Download pdf - Tziralis & Ipeirotis at 3rd Prediction Markets Workshop

Detecting Important Events using Prediction Markets, Text Mining, and Volatility Modeling

George Tziralis & Panos Ipeirotis

markets and efficiency

• strong: prices reflect all information, public and private

• semi-strong: prices reflect all publicly available information

• weak: past prices cannot be used for market prediction

prediction markets?

• Perhaps, the relationship between price and information is no more clear than in prediction markets (Pennock et al. 2002)

• let’s start from the weak form of efficiency

• is it possible to predict future prices using only past ones?

playground

• InTrade

• political contracts

• US nominee elections 08

• 800 - 1000 time series instances for each contract

machine learning

• various algos, potential inputs etc

• lots of both trials and errors

• finally selected Support Vector Machines

AI for PM prediction

• poor results

• very close to / worse than random walk

• suggesting weak efficiency

prices, diffs, returns

• low autocorrelation

prices, diffs, returns

• low autocorrelation

• garch analysis: ARMAX(0,0,0) for the mean

• TS ~ noise

• evidence of weak efficiency

variance

• typically stable through time

• not in our case

• GARCH(1,1) !!

• autocorrelation

• volatility clustering

volatility clustering

• large price changes are followed by similarly large changes in price, of either sign, and its opposite

• typical in all financial markets

• not yet utilized appropriately for event detection

• big price changes in periods of high volatility should not suggest significant events

event detection

• the task of monitoring news corpus to discover stories that discuss a previously unidentified event

• the vision of a robust system that would monitor news streams and alert on events

• principal assumption: information organization by event, rather than by subject

event detection, so far

• correlate a simple change in market prices or returns with a signal of an event

• naive approach, assumption that volatility remains stable

• this is not the case

volatility modeling

• introduce a GARCH model for the variance of prices

• concurrent analysis of volatility and text

• identify important events more robustly

text mining

• various options, lots of experiments, too

• let’s try a naive one here

• data from google trends

• keywords: nominee’s name

events affecting volatility

• unanticipated events


• unanticipated events

• introduce unexpected new information in the market and

• increase volatility after they become known


• anticipated events


• anticipated events

• increase volatility the days before they happen,

• introduce new information when they happen and then

• decrease the volatility allowing the market to stabilize in a new “equilibrium”

discussion

• prediction markets + volatility modeling + text mining ➜ strong potential for event detection

• work in progress, need ur feedback

• various future research directions

thank you!