Predicting Asset Price Movements With A High Degree Of

Such an intense focus has been placed on quarterly earnings as an indication of a company’s success by everyone from analysts to executives that ethics have for the most part been thrown out the window, sacrificed to the all important number, i.e. earnings per share. This is the theory in Alex Berenson’s book “The Number: How the Drive for Quarterly Earnings Corrupted Wall Street and Corporate America.” This number has become part of a game to be played, a figure to be manipulated – beat the number and Wall Street all but throws a parade, miss it and a company’s stock may be abandoned. Take into account the incentives that executives have to beat the number and one can find plenty of reasons to manage earnings.

Investors often come to believe that a stock is undervalued or overvalued compared to other stocks in its industrial group. To calculate an alternate target price for the current and next fiscal year based on those beliefs, investors can apply the average PE multiple for a company 's industrial group to the average professional analyst 's earnings estimate for the company in those periods. Valuation using the industry 's

The weekly performance of IBM stock presented a contestant growth. One highlight of the falling of stock price in the 6th week in the investment period was when IBM presented the 3rd quarter financial report. The investors weren’t satisfied with the profit report which they expected to be better especially when other IT companies were doing well in the 3rd quarter. One mistake I made was that I didn’t follow closely to the financial report of the company; therefore, I missed the peak of the stock price. From this experience, I learned that financial reports and current news are important indicators of the stock price. By following closely to the current event and analyzing the financial report, investors can maximize the profit and also become more familiar to the market.

When analysts question a firm’s earnings quality, it raises concerns regarding under or over aggressive accounting practices that may be allowing the firm to manipulate the earnings. Earnings quality is defined as the strength of the current earnings in being used to predict future earnings and cash flows. Since earning quality is indicative of future performance, analysts are more likely to address issues that have substantial impact on the earnings quality. An issue arises when the nature of the earnings is questioned. While permanent earnings are part of normal operations, any irregular, one time earnings can skew the earnings, making the firm look more profitable than it is. This is due to the inability to recreate similar one-time transactions that will give rise to such numbers. Investors prefer predictable

A great number of studies further identified several factors which particularly concerns market capitalisation, effective of stock market, etc., which explains the dynamic forces of stocks returns during the earnings announcements date in an organised manner. For instance, Atiase (1985) found that unexpected information pass on to the market by actual earnings report is inversely correlated to the company’s capitalisation. Grant (1990) observed that the market in which a company’s securities are traded often determined the behaviour of stocks return around the earnings announcements. Several other studies have aimed to organise for synchronise factors by using time series data such as intra-day and daily data. However, a fairly number of more robust studies has examined the information content of macroeconomics news releases. Elsharkawy and Garrod (1996), Pope and Inyangete (1992).

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

The correlation between the market portfolio and HML and the correlation between intercept and HML is -0.335 and -0.070, which indicates a moderate negative relationship between market portfolio and HML, and weak negative relationship between intercept and HML. Also, the correlations between the market portfolio and SMB, and between the SMB and HML are 0.348 and 0.191 respectively, which means that there are some positive relationships between them.

Danielsson and De Vries explain how predictions of low probability, worst-case outcomes are extremely poor using Value at Risk. These, importantly, are the most influential events that deserve the most attention. Such a blind spot is unacceptable. As such, they offer an alternative: an extreme value estimator. When tested against VaR and historical simulations far out in the tails, it performs much better in locating the depths of a negative shock. It charts tails according to particular parametric distributions, “develop[ing] a straightforward rule for obtaining multi-period VaR from the single period VaR.” It is still fraught with several problems, but makes much more realistic assumptions about the distribution of stock market returns. Moreover, it more closely mimics the actual returns of the market.

We also predicted this portfolio would perform better in a bear market. This is because when hype-stocks fall, they tend to fall really hard. And since we are buying strong stocks that fall in what we feel is a reasonable Price-to-Earnings range, downside reaction may not be as strong as for those stocks that are relying on more highly speculative assumptions. Given the Federal Reserve’s increasingly hawkish behavior, we felt our first executed trade was an appropriate time to implement this strategy since we had began seeing signs of weakness in the

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.

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.

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

Smart beta works by weighting securities by factors such as earnings, dividends or risk, rather than by market capitalisation. This approach allows investors to exploit anomalies in security valuations, while still offering low-cost index-like fund management. However, by creating one-size-fits-all asset allocations, smart beta may not provide the smarter alternative to public benchmarks that its advocates suggest, especially for insurance companies as smart beta benchmarks are not customised for their unique liability profiles and objectives.

Key Words: Financial Momentum Effect, Momentum Strategy, Market Efficiency Hypothesis, Fama-French Three Factors Model, Behavioral Finance

For many people, the idea of sifting through financial reports on a regular basis is something of great abhorrence to them, and yet if you wish to invest in winning shares, you have to do a “fair” amount of research and work. However, you do not need fancy qualifications, and there are plenty of intelligent people that make highly complex models that are useless. What you need to do is find a happy middle ground where you are neither doing too little or suffering from paralysis by analysis.