Further Considerations and Conclusion

Portfolio Balancing

• Is it more difficult to maintain a value-weighted or an equal-weighted portfolio?

• What does it mean to maintain a (zero-investment) portfolio when there are differential rates of return?

  • only value-weighted portfolios require no rebalancing activity for constant weights
  • worse, after one period, the portfolio is typically longer zero-investment

• Should you use rebalanced zero-investment portfolios, or actual portfolio returns?

  • Actual portfolio returns generate cash balances. You would need to count in returns.
  • Even if you do adjust them, what does it mean if your returns come from cash?

• “Easy” question in a perfect capital market without transaction costs

  • Just rebalance after every time period, and sum up (do not compound) cash.

UCLA Endowment Considerations

• We are tax advantaged
  • Do we like interest and dividend receipts?
  • Be Smart: Tax-exempts should not purchase stocks after dividend payment; and should not sell them before dividend payment!
• What does it mean for us to reduce risk?
  • How can we make money for Anderson when/if Anderson is going to be in trouble?
  • California correlation?
  • UC correlation?
  • MBA program correlation?
• Overall Portfolio — how do we fit into UCLA’s endowment?
  • Should we be negatively correlated with the UCLA endowment?
• Where is our competitive advantage? Where do we choose to play?
  • Could the UCLA endowment run a UCLA research VC fund?
• What are our agency incentives?
  • Do you want to be replaceable?

Other Important Questions

• Good Weights?

  • Equal or value weighting legs?
  • Signal Weighting?

• Use index (futures) as cheap hedging/market timing?

• Can we use ETFs as cheap inputs?

• Can we trade events?

  • Probably not with your attention span
  • Probably not given that you are active only 6 out of 12 months

• Are non-investment like ``shorts’’ (holes)?

  • Yes
  • Especially if you think of your strategy in the context of a portfolio overlay

• How should you handle outlier rates of return in the backtests?

  • If the rocket exploded, is it representative for the future?

• Should you worry about data errors?

  • Fortunately, less with CRSP

The Gorilla

The gorilla in the room is that we do not know if our historical behavior is representative of the future.

• Did we just find spurious correlations from trying too many signals?

• Are there factors that have not reared their heads? That could wipe us out?

• Is the experience from the last x years still even relevant or has the world changed?

  • How many years should we/you choose for data evals?

    • Probably not less than 20 or more than 60.

  • As X-costs have come down, what is their effect then vs. now? How does it influence backtests?

  • What do other investors and funds do?

    • In a sense, we are playing both with and against them.
    • Are they reading our research?
    • Are they piling into the same regularity, too?
    • Can we ride their wave and get out in time?
    • Or are we the dummies?

  • What would be an expected performance pattern on strategies?

    • If you think these are inefficiency, they should go away
    • If you think these are just stable risk factors, they should not go away unless their aggregate diversification improves
    • If you think these were inefficiency, they should go away after being “discovered”

Harvey-Liu-Zhu (2015)

Pontiff-McLean (2015)

The Megalodon

• Should we use intuition or quant empiricism?
  • Both?
• What kind of findings do mistakes in thinking/coding/etc. tend to favor?
  • Fortunately, efficient markets mean than pricing is ok
  • Will you move on if the result is zero?
    • What if you write 50 mistaken programs and 50 correct programs?
  • Coding mistakes make for unbelievable performance.
• How can you be sure that your code is right?
  • Someone else must code it independently, just using a reasonable verbal description
  • it makes no sense to describe weird contortions
    • if they are required, you were just data mining

Alternative Known Regularities

It is not clear whether these are inefficiencies or risk factors. That is, it is not known whether the historical ability to earn higher rates of return in them was due to stupidity by the rest of the market or due to a risk that other investors really disliked. In either case, from 1963-2018, these helped investors earn positive average rates of return on zero-investment portfolios.

##  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  2017  2018 
##  21.6  13.2 -82.2   6.0   7.4   1.4   7.9   1.7  20.7 -20.4   5.0   9.3

mean(umd)

## [1] -0.7

##  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  2017  2018 
## -14.7   0.8  -9.2  -5.3  -8.5   9.7   1.5  -1.6  -9.6  22.9 -13.9  -9.4

print(mean(hml))

## [1] -3.108

##  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  2017  2018 
## -7.19  3.20  9.23 13.80 -6.01 -1.15  7.27 -8.08 -4.05  6.60 -4.77 -3.32

mean(smb)

## [1] 0.4608

## 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 
##  1.4 -6.4 -8.1 -3.4  2.0  5.0  6.0 -3.3 -3.1 -5.8  1.9  5.6  8.4

mean(jan)

## [1] 0.01538

Qualitative Investments?

• Given all these problems, should you just switch over to qualitative investments?

  This is just stupid.

• It’s like arguing that because we have evidence that falling 10 meters will kill you, we are better off jumping out from 100 meters

• If you do qualitative research, you can use it as a quantitative input signal!

Other Important Statistics

• Transaction Cost Analysis

• Turnover analysis

• Use of Collateral (shorting costs)

• Year-by-Year Performance

• Risk analysis (idiosyncratic and factor risk)

• Sharpe Ratios

• Longest Time To Recover

• Longest Negative Stretch

• Worst Performance