.g., bonds, international stocks, international small stocks and international small value stocks).

The Bottom Line
So, what have you learned? Investors can structure portfolios that deliver above global market index returns by designing a strategic portfolio tilt. Fundamental theories like asset allocation and the three-factor model can have a dramatic impact on the way you invest.

Remember, designing a portfolio that favors small and value companies over pure market risk should deliver higher expected returns over extended periods of time. These benefits can be reliably captured by passive strategies (like index funds) that do not rely on either individual stock selection or market timing. As with many things in life, a little strategic planning goes a long way.


The Capital Asset Pricing Model (CAPM)

No matter how much we diversify our investments, it's impossible to get rid of all the risk. As investors, we deserve a rate of return that compensates us for taking on risk. The capital asset pricing model (CAPM) helps us to calculate investment risk and what return on investment we should expect. Here we look at the formula behind the model, the evidence for and against the accuracy of CAPM, and what CAPM means to the average investor.

Birth of a Model
The capital asset pricing model was the work of financial economist (and, later, Nobel laureate in economics) William Sharpe, set out in his 1970 book "Portfolio Theory And Capital Markets." His model starts with the idea that individual investment contains two types of risk:

  1. Systematic Risk - These are market risks that cannot be diversified away. Interest rates, recessions and wars are examples of systematic risks.

  2. Unsystematic Risk - Also known as "specific risk," this risk is specific to individual stocks and can be diversified away as the investor increases the number of stocks in his or her portfolio. In more technical terms, it represents the component of a stock's return that is not correlated with general market moves.
Modern portfolio theory shows that specific risk can be removed through diversification. The trouble is that diversification still doesn't solve the problem of systematic risk; even a portfolio of all the shares in the stock market can't eliminate that risk. Therefore, when calculating a deserved return, systematic risk is what plagues investors most. CAPM, therefore, evolved as a way to measure this systematic risk. (To learn more, see Modern Portfolio Theory: An Overview.)

The Formula
Sharpe found that the return on an individual stock, or a portfolio of stocks, should equal its cost of capital. The standard formula remains the CAPM, which describes the relationship between risk and expected return.

Here is the formula:

CAPM's starting point is the risk-free rate - typically a 10-year government bond yield. To this is added a premium that equity investors demand to compensate them for the extra risk they accept. This equity market premium consists of the expected return from the market as a whole less the risk-free rate of return. The equity risk premium is multiplied by a coefficient that Sharpe called "beta."
More than 50% of retirement age individuals to not have enough savings
 
Beta
According to CAPM, beta is the only relevant measure of a stock's risk. It measures a stock's relative volatility - that is, it shows how much the price of a particular stock jumps up and down compared with how much the stock market as a whole jumps up and down. If a share price moves exactly in line with the market, then the stock's beta is 1. A stock with a beta of 1.5 would rise by 15% if the market rose by 10%, and fall by 15% if the market fell by 10%. (For further reading, see Beta: Gauging Price Fluctuations and Beta: Know The Risk.)

Beta is found by statistical analysis of individual, daily share price returns, in comparison with the market's daily returns over precisely the same period. In their classic 1972 study titled "The Capital Asset Pricing Model: Some Empirical Tests," financial economists Fischer Black, Michael C. Jensen and Myron Scholes confirmed a linear relationship between the financial returns of stock portfolios and their betas. They studied the price movements of the stocks on the New York Stock Exchange between 1931 and 1965.


Beta, compared with the equity risk premium, shows the amount of compensation equity investors need for taking on additional risk. If the stock's beta is 2.0, the risk-free rate is 3% and the market rate of return is 7%, the market's excess return is 4% (7% - 3%). Accordingly, the stock's excess return is 8% (2 X 4%, multiplying market return by the beta), and the stock's total required return is 11% (8% + 3%, the stock's excess return plus the risk-free rate).

What this shows is that a riskier investment should earn a premium over the risk-free rate - the amount over the risk-free rate is calculated by the equity market premium multiplied by its beta. In other words, it's possible, by knowing the individual parts of the CAPM, to gauge whether or not the current price of a stock is consistent with its likely return - that is, whether or not the investment is a bargain or too expensive.

What CAPM Means for You
This model presents a very simple theory that delivers a simple result. The theory says that the only reason an investor should earn more, on average, by investing in one stock rather than another is that one stock is riskier. Not surprisingly, the model has come to dominate modern financial theory. But does it really work?

It's not entirely clear. The big sticking point is beta. When professors Eugene Fama and Kenneth French looked at share returns on the New York Stock Exchange, the American Stock Exchange and Nasdaq between 1963 and 1990, they found that differences in betas over that lengthy period did not explain the performance of different stocks. The linear relationship between beta and individual stock returns also breaks down over shorter periods of time. These findings seem to suggest that CAPM may be wrong.

While some studies raise doubts about CAPM's validity, the model is still widely used in the investment community. Although it is difficult to predict from beta how individual stocks might react to particular movements, investors can probably safely deduce that a portfolio of high-beta stocks will move more than the market in either direction, and a portfolio of low-beta stocks will move less than the market.

This is important for investors - especially fund managers - because they may be unwilling to or prevented from holding cash if they feel that the market is likely to fall. If so, they can hold low-beta stocks instead. Investors can tailor a portfolio to their specific risk-return requirements, aiming to hold securities with betas in excess of 1 while the market is rising, and securities with betas of less than 1 when the market is falling.

Not surprisingly, CAPM contributed to the rise in use of indexing - assembling a portfolio of shares to mimic a particular market - by risk averse investors. This is largely due to CAPM's message that it is only possible to earn higher returns than those of the market as a whole by taking on higher risk (beta). (To learn more, see The Lowdown On Index Funds.)

Conclusion
The capital asset pricing model is by no means a perfect theory. But the spirit of CAPM is correct. It provides a usable measure of risk that helps investors determine what return they deserve for putting their money at risk. To learn more, see Achieving Better Returns In Your Portfolio.

 

 

Source: Investopedia



Value At Risk (VAR)

A statistical technique used to measure and quantify the level of financial risk within a firm or investment portfolio over a specific time frame. Value at risk is used by risk managers in order to measure and control the level of risk which the firm undertakes. The risk manager's job is to ensure that risks are not taken beyond the level at which the firm can absorb the losses of a probable worst outcome.


Value at Risk is measured in three variables: the amount of potential loss, the probability of that amount of loss, and the time frame. For example, a financial firm may determine that it has a 5% one month value at risk of $100 million. This means that there is a 5% chance that the firm could lose more than $100 million in any given month. Therefore, a $100 million loss should be expected to occur once every 20 months.  

 

Source: Investopedia

Recent Blog Entries

News Links



The essence of quantitative investing is crunching numbers. Anything that can go into a digital computer is fair input. And since computers are mostly digital and linear programs are rigid, “quant” analysis tends to be repetitively structured and rich in reliance on back-testing.

The central themes of quant investing are that history reveals enduring patterns of price behavior, which can be unlocked by statistical techniques; that risk of loss is closely related to volatility, which is related to return; and that management of risk, return, co-variance and time frames can be usefully predictable.

Even quantitative back-testing is intuitive “data mining” — determining what patterns exist in a finite sample of numbers. Investment strategies based on hindsight often fail.
The role of derivatives for managing risk through the financial markets is frequently misunderstood. Yet these instruments — futures, options and a multitude of variations are packages of the basic components of risk: they more than anything else traded come close to the theoretically ideal instruments for the trading of risk.

Derivatives can turn stocks into bonds and vice versa, and can pinpoint, very precisely, specific risks and returns that are packaged within a complex structure.
Risk management is essential in a modern market economy.
Going long” an investment means buying it in the expectation of a future price rise. “Going short” is the opposite: selling something you do not own in the hope of buying it back more cheaply in the future.
When you go long, your loss is limited to what you paid for the stock.

But when you go short, your losses are potentially without limit as the buyback price may rise ever higher above the price at which you sold. Why sell short?

The obvious answer is to profit from the impending decline of an overpriced stock. But shorting requires a tough-minded pessimism, a contrarian turn of mind or a “gloom and doom” view of the world.
There are essentially two ways of analyzing investments: fundamental analysis and technical analysis. With the former, investors try to calculate the value of an asset, comparing the present value of the likely future cash flows with its current price.
With the latter, they focus exclusively on the asset’s price data, asking what its past price behaviour indicates about its likely future price behaviour. Market strategists believe that history tends to repeat itself.

They make price predictions on the basis of published data, looking for patterns and correlations, assessing trends, support and resistance levels. The true objective of technical analysis is to determine whether or not the ingredients of a healthy bull market are present — and to watch out for possible warning flags before a major decline or bear strikes.
The “efficient market hypothesis” (EMH) says that at any given time, asset prices fully reflect all available information —that price movements do not follow any patterns or trends.
This means that past price movements cannot be used to predict future price movements. Rather, prices follow what is known as a “random walk,” an intrinsically unpredictable pattern.

In the world of the strong form EMH, trying to beat the market becomes a game of chance not skill.

A central challenge to the EMH is the existence of market anomalies: reliable, widely known and inexplicable patterns in returns, such as the “January effect.” In reality, markets are neither perfectly efficient nor completely inefficient.

All are efficient to a certain degree, but some more than others.
A caricature of the investment world divides it into two camps: value investors, who buy stocks that have fallen in price in the belief that the rest of the market has missed a bargain; and growth or momentum investors, who buy stocks that have gone up in the hope that they turn out to have been “cheap at any price.”

Value investors dispute the efficient market hypothesis, which suggests that prices reflect all available information, and see investment opportunities created by discrepancies between stock price and the underlying value of company assets.
One of the most high profile features of the business and investment worlds is corporate restructuring — mergers and acquisitions (M&A), leveraged buyouts, divestitures, spinoffs and the like.

The number and value of mega-mergers in 1998 set new records. This has reawakened the populist cry that such mergers do not create new wealth, that they merely represent the trading of existing assets — rearranging the deck chairs on the Titanic.

The primary argument in favor of M&A is that they are good for industrial efficiency. Takeovers are a radical solution for remedying poor performance and safeguarding against economic mediocrity.
Contrary thinking is intellectual independence with a healthy dash of agnosticism about consensus views. If a consensus grows to be a “crowd,” the contrarian will flee, but not necessarily to the exact opposite.

Instead, identification of a herd motivates the contrarian to be more rigorous in independent thinking.

And the contrarian is more likely to be attracted to a point of view that has not yet been thought of — the “empty file drawer” idea — than one that has been considered and rejected.
Financial engineering is, in essence, the phenomenon of innovation in the financial industries: securities innovation; innovative financial processes; and creative solutions to corporate finance problems.

The biggest challenges over the next few decades for financial engineering will focus on aspects of human judgment that are now considered impossible to mimic computationally: fear, greed and other emotional aspects of decision-making.

Recent advances in the cognitive sciences, neurobiology and computer science may provide some clues to solving these tantalizing problems in financial contexts.
Fixed income securities are generally thought of as safe rather boring investments, lacking the risks associated with equities. In practice, though,

it is possible to lose vast amounts of money by getting the bond markets wrong. Inflation is bad for bonds, eroding their value as prices and yields, unless index-linked, fail to keep pace with rising prices.

Inflation drives interest rates higher and bond prices down. In terms of inflationadjusted returns, bonds have actually been more risky than equities for most of the nineteenth and twentieth centuries.

However, many believe that bonds may outperform stocks over the next few years as stocks’ recent strong performance makes them less attractive and as deflation becomes a more potent force than inflation.
Global investing is, in the first instance, about asset allocation between equities, bonds, cash and other instruments; and second, about investing in global markets.

International investors benefit by diversifying their portfolios across assets in a range of different countries.

With many national markets often highly uncorrelated, this form of diversification would seem to offer the strongest potential for reducing risk, while at the same time promising enhanced returns.

But the more markets move together, the fewer will be the benefits of international diversification.