1. On-Line Portfolio Selection Using Multiplicative Updates Written by David P. Helmbold (Cal), Robert E. Schapire (Cal), Yoram Singer (AT&T) and Manfred K. Warmuth (Cal) Presented by Ryan M. McCabe

2. Goal Within a menu of a fixed number of stocks, we want to make as much money as possible without relying too much on luck Within a menu of a fixed number of stocks, we want to make as much money as possible without relying too much on luck We’ll compare our results to how well the best single stock, another form of on-line learning (Cover) and a batch learner (BCRP) each performed We’ll compare our results to how well the best single stock, another form of on-line learning (Cover) and a batch learner (BCRP) each performed

3. Context Remember, this is on-line learning Remember, this is on-line learning Unlike batch learning, the data is coming to us in a stream, and we learn from each example Unlike batch learning, the data is coming to us in a stream, and we learn from each example Still, we do not want to completely ignore what we have learned from history Still, we do not want to completely ignore what we have learned from history

4. More Context We have a bunch of stocks We have a bunch of stocks We have some wealth We have some wealth Every day we get a report on the stocks Every day we get a report on the stocks Every day we update our current wealth, based on their performance yesterday Every day we update our current wealth, based on their performance yesterday Every day we re-allocate our wealth over the stocks Every day we re-allocate our wealth over the stocks

5. Preliminaries We have N stocks We have N stocks w is a vector of weights over N stocks w is a vector of weights over N stocks w i from i = 1 to N, sums to 1 w i from i = 1 to N, sums to 1 every w i >= 0 every w i >= 0 We have T total time, superscript t denotes a specific time We have T total time, superscript t denotes a specific time

6. Preliminaries w t is the vector of weights at time t w t is the vector of weights at time t w t is chosen at the beginning of day t w t is chosen at the beginning of day t x t is the vector of relative performance of all the stocks over the course of day t x t is the vector of relative performance of all the stocks over the course of day t x t = closing price on t / opening price at t x t = closing price on t / opening price at t The wealth resulting from day t is w t * x t The wealth resulting from day t is w t * x t We change w t every day in some way We change w t every day in some way

7. Follow-Ups If we have time at the end of this presentation, we’ll talk about some things of practical importance If we have time at the end of this presentation, we’ll talk about some things of practical importance Transaction costs Transaction costs Side information Side information Implementation details Implementation details

8. Four Types of Portfolio Mangers (Best) Constant-Rebalanced Portfolio (Best) Constant-Rebalanced Portfolio Cover Universal Portfolio Cover Universal Portfolio Exact Exponentiated Gradient (ExactEG(  )) Exact Exponentiated Gradient (ExactEG(  )) Approximate Exponential Gradient (EG(  )) Approximate Exponential Gradient (EG(  ))

9. Constant-Rebalanced Portfolios In a CRP w t is learned over all T by looking back over the data (this is our batch method) In a CRP w t is learned over all T by looking back over the data (this is our batch method) Although the wealth is redistributed every day over the N stocks, w t stays the same from 1…T Although the wealth is redistributed every day over the N stocks, w t stays the same from 1…T w* denotes the w t that maximizes wealth over the given set of x t from 1…T w* denotes the w t that maximizes wealth over the given set of x t from 1…T w* is associated with the Best Constant- Rebalanced Portfolio (BCRP) w* is associated with the Best Constant- Rebalanced Portfolio (BCRP)

10. Cover Universal Portfolio Another on-line method Another on-line method w t is updated every day w t is updated every day w t is a weighted average over all feasible portfolios w t is a weighted average over all feasible portfolios Guarantees the same asymptotic growth rate as BCRP for any given set of x t Guarantees the same asymptotic growth rate as BCRP for any given set of x t Exponential complexity in N Exponential complexity in N

11. Exact Exponentiated Gradient Remember on-line regression? Remember on-line regression? F(w t+1 ) =  log(w t+1 * x t ) – d(w t+1, w t ) F(w t+1 ) =  log(w t+1 * x t ) – d(w t+1, w t ) Maximize F(w t+1 ) over w t+1, given w t and x t Maximize F(w t+1 ) over w t+1, given w t and x t log(w t+1 * x t ), maximizes wealth if x t stays still log(w t+1 * x t ), maximizes wealth if x t stays still d(w t+1, w t ), penalizes moving too far from w t d(w t+1, w t ), penalizes moving too far from w t , learning rate - shifts importance between main two terms , learning rate - shifts importance between main two terms But F(w t+1 ) is difficult to maximize But F(w t+1 ) is difficult to maximize

12. How do we learn w t ? So we use an approximation So we use an approximation Using a first-order Taylor approximation of the first term at w t+1 = w t and a relative entropy distance measure for the second penalty term, waving some hands, we get the EG(  ) update: Using a first-order Taylor approximation of the first term at w t+1 = w t and a relative entropy distance measure for the second penalty term, waving some hands, we get the EG(  ) update:

13. Exponential Gradient Update This approximate version performs indistinguishably as well as the original Exact EG(  ) = F(w t+1 ) =  log(w t+1 * x t ) – d(w t+1, w t ) This approximate version performs indistinguishably as well as the original Exact EG(  ) = F(w t+1 ) =  log(w t+1 * x t ) – d(w t+1, w t ) It is only linearly complex in N It is only linearly complex in N

14. Quick ReCap So now we have defined our four methods So now we have defined our four methods Best Constant-Rebalanced Portfolio (BCRP) Best Constant-Rebalanced Portfolio (BCRP) Cover Universal On-Line Portfolio Cover Universal On-Line Portfolio Exact EG(  ) Exact EG(  ) Common EG(  ) Common EG(  ) Let’s see how they perform under pressure…

15. The Experiments 22 years of NYSE data (T > 5,000) 22 years of NYSE data (T > 5,000) 36 equities (N = {2, 3,…,36}) 36 equities (N = {2, 3,…,36}) Usually 2- or 3-stock subsets were used Usually 2- or 3-stock subsets were used Reproduced each Cover experiment Reproduced each Cover experiment Stocks chosen for volatility reasons Stocks chosen for volatility reasons Found BCRP, then ran w* through from the beginning Found BCRP, then ran w* through from the beginning Ran EG(  ), ExactEG(  ) through from the beginning Ran EG(  ), ExactEG(  ) through from the beginning

16. Commercial Metals and Kin Ark (Figure 5.1)

17. IBM and Coca Cola (Figure 5.2)

18. Gulf, HP, and Schlum (Fig 5.3)

19. Volatility Elasticity (Table 5.5)

20. Results Analysis Summary EG(  ) and ExactEG(  ) were always about 1% from each other with EG(  ) running much faster EG(  ) and ExactEG(  ) were always about 1% from each other with EG(  ) running much faster BCRP always did the best BCRP always did the best EG(  ) always outperformed Cover’s Universal Portfolio, despite Cover’s superior analytical worst-case bound EG(  ) always outperformed Cover’s Universal Portfolio, despite Cover’s superior analytical worst-case bound

21. Talking Points “[S]urprisingly, the wealth achieved by the EG(  ) update was larger than the wealth achieved by the universal portfolio algorithm. This outcome is contrary to the superior worst- case bounds proved for the universal portfolio algorithm.” “[S]urprisingly, the wealth achieved by the EG(  ) update was larger than the wealth achieved by the universal portfolio algorithm. This outcome is contrary to the superior worst- case bounds proved for the universal portfolio algorithm.” Cover = O((N log T)/T) Cover = O((N log T)/T) EG(  ) = O(√((log N)/T)) EG(  ) = O(√((log N)/T)) Any ideas why? Any ideas why?

22. Talking Points So, the size of N affected relative running times, but how did stock volatility affect relative overall wealth? So, the size of N affected relative running times, but how did stock volatility affect relative overall wealth? Would running time matter in this domain if the algorithms were applied? Why did it matter so much to the authors? Would running time matter in this domain if the algorithms were applied? Why did it matter so much to the authors?

23. Follow Up Transaction Costs Transaction Costs Scottrade.com charges $7 per transaction Scottrade.com charges $7 per transaction Would you update every stock every day? Would you update every stock every day? Side Information Side Information K-finite states of side info, available to algorithm K-finite states of side info, available to algorithm Computationally the same as K parallel versions running, so no big deal and may increase wealth Computationally the same as K parallel versions running, so no big deal and may increase wealth Implementation Details Implementation Details How do we pick  ? How do we pick  ? How do we pick w 1 ? How do we pick w 1 ?

24. Done