Application of Machine Learning to

Financial Trading

January 2, 2015

Some slides borrowed from:

Andrew Moore’s lectures,

Yaser Abu Mustafa’s lectures

About Us

Our Goal : To use advanced mathematical and statistical concepts to create

situational trading algorithms generating uncorrelated alpha.

Our Background: A Mathematician with some market experience started

AlgoAnalytics in October 2009.

Global Equivalent: Systematic (non-discretionary) Managed Futures Advisors

CEO: Aniruddha Pant, PhD (Berkeley, USA)

Financial Engineering, Quantitative Trading, Derivative Trading, Hedging, Analytics/Machine learning, Control Theory

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Aniruddha Pant � +91-9822873624

@ apant@algoanalytics.com

� www.algoanalytics.com

+6 Quantitative Analysts

Trading, Hedging, Analytics/Machine learning, Control Theory

CFO: Girish Patil, BE, PGDBA

Fundamental equity research covering Indian, US and Middle East markets. Experience in technical trading of markets.

Outline

� What is machine learning

- Binary classification

� What are we trying to classify

- Why is this problem unique

� Machine Learning Techniques

- Different Techniques

- Support Vector Machines (SVM)

- Ensemble Learning

- Unsupervised Learning

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- Unsupervised Learning

- Overfitting –Approach

� Newer techniques

- MKL

- Deep Learning

� Money Management

� What we do? – AlgoAnalytics Portfolio

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DEFINING & UNDERSTANDINGTHE PROBLEM

What is machine learning?

“ A computer program is said to learn from experience

E with respect to some class of tasks T and

performance P, if its performance at tasks in

T, as measured by P, improves with experience E”

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T, as measured by P, improves with experience E”

– Tom M. Mitchell

Daily Returns of NIFTY Index since January 2003

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• Daily Return Average: 0.081% , Standard Deviation: 0.016

• Ratio of Std/Mean = 19.27

• Kurtosis: 13,

• 2% of the moves bigger than 3-sigma

• 3 moves bigger than 6 sigma in@2800 days

• 6-sigma moves @350times more likely than Gaussian

• Non stationary distribution

Autocorrelation of Daily returns

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• Mean absolute daily move 1.1%

• 52% Accuracy leads to losses/break-even

• 56 % Accuracy leads to phenomenal profit

• 4% improvement over break-even accuracy leads to 8.8% profit every 100

days, which is huge!

• Working very close to randomness

Random Trading Systems: Pitfalls of working with close to random systems

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• Daily signals generated randomly 100 times

• Only constraint: Number of positive moves same as original dataset

• Best random system accuracy: 53.1%

• Worst random system accuracy: 47.3%

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MACHINE LEARNING TECHNIQUES

Supervised vs. Unsupervised Learning

Supervised Learning

� Goal: to learn a classification/regression

model

� TASK: well defined (the target function)

Unsupervised Learning

� Goal: to find structure in the data

� TASK: vaguely defined

� No TEACHER

Primarily, supervised learning

used in the case of financial

data

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� TASK: well defined (the target function)

� EXPERIENCE: training data with teacher

provided

� PERFORMANCE: error/accuracy on the

task

� No TEACHER

� No PERFORMANCE (but there are some

evaluation metrics)

data

Supervised Learning Techniques

Decision Trees

• Flow-chart like

Random Forests

• Extension of single

Artificial Neural Networks

Logistic Regression

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• Flow-chart like structure

• Valuable with small –width datasets

• Maps observations of an item to conclusion about the items target value

• Extension of single classification trees

• Many classification trees grown into a “FOREST”

• High accuracy and efficient on large databases

Networks

• Analogous to biological neural networks

• Used to find complex data patterns

• Interconnected artificial neurons used for computation

Regression

• Probabilistic statistical classification model

• Binary Predictor

Supervised Learning Techniques

SVM

• Used for classification

Multiple Kernel Learning

Deep Learning

• Attempts to model

Bayesian Networks

• Probabilistic model

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• Used for classification and regression analysis

• Constructs hyperplanein high dimensional space with maximum margin

• Most widely used and popular method

Learning

• Extension of kernel trick used to handle non-linear classification

• Combines information from multiple sources

• Attempts to model high-level abstractions in data

• Model architecture composed of multiple non-linear transformations

• Uses many layers of non-linear processing units for feature extraction and transformation

• Probabilistic model

• Based on the Bayesian rule

• Assumption that input attributes are indepedant

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WHAT WE DOFINANCIAL MARKETS

Try to predict many things which look like this

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• Daily Return Average: 0.081% , Standard Deviation: 0.016

• Ratio of Std/Mean = 19.27

• Kurtosis: 13,

• 2% of the moves bigger than 3-sigma

• 3 moves bigger than 6 sigma in@2800 days

• 6-sigma moves @350times more likely than Gaussian

• Non stationary distribution

AA Portfolio

• Daily predictions using machine learning techniques

• Predictions based on economic factors affecting the underlying security

Intra-Day Low Frequency

• Pair Trading – Long-short pairs of Nifty stocks and indices

• Market neutrality achieved by making the pair beta neutral.

• Based on the idea of statistical arbitrage

Market Neutral Multi-Day

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• Momentum Strategy – Indentifying momentum in stocks/indices

• Mean-Reversion Strategy – Assumption that each security returns to its historical mean

Directional Strategy

• Alpha comes from underlying direction

• Butterfly spread – long ITM strike, short 2 ATM strike, long OTM strike

• No naked short options

Options

Portfolio Performance

1

1.5

2

2.5Equity Curve

AA Portfolio

Niftybees

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*Nifty BeES, an ETF tracking the S&P CNX Nifty index, is used as the benchmark.

AA Equity Backtesting Performance :

Backtesting Period: 4th Jan 2010 – 31st Oct 2014

PortfolioAnnualized

ReturnsDrawdown

Max DD Period

(Months)

Leverage

Factor

Max Loss in

Rs. LSharpe

Calmar

ratio

AA Equity 15.86% 4.80% 4 1 48 3.75 3.30

NiftyBees 10.63% 27.50% 38 0.62 0.39

0.5

Jan-10 Jan-11 Jan-12 Jan-13 Jan-14

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WHAT WE DOOTHER DOMAINS

BFSI

• Trading Strategy and Analysis

Healthcare

• Recognizing potential Pulmonary Embolism

Some of our previous work and future possibilities

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• Trading Strategy and Analysis

• Bank Credit Classification• Portfolio Analysis

• Financial Market Forecasting

• Predict customer interest in Caravan Insurance Policy

• Predictive Customer Relationship Analytics(CRA)

• Risk management and prediction

Future Work• Detect money laundering

• Customer segmentation and Branding

• Recognizing potential Pulmonary Embolism candidates from CAT scan data

• Hepatitis B and Hepatitis C patients using non-biopsy test data

• Cancer cell classification

Future Work • Patient care aid

• Predict premature birth based on peptide biomarkers

• Risk of death in surgery

• Hospital admission – predict readmission for same illness

Human Resources Management

Telecom

• Accurately predict as many

Other• Electricity Load Forecasting•Airline Passenger Forecasting

Some of our previous work and future possibilities (Contd…)

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Management

• Manpower Asset Allocation

• Recruitment Model – Talent Forecasting

• Worker’s Compensation Policy

Future Work• Turnover modeling for

businesses

• Targeted retention

• Accurately predict as many current 3G customers

• Identify 2G customers likely to convert to 3G customers

Future Work• Forecast traffic patterns and

peak period routing

• Identify at-risk customers; convert them to loyal customers

•Airline Passenger Forecasting•Sentiment Analysis using twitter data•Cross-selling – predicting potential customers

Future Work

• Predicting player

performance in sports• Efficient building design• Power grid management

Work in progress

MRI Analytics

Efficient evidence based healthcare system

Image Processing + Machine learning + Radiologist = decision support systems

Recommender Systems

Recommend items sold online to potential customers

Machine learning - predicting that an item is worth recommending

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Automated detection of diabetic retinopathy and macular edima

Efficient evidence based healthcare system

Image Processing + Machine learning + eye specialist

Predictive Maintenance in Refrigeration Systems

Fault detection in refrigeration systems

Energy optimization