Market Theories

Stochastic processDescriptionApplicability to real marketsNotes
diffusion processsatisfies the diffusion equationpoorRegnault (1863) and Osborne (1959) discovered that price deviation is proportional to the square root of time, but the nonstationarity found by Houthakker (1961) and Osborne (1962) compromises the significance of the process.
Gaussian processincrements normally distributedpoor Financial markets exhibit leptokurtosis (Mitchell (1915, 1921), Olivier (1926), Mills (1927), Osborne (1959), Larson (1960), Alexander (1961)).
Lévy processstationary independent incrementspoor Houthakker (1961) and Osborne (1962) found nonstationarities in markets in the form of positive autocorrelation in the variance of returns.
Markov processmemorylesspoorHouthakker (1961) and Osborne (1962) found positive autocorrelation in the variance of returns.
martingalezero expected returnsubmartingale: good for stock marketBachelier (1900) and Samuelson (1965) recognised the importance of the martingale in relation to an efficient market. Whilst Cox and Ross (1976), Lucas (1978) and Harrison and Kreps (1979) pointed out that in practice investors are risk averse, so (presumably as compensation for the time value of money and systematic risk) they demand a positive expected return. In a long-only market like a stock market this implies that the price of a stock follows a submartingale (a martingale being a special case when investors are risk-neutral).
random walkdiscrete version of Brownian motionpoorLeRoy (1973) and (especially) Lucas (1978) pointed out that a random walk is neither necessary nor sufficient for an efficient market.
Wiener process/Brownian motioncontinuous-time, Gaussian independent incrementspoorBachelier (1900) developed the mathematics of Brownian motion and used it to model financial markets. Note that Brownian motion is a diffusion process, a Gaussian process, a Lévy process, a Markov process and a martingale. On the one hand this makes it a very strong condition (and therefore the least realistic), on the other hand it makes it a very important “generic” stochastic process and is therefore used extensively for modelling financial markets (for example, see Black and Scholes (1973)).

Graphical Theories

  1. Breakout theory
  2. Candlestick psychology
  3. Clayburg theory
  4. Indicators combined
  5. Ross theory
  6. Wolf theory
  7. Dacharts

    Geometrical Theories

  1. Angles method
  2. Crane theory
  3. Carney theory
  4. Di Napoli theory
  5. Fractal theory
  6. Gann theory
  7. Pesavento theory
  8. Pyrapoint
  9. Sacred geometry
  10. Symmetry & Swing
  11. Cycle Studies
  12. Clyde Lee method

    Numerical Theories

  1. Constant application
  2. Murrey Math theory
  3. Personal numerical theory
  4. sacred numbers & ratio
  5. moonXmoon theory
  6. signXsign theory
  7. seasonXseason theory
  8. moonXsign theory
  9. combined X combined theory
  10. mml-x-moon theory

    Mathematical & Physical Theories

  1. FFT theory
  2. Mesa theory
  3. Neural Nets theory
  4. Time & Space theory
  5. Quantum method
  6. General Statistical Methods
  7. Fisher Transform method
  8. Monte Carlo Methods
  9. Chaos Trading B.Williams

    Astro & Terrestrial Theories

  1. Astrotides
  2. Berg theory
  3. Cycle theory
  4. Delta theory
  5. Fishtrading method
  6. Ions theory
  7. Larson theory
  8. Moon theory
  9. Solar-Terrestrial theory
  10. WavesAstrology theory
  11. harmonics method
  12. cosmo-trend application
  13. Astro-NeuralNets Alphee Theory
  14. Astro Cycles Research
  15. 56 Year Cycle McMinn

    Econophysics

  1. Physics of Finance
  2. Quantitative Finance
  3. Mathematical statistics and Econometry
  4. Chaos& Non Linear Dynamics Models
  5. Complex Systems in Financial Market
  6. Finance and Time Series
  7. Gauge Physics of Finance

    Theories Combined- Forecast & Lessons Miscellaneous

  1. Financial Engineering
  2. Mathematical Models in Finance
  3. Market Models
  4. Mathematical Models for Trading
  5. Genetic Algorithms for trading
  6. Neural Network Trading Models
  7. Bayesian Networks Applications
  8. Financial Forecasting with Support Vector Machines
  9. Computational Methods for Financial Applications
  10. Gauge geometry of financial markets
  11. Quantum Electrodynamics Analogy
  12. Mesoscopics of stock market
  13. Path Integrals Financial Markets
  14. Quantum Finance
  15. Dynamic Models
  16. Brownian motion stock markets
  17. Option Theory Stochastic Analysis
  18. Option Pricing under Quantum Theory
  19. Microscopic Models of Financial Markets
  20. Intelligent Finance