MCarloRiskHD

Don't be "fooled by randomness!" Run your hunches, tips, analysis, and advice from cab drivers through our random walk stock analyzer!

Try our new MCarloRisk3D app for more features and 3D visualization.

Estimates future price distribution using random walk theory.

This is the HiDef version of MCarloRisk app, customized for iPad.


Background discussion: E. Fama article on early random walk studies from the 1960's:

 http://www.ifa.com/Media/Images/PDF%20files/FamaRandomWalk.pdf



New model calibration tutorial:

 http://diffent.com/tuning1.pdf



Example use case & training guide for studying "AAPL to $320" can be found at:

 http://diffent.com/AAPL320arialP.pdf



The app uses prior data from the stock in question for volatility estimates. 

User can control how far back in time to use historical data to capture only the current "epoch" of a company or of the market as a whole if desired.


Built-in backtesting, verification, and model tuning tools.



-- Details --



This app models daily stock returns as a stable stochastic process and estimates a future price distribution by Monte Carlo re-sampling from an "empirical distribution" of a user-specified subset of prior (known) daily returns. 

Be sure to press the Run Monte button on the Monte Carlo tab after changing settings or downloading a new data set. 

This app downloads historical data from Yahoo Finance as base data to resample. Prices are converted to daily returns [P(t)/P(t-1)] before resampling. The user can choose how far back to resample. By estimating a probability distribution of future prices at the user-specified investment horizon in this manner, we can give risk-of-loss estimates in thumb-rule fashion. 

Reports out estimated price and %loss estimates at the commonly used levels of 1st percentile and 5th percentile (1% and 5% risk). Also reports out median (50th percentile) price estimates at the given number of days forward. Calculations may be performed on Yahoo daily Closing or Adjusted daily Closing price data. An artificial shock filter is provided, which can be used to reject the resampling of prior returns that are artificially large (due to splits or other artificial re-valuations that do not affect the underlying value of the asset).

The stochastic model may be tuned or calibrated only by adjusting the maximum number of days backwards to sample or adjusting the black swan parameters.

Model Validation features:



On the Monte Carlo tab, you can withhold any number of recent days from the model and then plot the results of the stochastic risk forecast as lower-bound envelopes at 1% and %5 estimated probability (risk) levels. 



Validate tab:



This allows you to perform an exhaustive validation on your model by withholding several points, computing the model, comparing the forward prediction of the model versus the actual reserved data, and repeating this in increasing time sequence for all withheld points.



A vertical "Cursor Beam" is provided that you can drag across the new plots in the Monte Carlo tab and the Validate tab to show the plotted values from several curves at once, with the values color-coded to the curves.



Recent additions:

1. In the Monte Carlo tab, compute actual percentile for the user-settable probability trace when points are withheld for backtesting. This % is shown in black underneath the blue "actual $" notation on the upper left of the main graph.



2. Add "black swan" features to the model (access this via the Opt menu in the Monte Carlo tab). User may specify amount of the black swan event in percent and the probability of such an event occurring on any given day. A black swan event is a large 1 day drop.

The app provider makes no claims as to the suitability of this app for any purpose whatsoever, and the user should consult an investment advisor before making investment decisions.

更新履歴
Adjust the X location where the probability plot flips, to better support the iPad screen size.