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Anomaly – The True Architects of the Economic Crisis?
Those of you who followed Nouriel Roubini during the Asian Currency crisis over a decade ago* should have already recognized the similarity between that crisis and this one. Roubini was recently interviewed and gave his opinion: “The U.S. has been living in a situation of excesses for too long. Consumers were out spending more than their income and the country was spending more than its income, running up large current-account deficits. Now we have to tighten our belts and save more. The trouble is that higher savings in the medium term are positive, but in the short run a consumer cutback on consumption makes the economic contraction more severe.”
That’s the paradox of thrift. But we need to save more as a country, and we have to channel more resources to parts of the economy that are more productive. And when you have too many financial engineers and not as many computer engineers, you have a problem……I think this country needs more people who are going to be entrepreneurs, more people in manufacturing, more people going into sectors that are going to lead to long-run economic growth. When the best minds of the country are all going to Wall Street, there is a distortion in the allocation of human capital to some activities that become excessive and eventually inefficient.” However, Nobel laureate Robert Merton of the Harvard Business School has a different perspective:
we need more financial engineers, not fewer risk and innovation, including derivatives, are not going away, and we need senior managements, boards, and regulators of financial institutions who understand them.” Who are the Financial Engineers? And What the Hell Are They Talking About? I received my Master of Science in Financial Engineering degree back in 2002 and still to this day no one knows what the hell that means. Ok, Financial Engineers are often “rocket scientists” (literally) that are hired by large banks and multinational corporations to build sophisticated mathematical models with the intention to predict the likelihood of risky events, to provide valuations for instruments that are traditionally hard to price, and to create synthetic securities for the hedging risk (and sometimes for speculating).
“As LBO specialist Ted Stolberg once told Inc. Magazine, ‘Financial engineering is a lot like building a bridge. You can build it anyway you like as long as it doesn’t collapse when heavy trucks run over it and you can add additional lanes when you want more traffic to go over it. And when it’s all done, it should be a thing of beauty, like the Golden Gate'” (Warsh, 1993, p. 296). These “quants”, as they are lovingly called, are often lured from poor paying academic jobs by Wall Street to high paying jobs in London, New York, Chicago, or California. The corporate executives that hire these Quants often like to remind their investors that everything will be alright because of the brilliant minds they now have on the payroll. Unfortunately, there are two large problems in financial engineering that have emerged in hindsight. First, finance is ultimately about human beings and their relationships to each other.
Real finance bears little resemblance to the logical order of math and physics. Most models in finance begin with the basic assumption of “Homo Economus”, the assumption that man is a rational being. This has largely been proven to be a faulty assumption thanks to the recent research of cognitive neuroscience. Second, the output from the financial models is misinterpreted by the decision makers in senior level management. As Alfred Korzybski said, “The map is not the territory”. Much too much decision making has been based upon these models, giving them far too much weight. Senior executives seem all to eager to confirm their successes and deny their failures, it is human nature after all. Financial Models: Stock Market Rationality or Irrationality? “It is more than a metaphor to describe the price system as a kind of machinery, or a system of telecommunications which enables individual producers to watch merely the movement of a few pointers, as an engineer might watch the hands of a few dials, in order to adjust their activities to changes of which they may never know more than is reflected in the price movement.” – F.A. Hayek The efficient market hypothesis is quite appealing conceptually and empirically, which accounts for its enduring popularity.
In a nutshell, efficient stock markets are generally thought of as equilibrium markets in which security prices fully reflect all relevant information that is available about the “fundamental” value of the securities (Tangentially, Benjamin Graham, famous for co-authoring the fundamentalist treatise Security Analysis with David L. Dodd, was quoted as saying shortly before his death, “I am no longer an advocate of elaborate techniques of security analysis in order to find superior value opportunities… I doubt whether such extensive efforts will generate sufficiently superior selections to justify their costs… I’m on the side of the ‘efficient market’ school of thought…” [Malkiel, 1996, p. 191]). Despite its popularity, efficient capital markets theory has weathered some very appropriate criticisms. Since a theory is a model of reality and not “reality” itself, anomalies arise where theory does not mirror reality and the theory of efficient capital markets is no exception.
Ray Ball’s article The Theory of Stock Market Efficiency: Accomplishments and Limitations (Ball, 1994, p. 40) presents a mostly balanced perspective and illuminates some interesting anomalies: 1) A study by French and Roll suggests that prices overreact to new information which is then followed by a correction, allowing contrarian investors to take profits. 2) Excess volatility of prices due to the “extraordinary delusions and madness of crowds”. 3) Prices underreact to quarterly earnings reports, which in itself seems an anomaly in the tendency of prices to overreact to new information. 4) A recent study by Fama and French provides evidence that there is no relationship between historical betas and historical returns which has lead many to believe the equilibrium-based CAPM, developed greatly due to the enormous amount of empirical data on efficiency, has failed. (Not included in Ball’s article, but told in Malkiel’s A Random Walk Down Wall Street is the story of how Fama and French also determined that buying a stock that has performed poorly for the past two years will often give you above average returns during the next two years (Malkiel, p. 198), thereby allowing contrarians to take a profit once more.) 5) There are seasonal patterns to be found in the data on stock returns or small firms, such as the “January effect”, where stock prices are unusually higher during the first few days of January or the “weekend effect” where average stock returns negatively correlated from closing on Friday to closing on Monday.
Anomalies missing from Ball’s article include: 1. the evidence that firms with low price-earnings ratios outperform those with higher P/E ratios. 2. the evidence that stocks that sell with low book-value ratios tend to provide higher returns. 3. the evidence that stocks with high initial dividends tend to provide higher returns (Malkiel, pp. 204 -207). Where Ball’s article differentiates itself from most other summaries of the trials and tribulations of the theory of efficient capital markets is in a section titled “Defects in ‘Efficiency’ as a Model of Stock Markets” (Ball, p. 41 – 46) where he discusses the general neglect within the theoretical and empirical research on stock market efficiency of the processing and acquisition costs of information. This neglect could be the reason for the anomalies, such as the “small firm effect”, the tendency of small cap stocks to provide higher returns. He also criticizes the assumption in the efficient markets hypothesis of investor “homogeneity” and suggests the need for a new research program. Ball also considers the role of both transactions costs in the efficient markets theory literature “largely unresolved” and the effect of the actual market mechanism on transacted prices, also known as “market microstructure effects”.
He defends efficient markets theory from Robert Shiller’s argument (that the historical variance of stock prices has been much more volatile than can be justified by historical variance in actual dividends) by challenging Shiller’s use of a constant market expected return in nominal terms. Since CAPM assumes a constant risk free rate of return and a constant market risk premium it is impossible to determine a “correct” amount of variance in the market index. Ball also defends market efficiency from Shiller and other behavioralists in maintaining that the mean-reversion in stock returns does not necessarily imply market irrationality. CAPM does not claim to dismiss the trend for periods of relatively high returns to be followed by periods of relatively low returns. In fact, such cyclical patterns may be the result of rational responses by investors to political/economic conditions and corporations to changes in investor demand for stocks.
Ball then grants more space to Shiller and the behavioralists by ending his piece with the rhetorical question “Is ‘behavioral’ finance the answer?” He very quickly answers, “I don’t think so” (Ball, p. 47). I would rephrase the question so it reads “Does ‘behavioral’ finance yield useful answers?” and my answer would be “yes.” Whether or not investors behave rationally, that is, whether or not investors accurately maximize expected utility is an important assumption of the efficient market hypothesis and if it is not true, it may explain why the anomalies exist. Work in prospect theory by Allias, Kahneman and Tversky provides important evidence that the standard assumption of expected utility maximization assumed by most financial economists may not furnish accurate representations of human behavior (prospect theory states that individuals are better represented as maximizing a weighted sum of “utilities,” determined by a function of true probabilities which gives zero weight to extremely low probabilities and a weight of one to extremely high probabilities). While such evidence is not damning, it is troubling to say the least (Shiller, 1997).
Interestingly enough, Ball’s article omits the common practice of financial economists to categorize the theory of the stock market efficiency into three types which, from least to most orthodox, are as follows: 1. The weak form states that the history of stock price movements contains no useful information enabling investors to consistently outperform a buy-and-hold portfolio management theory. 2. The semi-strong form maintains that no available published information will help security analysts select “undervalued” securities. 3. The strong Form holds that everything known or even knowable about a company is reflected in the price of the stock. Statistical evidence lends credibility to the weak and semi-strong forms, and discounts the strong form revealing that corporate insiders have earned excess profits trading on inside information. In support of the weak and semi-strong forms, the results of Ball and Brown’s mid-1960’s study (Ball, p. 35) of how the stock market actually responds to announcements of annual earnings suggests that the market anticipates approximately 80% of the new information found in annual earnings before the earnings were actually announced.
In other words, investors were mostly deprived of future opportunities to profit from the new information since stock prices had already processed the information released in the annual earnings reports. It seems to me investors and “Quants” alike would do well to not to swallow any one approach whole, warts and all, but to carefully weigh the evidence of all the different approaches. In scientific experimentation, where Quants feel at home, there are no success and failures, only outcomes or results. All that emerge are data points that tell you if you hypothesis is correct or not. Unfortunately, in capital markets, if an “experiment” is leveraged enough, you can bankrupt entire countries, and now, perhaps even the world. In capital markets, the real risk of experimentation like this can result in people not eating. What is Risk and Where Does Financial Engineering Come In? Well, we can intuitively say there seems to be a positive relationship between risk and uncertainty. The more certain we can be of a particular outcome, the less risky it is. However, in a dynamic world such as ours where we can barely (and usually inaccurately) predict the weather five days from now, how can a financial manager, farmer, or any interested party expect to predict, say, the price of tea in China weeks, months, or even years from now?
This is where the beautiful asymmetric nature of a financial instrument called an “option” comes in: “A call option is the right to buy a specified quantity of some underlying asset by paying a specified exercise price, on or before an expiration date. A put option is the right to sell a specified quantity of some underlying asset for a specified exercise price, on or before an expiration date” (Figlewski and Silber, 1990, p. 4). An investor’s potential loss is limited to the premium, while the potential profit is unlimited. So while it may be impossible to predict the future price of tea in China, it is possible to set a floor for the amount of loss allowed to occur without setting a ceiling on the profits reaped. Options belong to a class of financial instruments called derivatives, aptly named because they derive their value from something else. Options, for example, derive their value from an underlying asset. Other derivatives include interest rate and exchange rate futures and swaps, whose values depend on interest and exchange rate levels (some parties exchange cash payment obligations because they may prefer someone else’s payment stream), commodity futures, whose value depend on commodity prices, and forward contracts, which are similar to future contracts except that the commodity under contract is actually delivered upon a specified future date. But how can we use these instruments to minimize our exposure to risk?
“Financial engineering is the use of financial instruments to restructure an existing financial profile into one having more desirable properties” (Galitz, 1995, p. 5). In other words, it is the province of the financial engineer to design “synthetic” securities to achieve desired risk-return results. You take combinations of option, futures, swaps, etc. and create new securities to mitigate unforeseen risks. Assuming that the cash flows between the straight security and the synthetic portfolio are equivalent, then any difference in the present market values of the two is an arbitrage opportunity. An arbitrage is trade in which one buys something at one price and simultaneously sells essentially the same thing at a higher price, in order to make a riskless profit (In an efficient market such opportunities should be rare, and when the wily investor took advantage of it the very process should drive the price of what they are buying up and the price of what they are selling down).
A Simple Example of How Financial Engineering Actually Works In his article, The Arithmetic of Financial Engineering (Smith, 1999, p. 534) Donald J. Smith uses simple arithmetic and algebra to illustrate the relationships of a variety of different security combinations (synthetic securities) used by financial engineers to create these unique risk-return trade-offs. His basic explanatory formula looks like this; A + B = C where, A + B comprise the synthetic portfolio C is the straight security + sign denotes a long position, or a lending posture – sign denotes a short position, or a borrowing posture Using the arithmetic outlined above, Smith can illustrate the relational structure of such synthetic securities as; Interest rate swaps + Interest Rate Swap = + Unrestricted Fixed Rate Note – Floating Rate Note The coupon for most bonds is fixed ahead of time, hence the name fixed-income securities, but many issues have coupons that are reset on a regular basis and therefore float, these are called floating rate notes.
Collars + Collar = + Cap – Floor “Caps” and “Floors” are option contracts that guarantee the maximum [cap] and minimum [floor] rate that can be reached. Caps and floors are essentially interest rate insurance contracts that insure against losses from the interest rates rising above or falling below determined levels. Mini-Max Floater + Mini-Max Floating Rate Note = + Typical Floating Rate Note – Cap Inverse Floaters – Inverse Floater = – Two Fixed Rate Notes + Unrestricted Floating Rate Note -Cap Inverse floaters appeal to those investors who are bullish on bond prices and expect interest rates to drop. This is the synthetic security that Robert Citron used wrongly and ended up bankrupting Orange County, California when the Federal Reserve sharply raised interest rates in 1994. This folly ended up costing Orange County $1.7 billion in 1994 dollars! Participation Agreements + Participation Agreement = + Cap – Floor This simple arithmetic formula wields great explanatory power for those who seek to an easy understanding of the complexities of financial engineering.
However, the financial engineer must be cautious with the double edged sword of derivative instruments. When used to hedge, derivatives can be invaluable guards against risk, however if used to speculate, they can invite unnecessary risks. Also, hubris can be devastating as sometimes the payoffs can be too complex to fully understand. Unintended consequences can be a bitch (see credit default swaps) The United States Government = The Paleo-Financial Engineers “Blessed are the young, for they shall inherit the national debt” -Herbert Hoover Let’s look at one of the most complicated financial engineering schemes of all time, the relationship between the United States Treasury and the Federal Reserve system. The Federal Reserve is a privately owned corporation. In other words as the popular phrase goes, “The Federal Reserve is as ‘federal’ as Federal Express”. The largest stock holders of the Federal Reserve bank are the 17 largest banks on the planet. As a matter of record, for the United States the last century has been one of deficits and debt.
Simply put, a deficit occurs whenever you spend more than you have. Every time the government spends more than it has it must issue a debt instrument or I.O.U., usually a U.S. Treasury bond, to cover the expenses. The Federal Reserve banking cartel buy these bonds (with paper currency literally created out of thin-air) on the promise that the government will pay the Federal Reserve back both the principal and a fixed rate of interest. In exchange for this interest payment, the Federal Reserve literally creates money (mostly electronically and completely out of thin air) through manipulated ledger accounts. What most people fail to recognize is that the main way Treasury generates the revenue to pay off it’s debt to the Federal Reserve is through taxation. Simply put, our income taxes goes directly to bankers. A more sobering fact is this, to get an idea of how much the U.S. owes to bondholders (i.e., the Federal Reserve banking cartel) just take a look at the National Debt. It towers at over $11 trillion (remember a trillion is a thousand billion, and a billion is a thousand million, and million is a thousand thousand.
With an estimated population of the United States of 305,367,770, that means that each United States citizen’s share of the outstanding public debt is nearly $40K at this writing. The tricky part is this, if the growth of the debt is constant and greater than the rate of growth of average real income, then what should we expect the government to do when tax revenues are no longer sufficient to pay the interest on the debt? Then once the money (again, which was created out of thin-air) trickles down back into the economy as the government spends it, and finds its way back into the private banks. Once there, the real inflation begins through the magic of fractional reserve banking. This is all documented in the Federal Reserves’ own manual entitled “Modern Money Mechanics”. In a nutshell, since they only maintain a fraction of the actual reserves on-hand (while their ledgers falsely say they have the whole amount) the currency is inflated and the risk of bank runs are ever present.
There are only three basic courses of action the government can take; repudiate, hyperinflate, or liquidate. I favor the liquidation of governmental assets (non-essential governmental properties like the FDA, FCC, or the IRS) over repudiation or hyperinflation simply because liquidation of governmental assets is the surest way to end big government as we know it. Repudiation would shock the economy, interest rates would skyrocket, and bond prices would plummet; too much risk involved. Hyperinflation would only devalue the currency and impoverish everyone concerned. In Conclusion All this brings me back full circle to Nouriel Roubini’s quote again: “The U.S. has been living in a situation of excesses for too long. Consumers were out spending more than their income and the country was spending more than its income, running up large current-account deficits. Now we have to tighten our belts and save more. The trouble is that higher savings in the medium term are positive, but in the short run a consumer cutback on consumption makes the economic contraction more severe.
That’s the paradox of thrift. But we need to save more as a country, and we have to channel more resources to parts of the economy that are more productive. And when you have too many financial engineers and not as many computer engineers, you have a problem……I think this country needs more people who are going to be entrepreneurs, more people in manufacturing, more people going into sectors that are going to lead to long-run economic growth. When the best minds of the country are all going to Wall Street, there is a distortion in the allocation of human capital to some activities that become excessive and eventually inefficient.” I wholeheartedly agree that the solution lies in entrepreneurship. However, the quote is bookended by the concept of “excess” and associates it with our economic crisis. This begs the question though, who are the true architects of this excess, the Financial Engineers alone or are the Federal Reserve and the U.S. Treasury complicit as well?
Hayek, F. A. (September, 1948). The Use of Knowledge in Society.
The American Economic Review, XXXV, No. 4. Malkiel, B. G. (1996).
A random walk down wall street. New York, N.Y. Ball, R. (1994).
The theory of stock market efficiency: accomplishments and limitations. In D. H. Chew, Jr. (Ed.),
The new corporate finance; where theory meets practice (pp. 35 – 48). Boston, MA. Shiller, R. J. (1997). Human Behavior and the Efficiency of the Financial System. [online]. Available: [http://www.econ.yale.edu/~shiller/handbook.html].
Warsh, D. (January 17, 1988). After the Crash (financial engineering). economic principals.
New York, N. Y. Figlewski, S. and Silber, W. L. (1990).
financial options: from theory to practice. New York, N. Y. Galitz, L.C. (1995).
financial engineering: tools and techniques to manage financial risk. Burr Ridge, Illinois. Smith, D. J. (1999). The Arithmetic of Financial Engineering. In D. H. Chew, Jr. (Ed.), The new corporate finance; where theory meets practice (pp. 535 – 543). Boston, MA. (June 20, 1999).
*The Lessons of the Yen (I wrote this back in 1998 for the Golden Gate University student newspaper, if you substitute “Japan” for “America” it could be true today) As little as ten years ago it was thought that America’s unemployment and growth rates would never be more appealing than those of Japan’s. Such thinking has proven wrong, and the sting is being felt around the world. What effect, if any, do problems in one part of the world have on the others? Well, the sinking Japanese economy, the latest of the Asian Tigers to be struck by the Asian currency crisis iceberg is cause for concern for some Golden Gate University students in San Francisco. International students receiving funds from Japan are the most immediately affected. Erina Ishikawa (MBA, entrepreneurship) and Dongil Yun (masters, computer information systems), have both felt the effects of an unfavorable exchange rate since the decline of the Yen.
“When I came (to America) ten years ago, things were much cheaper for us in Japan, now the opposite is true,” said Yun. Anticipating economic problems in Japan and noticing higher interest rates in the US, Misa Aoki (MA, Public Relations) changed her Yen savings to dollars over a year ago. While not impacted by the threat of waning purchasing power due to her foresight, she still worries about finding a job after graduating and returning to Japan. Such fears are not unfounded. The rising unemployment rate of 4.1% is the highest in Japan since World War II. Fortunately, none of those interviewed knew of anyone who has had to drop out of school and return to Japan because of the crisis. They all said that they were concerned for the future of Japan’s economy, but that they ultimately do not think that the current crisis is that big of a deal. Jiro Ushio, chairman of the powerful Japan Association of Corporate Executives echoes the same sentiment, “[t]he realities of Japan’s economy are not as bad as the world thinks.” The president of the American Chamber of Commerce in Japan, Glenn S. Fukushima, said, “[f]undamentally it comes down to the fact that people in Japan generally don’t think that things are so bad that they need to have fundamental change.” Even some in Japan feel that the US expects its own bubble economy to pop soon and is merely looking for a scapegoat.
Obviously, there were problems enough for Secretary of the Treasury, Robert Rubin, to intervene to prop up the falling Yen in mid-June. His multi-billion dollar gamble paid off in the short run, reversing the Yen’s slide by 8% within one day. Critics of Japan’s government maintain that the under guidance by the Ministry of Finance, Japanese banks made bad loans to weak companies instead of letting the market work. The bad loans account for more than $600 billion, an amount larger than the entire economy of China, the world’s most populated country. Surprisingly however, the Japanese people overwhelmingly re-elected the current government. Prescriptions for recovery are everywhere, MIT’s Paul Krugman suggests that Japan’s central bank should inflate the money supply and lower interest rates to stimulate domestic demand, while others say that Japan’s April deregulatory “Big Bang” liberalization program will ultimately pay off in the long run. Whether the “big bang” or a more Schumpeterian “evolutionary” course is taken, with last week’s resignation of Prime Minister Hashimoto, the future is uncertain.
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