commodity price prediction using an artificial prediction market based approach · ·...

Commodity Price Prediction using An Artificial Prediction Market based

Approach

July 18th, 2017

Rohith D. Vallam

Ramasuri Narayanam

Gyana R. Parija

India Research Lab2 © 2016 IBM Corporation

Agenda

Prediction Markets: Introduction

Problem Definition

Artificial Prediction Market: A Vanilla Version

Results & Comparison

Next Steps


How do people predict ?

Opinion Polls Delphi methods Peer Prediction Methods

Wagering

mechanisms

Prediction Markets (Our Focus)

Source of images :

http://images.google.com

© 2017 IBM Corporation

What is a Prediction Market?

▪ Tool for collecting and aggregating opinion using market principles

▪ Price: probability of event occurring

▪ Value: leading indicator, expose hidden information

▪ Pay-off: monetary, reputational, indirect

▪ Accuracy: better than conventional forecasting1

Clinton

Obama

Image: http://upload.wikimedia.org/wikipedia/commons/1/13/IEM_DCON2008.svg

Iowa Electronic Markets:

2008 US Democratic Convention Market

Defintion: A place where information is aggregated via market (or other) mechanisms for the

primary purpose of forecasting events, or the probability that an event will occur

1Source: Arrow, K.J. et al “The Promise of Prediction Markets” Science, 2008, 320, 5878, 877-878

http://www.sciencemag.org/cgi/content/summary/320/5878/877


Prediction Markets: Scientific Background


When to use Prediction Markets?

▪ Complexity (ecosystem)

▪ Uncertainty

▪ Many decision points

▪ Clear outcomes

▪ Market liquidity

▪ Diversity of opinion


Advantages of Prediction Markets with Other Approaches of Information

Aggregation


Prediction Markets At Work: Consumer Prediction Markets

Intrade Prediction Market

Hollywood Stock ExchangeViral Loop (Prediction market mobile app)


Prediction Markets At Work: Consumer Prediction Markets (Cont.)

LongBets Inkling

New kid on the block:

Blockchain Prediction Markets

and many

more …..

(Wikipedia) The Augur project seeks to leverage the

open, global, peer-to-peer ledger functionality

that blockchain technology provides, as well as game

theory and financial incentives, to better explore the

concept of the wisdom of crowds (also known

as collective intelligence) and try to get more accurate

predictions about future events.

https://en.wikipedia.org/wiki/Peer-to-peer

https://en.wikipedia.org/wiki/Ledger

https://en.wikipedia.org/wiki/Blockchain_(database)

https://en.wikipedia.org/wiki/Game_theory

https://en.wikipedia.org/wiki/Wisdom_of_the_crowd

https://en.wikipedia.org/wiki/Collective_intelligence


Prediction Markets At Work: Corporate Prediction Markets

▪ PMs in use at 100-200 US organizations (July 2010)▪ Ex: HP, BestBuy, Electronic Arts, Boeing, Amazon, Harvard, GM, Hallmark, P&G, Ford,

Microsoft, Chevron, Lockheed Martin, CNN, Adobe, American Express, Bosch

▪ Applications▪ Project management, risk management

▪ Revenue forecasting, demand planning, capital budgeting

▪ Idea management (rate, filter, prioritize ideas)


Agenda


Problem Definition

Artificial Prediction Market: A Vanilla Version


Next Steps


Price Prediction for Raw Materials

Feedstock SuppliersFeedstock Types

Raw Material

Manufacturing (Plants,

Process, Capacity)

Port of Origin /

Destination Port

Raw Material Supply(Countries / Regions / Company)

Raw Material Orders Raw Material InventoryMfg. End Product (Products, Demand,

Regions)Raw Material Prices

Raw Material & Key Information which Experts seek from High Impact Factors

Knowledge Search & Results

Expert 1

Knowledge Type 1Expert 2

Knowledge Type 2Expert 3

Knowledge Type 3

Artificial Prediction

Market based

Approach

(to predict the price

for raw materials)


Prediction Markets: High Level Outline

Expert 1’s Prediction

Price: 1560

Confidence Level: 5

Expert 2’s Prediction

Price: 1620

Confidence Level: 7

Expert n’s Prediction

Price: 1650

Confidence Level: 9

.

.

.

Prediction Market based

System

Output:

Market

Prediction

of Raw

Material


Agenda


Problem Definition

Artificial Prediction Market


Next Steps


ARCHITECTURAL DIAGRAM of ARTIFICIAL PREDICTION MARKETS (for Price Prediction)

Market Participants

Agent_IRL_ML

BettingStrategy

(Q Learning)

β1

Agent_IRL_FeedStock_ML

Agent_IRL_EM

Budget

Betting Strategy

(Q Learning)

β3

Budget

Betting Strategy

(Q Learning)

β2

Budget

Prediction Market

Market Price Prediction(from market price

equations)(Prediction2, bet2)

Budget Updationfor all agents

β1 <- β1 – bet1 + revenue1



Raw Material

PRICE DATA

SOURCE


Timeline of Artificial Prediction Market (with 2 Agents)

Week 1 starts Week 2 starts

Player 1 bets 20$ on the prediction 5345.45

Player 2 bets 56$ on the prediction 5435.32

Player 1 places a revised bet 12$ on the prediction 5679.26 after observing the market prediction

Player 2 places revised bet 35$ on the prediction5467.87 based on market prediction

Market Maker reveals current market prediction

Prediction market closes. Ground Truth is revealed

Players are rewarded based on their predictions and the realized outcome.

Note: 1. We can run the above market using the Data whose time duration: 1-May-15 to 31-Mar-17 (weekly data)

……..


Artificial Prediction Market Idea to Raw Material Price Prediction

Algorithm sketch based on the paper on Continuous Artificial Prediction Market (c-APM). Details of reference given below:Online Prediction via Continuous Artificial Prediction Markets - IEEE Intelligent Systems (2017) - Fatemeh Jahedpari, Talal Rahwan, Sattar Hashemi, Tomasz Michalak, Marina De Vos, Julian Padget, Wei Lee Woon.

Agenda


Problem Definition



Next Steps


Preliminary Results (using the Proposed Artificial Prediction Market)

Metrics For Entire Data (05-Jun-15 to 30-Jun-17)

✓RMSE Score = 45.01

✓MAPE Score = 0.0212

Metrics For Q2 2017 (Apr-Jun 2017)



Metrics For Q1 2017 (Jan-Mar 2017)



Metrics For Q4 2016 (Oct-Dec 2016)



Metrics For Entire Data (05-Jun-15 to 30-Jun-17)



Metrics For Q2 2017 (Apr-Jun 2017)



Metrics For Q1 2017 (Jan-Mar 2017)



Metrics For Q4 2016 (Oct-Dec 2016)



Artificial Prediction Market Dynamic Opinion Formation Model


Preliminary Results (using Vanilla Version of Artificial Prediction Market)

Maximum Absolute Percentage Deviation of the proposed Artificial Prediction Markets based approach below 4% for the last 3 Quarters.

Agenda


Problem Definition



Next Steps


Next Steps

Engineering with ``Parameter Configurations” to improve the performance of ``Vanilla Version of Artificial Prediction Market”

Explore advanced mathematical constructs to prediction performance ✓ Work with different ``proper scoring rules” to determine payments to the agents

✓ Work with different strategies for the ``market maker” to define the market prediction

✓ Work with different learning algorithms for the agents to improve their own predictions after observing the market prediction

Design an ``artificial expert / agent” who can observe other agents’ predictions and then predict most accurate price of raw material:✓ Based on ``deep reinforcement learning” paradigms

✓ Based on algorithms in online learning especially ``prediction with expert advice” literature

Design of “Artificial Prediction Markets” to derive predictions in the form of “Probability Distribution” for a given task.


References

✓Online Prediction via Continuous Artificial Prediction Markets - IEEE Intelligent Systems (2017) -Fatemeh Jahedpari, Talal Rahwan, Sattar Hashemi, Tomasz Michalak, Marina De Vos, Julian Padget, Wei Lee Woon.

✓An Introduction to Artificial Prediction Markets for Classification – Adrian Barbu and Nathan Lay –Journal of Machine Learning Research (2012)

✓Artificial Prediction Markets for Online Prediction of Continuous Variables-A Preliminary Report -Fatemeh Jahedpari et.al. (2015)

✓Simulating Prediction Markets that Include Human and Automated Agents - Wendy Chang (Masters thesis, MIT 2009)

✓Betting and Belief: Prediction Markets and Attribution of Climate Change - John J. Nay et. al (2016)


Thank You

commodity price prediction using an artificial prediction market based approach · ·...

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