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Volume–price trend (VPT) (sometimes price–volume trend) is a technical analysis indicator intended to relate price and volume in the stock market.VPT is based on a running cumulative volume that adds or subtracts a multiple of the percentage change in share price trend and current volume, depending upon the investment's upward or downward movements.
The efficacy of technical analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, [5] and research on whether technical analysis offers any benefit has produced mixed results. [6] [7] [8] Technical analysts or chartists are usually less concerned with any of a company's ...
Over this period the average return was 13.9% of 30-stock Magic Formula portfolio versus 9.3% for the BSE Sensex. [9] An analysis of the Hong Kong stock market from 2001 to 2014 found Greenblatt's formula was associated with long-term outperformance of market averages by 6-15% depending on company size and other variables. [10]
For example, a chartist may plot past values of stock prices in an attempt to denote a trend from which he or she might infer future stock prices. The chartist's philosophy is that "history repeats itself". [2] Technical analysis assumes that a stock's price reflects all that is known about a company at any given point in time. [disputed ...
The Brownian motion models for financial markets are based on the work of Robert C. Merton and Paul A. Samuelson, as extensions to the one-period market models of Harold Markowitz and William F. Sharpe, and are concerned with defining the concepts of financial assets and markets, portfolios, gains and wealth in terms of continuous-time stochastic processes.
Stock advisor websites provide individual investors with useful tools, research and guidance to build their wealth. Many investors count on these handy online sources for ideas and information on ...
High frequency data employs the collection of a large sum of data over a time series, and as such the frequency of single data collection tends to be spaced out in irregular patterns over time. This is especially clear in financial market analysis, where transactions may occur in sequence, or after a prolonged period of inactivity. [7]
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