matt's notes

indiehead/stock-tracker @ github

Game theory approach:
 
Operations on Time Series Data
 
   * A typical framework is that of the FAME system, since it embodies an excellent understanding of the special properties of time series. FAME stands for forecasting, analysis and modeling environment
     FAME information systems, Ann Arbor Michigan.
     www.fame.com
   * Data Preparation (i.e. interpolating and time scale conversion) -- curve-fitting
   * Queries (e.g. moving averages and sums) -- aggregates over time.
   * Forecasting (e.g. statistical or data mining-based extrapolation) -- regression, correlation, Fourier analysis, and pattern-finding.
 
Forecasting
 
# Autoregression uses a weighted sum of previous values to predict future ones. There are also seasonal autoregressive models.
# These and other models are incorporated in time series products such as FAME, SAS and SPLUS.
# In options finance, the basic approach is to assume that the price of an equity is based on a random walk (Brownian motion) around a basic slope. The magnitude of the randomness is called the volatility. In a result due to Norbert Wiener (he worked it out to shoot down bombers over London), for this model, the standard deviation of the difference between the initial price and the price at a certain time t rises as the square root of time t.
 
Steps in a Typical FAME Session
 
    * Specify frequency. Say monthly, starting at January 1, 1996 and ending at the current time.
    * Create sales and expenses time series by importing these from a file or typing them in. Specify that these are flow type time series.
    * Create a new time series:
      formula profit = sales - expenses.
    * Create a fourth time series with weekly frequency on inventory. Specify that inventory is a level type time series.
    * Convert the first three time series to a weekly frequency (by dividing the monthly values by 4.2 or by constructing a cubic spline to make the sales, expenses, and profits curve look smooth).
      This interpolation depends on knowing that sales and expenses are flow-type values.
    * Now, use autoregression to predict future time series values.
 
Because of what is known about stock market time series data (random walks:source)
Possibly not so random http://www.castrader.com/2007/03/why_are_informa.html
 
Finance Applications of Game Theory
  * Higher order beliefs
 
http://www.gametheory.net/news/Items/110.htmlwww.fame.com
   * Data Preparation (i.e. interpolating and time scale conversion) -- curve-fitting
   * Queries (e.g. moving averages and sums) -- aggregates over time.
   * Forecasting (e.g. statistical or data mining-based extrapolation) -- regression, correlation, Fourier analysis, and pattern-finding.
 
Forecasting
 
# Autoregression uses a weighted sum of previous values to predict future ones. There are also seasonal autoregressive models.
# These and other models are incorporated in time series products such as FAME, SAS and SPLUS.
# In options finance, the basic approach is to assume that the price of an equity is based on a random walk (Brownian motion) around a basic slope. The magnitude of the randomness is called the volatility. In a result due to Norbert Wiener (he worked it out to shoot down bombers over London), for this model, the standard deviation of the difference between the initial price and the price at a certain time t rises as the square root of time t.
 
Steps in a Typical FAME Session
 
    * Specify frequency. Say monthly, starting at January 1, 1996 and ending at the current time.
    * Create sales and expenses time series by importing these from a file or typing them in. Specify that these are flow type time series.
    * Create a new time series:
      formula profit = sales - expenses.
    * Create a fourth time series with weekly frequency on inventory. Specify that inventory is a level type time series.
    * Convert the first three time series to a weekly frequency (by dividing the monthly values by 4.2 or by constructing a cubic spline to make the sales, expenses, and profits curve look smooth).
      This interpolation depends on knowing that sales and expenses are flow-type values.
    * Now, use autoregression to predict future time series values.
 
Because of what is known about stock market time series data (random walks:source)
Possibly not so random http://www.castrader.com/2007/03/why_are_informa.html
 
Finance Applications of Game Theory
  * Higher order beliefs
 
http://www.gametheory.net/news/Items/110.html
 
Information Theory combined with game theory