The authors jointly examine momentum and value in eight different markets and asset classes. Asness, Moskowitz and Pederson found two main phenomena associated with returns across 8 various markets and asset classes in their research. These findings challenge previous, well-established theories, like the existence of significant premia in value and momentum return strategies across asset classes and global markets. This innovative research includes some new asset classes not previously used, such as government bonds, currencies, and commodities. The authors look to prove the
During a study by Jegadeesh and Titman (1993), the portfolios created using a strategy, which is based on the assumption that within a period of 3-12 months (short-term), the stocks that made gains (led to losses) in the past will make gains (lead to losses) in the future, obtained abnormal returns of 1% in each consecutive year. When the literature is examined, it is seen that very small number of studies paid regard to the power of adding the momentum factor to SMB and HML factors of Fama and French (WML-winners minus losers) in explaining stock returns. (Carhart 1997, Jegadeesh 2000, Liew and Vassalou 2000, Kim and Kim 2003, L’Her; Masmoudi; Suret 2004, Bello 2007, Carmichael and Coën 2008, Lam; Li; So 2009; Unlu,2012).
There is only one reason investors receive higher returns make that investment in high-risk stocks. Therefore, CAPM occupy dominant position in this modern financial environment (Fama and French, 1993).
This document is authorized for use only by Yen Ting Chen in FInancial Markets and Institutions taught by Nawal Ahmed Boston University from September 2014 to December 2014.
In February 1995, Adam Bain, investment advisor in the London, Ontario branch of RBC Dominion Securities Inc. (RBC DS), was considering whether or not to implement a price momentum strategy for his clients. Trend and Cycle, DS’s technical research department, had recently circulated a copy of a study which described a simple price momentum model and referred to its “startling results” based on back testing the strategy over a 15 year period. The Trend and Cycle group had long promoted the importance of price momentum and relative strength to potential clients. Bain needs to determine whether the proposed model was “too good to be true” or, if it did not look promising, how he would go about
2) What are the advantages and disadvantages of investing in property? Despite economic downturn in recent periods, there are various incentives provided by
The premise of an efficient market is that stock prices adjust accordingly as information is received. The speed and accuracy of the pricing changes are a reflection of the strength of the market efficiency, where in theory a perfectly efficient market will re-adjust prices immediately and precisely with new information. The efficient market hypothesis aligns with beliefs about whether technical and fundamental analyses are useful in making investment decisions or whether a passive approach is appropriate. In a perfectly efficient market, these types of analyses are not able to predict stock price trends (based on market inefficiencies or price abnormalities) which could assist in portfolio positioning or investment management. However, some investors belive that the market pricing is not precise and that there are timing windows and pricing trends that can be identified through analysis of past performance and finding price abnormalities where all information is not correctly reflected in the stock price (Hirt, Block and Basu, 2006).
Every individual whether they are aware of it or not, base their decision-making on some form of statistical data. Simple everyday decisions are made through rationalizing a problem or opportunity, forming a hypothesis, analyzing information, and determining a decision based on the gathered information. For the purpose of practicality, Team A has chosen real estate market data gathered from the website for the Statistical Techniques in Business and Economics (2008) textbook to formulate and define a chosen problem, attempt to delineate the purpose of the research into the variables that affect
which is better on each attribute. So, the investors needed to make choices depending on what is available and what are his own priority ratings of attribute he wants in his product. The rank preferences of investors were post office, bank deposits, gold, real estate, equity investment and mutual fund.
Although it is understandable that the author votes Adams Realty as a more superior real estate agent than the Fitch Realty based on his personal experience, it is necessary that a more logically convincing argument be fashioned before this conclusion is ratified and shared in public. There are several fragile parts in the argument on behalf of such a comparison. First of all, the argument insufficiently connects the Realtors' performance to their employees' work schedules. Second, the argument does not present enough logical data to justify its claims that Adam Realty performs stronger than Fitch Realty. Further, the data presented are quite vague, not providing enough information regarding the details of the supposedly higher achiever, Adams Realty, that sold properties faster than Fitch Reality. Finally, it is not guaranteed that the promoted realtor sells any home more quickly and at a better price compared to others especially Fitch Realty. While the author's hypothesis is certainly one possibility, more evidence is needed to eliminate other conclusions and to bolster the strength of this argument.
However, Housing plays different roles in the different household portfolio selection models, some of them consider housing as the consumer goods, and others think housing as a large investment. Brueckner (1997) argues that the housing investment is inefficient based on the mean-variance sense. Arrondel and Lefebvre (2001) illustrate that the probability of housing investment shows an inverted U shaped, which can
In order to analyze the momentum effect of different specifications, stocks were divided into ‘winner’ stocks and ‘loser’ stocks according to their rankings.
The success of the model is attributed to Yale’s ability to combine both quantitative analysis (mean-variance analysis) with market judgments to structure its portfolio. In addition, Yale also uses statistical analysis to actively test their models with factors affecting the market, therefore understanding the sensitivity of their portfolio in response to various market changes. Yale also follows and forecasts the cash flow of private equity and real assets in its portfolio to decide the need for hedging.
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.)
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.