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Data Analytics: Risk Mitigation in Financial Investments v01
- The document discusses risk mitigation in financial investments through the use of data analytics. It covers assessing different investor types and their varying objectives, analyzing different asset classes and their risks, and using analytical tools like technical analysis and fundamental analysis to develop investment strategies and manage portfolio risk. The focus is on collecting and analyzing relevant data from multiple sources to predict market movements and make informed investment decisions.
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Data Analytics: Risk Mitigation in Financial Investments v01
- 1. Data Analytics Risk Mitigationin Financial Investments Sumir Nagar - Chief Operating Officer Agile FT LLC, Dubai, UAE www.agile-ft.com at Shailesh J Mehta School of Management - IIT Powai March 18, 2017 The opinions of the presenter are his individual opinions and/or advise, and are not to be construed as those of Agile FT LLC, Dubai, UAE. Investments are subject to market risk and prudence is advised when investing in financial instruments.
- 2. Thanks to theSchool, Vinod, Aditya, and Shasti House Rules & Etiquette You can survive without your cellphones for a few minutes I’m a big fan of interactive, let’s try and wait for logical breaks before we debate, discuss, comment So that we don’t talk over others, please indicate that you have something to say 2
- 3. I’m Usually Unpopular •Just a little about me • This is a much larger topic & I will try to do justice in 60 minutes • What I have to say can be construed to be CONTROVERSIAL • What I have to say is NOT ROCKET SCIENCE • Simply because Data Analytics is NOT ROCKET SCIENCE, (despite our best efforts) to cast that impression • I deal with • Basics - (usually forgotten) • Common Sense - (The Most Uncommon Thing) • Prudence - (Whatever That Means) 3
- 4. Who Is The“Investor” • Individual • HNI • Mid Income • Low Income 4
- 5. Who Is The“Investor” • Corporate 5
- 6. Who Is The“Investor” • Financial Institution • Bank • Insurance • Pension 6
- 7.
- 8. Risk & RewardAre Closely Intertwined 8
- 9. You Cannot HideFrom Risk 9
- 10. All You CanDo Is Manage Risk Down To Manageable Levels 10
- 11. Risk Management IsA Simple Matter of Assessing/Analysing Data Points 11
- 12. Risk Framework • NeedsAssessment - The Purpose • Expected Outcome - The Result • Horizon - Time Element • Appetite to Loose & Absorb Shocks - Risk Appetite • Ability to Manage Risk - Strategies • Available Cash 12
- 13. Varying Objectives? • Individuals •Life Events: Education, Marriage, Housing, Retirement • Corporate • Manage surplus cash, even our cash flow cycles, growth, taxation • Bank • Manage cash flow mismatches between deposits, withdrawals, lending, defaults, growth, taxation • Insurance • Risk Events, Cash flow mismatches, growth 13
- 14. Varying Needs Analysis • Regardlessof the Investor Type the measures are identical • What differs is the Analysis of the Need • For Individual Investors the cycle begins with a Risk Assessment Questionnaire (Why, When, etc) • For Corporates it depends on their Business Plans • For Banks it depends on ALM • For General Insurance Companies it depends on • Risk Events • Actuarial Calculations • For Life & Pension Companies • Risk Events (death, disability, medical conditions, retirement) • Risk Assessments in case of Investment Backed or Unit Linked Plans 14
- 15. Individuals 15 Income Group Sources Horizon AppetiteAvenues Returns % Share Low Wages Short Low Banks Insurance Fixed Variable 60% 40% Medium Wages Intermediate Medium Banks Insurance MF’s Fixed Variable Markets 40% 30% 30% High Wages Business Long High Varied Fixed Variable Insurance Markets Hedges PE 20% 20% 30% 20% 10%
- 16. Corporate 16 Size Surplus HorizonAppetite Avenues Returns % Share Small Low Temporary Low Banks Fixed Variable 60% 40% Medium Medium Intermediate Medium Banks MF’s Treasuries ICD’s Family Offices Fixed Variable Markets 40% 30% 30% Large High Long High All Fixed Variable Markets Hedges 20% 20% 30% 30%
- 17. Financial Institutions 17 Size SurplusHorizon Appetite Avenues Returns % Share Small Low Temporary Low Banks Fixed Variable 60% 40% Medium Medium Intermediate Medium Banks MF’s Treasuries Fixed Variable Markets 40% 30% 30% Large High Long High All Fixed Variable Markets Hedges 20% 20% 30% 30%
- 18. Risk Types • MarketRisk • Operational Risk • Systemic Risk 18
- 19. Analytics • Is largelydependent on various factors • History - Hindsight is a great thing - (BUT The Bullet has been fired) • Future Prospects - Predictions have DEPENDENCIES (Variables) • Guesswork - Probability (LACK of Certainty) • Triggers - Events (we usually have little or no control) • Availability - QUALITY Data (Sources, Cleanliness, Quantum) • Methods, Techniques, Tools - Collection, Processing & Interpretation 19
- 20. Risk Frameworks • Thereare frameworks • There are no global agreements • If there are agreements, they are debated to death in the event of failures • We are only as good as our last predictions 20
- 21. Asset Classes &Their Nature 21
- 22. Equity Shares • UsuallyConsidered to be High Risk • Investor Types • Buy & Hold vs Dividend Stripping • Market Investors - (Price Appreciation) • Valuations - Driven by Market Perceptions • Outstanding Shares • Trading Volumes • Follow herd mentality • Informed sources close to the management • Times EPS - (How Many Times) • By what factor • There is not a single yardstick 22
- 23. Interest Bearing • Consideredto be Low Risk • Subject to Ratings • Sanctity of Ratings • Rating Agency Advisories are Conflicting • Rating Agencies depend on Data Points 23
- 24. Contracts • Derivatives &Futures • Are actually SIMPLE to Understand • They depend on an Underlying Instrument (or Basket) • They were created as a means to safeguard physical deliveries of an underlying at predetermined rates/prices • However, they are Seldom held till expiry • The complexities arise from • End Use • Lack of Standardised Pricing Models • Traders usually have a pet Quant who works off complicated Excels • And no Quant agrees with the other 24
- 25. Contracts • Foreign Exchange •Markets are driven by International Trade Flows • Subject to Government Intervention • Easily swayed by unscrupulous Traders 25
- 26. Commodities • Depend onActs of God • Discoveries • Extraction Methods 26
- 27. Real Estate • Consideredone of the safe investment avenues • Subject to Infra • Usually considered to be illiquid 27
- 28. Metals & Crude •Gold • Indian housewives hold 11% of the world's gold. That is more than the reserves of the USA, the IMF, Switzerland, and Germany put together • Earlier a country’s currency was valued in terms of its gold reserves • The Gold Standard was abandoned • This led to the advent of deficit financing • Silver • Platinum • Copper • Oil • That can quickly change, as alternative energy technologies do exist, its just a matter of time until Black Gold looses its lustre 28
- 29. Alternate Asset Classes •MBS • ABS • Strips • Exotics • Digitals • Structures 29
- 30. Invisible Cash Flows •Arms • Drugs • Though illegal the income from these make it into mainstream investing 30
- 31. Investment Scenarios • ScenarioAnalysis - What If? • Show Me The Money • Money is like WATER • It finds its own LEVEL • It usually flows to where Money ALREADY exists • Few take a Contrarian View • The TREND is your FRIEND • FOLLOW the TREND 31
- 32. Ever Heard ofthe 80:20 Rule? 32
- 33. Well here itis 33
- 34. 80% of Investorshave to LOOSE for the 20% to GAIN 34
- 35. This is calledThe SMART MONEY 35
- 36. They are SMART‘cause they have what 80% DON’T 36
- 37.
- 38. So Let’s TalkAbout DATA ANALYTICS • It deals with Histories • It seeks to PREDICT basis the PAST • History does’t always repeat itself • It means throwing several variables into the MIX • One of the biggest variables is The CYCLIC NATURE of market movements • It must predict when Cycles begin and End • There are Macro Cycles and Micro Cycles (wheels within wheels) • There are ALWAYS LEAD and LAG Indicators 38
- 39. Data Quality • Datacomes from various sources • Its usually delayed • To clean Data various interpolation techniques have to be applied • Log Linear • Cubic Spline 39
- 40. Data Sources • Datacomes from various sources • Markets - Prices • Governments - Economic • Agencies - Feeds • Its usually delayed • To clean Data various interpolation techniques have to be applied • Log Linear • Cubic Spline 40
- 41. Analytical Challenges • CrossReferencing • Feed Delays pose challenges • Conflicting Data Sets need careful analysis • Interpretation needs to be led by Humans • Humans are usually subjective • Algorithms are written by Humans • Self Learning & AI are the new buzz words, BUT their accuracy will take time to establish • We will get it all wrong before we get it right 41
- 42. So Do Weneed Analytics • Unequivocally - YES • What we need is - Standardisation • We need Global and Regional Models • What works for the Advanced Economies Doesn't necessarily work for the LDC’s 42
- 43. Decision Support • DecisionSupport Systems are nothing but Algorithms • Collect Data • Clean Data • Query Data • Fetch Data • Run Algorithm factoring in Variables we referenced earlier • Publish on Screen or via Report • Run various scenarios by changing around a few variables • And Finally, in the face of a plethora of information taking a decision 43
- 44.
- 45. Technical Analysis • Prices •Volumes • Trends • Cycles • Macro • Micro • Indicators • Strategies 45
- 46. Strategies • The Long& Short of It All • Taking Long Positions • Taking Short Positions • Naked vs Covered • Alternating Long & Short within Cycles • Butterflies • Long Butterfly • Short Butterfly • Straddles • Strangles • Combining Holdings & Derivatives 46
- 47. Tools • MACD • DayMoving Average • Signal • Crossover • Japanese Candlesticks • Fibonacci Numbers • Finally its all about PATTERNS & CYCLES (My favourite is a Double Top Followed by a Double Bottom) • Thought to be PURE in terms of indication of market movements • Markets are moved by Money Flows • Money Flows are controlled by People • So, someone makes the first move and the Markets follow • That SOMEONE is the SMART MONEY (the 20%) 47
- 48. Fundamental Analysis • BalanceSheet Analysis • Key Financial Ratios • Past Performance • Future Prospects • Sector Analysis • Industry Analysis • Competitor Analysis • Technology Obsolescence & Ever-greening • Distribution Framework • Capital Adequacy • Management Track Record 48
- 49. Hybrid Analysis • CombineFundamentals & Technicals • Base macro decisions on Fundamentals • Base Entry & Exit on Technicals • Use the Snake in the Tunnel Technique 49
- 50. Tips & Tricks •DON’T take a NAKED Short unless you have money to throw • Market Tops & Bottoms are predicted by FOOLS or GENIUSES • One Prediction does not a Guru Make • Analyse, Analyse, Analyse • Pick an Investment Strategy based on your Risk Appetite • Stick with your Strategy, give it time • If you’ve picked a good stock, don’t panic at the first downturn • Cut Your Losses, Let your Profits Run • Book Profits periodically, you’re bound to run afoul of the Trend at some time • TRY (hard) not to average a loosing position 50
- 51.
- 52. Models • Build aRisk Based Model • Models can be as Generic or Specific as You like • Balance your Risk/Model • Hedge your Bets • Do Dry Runs BEFORE entering the Market • Play out your Hypothesis • Course Correct as You go • Don’t let EGO get in the way of your Investment Decisions • Finally, its about MAKING Money • Prudence and Balance are your FRIENDS, NOT Greed 52
- 53.