Correlation Between Past Futures And Trading Positions Of Speculators

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

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

Given the event date and stock price data, the EP and TP can be constructed in order to estimate the normal returns and abnormal returns respectively.

If you are a new investor who is interested in investment history or how to make investments, purchase this book by Burton G. Malkiel. This book is ideal for any experienced investor who wants to brush up on their knowledge of investment techniques and theories also. There are not many books that have been written about investing. A Random Walk Down Wall Street is broken down into four parts which include; Stocks and Their Value, How the Pros Play the Biggest Game in Town, The New Investment Technology and A Practical Guide for Random Walkers and Other Investors. In total, there are fifteen chapters that cover a lot of key points that many will find interesting and informative.

Not only the volatility prediction were accurate, the mean part also provides considerably nice fit. With the excellent fit from our prediction model, we expect our predictions to be fairly dependable. Nonetheless, when comparing actual future return with our predicted return and volatility obtained from 5-day ahead rolling forecast procedure, the results were rather unsatisfactory. All of the predicted volatilities were considerably high and did not move along with real fluctuations in return series, which resulted in very significant value at risk. In addition, the return predictions were no much better than just using sample means, which were all very close to zero, to predict future return. The prediction vs actual return plot for 60 days is shown below.

Jegadeesh, Narasimhan , “Evidence of predictable behavior of security returns,” Journal of Finance 45, 881-898., 1990

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).

To justify whether it works, we first ran a regression on the Three-Factor Fama French Model (Market Excess Return, SMB and HML) from Jan 1972 to Dec 2012 respectively for the High-, 5-, Low-, and High-Minus-Low Portfolio. Then, we added momentum into the model to create a four-factor model (Momentum, Market Excess Return, SMB and HML) for the same period on a monthly basis. (See Exhibit 2).

In order to analyze the momentum effect of different specifications, stocks were divided into ‘winner’ stocks and ‘loser’ stocks according to their rankings.

The first law of forecasting is to assume the future will behave like the past .Event at that, there is a limit to how accurate the forecast can be even when using pass data. This research paper aims to forecast Walmart stock prices over a two year period. But because stock prices are more substantial when they are presented either in monthly or weekly data, the paper will be forecasting over two years but on a monthly basis i.e. Walmart stock prices is forecasted over a 24 month period.

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.)

The analysis of this paper will derive the validity of the Fama and French (FF) model and the efficiency of the Capital Asset Pricing Model (CAPM). The comparison of the Fama and French Model and CAPM (Sharpe, 1964 & Lintner, 1965) uses real time data of stock market to practise its efficacy. The implication of the function in realistic conditions would justify the utility of the CAPM theory. The theory suggests that the expected return demanded by investors on a risky asset depends on the risk-free rate of interest, the expected return on the market portfolio, the variance of the return on the market portfolio, and

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).

Behavior analysis of stock return attain more stockholder attention in the market research than any other factor. Among many other factors which are associated with stock return capital gain and dividend yield can be at higher significance level because it is often said that the survival of short term investment is due the capital gain factor and the long term investment is done for the sake of dividend yield.