mean reverting asset trading research topic presentation csci-5551 grant meyers

Mean Reverting Asset Trading

Research Topic PresentationCSCI-5551

Grant Meyers

Table of Contents

1. Introduction + Associated Information2. Problem Definition3. Possible Solution 14. Problems with Solution 15. Possible Solution 2 / Research Topic6. Specific Questions to be Answered

1. IntroductionAsset Definition + Properties of a Mean Reverting Asset

Asset Definition

1. A resource with economic value that an individual, corporation or country owns or controls with the expectation that it will provide future benefit.

2. A balance sheet item representing what a firm owns.

This presentation will cover only – stocks which represent an ownership interest in a business.

Properties of a Mean Reverting Asset Needs some level of volatility in price.

Needs to vacillate around a center value; rising / falling around a dependable ‘Mean’ value.

Properties of a Mean Reverting Asset Required for a good Mean Reverting Asset:

Preferably a seasonal or otherwise dependable cycle up and down.

High liquidity, being able to buy and sell at optimum prices. Minimal chance of ‘insider trading’ or other ‘exceptional’ events.

Examples of a Mean Reverting Asset Chevron over last 5 years

Examples of a Mean Reverting Asset Disney this year

Table of Contents


Problem DefinitionWhat does ‘Mean Reverting Asset Trading’ encompass?

Core Questions

Can you make money from the ‘Stock Market’ by trading?

Which companies do you choose?

What are the costs?

Problem Components 1 - Timing Can you make money from the ‘Stock Market’ by trading?

Maximize profit from buying low + selling high.

When do you buy? (A) $10,000 of Netflix (NFLX) bought on 16 Dec 2014 @ $45.21 / share = 221 shares (B) $10,000 of Netflix (NFLX) bought on 6 Aug 2015 @ $126.45 / share = 79 shares

When do you sell? (A) 221 shares sold on 6 Aug 2015 @ $126.45 / share = $27,945.45 (+$17,945.45) (B) 79 shares sold on 22 Oct 2015 @ $97.32 / share = $7,688.28 (-$2,311.72)

Problem Components 2 – Options

Which companies do you choose? There are 1,868 stocks listed on New York Stock Exchange. There are 3,300 stocks listed on the Nasdaq. There are 1,299 stocks listed on Euronext.

Problem Components 3 - Costs

Transaction Cost Online Self Directed Trade - $8.90 Broker Assisted Trade - $30.99

Opportunity Cost $10,000 of Amazon (AMZN) bought on 24 Oct 2014 sold today is

worth $20,367.02 $10,000 of Apollo Education Group (APOL) on 22 Dec 2014 sold

today is worth $2,151.8

Emotional Loss Aversion - Humans fear loss much more than possible

winnings

Variables + Unknowns

Maximize Gain, Minimize Loss Timing the Buy Timing the Sell Minimizing costs

There is no obvious solution, no method always works.

Hindsight may be perfect, but predicting the future with precision is literally impossible.

Table of Contents


Possible Solution 1Based on analytic solution to asset price prediction algorithm.

Possible Solution 1

Possible Solution 1 – Analytical Solution

Possible Solution 1 – Analytical Solution

Buy at x1 and sell at x2

Table of Contents


Problems with Solution 1

Problems with Solution 1

Requires model for underlying asset to set calculation constants and determine the rate of reversion to the mean, and the equilibrium level / mean value.

Allows adjustments via main 2 parameters only.

Nearly impenetrable math…

Table of Contents


Possible Solution 2 / Research TopicStochastic Approximation Methods and Applications in Financial Optimization Problems - Chapter 2: Mean-Reverting Asset Trading

Mean Reverting Asset Prediction Equation

Components 1

Stochastic Approximation Used to recursively estimate some quantities based on

noise corrupted observations. Originally introduced in 1950s.

Noise Sources Imperfect sampling period. Multiple trades executing ‘simultaneously’. Sampling technique. Midpoint between bid / sell, or last

trade price

Components 2

Mean Reverting Asset Prediction Equation - Estimation

Advantages Over Solution 1

No model for the underlying asset. Less rigid, less dependent on human ‘intuition’. Easily updated for new data & ‘paradigm’ shifts in whole

sectors.

Data for stocks is easily available & in an easily processed format.

Advantages Over Solution 1, continued Multiple asset data time-resolutions allow for

variable scaled action speeds. If broker takes, on average, 10 seconds to execute a trade,

having a regression based on faster time would not necessarily work well.

Using 24 hour scale data, may allow for a more macroscopic view of the asset’s movement.

Table of Contents


Specific Questions to be Answered 1Data Sample Related Does the algorithm work when there is a macroscopic change in

the overall market?

Does changing the training & applying time windows affect the return? How much? Do longer windows fair better or shorter ones?

Are there any dependable seasonal fluctuations?

Does the asset ‘class’ affect the effectiveness of the algorithm?

Specific Questions to be Answered 2Performance Related How fast can the Xeon server crunch the numbers?

How fast can the Hydra server crunch the numbers?

Is there a better way to format the data than the default JSON format?

Given the use of common mathematical operations, could they be switched out to a format that uses matrix multiplication?

References

Human Loss Aversion - http://www.sciencemag.org/content/313/5787/684 Asset Definition - http://www.investopedia.com/terms/a/asset.asp NYSE Listing Size: https://en.wikipedia.org/wiki/New_York_Stock_Exchange NASDAQ Listing Size: https://en.wikipedia.org/wiki/NASDAQ EuroNext Listing Size: https://en.wikipedia.org/wiki/Euronext Average Online Trading Cost: http://www.valuepenguin.com/average-cost-

online-brokerage-trading Zhang and Zhang Reference: Hanqin Zhang, Qing Zhang, Trading a

mean-reverting asset: Buy low and sell high, Automatica, Volume 44, Issue 6, June 2008, Pages 1511-1518, ISSN 0005-1098, http://0-dx.doi.org.skyline.ucdenver.edu/10.1016/j.automatica.2007.11.003.

mean reverting asset trading research topic presentation csci-5551 grant meyers

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