Research On Low Risk Phenomenon Essay

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.

Due to the turbulence of global economic crash, the high volatility appeared in Woolworth’s share price returns from January 2007 to June 2009, the return rate is floating widely. The most serious market crash happened on 9th September 2008 （－13.50％）. During this period of time, the investors had a difficult time. By contrast, there is a low volatility clustering from Jan-11 to Jun-11

In the years since 2000, a few investors have determined that their small cap portfolio outperformed their shares held in larger companies in terms of ROI (return on investment). The total dollar price earned was lesser; nevertheless the benefit percentage was

We believe, however, that returns response uses only partial market information about stocks. The Traditional Capital Asset Pricing Model (CAPM) holds the opinion that systematic risk would be the only factor that influences asset prices since idiosyncratic risk could be eliminated away by holding a well-diversified investment portfolio. However, recent empirical studies have found that the risk intensity of stocks is mainly due to the idiosyncratic risk of individual stocks. This conclusion was different from CAPM’s argument that only systematic risk would have an effect on returns. Volatility, in many studies, has also been found to carry current and future information. A recent study that attempted to break through the traditional method of calculating returns was performed by Campbell, Lettau, Malkiel and Xu (2001). They successfully separated the volatility of stock returns into market, industry and firms’ idiosyncratic volatility by use of a disaggregated approach. Xu and Malkiel (2003) applied a decomposition method and a disaggregated approach method to decompose volatility of stock returns into systematic volatility and idiosyncratic volatility and found that corporate private information could be reflected to its stock price faster when the institutional investors held a higher percentage of that company’s stock. Hatemi-J, A. and M. Irandoust

Among the more notable risk measures identified, the author states, Basu (1977) found a positive connection between expected stock returns and earnings to price ratio. Following that, Banz (1981) concluded that small firms have, on average, higher risk-adjusted returns than large firms. Later, Bhandari (1988) showed a positive connection between debt to equity and expected stock returns, even when controlling such variables as systematic risk, firm size and the January effect. Chan et al (1991), while analysing the relationship between expected stock returns

In the beginning of this investment period (9-9-2015), the igorpinto5 portfolio was properly distributed among 5 sectors: Services: Starbucks Corporation (30%), Banks: Goldman Sachs Group Inc. (28%), Industrials: JBS SA (23%), Technology: Amazon.com, Inc. (10%), and Homebuilding: D.R. Horton, Inc. (9%). The Igorpinto5 stock fund had close to no variation when compared to the market (9/15 until 11/13). Despite high volatility in the stock market in the period, the beta of the last four months of Igorpinto5 fund in relation to the S&P500 was 0.97. On 11/13/2015, the beta of Igorpinto5 represented

“Higher risks lead to higher returns” is one of the basic concepts in the investment theory. Also, the CAPM, thought for decades at universities as one of the basic asset pricing models, supports it.

When we invest money into the stock market we do it with the intention of generating a return on the capital invested. Many investors try not only to make a profitable return, but also to outperform, or ‘beat the market’. However, market efficiency - championed in the efficient market hypothesis (EMH) formulated by Eugene Fama in 1970, suggests that security prices instantly and fully reflect all available information and that it would not be possible for an investor to make consistent excess profits. When new information

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.)

In the past 12 months, the world’s stock market has experienced a particularly high degree of volatility that has never been seen in recent years. “On Monday 24 August 2015, about £74billion was wiped off the value of Wall Street and FTSE 100, and the Down Jones Industrial Average lost more than 1000 points at one stage on that day” (Allen, 2015).

This report aims at implement two distinct approaches, which can indicate the expected return and risk of a two-stock portfolio, to generate a practical solution to risk-analyzing for stock-investing. The two approaches are Mean-Variance Approach and CAPM Approach. While we apply the Mean-Variance Approach to determine the expected return and standard deviation, we employ the CAPM approach to measure the beta and expected return of each stock. The calculations of the aforesaid mathematical characteristics will contain the weekly returns during a seven-year time period integrated with the ASX all ordinaries Accumulation Index as a substitute for the market index and Official Cash Rate (thereafter, OCR, which is the interest

Over a period of 15 consecutive years from 1991 to 2006, Miller’s Legg Mason Value Trust (LMVTX) was able to outperform the S&P 500. In the recent 22 years, there were two non-ideal periods of LMTVX. The first one was during the bear markets of early 2000s (from 2000 to 2002) caused by the crisis of tech companies and the 911. The 2nd one started from 2006 to 2009 during the world economic recession resulted by subprime mortgage crisis. However, during year 2000 to 2001 LMVTX still outperformed the S&P500. Year 2006 was the 1st first LMVTX tailed the return of the S&P 500 since 1991.

Even though there are flaws in the CAPM for empirical study, the approach of the linearity of expected return and risk is readily relevant. As Fama & French (2004:20) stated “… Markowitz’s portfolio model … is nevertheless a theoretical tour de force.” It could be seen that the study of this paper may possibly justify Fama & French’s study that stated the CAPM is insufficient in interpreting the expected return with respect to risk. This is due to the failure of considering the other market factors that would affect the stock price.

Because of this limitation in using variance as a risk measurement, various downside-risk measurements have been proposed and developed. One of the downside risk measurements is semi variance. By definition, a downside-risk measurement measures only the returns below a certain threshold. This threshold captures the risk perspectives from investors to investors. Unlike standard deviation, downside risk accommodates different views of risk.

Pettengill et al. (1995) suggested a new method for testing the relation between return and beta. They established a conditional model which anticipates whether the risk premium on the market index is positive or negative. When the excess return on the market index is positive there should also be a positive relationship between beta and return, and when the risk premium is negative and return also negatively connected. It is based on a fact that high beta stocks are very likely to be more sensitive to the negative risk premium and even have a lower return than low beta stocks. Their research conducted on the US market confirmed that there is a positive relation between betas and returns.