Applicatons of AI in finance

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Applicatons of AI in finance

• Amit
• Anshum
• Pratyush
• Siddharth

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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

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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

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Areas where AI is applied

• Financial Data mining
• Arbitrage Opportunities
• Hedging and Trading Strategies
• Financial Time Series Forecasting
• Supply Chain Management
• Fraud Detection

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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!!

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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

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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.

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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

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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

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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

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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.

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Genetic Algorithms Method Overview

• Recombination MutationTake 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.

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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?

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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.

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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).

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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.

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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.

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Benefits of AI in Manufacturing

• Production Scheduling
• Advanced Planning and Scheduling
• Production Reporting
• Inventory Management
• Accounting
• Capacity Planning
• Materials Requirements Planning
• Process Control.

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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.

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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 exampleConsider the case of credit card
  company.In this case fraud must be detected while
  transaction going on.

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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.

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Rule Based Expert System

• Rule Based Expert System work on set of rules
  given to it(fraud rule),Based on experts
  experience.
• For exampleIf pin for ATM card is entered
  wrongly for more than three times,An expert
  system might detect the possibility of fraud.

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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.

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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
  asnumber of misused cases covered by the
  rule/total number of cases covered by the rule
• More confidence means greater accuracy and less
  false alarm.

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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.

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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

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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

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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 cant
  capture?

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Need for forecasting

• High incentives
• Strategic decision and Policy making
• Manage risk
• Capture the dynamics of market and complex
  patterns in data

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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.

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Flow Diagram and basic model of Neural Network
Data Collection
Data Preprocessing
Extract Test Data Set
Select Network Architecture
Training
Forecasting
Result Analysis
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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.

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What all things Intelligent Systems still cant
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.

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Questions and Answers
QUESTIONS ARE GAURANTEED IN LIFE ANSWERS ARENT
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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.

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• 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