Foundations of technical analysis: computational algorithms, statistical influence, and empirical implementation

Article Abstract:

The underlying principles of technical analysis or 'charting' in financial practice are discussed. Technical analysis of finance has largely failed to receive academic scrutiny or acceptance as more traditional disciplines, partly because it is highly subjective. A systematic and automated approach for technical pattern recognition is presented. The method relies on non-parametric kernel regression and is exemplified through a sample application to the performance of US stocks between 1962 and 1996; statistical tables, line plots and scatter plots are included. Non-parametric kernel regression is a smoothing technique that can effectively incorporate the essence of technical analysis, although visual pattern recognition still relies on human judgment more than computational algorithms. Among the included results are tables of Monte Carlo analyses.

Prepackaged software, Science & research, Usage, Computer software industry, Software industry, Product information, Algorithms, Spreadsheets, Spreadsheet software

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The price impact and survival of irrational traders

Article Abstract:

A study involving price impact of irrational traders and their long term survival is presented. It is assumed that both the factors are not related and that price impact has significant influence on asset prices even when the wealth of irrational traders becomes negligible. Also, the portfolio policy of irrational traders deviates from limits, even after price process approaches its long-limit.

United States, Management dynamics, Pricing Policy, Analysis, Management, Forecasts and trends, Market trend/market analysis, Company business management, Product price, Traders

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Implementing option pricing models when asset returns are predictable

Article Abstract:

The predictability of an asset's returns will affect the prices of options on that asset, even though predictability is typically induced by the drift, which does not enter the option pricing formula. For discretely-sampled data, predictability is linked to the parameters that do enter the option pricing formula. We construct an adjustment for predictability to the Black-Scholes formula and show that this adjustment can be important even for small levels of predictability, especially for longer maturity options. We propose several continuous-time linear diffusion processes that can capture broader forms of predictability, and provide numerical examples that illustrate their importance for pricing options. (Reprinted by permission of the publisher.)

Models, Prices and rates, Measurement, Options (Finance)

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