Applicatons of AI in finance

Amit

Anshum

Pratyush

Siddharth

The AI view of money

''Money is just a type of information, a pattern that, once digitized, becomes subject to persistent programmatic hacking by the mathematically skilled. As the information of money swishes around the planet, it leaves in its wake a history of its flow, and if any of that complex flow can be anticipated, then the hacker who cracks the pattern will become a rich hacker." -- from Cracking Wall Street

Why Computers?

Computers can process lot more information per unit time than we can, without getting tired

Computers can recognize patterns in data easily

Computers can do calculations for you, so that you can work at a higher level of abstraction

You don't have to pay a computer on an yearly basis

Areas where AI is applied

Financial Data mining

Arbitrage Opportunities

Hedging and Trading Strategies

Financial Time Series Forecasting

Supply Chain Management

Fraud Detection

Arbitrage

Arbitrage: is an investment, where there is no chance of loss in any case (state), and a positive cash inflow in atleast one case.

Liquid market: Minimal Arbitrage opportunities

For example: In India: 1 Euro = 65Rs, 1$ = 50 Rs

In US: 1 Euro= 1.5$

Purchase Euros from India, sell them in US to get $, sell them back in India. Sure Profit!!

How can we make MONEY

Arbitrage opportunities are mostly present after following a long chain of relationships

In an efficient market, arbitrage opportunities exist for very small periods of time

Can be taken advantage of, using fast computers, and launching automatic trades

Statistical Arbitrage -- Casinos

The arbitrage opportunity, which are true in expectations, i.e. In the long run, repeating a trading strategy

In financial markets, wherever statistical arbitrage is used, it involves hundreds and thousands of transactions of various securities over short holding periods, days to seconds.

Clearly, we need intelligent systems to gain from them.

Online Auctions

Various bidding strategies possible: Bid shading, Chandelier binding

Data needs to be processed on the fly

Complicated models to select a good Opening Bid

Probabalistic models

Need for intelligent systems

False-name bids possible: Leveled division set protocol

Genetic Algorithms for our aid

Genetic Algorithms : Good for optimization problems.

Provide quick acceptable solution

Particularly good for noisy and discontinuous functions appearing so frequently in market modelling and asset allocation

Also very good for combinatorial optimisation

Genetic Algorithms

GAs work with a population of ”individuals”

Fitness Score of Individuals

”Fit individuals” are given opportunities to reproduce by ”cross breeding”. Least fit members ”die out”

A well designed GA, converged to optimal solution of the problem

Genetic Algorithms: Method Overview

Evaluation Function: Provides a measure of performance wrt the set of parameters

Fitness Fuction: Provides a relative measure of fitness using the evaluation function. Generally it is the ratio of my evaluation function to the avg of evaluation function

Each individual gets to place number of copies in the population depending upon the ratio. Higher your ratio, more you represent.

Genetic Algorithms: Method Overview

Recombination & Mutation:Take any two parent strings, choose a 1 point crossover. Swap the strings on either side & mutate with some low probability.

The recombination probabilities depend on the type of coding which you choose for the problem.

Mutation is done so that no point in the search space has zero probability of being examined.

AI in Financial Data Mining and Manufacturing

What is the role of AI in data mining?

What is the nature of its contribution towards Business?

What is the role of an intelligent machines in manufacturing?

AI in Data Mining

Data mining is the process of extracting hidden patterns and useful knowledge from a set of raw data.

Computers come into picture when the data is too large to be analysed manually and when greater speed and accuracy is required.

Modern computers have largely enhanced data mining by use of sophisticated tools and complex algorithms. An important part of this is performing complex calculations in feasible time.

Automated data mining in Finance

The need for data mining in finance arises due to the following (and many others) :

Benefit from short-term subtle patterns.

Read the impact of market players on market regularities.

Make coordinated multi resolution forecast (minutes,days,weeks,months,and years).

AI in manufacturing

AI provides the edge required to stay in competition in today's highly competitive market.

On the factory floor, Artificial Intelligence will enable machines of automated reasoning thus providing solutions to manufacturing problems during the production process.

Automatic scheduling of manufacturing operations helps in better utilization of resources.

Practical applications of AI in manufacturing.

Nissan and Toyota, for example, are modeling material flow throughout the production floor that a manufacturing execution system applies rules to in sequencing and coordinating manufacturing operations.

Many automotive plants use rules-based technologies to optimize the flow of parts through a paint cell based on colors and sequencing, thus minimizing spray-paint changeovers.

Benefits of AI in Manufacturing

Production Scheduling

Advanced Planning and Scheduling

Production Reporting

Inventory Management

Accounting

Capacity Planning

Materials Requirements Planning

Process Control.

How AI has fared so far

Abundance of data in financial market and diversity of the requirements provide a suitable environment for testing the data mining techniques and models.

Since 1990 there has been a huge revolution in application of AI in business and manufacturing.AI has become a mainstream phenomenon and has largely benefited those who have adopted it.

Fraud detection

Fraud cases has a severe impact on company profit and reputation.

Number of fraud cases are increasing day by day.

Fraud detection might need to be done at real time,For example:Consider the case of credit card company.In this case fraud must be detected while transaction going on.

Expert system in fraud detection.

Although a given case may look legal,Experienced expert may tell that it is the case of fraud

We can Extract the experience of the expert and put them into the system.

Rule Based Expert System

Rule Based Expert System work on set of rules given to it(fraud rule),Based on experts experience.

For example:If pin for ATM card is entered wrongly for more than three times,An expert system might detect the possibility of fraud.

Share and Confidence of Rule

We define the share of fraud rule as the percentage of fraud cases which is covered by the rule.

Share of fraud rule does say about acurracy of the rule.

Confidence of fraud rule

Some non-fraud cases may also be flagged as a case of fraud,which may lead to wrong diagnosis.

We define the confidence of the fraud rule as:number of misused cases covered by the rule/total number of cases covered by the rule

More confidence means greater accuracy and less false alarm.

Problem with rule based System

Number of rules increases substantially over the years,slowing the process of fault detection

Rules valid few years ago might not be valid now or may be of very little use,Which might still be there in the system.

Fraud Detection using neural Networks

fraud detection in many operation falls neatly in principle within the scope of pattern recognition procedures.Hence neural network as fraud detection technique is a good option

Neural Networks can even detect new types of fraud

Problems with Neural Networks

Number of fraud cases as compared to legal cases is very low.

Difficult to collect data and training set for the network.

Data set are given in different ratio of fraud cases to legal cases,then it occur in practice.

neural network will start flagging legal cases as the case of fraud

Market Forecasting

What is forecasting?

Need for forecasting?

What is the role of AI in forecasting?

Applications of forecasting in various domain

What all things Intelligent System still can’t capture?

Need for forecasting

High incentives

Strategic decision and Policy making

Manage risk

Capture the dynamics of market and complex patterns in data

Where does AI fit in?

Sum up the experience of seasoned investor

Indicators for different phases of business life cycle.

Recession consolidation/ fiscal recovery growth fiscal decline

Efficient market hypothesis

Different methods of forecast eg. GARCH, ARCH, ARIMA,

Neural Networks.

Flow Diagram and basic model of Neural Network

Data PreprocessingData Preprocessing

Extract Test Data SetExtract Test Data Set

Training

Forecasting

Result Analysis

Select Network ArchitectureSelect Network Architecture

Data CollectionData Collection

Uses of Intelligent System

Manage Risk eg. Currency market average daily turnover is $ 3.2 trillion as reported in April 2007.

Building up portfolio eg. Hedge funds, mutual funds, fund managers use intelligent system to build up portfolio from different asset classes.

Forecast future returns.

Analyze “risk-reward” ratio.

Trend analysis and pattern recognition.

Trading strategies and economic indicators

eg.Projecting Inflation and GDP figures.

What all things Intelligent Systems still can’t capture?

Market sentiments eg. War situations, natural calamities etc.

Emotional attachment to an investment. eg. Gold in india people are attached.

Market reaction to scams and scandals eg. Satyam fraud.

Questions and Answers

QUESTIONS ARE GAURANTEED IN LIFEANSWERS AREN’T

Bibliography

 Data Mining For Financial Applications.  Boris Kovalerchuk , Central Washington University USA ; Evgenii Vityaev , Institute of Mathematics Russian Academy of Sciences Russia

Artificial Intelligence in  Manufacturing - improving the bottom line. Dawn Tupciauskas, Tuppas Software Corporation ,2008.

Financial forecasting using neural networks, Ed. Gately 1996.

Genetic algorithms overview, Franco Busset.

Wikipedia for most of the other references.

R. Brause, T. Langsdorf, M.Hepp: Neural Data Mining for Credit Card Fraud Detection,IEEE Int. Conf on Tools with Art. Intell. ICTAI-99, IEEE Press 1999, pp.103-106