Thursday, July 25, 2019

Can Dow Jones Industrial Average (DJIA) Predict the Housing Market and vice versa?

Suraj, a Harvard graduate, is interviewing for a Senior Quantitative Strategist position.

Question # 1
Interviewer: Would you consider these two market segments predictive of each other?

Suraj: Absolutely. The Correlation Coefficient and Regression R-squared are showing they move in lockstep and in the same direction.

Question # 2
Interviewer: Is the linear trendline the best fit? Eyeball the scatter and use your best quantitative judgment.

Suraj: For a management presentation, the linear trendline is fine. For a technical presentation, I would use Polynomial trendline with 3rd order which would reduce the noise on the outer end of the curve.

Question # 3
Interviewer: By doing so, how much improvement do you expect to see? 

Suraj: I would expect the R-squared to move up in the vicinity of 0.96.

Interviewer: Okay, please give me a minute and let me find out. Yes, you are right. It's 0.956, so it's actually 0.96 rounded. I must say, you have developed an excellent eye for the data distribution.

Question # 4
Interviewer: Let's assume we are trying to hang our hat on this solution. Would you recommend this to our clients who enjoy short-term trading?

Suraj: No. This analysis is developed off the monthly data so it is not viable for the short-term traders. For the short-term housing traders, the analysis must be based off the local home sales data and for the short-term equity traders, it must be developed off the most recent 3-months of daily closing data or most recent 6-months of weekly closing data.  

Question # 5
Interviewer: Agreed, this is an analysis, not a solution. Either way, who would you recommend this analysis to? 

Suraj: Those who have much longer time horizon, like the Mutual and Pension Fund Managers, and other long-term investors.  

Question # 6
Interviewer: How would you improve upon this analysis in a very short period of time?

Suraj: I would try to study and isolate the seasonality in both data. For example, for the residential investors, Q1 might be better than Q3. Likewise, Q3 might be the best quarter to sell stocks to book profit. Analysis of seasonality is part and parcel of any long-term trend analysis.  

Question # 7
Interviewer: Would you stick to this data and time series to study the seasonality?

Suraj: No. The study of seasonality requires at least one full cycle of data, preferably more, so I would go back a few more years. Of course, this is a large enough sample to study the basic collinearity so I would expect the collinearity would still remain in the ballpark.

Question # 8
Interviewer: Don't you think the impact of new economic and fiscal policies and other major economic events would distort the seasonality analysis?

Suraj: No. Those impacts can be separated out. For instance, the new cap on SALT has been impacting the high-end residential market in high tax areas so the co-mingling of that sort of data would be imprudent. 

Question # 9
Interviewer: How would you (physically) separate out that data? Give me examples from both data series.

Suraj: In terms of the housing data, you are using the Case-Shiller Composite 20, meaning the largest 20 MSAs in the country. We know the pockets hit hardest by the SALT cap so they must be removed from the data. Similarly, I would not use the stretch of Dow Jones data post 9/11.     

Disclaimer - The author is not advocating the indices listed here. Consult your Financial Planner for an appropriate asset allocation model and/or trading strategies for different markets, including housing.

Good Luck!

Sid Som, MBA, MIM
President, Homequant, Inc.

Coming soon: Sid's New Book: Modern Interviewing Techniques and Skills - Live Simulations with actual Market Data

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