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The choice of stock analysis is determined by the investor's belief in the different paradigms for "how the stock market works". For explanations of these paradigms, see the discussions at efficient-market hypothesis , random walk hypothesis , capital asset pricing model , Fed model , market-based valuation , and behavioral finance .
Bottom-up may refer to: Bottom-up analysis, a fundamental analysis technique in accounting and finance; Bottom-up parsing, a computer science strategy; Bottom-up processing, in Pattern recognition (psychology) Bottom-up theories of galaxy formation and evolution; Bottom-up tree automaton, in data structures; Bottom-up integration testing, in ...
Typically, equity long/short investing is based on "bottom up" analysis based primarily on the analysis of the financial statements of the individual companies, in which investments are made. There may also be "top down" analysis of the risks and opportunities offered by industries, sectors, countries, and the macroeconomic situation.
This redirect may meet Wikipedia's criteria for speedy deletion because it is holding up a page move that is non-controversial or consensual, for instance reversing a redirect. The page to be moved to this name is Bottom–up and top–down design
In finance, an investment strategy is a set of rules, behaviors or procedures, designed to guide an investor's selection of an investment portfolio.Individuals have different profit objectives, and their individual skills make different tactics and strategies appropriate. [1]
In the bottom-up approach, we calculate the smaller values of fib first, then build larger values from them. This method also uses O( n ) time since it contains a loop that repeats n − 1 times, but it only takes constant (O(1)) space, in contrast to the top-down approach which requires O( n ) space to store the map.
A first approach was made by Beckers, Rudd and Stefek for the global equity market. They estimated a model involving currency, country, global industries and global risk indices. This model worked well for portfolios constructed by the top down process of first selecting countries and then selecting assets within countries.
In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the ...