nullModel {ttrTests}R Documentation

Hypothesis test for efficacy of TTR

Description

One of the four main functions in the package. Creates a confidence interval for the observed excess return via bootstrap resampling. Can write summary of output to a file as a latex figure.

Usage

nullModel(x, model = "stationaryBootstrap", userParams = 4, nSamples = 100, 
ttr = "macd4", params = 0, burn = 0, short = FALSE, condition = NULL, silent = TRUE, loud = TRUE, 
alpha = 0.025, TC = 0.001, benchmark = "hold", latex = "")

Arguments

x A univariate series
model Passed to the function 'generateSample'
userParams Passed to the function 'generateSample'
nSamples How many bootstrapped samples to generate
ttr Could be a character string for a built in TTR, or a user defined function. User defined functions must take a univarate series and a list/vector of inputs and must output a series with values 1,0,-1 only
params Used to calculate the position based on the given TTR
burn When computing the position function s(t), values for t < burn will be forced to 0, i.e. no position held during the 'burn' period
short Logical. If false the position function s(t) will be forced to 0 when it would otherwise be -1, i.e. no short selling
condition An extra opportunity to restrict the TTR so that position is forced to 0 under some condition. Must be a binary string of the same length as the data 'x'. See 'position' for more details.
silent Logical. If TRUE, output from subroutines will be supressed.
loud Logical. If FALSE, output from the main function will be supressed.
alpha Confidence interval for 2-sided hypothesis test
TC Percentage trading costs. Used to adjust return statistics.
benchmark When computing 'excess' returns, all functions in this package subtract the conditional returns based on a given "ttr" from the "benchmark" returns. Two different TTRs can be compared this way if desired.
latex Full path name for a writable file. The laTeX code that generates a figure with a summary of the output will be appended to file.

Value

CR A vector of conditional returns of length 'nSamples'
AR CR, adjusted for trading costs
SR Sharp ratio for these returns using r_f = 0
Z Z-score for observed excess return, using mean and standard deviation of CR for a confidence interval
P P-value associated with observed Z-score

Note

A significant P-value is enough to reject the null hypothesis that the TTR had results due solely to randomness in the data. However, there are several other null hypotheses to explain good results, chiefly the data snooping hypothesis, addressed using the function 'realityCheck'.

EXTREMELY IMPORTANT NOTE: The functions in this package evaluate past performance only. No warranty is made that the results of these tests should, or even can, be used to inform business decisions or make predictions of future events.

The author does not make any claim that any results will predict future performance. No such prediction is made, directly or implied, by the outputs of these function, and any attempt to use these function for such prediction is done solely at the risk of the end user.

Author(s)

David St John

References

William Brock, Josef Lakonishok, and Blake LeBaron. Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5):1731-1764, 1992.

Examples


spData <- as.vector(getYahooData("SPY",start="20060101",end="20081231")[,"Close"])
null <- nullModel(spData,nSamples=5)


[Package ttrTests version 1.4 Index]