The Mysterious Flame of Queen Loana Review Essay Example

The Mysterious Flame of Queen Loana Review Paper Essay on The Mysterious Flame of Queen Loana Postmodernism clean water. So pure that even bacteria do not have))) To take though required direction roll with other authors there are not only woven into the fabric of the text, they are here the text itself! This is justified by the plot the hero reads a book of his childhood and gradually starts to recover lost memories As for nadsyuzhetnyh associations with authors, here I will mention only those who are particularly struck me: Fowles ( mantissa the beginning of a very similar the same awakening in the hospital, the same memory loss, only Fowles this topic did not disclose), Proust (apparently by the flow of thoughts, while some little thing awakens mysterious flame memories ) and Kundera (strange hero :)) We will write a custom essay sample on The Mysterious Flame of Queen Loana Review specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on The Mysterious Flame of Queen Loana Review specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on The Mysterious Flame of Queen Loana Review specifically for you FOR ONLY $16.38 $13.9/page Hire Writer it is a pity that it was not Eko ohozhe. Of course, the rejection of their own style, too, is in the spirit of postmodernism, but I wished that from eco have only the richness of vocabulary so book subjects ( Name of the Rose remembered) In general, post-modernism in the Mysterious Flame of Queen Loana blooms so that drowns out all the charm and subtlety of the novel. The plot is like there, but it has no content. The composition is built, but not completed. Style rovnehonko so that does not catch the eye and reveal the authors traits. What we have as a result? So that whether the novel was signed in the name of not Umberato Eco, he would have become the object of a caustic criticism: the idea is not worked out, talent, strength is not enough, the plot is stilted, the heroes of cardboard, and the author zauchka, zaznayka and upstart trying to swim to the other peoples quotes. but the time is Umberto Eco, then we say, the Apotheosis of postmodernism I can well believe that the views of other readers can be very different, but personally I would have made this novel a little more alive and natural, would both storylines (and childhood and adulthood), would reduce the attic part and asked for b s author to deliver elegant, but a clear point at the end, which in the novel is not there. Yes, it would be a completely different affair, and postmodern sequined he would not have shone. Instead, he would become one of my favorites! 🙂

Encana Report Essay

Encana Report Essay Encana Report Essay Objective To find the Weighted Average Cost of Capital for the energy firm EnCana. Finding the firms market value capital structure Value of debt: Short Term Debt: We assume that Encana’s short-term debt is NOT a part of its permanent capital structure because Encana’s business activities are project based (mining, oil and gas) and hence assumedly require different frequencies and magnitudes of short-term debt that vary with each project. Long-term Debt: We do not have the resources to calculate the market value of debt (as we don’t know how many of which bonds there are), therefore we will use the book value which is $6,629m. Value of equity: Barb asks if we can use book value of equity. Considering book value of equity is rarely close to its market value(intuition – book value is just assets less liabilities, whereas market value is value of all expected future cash flows), this is not a viable option. EnCana has 854.9 mil shares outstanding, trading at $56.75. Therefore market value of equity = 854.9 x 56.75 = 48,515.575 (Means 3:1 market to book ratio – can see clearly why you cannot use the book value of equity) Therefore: Debt = 12%, Equity = 88% of capital structure (Apx.1). Find cost for individual capital structure components Historical interest rates are irrelevant in calculating cost of capital, what matters is current yield which reflects the return currently required by investors. Cost of debt Publicly Traded Debt: Yield is 5.81%. We use this yield assuming that since long-term interest rates are the average of short-term rates - amidst 30 years 1-2 years difference should not particularly skew the average, therefore all four long-term bonds should be trading at a similar yield. Other long-term debt: Yield (assuming EnCana qualifies for the prime rate) is 5.25% Therefore weighted average cost of debt is 5.81 x 0.81 + 5.25 x 0.19 = 5.7% (Apx.2) Cost of common equity CAPM approach (SML equation): We know: rf = long term bond yield = 4.20%, ÃŽ ² =1.27 (assuming historical beta is good representation of current beta – problem – only based on 3 years of data) We do not know the risk premium on the market Historical risk premium: Find the average historical risk premium by subtracting average historical risk free rate from average historical return on market: Arithmetic average: 13.9 – 6.5 = 7.4%, Geometric average: 12.9 -5.6 = 7.3% Problems: Bond average is only 1 year bonds rather than long term rates Increase in risk premium can actually contribute to decline in stock market returns Forward looking risk premium Use discount cash flow model to estimate rpM: (Apx.3). We find the growth rate with (assuming historical growth rates are accurate representation of the future): 25yr growth rate = 5.54 %( Apx. 4) Problems: No reason to believe future growth will be like past growth Growth rates sensitive to period over which growth is measured (Apx.5) We use the 25 year growth rate (due to long-term nature) to obtain a rpM of 3.3 %( Apx.6), which seems unusually low considering risk premium generally is within 3.5-6.5%. Which risk premium to use? If we consider that the historical risk premium tends to overstate the risk premium (we are less risk adverse due to various other forms of financial stability), and the five year growth rate of the market (i.e. the current trend) is much higher (if assumed to continue would result in a much higher risk premium, approx. 8%), it is reasonable to take the average of the two: (7.4% +3.3%)/2 = 5.35%. This value for rpM seems more reasonably within the typical range. Using this rpM we calculate the cost of equity: re = 4.2 + 1.27(5.35) = 11% Constant growth dividend discount model , r = + 0.1187 =12.42% Problems: Not a lot of historical data to work off Which model do we use? Considering the extra step of calculating the risk premium in

Discussion topic Assignment Example | Topics and Well Written Essays - 250 words - 11

Discussion topic - Assignment Example On the other hand, quantitative research method has a major merit in that it can be administered and evaluated very quickly and the responses tabulated very quickly. In addition, the numerical data obtained in this method facilitates quick comparisons between groups as well as the extent of congruence between respondents. This advantage is majorly used in nursing research when a comparison is needed after a new nursing intervention is initiated for example nursing rounding (Carr, 2014). Quantitative and qualitative research study methods have some of their limitation in nursing research. A study done by Carson (2011) on the strengths and weakness of research designs involving quantitative measures, found out that experimental research has several methodological limitations. These limitations were seen to jeopardize the internal and external validity of the research results thus limiting their applicability for practice. Some of the threats noted were sampling and recruitment. Sampling technique may have a problem in randomised control trials when the potential participants are not prepared to opt for treatment in randomised basis. Similarly, recruiting subjects to participate in clinical trials may be difficult. On the other hand, qualitative research has been noted to be time consuming and important issues may be overlooked during the study. in essence both methods are appropriate to conduct a research, and can contribute greatly to the scientific body of knowledg e (Carr,

THE ACTIONS OF DRUGS ON THE GUINEA-PIG ISOLATED ILEUM Lab Report

THE ACTIONS OF DRUGS ON THE GUINEA-PIG ISOLATED ILEUM - Lab Report Example Q2 (ii): When testing the agonist action of the morphine-like drugs, it is observable that, through the depressant action of the morphine-like drugs, it was difficult to assess the potencies because the tachyphylaxis developed rapidly. In this case, it is important to use small doses of the drug while exposing the gut to the drugs at the intervals that do not go below 30 minutes. The inhibitory effect of morphine on the twitch of longitudinal muscle was induced by the coaxial stimulation, hence leading to the dose-response curve of order ââ€" . Upon sing nalorphine-like drugs, the depressant action of the N-allyl analogue of the morphine was having the similar order to that of morphine. However, tachyphylaxis development was much more rapid with nalorphine than with morphine. When testing the antagonist action of the morphine-like drugs, tachyphylaxis was able to develop with all compounds tested, which was a strong indication on the possibility of exhibiting antagonist action under suitable conditions. In this experiment, techyphylaxix was able to develop more rapidly than compared to using the agonist. Basing on the agonist activity of the antagonists, the conventional method used for testing antagonism did not yield the decisive results. The antagonism through low concentrations of morphine of the inhibitory effect of morphine upon twitch of the longitudinal muscle was able to induce coaxial stimulation. Q3: Through using the experimental protocol or two log curves, there is a possibility of an error occurring. To avoid such errors, the formula can be modified into that of the critical ratio approach (CR). The CR is the concentration of agonist at the presence of the antagonist required for producing a fixed response to the linear part of the concentration. It is thus advisable to use the equation that relates CR to KB, which is expressed

How do you think magic makes itself felt in contemporary life Essay

How do you think magic makes itself felt in contemporary life - Essay Example But how can we truly say that a certain situation or feeling is truly magic Like in the movies, one seems to be cast on a spell as we hold our breaths and take in every scene and make it our own. We can see the story unfold before our eyes as how the director sees it in his mind. We are transported into another world, another dimension with each character that somehow looks surreal. We find traces of personality that is distinct and personal, as if our own. And with these movies we can escape even for a few hours, a few minutes the commonplace tragedies that beset our everyday lives. It is also the question of a supernatural being that has created characters such as vampires. These creatures have long been associated as evil and ungodly from the creative minds of writers. Man's fascination with the unknown has spawned various characters, but in today's fiction these characters are given a more human touch than the stories written centuries ago. Creatures long depicted as human predators are given a human side and can experience the same pains and anguish as that of a common man. The idea of vampires living amongst us is another magical transformation.

Analysis Of Hedge Fund Performances

Analysis Of Hedge Fund Performances 1. INTRODUTION: Hedge funds are actively managed portfolios that hold positions in publicly traded securities. Gaurav S. Amin and Harry M. Kat (2000) stated on their report that A hedge fund is typically defined as a pooled investment vehicle that is privately organized, administrated by professional investment managers, and not widely available to the public? It charges both a performance fee and a management fee. It allows a flexible investment for a small number of large investors (usually the minimum investment is $1 million) can use high risk techniques. Nowadays it is very clear that in the matter of alternative investment mutual fund is not performing well. As a high absolute returns and typically have features such as hurdle rates and incentive fees with high watermark provision hedge fund gives a better align to the interests of managers and investors. Moreover mutual funds typically use a long-only buy-and-hold type strategy on standard asset classes, which help to capture risk premia asso ciate with equity risk, interest rate risk, default risk etc. However, they are not very helpful in capturing risk premia associate with dynamic trading strategies. That is why hedge fund comes into the picture. This is the year of 2009, which takes the greatest history of the world in the following century. In the year of 2008 the world saw the greatest fall down of the world economy. Lots of people missing their jobs, lots of company were stopped. The world economy faced the highest losses in the history. These all factors are showing only one way to makeover from that greatest downfall that is hedging. 3The last couple of decades have witnessed a rapidly growing in the hedge funds. Relative to traditional investment portfolios hedge funds exhibit some unique characteristics; they are flexible with respect to the types of securities they hold and the type of the position they take. 1 Agarwal, V. and Naik, N. (2000). Multi-period performance persistence analysis of hedge fund s?. The journal of financial and quantitative analysis. Vol. 35, No,3. PP-327. 2 Agarwal, V. and Naik, N. (2004). Risks and portfolio decisions involving hedge funds?. The review of financial studies, Vol. 17, No.1. PP-64. 3 Journal of banking and finance 32(2008) 741-753- Hedge Fund Pricing and Model Uncertainty? by Spyridan D. Vrontos, Ioannis D. Vrontos, Daniel Giomouridies. 4The number of FOHFs increase by 40% between 2001 and 2003, and now comprised almost two third of the $650 billion invested in the USAs hedge fund market. Due to its nature it is difficult to estimate the current size of hedge fund industry. 5Van Hedge Fund Advisors estimates that by the end of 1998 there were 5380 hedge fund managing $311 in capital, with between $800 billion and $1 trillion in total assets, which indicates the higher number of recent new entries. So far, hedge fund is based on American phenomena. About 90% hedge fund managers are based in the US, 9% in Europe and 1% in Asia and elsewhere. Now a days around 5883 hedge funds are trading around the world. (*Barclay Hedge database) 4 Financial times, 29th October, 2003. www.vanhedge.com http://www.barclayhedge.com/products/hedge-fund-directory.html 1.1 Categories of Hedge fund investment objectives: Event Driven: Distressed securities- manager focuses on securities of companies in reorganization and bankruptcy, ranging from senior secured debt to the common stock of the company. Risk arbitrage- manager simultaneously buys stock in a company being acquired and sells stock in its acquirers. Global: International- manager pays attention to economic change around the world (except the United States) but more bottom-up oriented in that managers tend to be stock-pickers in markets they like. Uses index derivatives to a much lesser extent than macro managers. Emerging- Manager invests in less mature financial markets of the world, e.g. Hong Kong, Singapore, Pakistan, India. Because shorting is not permitted in many emerging markets, managers must go to cash or other markets when valuations make being long unattractive. Regional- Manager focuses on specific regions of the world, example- Latin America, Asia, and Europe. Global macro: Opportunistic trading manager that profits from changes in global economies typically based in major interest rate shifts. Uses leverage and derivatives. Market neutral: Long/short stocks- half long/half short. Manager attempts to lock-out or neutralize market risk. Convertible arbitrage- Manager goes long convertible securities and shorts the underlying equities. Stock index arbitrage- Manager buys a basket of stocks and sells short stock index futures, or the reverse. Fixed income arbitrage- Manager buys T-bonds and sells short index futures or the reverse. Short sales: Manager takes a position that stock prices will go down. Used as a hedge for long only portfolios and by those who feel market is approaching a bearish trend. U.S Opportunistic: Value â€Å" Manager focuses on assets, cash flow, book value, out-of-favor stocks. Growth â€Å" Manager invests in growth stocks, revenues, earnings, and growth potential are keys. Short term â€Å" Manager holds positions for a short time frame. Fund of fund: Capital is allocated among a number of hedge funds, providing investors with access to managers they might not be able to discover or evaluate in their own. Usually has a lower minimum than a hedge fund. Source: Carl Ackermann, Richard McEnally, and David Ravenscraft, The performance of hedge funds: Risk, Return and Incentives,? Journal of finance 54, no.3 (June 1999) figure 1, page-843. Reproduced from a hedge fund database firm named Managed Account Report (MAR) Inc, and distributed through LaPorte Asset Allocation System. 2. Literature review: Despite the increasing interest and recent development, few studies have been carried out on hedge funds comparing to other investment tools like mutual funds. An analysis of Hedge Fund performance 1984-2000? by Capocci Daniel using one of the greatest hedge fund database ever used on his working paper (2796 individual funds including 801 dissolved), to investigate hedge funds performance using various asset-pricing models, including an extension from of Carharts (1997) model combined with Fama and French (1998), Agarwal and Naik (2000) models that take into account the fact that some hedge funds invest in emerging market bond. At the end they found that their model does a better job describing hedge funds behaviour. That appears particularly good for the Event Driven, Global Macro, US Opportunistic, Equity non-Hedge and Sector funds. Since the early 1990s, when around 2000 hedge funds were managing assets totalling capital of $60 billion, the subsequent growth in the number and asset base of hedge funds has never really been refuted. The industry only suffered from a relative slowdown in 1998, but since then has enjoyed a renewed vitality with an estimated total of 10,000funds managing more than a trillion US dollars by the end of 2006. The growing trend of the sector remained remarkably sustained during the stock market collapse that started in March 2000, when the NASDAQ composite Index reached an all-time high of 5,132 and finished three years later with a floor level of 1,253. In the meantime, the global met asset value (NAV) of hedge funds continued to grow at a steady rate of 10.6% (Van Hedge Funds Advisors International, 2002), contrasting with a decrease of 2.7% in the worldwide mutual fund industry ( Investment Company Institute, 2003). In 2001, Capocci and Hubner(2004) estimated that there were 6,000 he dge fund managing around $400 billion. In 2007, Capocci, Duquenne and Hubner (2007) estimated that there were 10,000 hedge funds managing around $1 trillion. This is a growth of 11% in the number of funds and 26% in assets over six years (6PhD thesis paper by Daniel P.J. Capocci). Other studies from practitioners Hennessee (1994), and Oberuc (1994) also showed an evidence of superior performance in the case of hedge funds. Ackernann and Al. (1999) and Liang (1999) who compared the performance of hedge funds to mutual funds and several indices, found that hedge funds constantly obtained better performance than mutual funds. Their performance was not better than the performance of the market indices considered. They also indicated that the returns in hedge funds were more unstable than both the returns of mutual funds and those of market indices. According to Brown and Al. (1997) hedge funds showing good performance in the first part of the year reduce the volatility of their portfolio in the second half of the year (Capocci Daniel- An analysis of hedge fund performance 1984-2000). Taking all these results into account hedge funds seems a good investment tool. 6 PhD thesis paper by Daniel P.J. Capocci. Electronic copy available at: http//ssrn.com/abstract=1008319. 3. Research design and Methodology: In this section I would like to describe the empirical methodology to be used to measure the performance of hedge fund as well as the performance of FTSE 100 and SP 500. My aim is to identify which will give the better return for an investor. To investigate hedge funds performance and performance of FTSE 100 and SP 500 my study will follow some models like 4-factor model from of Carharts (1997) model, the 3-factor model from Fama and French (1993) models, the Sharpe ratio (1966) and Jensens alpha (1968) and CAPM. I divide my research into three sections. First section will analyse the performance of hedge funds, FTSE 100 and SP 500. This section sets out the models of performance measurement I will use. Second section will made correlation between Hedge fund vs. FTSE 100 and Hedge fund vs. SP 500 to find out the better portfolio. Third section will exposes a discussion as well as a description of my database and finally concludes the paper. 3.1. Performance measure models: The 4-factor model from Carhart (1997) Carharts (1997) 4-factor model is an extension of the Fama and French (1993) factor model. It not only takes into account the size of the firms, the book to market ratio, but there is an additional factor for the momentum effect. Grinblatt, Titman and Wermers (1995) define this effect as buying stocks that were past winners and selling past losers. This model is estimated with the following regressions: Rpt-Rft=ÃŽÂ ±p+ÃŽÂ ²pi (Rmt â€Å"Rft) + ÃŽÂ ²p2 SMBt +ÃŽÂ ²p3 HMLt + ÃŽÂ ²p4 PR1YRt + ept t= 1,2,,T Where SMBt= the factor mimicking portfolios for size; HMLt= the factor mimicking portfolio for book to market equity; PR1YRt= the factor mimicking portfolio for the momentum effect7 7 for a description of the construction of PR1YR see Carhart (1997). As stressed by Daniel et al. (1997), this model, which is effectively a four factor Jensen measure, assumes that betas with respect to the returns of four zero investment factor mimicking portfolios, are appropriate measures of multidimensional systematic risk. According to this model, in the absence of stock selection or timing abilities, the expected return for a fund is the sum of the risk free return and the products of the betas with the factor risk premium, which are simply the expected returns of each of these zero investment portfolios. The Carhart (1997) approach identifies a matching passive portfolio return for each fund return. This passive return, which is subtracted from the fund return to generate ÃŽÂ ±p, is a weighted average of the returns of the Carhart factor portfolios and the return of a one month T-bill (Capocci Daniel 2001, Journal- European Private Bankers, Nov, 2001). The 3-factor model from Fama and French (1993): Fama and French (1993) 3 factor model is estimated from an expected form of the CAPM regression. It takes the size and the book to market ratio of the firm into account. It uses the time series approach from Black, Jensen, and Scholles (1972) in the sense that the monthly returns on stocks are regressed on the returns to a market portfolio of stocks and mimicking portfolios for size and book to market. It is estimated from the following extension of the CAPM regression: Rpt-Rft=ÃŽÂ ±p+ÃŽÂ ²pi (Rmt â€Å"Rft) + ÃŽÂ ²p2 SMBt +ÃŽÂ ²p3 HMLt + ept t= 1,2,,T Where, SMBt= the factor mimicking portfolios for size, and HMLt= the factor mimicking portfolio for book to market equity. SMLt which comes from small minus big meant to mimic the risk factor in returns related to size, and HMLt which comes from high minus low meant to mimic the risk factor in returns related to book to market equity8. HML (respectively SMB) is neutral relative to the size effect (respectively to the book to market). This means that these factors do a good job isolating the firm-specific components of returns (Fama and French 1993, 1995, 1996 and 2000). 8 See Fama and French (1993) for a precise description of the construction of SMBt and HMLt. The Sharp Ratio (1966): The Sharp ratios (1966) calculate the ratio of the average excess return and the return standard deviation of the fund that is being evaluated. As such it measures the excess return per unit of risk. Assuming all asset returns to be normally distributed, the CAPM tells us that in equilibrium the highest attainable Sharpe ratio is that of the market index. In more general terms, the market indexs sharp ratio represents the set of return distributions that is obtained when statically combining the market index with cash. With the market index being highly diversified, these distributions offer the highest achievable expected return for every possible standard deviation (Gaurav S. Amin and Harry M.Kat (2002), Hedge fund performance 1990-2000). Jensens Alpha (1968): Jensens alpha was introduced in Jensen (1968) and equals the intercept of the regression: (Rh-Rf)= ÃŽÂ ± + ÃŽÂ ² (Ri- Rf) + eh, Where Rh is the fund return, Rf is the risk free rate and Ri is the total return on the market index. Like the Sharpe ratio, Jensens alpha is rooted in the CAPM. According to the CAPM, in equilibrium all (portfolios of) assets with the same beta will offer the same expected return, any positive deviation therefore indicates superior performance (Gaurav S. Amin and Harry M.Kat (2002), Hedge fund performance 1990-2000). Capital Asset Pricing Model: The first performance model that will be used is a capital asset pricing based single index model (CAPM). This model developed by Sharpe (1964) and Linter (1965) is the oldest performance evaluation model. Its formula is the following: Rpt â€Å" Rft = ÃŽÂ ±p + ÃŽÂ ²p (Rmt-Rft) + ept t= 1,2,, T Where, Rpt= return of fund p in month t, Rft= risk free return on month t, Rmt= return of the market portfolio on month t, ept= the error term, ÃŽÂ ±p and ÃŽÂ ²p= the intercept and the slope of the regression estimated. The intercept of this equation, ÃŽÂ ±p commonly called Jensens alpha (1968) is usually interpreted as a measure of out or under performance relative to the market proxy used. There are several extension of this model have been developed like- the Breeden (1979) intertemporal CAPM or the Ferson and Schadt (1996) CAPM that allows time variation in the expected returns and the risk (Capocci Daniel 2001, An analysis of hedge fund performance 1984- 2000). 4. Data Preparation: For data preparation my first step will be to collect the monthly data of the hedge fund index, FTSE 100 and SP 500. For my data collection I will use some sources like- Credit Suisse/ Tremont Hedge Fund Index (CSTHFI hereafter) which is an appropriate representative of the entire hedge fund industry, there are three biggest database of hedge fund in the world these are Managed Account Reports (MAR), Hedge Fund Research, Inc (HFR) and TASS Management (TASS). These databases were the most used in academic and commercial hedge fund studies. For the FTSE 100 and SP 500 I will use yahoo finance. 4.1. Bias in Hedge fund data: According to Ackermann et al. (1999) and to Fung and Hsieh (2000), two upward biases exist in the case of hedge funds. They do not exist in the case of mutual funds, and they both have an opposite impact to the survivorship bias. Survivorship bias is an important issue in mutual funds performance studies (see Carhart and al. 2000). This bias is present when a database contains only funds that have data for the whole period studies. In this case, there is a risk of overestimating the mean performance because the funds that would have ceased to exist because of their bad performance would not be taken into account. The two upward biases exist because, since hedge funds are not allowed to advertise, they consider inclusion in a database primarily as a marketing tool. The first phenomenon stressed by Ackermann and al. (1999) and called the self-selection bias is present because funds that realize good performance have less incentive to report their performance to data providers in order to attract new investors. The second point called instant history bias or backfilled bias (Fung and Hsieh 2000) occurs because after inclusion a funds performance history is backfilled. This may cause an upward bias because funds with less satisfactory performance history are less likely to apply for inclusion than funds with good performance history (Capocci Daniel 2001, An analysis of hedge fund performance 1984- 2000). To avoid these biases I will try to take all funds both living and dissolved into account. Once I have collected all the data that I need I will use SPSS to test the correlation between my two benchmarks FTSE 100 and SP 500. 5. Contingency Plan: To make my research effective I made a well constructed plan. I have drafted a project plan (Appendix A) with scheduled dates for when I intend to complete sections for submission. After completing my final exam I will jump in to this field. Advises from previous students who completed their dissertation, I made my project plan flexible to keep some things in mind like supervisors holiday and any unforeseen events such as my illness. I will try to keep a good communication with my supervisor for checking that I am in right track. I plan to make some formal meetings with my supervisor to discuss my progress and I will try to inform him about the state of my work. It is hard to spending too much time over one task and going off track, I hope I will manage this if there is no rush at the very last minute. Another worry is the collecting and analysing the data, that is why I plan to collect the data early June once I have finished my research design. If I face any kind of difficulties I will inform him and make a cut-off point where I should stop searching the board data and start my own primary data. As I do all SPSS classes and briefly touched about this, I think it will be easy to analyze the data but I need to increase a bit of use of control on it by practicing more. So I will set aside time for collecting data and practice more SPSS for regression analysis. I hope if all these go well, I will make my dissertation very effectively. Analysis Of Hedge Fund Performances Analysis Of Hedge Fund Performances 1. INTRODUTION: Hedge funds are actively managed portfolios that hold positions in publicly traded securities. Gaurav S. Amin and Harry M. Kat (2000) stated on their report that A hedge fund is typically defined as a pooled investment vehicle that is privately organized, administrated by professional investment managers, and not widely available to the public? It charges both a performance fee and a management fee. It allows a flexible investment for a small number of large investors (usually the minimum investment is $1 million) can use high risk techniques. Nowadays it is very clear that in the matter of alternative investment mutual fund is not performing well. As a high absolute returns and typically have features such as hurdle rates and incentive fees with high watermark provision hedge fund gives a better align to the interests of managers and investors. Moreover mutual funds typically use a long-only buy-and-hold type strategy on standard asset classes, which help to capture risk premia asso ciate with equity risk, interest rate risk, default risk etc. However, they are not very helpful in capturing risk premia associate with dynamic trading strategies. That is why hedge fund comes into the picture. This is the year of 2009, which takes the greatest history of the world in the following century. In the year of 2008 the world saw the greatest fall down of the world economy. Lots of people missing their jobs, lots of company were stopped. The world economy faced the highest losses in the history. These all factors are showing only one way to makeover from that greatest downfall that is hedging. 3The last couple of decades have witnessed a rapidly growing in the hedge funds. Relative to traditional investment portfolios hedge funds exhibit some unique characteristics; they are flexible with respect to the types of securities they hold and the type of the position they take. 1 Agarwal, V. and Naik, N. (2000). Multi-period performance persistence analysis of hedge fund s?. The journal of financial and quantitative analysis. Vol. 35, No,3. PP-327. 2 Agarwal, V. and Naik, N. (2004). Risks and portfolio decisions involving hedge funds?. The review of financial studies, Vol. 17, No.1. PP-64. 3 Journal of banking and finance 32(2008) 741-753- Hedge Fund Pricing and Model Uncertainty? by Spyridan D. Vrontos, Ioannis D. Vrontos, Daniel Giomouridies. 4The number of FOHFs increase by 40% between 2001 and 2003, and now comprised almost two third of the $650 billion invested in the USAs hedge fund market. Due to its nature it is difficult to estimate the current size of hedge fund industry. 5Van Hedge Fund Advisors estimates that by the end of 1998 there were 5380 hedge fund managing $311 in capital, with between $800 billion and $1 trillion in total assets, which indicates the higher number of recent new entries. So far, hedge fund is based on American phenomena. About 90% hedge fund managers are based in the US, 9% in Europe and 1% in Asia and elsewhere. Now a days around 5883 hedge funds are trading around the world. (*Barclay Hedge database) 4 Financial times, 29th October, 2003. www.vanhedge.com http://www.barclayhedge.com/products/hedge-fund-directory.html 1.1 Categories of Hedge fund investment objectives: Event Driven: Distressed securities- manager focuses on securities of companies in reorganization and bankruptcy, ranging from senior secured debt to the common stock of the company. Risk arbitrage- manager simultaneously buys stock in a company being acquired and sells stock in its acquirers. Global: International- manager pays attention to economic change around the world (except the United States) but more bottom-up oriented in that managers tend to be stock-pickers in markets they like. Uses index derivatives to a much lesser extent than macro managers. Emerging- Manager invests in less mature financial markets of the world, e.g. Hong Kong, Singapore, Pakistan, India. Because shorting is not permitted in many emerging markets, managers must go to cash or other markets when valuations make being long unattractive. Regional- Manager focuses on specific regions of the world, example- Latin America, Asia, and Europe. Global macro: Opportunistic trading manager that profits from changes in global economies typically based in major interest rate shifts. Uses leverage and derivatives. Market neutral: Long/short stocks- half long/half short. Manager attempts to lock-out or neutralize market risk. Convertible arbitrage- Manager goes long convertible securities and shorts the underlying equities. Stock index arbitrage- Manager buys a basket of stocks and sells short stock index futures, or the reverse. Fixed income arbitrage- Manager buys T-bonds and sells short index futures or the reverse. Short sales: Manager takes a position that stock prices will go down. Used as a hedge for long only portfolios and by those who feel market is approaching a bearish trend. U.S Opportunistic: Value â€Å" Manager focuses on assets, cash flow, book value, out-of-favor stocks. Growth â€Å" Manager invests in growth stocks, revenues, earnings, and growth potential are keys. Short term â€Å" Manager holds positions for a short time frame. Fund of fund: Capital is allocated among a number of hedge funds, providing investors with access to managers they might not be able to discover or evaluate in their own. Usually has a lower minimum than a hedge fund. Source: Carl Ackermann, Richard McEnally, and David Ravenscraft, The performance of hedge funds: Risk, Return and Incentives,? Journal of finance 54, no.3 (June 1999) figure 1, page-843. Reproduced from a hedge fund database firm named Managed Account Report (MAR) Inc, and distributed through LaPorte Asset Allocation System. 2. Literature review: Despite the increasing interest and recent development, few studies have been carried out on hedge funds comparing to other investment tools like mutual funds. An analysis of Hedge Fund performance 1984-2000? by Capocci Daniel using one of the greatest hedge fund database ever used on his working paper (2796 individual funds including 801 dissolved), to investigate hedge funds performance using various asset-pricing models, including an extension from of Carharts (1997) model combined with Fama and French (1998), Agarwal and Naik (2000) models that take into account the fact that some hedge funds invest in emerging market bond. At the end they found that their model does a better job describing hedge funds behaviour. That appears particularly good for the Event Driven, Global Macro, US Opportunistic, Equity non-Hedge and Sector funds. Since the early 1990s, when around 2000 hedge funds were managing assets totalling capital of $60 billion, the subsequent growth in the number and asset base of hedge funds has never really been refuted. The industry only suffered from a relative slowdown in 1998, but since then has enjoyed a renewed vitality with an estimated total of 10,000funds managing more than a trillion US dollars by the end of 2006. The growing trend of the sector remained remarkably sustained during the stock market collapse that started in March 2000, when the NASDAQ composite Index reached an all-time high of 5,132 and finished three years later with a floor level of 1,253. In the meantime, the global met asset value (NAV) of hedge funds continued to grow at a steady rate of 10.6% (Van Hedge Funds Advisors International, 2002), contrasting with a decrease of 2.7% in the worldwide mutual fund industry ( Investment Company Institute, 2003). In 2001, Capocci and Hubner(2004) estimated that there were 6,000 he dge fund managing around $400 billion. In 2007, Capocci, Duquenne and Hubner (2007) estimated that there were 10,000 hedge funds managing around $1 trillion. This is a growth of 11% in the number of funds and 26% in assets over six years (6PhD thesis paper by Daniel P.J. Capocci). Other studies from practitioners Hennessee (1994), and Oberuc (1994) also showed an evidence of superior performance in the case of hedge funds. Ackernann and Al. (1999) and Liang (1999) who compared the performance of hedge funds to mutual funds and several indices, found that hedge funds constantly obtained better performance than mutual funds. Their performance was not better than the performance of the market indices considered. They also indicated that the returns in hedge funds were more unstable than both the returns of mutual funds and those of market indices. According to Brown and Al. (1997) hedge funds showing good performance in the first part of the year reduce the volatility of their portfolio in the second half of the year (Capocci Daniel- An analysis of hedge fund performance 1984-2000). Taking all these results into account hedge funds seems a good investment tool. 6 PhD thesis paper by Daniel P.J. Capocci. Electronic copy available at: http//ssrn.com/abstract=1008319. 3. Research design and Methodology: In this section I would like to describe the empirical methodology to be used to measure the performance of hedge fund as well as the performance of FTSE 100 and SP 500. My aim is to identify which will give the better return for an investor. To investigate hedge funds performance and performance of FTSE 100 and SP 500 my study will follow some models like 4-factor model from of Carharts (1997) model, the 3-factor model from Fama and French (1993) models, the Sharpe ratio (1966) and Jensens alpha (1968) and CAPM. I divide my research into three sections. First section will analyse the performance of hedge funds, FTSE 100 and SP 500. This section sets out the models of performance measurement I will use. Second section will made correlation between Hedge fund vs. FTSE 100 and Hedge fund vs. SP 500 to find out the better portfolio. Third section will exposes a discussion as well as a description of my database and finally concludes the paper. 3.1. Performance measure models: The 4-factor model from Carhart (1997) Carharts (1997) 4-factor model is an extension of the Fama and French (1993) factor model. It not only takes into account the size of the firms, the book to market ratio, but there is an additional factor for the momentum effect. Grinblatt, Titman and Wermers (1995) define this effect as buying stocks that were past winners and selling past losers. This model is estimated with the following regressions: Rpt-Rft=ÃŽÂ ±p+ÃŽÂ ²pi (Rmt â€Å"Rft) + ÃŽÂ ²p2 SMBt +ÃŽÂ ²p3 HMLt + ÃŽÂ ²p4 PR1YRt + ept t= 1,2,,T Where SMBt= the factor mimicking portfolios for size; HMLt= the factor mimicking portfolio for book to market equity; PR1YRt= the factor mimicking portfolio for the momentum effect7 7 for a description of the construction of PR1YR see Carhart (1997). As stressed by Daniel et al. (1997), this model, which is effectively a four factor Jensen measure, assumes that betas with respect to the returns of four zero investment factor mimicking portfolios, are appropriate measures of multidimensional systematic risk. According to this model, in the absence of stock selection or timing abilities, the expected return for a fund is the sum of the risk free return and the products of the betas with the factor risk premium, which are simply the expected returns of each of these zero investment portfolios. The Carhart (1997) approach identifies a matching passive portfolio return for each fund return. This passive return, which is subtracted from the fund return to generate ÃŽÂ ±p, is a weighted average of the returns of the Carhart factor portfolios and the return of a one month T-bill (Capocci Daniel 2001, Journal- European Private Bankers, Nov, 2001). The 3-factor model from Fama and French (1993): Fama and French (1993) 3 factor model is estimated from an expected form of the CAPM regression. It takes the size and the book to market ratio of the firm into account. It uses the time series approach from Black, Jensen, and Scholles (1972) in the sense that the monthly returns on stocks are regressed on the returns to a market portfolio of stocks and mimicking portfolios for size and book to market. It is estimated from the following extension of the CAPM regression: Rpt-Rft=ÃŽÂ ±p+ÃŽÂ ²pi (Rmt â€Å"Rft) + ÃŽÂ ²p2 SMBt +ÃŽÂ ²p3 HMLt + ept t= 1,2,,T Where, SMBt= the factor mimicking portfolios for size, and HMLt= the factor mimicking portfolio for book to market equity. SMLt which comes from small minus big meant to mimic the risk factor in returns related to size, and HMLt which comes from high minus low meant to mimic the risk factor in returns related to book to market equity8. HML (respectively SMB) is neutral relative to the size effect (respectively to the book to market). This means that these factors do a good job isolating the firm-specific components of returns (Fama and French 1993, 1995, 1996 and 2000). 8 See Fama and French (1993) for a precise description of the construction of SMBt and HMLt. The Sharp Ratio (1966): The Sharp ratios (1966) calculate the ratio of the average excess return and the return standard deviation of the fund that is being evaluated. As such it measures the excess return per unit of risk. Assuming all asset returns to be normally distributed, the CAPM tells us that in equilibrium the highest attainable Sharpe ratio is that of the market index. In more general terms, the market indexs sharp ratio represents the set of return distributions that is obtained when statically combining the market index with cash. With the market index being highly diversified, these distributions offer the highest achievable expected return for every possible standard deviation (Gaurav S. Amin and Harry M.Kat (2002), Hedge fund performance 1990-2000). Jensens Alpha (1968): Jensens alpha was introduced in Jensen (1968) and equals the intercept of the regression: (Rh-Rf)= ÃŽÂ ± + ÃŽÂ ² (Ri- Rf) + eh, Where Rh is the fund return, Rf is the risk free rate and Ri is the total return on the market index. Like the Sharpe ratio, Jensens alpha is rooted in the CAPM. According to the CAPM, in equilibrium all (portfolios of) assets with the same beta will offer the same expected return, any positive deviation therefore indicates superior performance (Gaurav S. Amin and Harry M.Kat (2002), Hedge fund performance 1990-2000). Capital Asset Pricing Model: The first performance model that will be used is a capital asset pricing based single index model (CAPM). This model developed by Sharpe (1964) and Linter (1965) is the oldest performance evaluation model. Its formula is the following: Rpt â€Å" Rft = ÃŽÂ ±p + ÃŽÂ ²p (Rmt-Rft) + ept t= 1,2,, T Where, Rpt= return of fund p in month t, Rft= risk free return on month t, Rmt= return of the market portfolio on month t, ept= the error term, ÃŽÂ ±p and ÃŽÂ ²p= the intercept and the slope of the regression estimated. The intercept of this equation, ÃŽÂ ±p commonly called Jensens alpha (1968) is usually interpreted as a measure of out or under performance relative to the market proxy used. There are several extension of this model have been developed like- the Breeden (1979) intertemporal CAPM or the Ferson and Schadt (1996) CAPM that allows time variation in the expected returns and the risk (Capocci Daniel 2001, An analysis of hedge fund performance 1984- 2000). 4. Data Preparation: For data preparation my first step will be to collect the monthly data of the hedge fund index, FTSE 100 and SP 500. For my data collection I will use some sources like- Credit Suisse/ Tremont Hedge Fund Index (CSTHFI hereafter) which is an appropriate representative of the entire hedge fund industry, there are three biggest database of hedge fund in the world these are Managed Account Reports (MAR), Hedge Fund Research, Inc (HFR) and TASS Management (TASS). These databases were the most used in academic and commercial hedge fund studies. For the FTSE 100 and SP 500 I will use yahoo finance. 4.1. Bias in Hedge fund data: According to Ackermann et al. (1999) and to Fung and Hsieh (2000), two upward biases exist in the case of hedge funds. They do not exist in the case of mutual funds, and they both have an opposite impact to the survivorship bias. Survivorship bias is an important issue in mutual funds performance studies (see Carhart and al. 2000). This bias is present when a database contains only funds that have data for the whole period studies. In this case, there is a risk of overestimating the mean performance because the funds that would have ceased to exist because of their bad performance would not be taken into account. The two upward biases exist because, since hedge funds are not allowed to advertise, they consider inclusion in a database primarily as a marketing tool. The first phenomenon stressed by Ackermann and al. (1999) and called the self-selection bias is present because funds that realize good performance have less incentive to report their performance to data providers in order to attract new investors. The second point called instant history bias or backfilled bias (Fung and Hsieh 2000) occurs because after inclusion a funds performance history is backfilled. This may cause an upward bias because funds with less satisfactory performance history are less likely to apply for inclusion than funds with good performance history (Capocci Daniel 2001, An analysis of hedge fund performance 1984- 2000). To avoid these biases I will try to take all funds both living and dissolved into account. Once I have collected all the data that I need I will use SPSS to test the correlation between my two benchmarks FTSE 100 and SP 500. 5. Contingency Plan: To make my research effective I made a well constructed plan. I have drafted a project plan (Appendix A) with scheduled dates for when I intend to complete sections for submission. After completing my final exam I will jump in to this field. Advises from previous students who completed their dissertation, I made my project plan flexible to keep some things in mind like supervisors holiday and any unforeseen events such as my illness. I will try to keep a good communication with my supervisor for checking that I am in right track. I plan to make some formal meetings with my supervisor to discuss my progress and I will try to inform him about the state of my work. It is hard to spending too much time over one task and going off track, I hope I will manage this if there is no rush at the very last minute. Another worry is the collecting and analysing the data, that is why I plan to collect the data early June once I have finished my research design. If I face any kind of difficulties I will inform him and make a cut-off point where I should stop searching the board data and start my own primary data. As I do all SPSS classes and briefly touched about this, I think it will be easy to analyze the data but I need to increase a bit of use of control on it by practicing more. So I will set aside time for collecting data and practice more SPSS for regression analysis. I hope if all these go well, I will make my dissertation very effectively.

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