Short Term Asset Management Pedro Candas Stepan Karpukhin Yoshinobu Kataya Robert Lee Luis Shirley TS Tactical Asset Allocation: Using a Sector Prediction Model

Short Term Asset Management TS Agenda Objective Intuition Methodology Some of Our Models Data Analysis Comparing Our Model Results Conclusion

Short Term Asset Management TS Objective Build a MSCI sector index prediction model using lagged MSCI sector index returns. Build a tactical asset allocation strategy using this prediction capability to outperform a strategic asset allocation of different MSCI sector indices

Short Term Asset Management TS Intuition Markets are highly inefficient Sectors are interrelated –Chemical sector depends on the Energy sector in their production process. –Japanese industries are reliant on U.S. market

Short Term Asset Management TS Methodology Gathered monthly MSCI sector index returns for Japan, U.K., and U.S. for Identified industry sectors for analysis. Developed prediction models with lagged industry data and other economic variables.

Short Term Asset Management TS Some of Our Models Japanese Financial Services Sector Japanese Chemical Sector –Japanese Energy Sector(lagged) Japanese Bank Sector(lagged) –Japanese Energy Sector(lagged) U.S. Energy Sector(lagged) Oil Prices(lagged) Dependent variablesIndependent variables

Short Term Asset Management TS Data Analysis Prediction models have up to a 64% hit rate –Accuracy may have improved with different economic indicators –Accuracy may have improved with different time frames on lagged data

Short Term Asset Management TS Comparing Our Model Benchmark- Mean Variance Portfolio of the seven MSCI sector indices described in our models(Strategic Asset Allocation) Who will perform better? Strategic Asset Allocation versus Our Tactical Asset Allocation Model

Short Term Asset Management TS Results At the same standard deviation (.034): –Benchmark: 11.1% annualized return –Our model: 23.0% annualized return

Short Term Asset Management TS Results

Short Term Asset Management TS Conclusion Predictive Models based on lagged sector returns may prove significant –Greater accuracy using different timeframes for lagged returns data and better economic indicators