Alternative Beta Funds Are All High Dividend Oriented

DFA’s investment strategy is based on their belief in the principle that stock market is efficient. They attempt to match a broad-based, value-weighted small-stock index and position themselves in the market as a passive fund manager that still claimed to add value by capturing specific dimensions of risks identified by financial science. DFA’s investment strategy incorporates elements of both passive and active management. It is passive in the sense that like many other index managers, it focuses on the importance of diversification, lower turnover and lower fees than actively managed portfolios. It is active in the sense that it develops its small-value stock focus based on academic research and uses certain techniques (such as

See Exhibit 2a for the curve for Exhibit 5, Exhibit 2b for the curve for Exhibit 6, Exhibit 2c for the curve for Exhibit 7, and Exhibit 2d for the curve for Exhibit 8. Exhibit 2a provides the baseline LTP which shows the highest Sharpe ratio that can be achieved without the introduction of a “real assets” is 1.01. The allocation of 23.4% in US Equities, 40.4% in Foreign Equities, and 36.2% in Bonds would result in an expected return of 10% and a

The extracted data used includes monthly returns from January 1972 to July 2011. The assets are selected so that the portfolio contains the largest, most liquid, and most tradable assets. The choice of such a variety of assets across several markets was used in order to generate a large cross sectional dispersion in average return. It helped to reveal new factor exposure and define a general framework of the correlated value and momentum effects in various asset classes.

Advisors and investors would do well to pay as much attention to the expected volatility of any portfolio or investment as they do to anticipated returns. Moreover, all things being equal, a new investment should only be added to a portfolio when it either reduces the expected risk for a targeted level of returns, or when it boosts expected portfolio returns without adding additional risk, as measured by the expected standard deviation of those returns. Lesson 2: Don’t assume bonds or international stocks offer adequate portfolio diversification. As the world’s financial markets become more closely correlated, bonds and foreign stocks may not provide adequate portfolio diversification. Instead, advisors may want to recommend that suitable investors add modest exposure to nontraditional investments such as hedge funds, private equity and real assets. Such exposure may bolster portfolio returns, while reducing overall risk, depending on how it is structured. Lesson 3: Be disciplined in adhering to asset allocation targets. The long-term benefits of portfolio diversification will only be realized if investors are disciplined in adhering to asset allocation guidelines. For this reason, it is recommended that advisors regularly revisit portfolio allocations and rebalance

Best fit index of fund: S&P 500 (the index of the pre-merger Amoco equity investment)

Each strategy will be used in a different way or ‘style,’ leaving funds of the same strategy with varying results. For example Australian Equity had an average one year return of -6.76 per cent, while Australian Fixed Interest had an average one year return of 6.99 per cent. Comparing the average one year return for each strategy illustrates which funds have the most reliable or steady rate of return, and in this case the most reliable funds are those with fixed interest. This is coherent with the risk factors associated with the different strategies, even though fixed interest is considered low risk and offers low returns, these returns are more reliable than strategies with a high risk, and therefore still perform positively under times of economic crisis.

market, and the S&P GSCI All metals, which measures the performance of investment in precious and industrial metals. The weight of 90% is given to the Russell 3000 index because we believe that our portfolio represents the U.S. market. Typically, we invest approximately 30% of the total assets each in large-cap stocks, mid-cap stocks, and small-cap stocks. The weight of 10% is given to the S&P GSCI All metals because, on average, we take a position in futures contracts that are worth up to 10% of the total assets.

We chose VW portfolio of NYSE, AMEX, and NASDAQ rather than EW of NYSE, AMEX, and NASDAQ because the value-weight portfolio can reflect the real market situation better.

Financial factor models are developed in an attempt to answer the question: ‘What really generates performance?’ Production Alpha models are rich and powerful, however, it is still a subset of information that has return and risk implication, many other investment signals should not be ignored. Therefore, the motivation of my project is to build, analyze, and monitor a factor library that satisfies the following criteria: First, it has a rich set of factors covering full spectrum of alphas and risks; second, it can be viewed as a robust and scalable platform that not only can build and maintain factors, but also can be used to analyze factors behaviors and fund exposures. The goal of the project is to trace insightful connection between the return of equity and the underlying factors to find out which factors generate returns for stocks. A factor can be thought of as any characteristics relating a portfolio of securities that is important in explaining their performance

Pine Street Capital’s Portfolio on July 26th,2000

Ticker shares share price total Allocation Beta Alpha R-Squared

To reduce a firm’s specific risk or residual risk a portfolio should have negative covariance or rather it should have no variance at all, for large portfolios however calculating variance requires greater and sophisticated computing power. As such, Index models greatly decrease the computations needed to calculate the optimum portfolio. The use of such Index models also eliminates illogical or rather absurd results. The Single Index model (SIM) and the Capital Asset Pricing Model (CAPM) are such models used to calculate the optimum portfolio.

We have used the data from Jan.2007-Dec.2011 (60 data points) to re-estimate βi’s which were averaged across securities within portfolios in order to obtain 10 initial portfolio βpt using the following regression

As indicated by the case study S&P 500 index was use as a measure of the total return for the stock market. Our standard deviation of the total return was used as a one measure of the risk of an individual stock. Also betas for individual stocks are determined by simple linear regression. The variables were: total return for the stock as the dependent variable and independent variable is the total return for the stock. Since the descriptive statistics were a lot, only the necessary data was selected (below table.)

Sanjun was tasked with developing a portfolio strategy based on our findings in the paper. As of date, he has delivered a quantitative portfolio that holds approximately 30 of 150 GIC Sub-Industries. Preliminary result suggests the performance can be significant. The team is excited to develop a strategy that is differentiated (higher tracking error & higher beta) from other quant portfolio currently being managed or tested.

Diversification is a method of investing that been shown to increase portfolio return while reducing portfolio risk as measured by standard deviation. This method specifically increases the efficient frontier for investors. The challenge to an investing firm is an appetite by its customers for an ever increasing efficient frontier. One area to explore to obtain this increase is through further diversifying through international diversification.