Private versus public markets - the new blending

 

There has been a flood of funds into private equity markets under the belief that there is some special investment magic with these firms and the managers who build these portfolios. The recent data suggests that this is not the case. The rationale for private equity is simple. Buy new quality firms vetted by managers who will engage with these firms to turn them into successful investments that can then be IPO'ed as an exit strategy. Investing in a private equity portfolio will have an illiquidity premium, you will be paid for your patience and inability access your money. Some of the latest evidence provided by Macro Hive suggests that the key assumptions do not work.  

Investors are not receiving the returns expected relative to public investments which would not be troubling in the short run if you were getting your money back. Unfortunately, are seeing long holding times because there are no exits. 

Perhaps holding liquid strategies may not as some have thought. Yes, you face mark-to-market risk, but if you are unhappy with returns, you can get your money back. 

Trend-following and equity markets - control the costs

 

There is money to be made trading long-only trends in equities but like all trading it is not easy and driven significantly by cost assumptions. In fact, it is the cost control that may be the most important alpha producer. Take what may seem like a good model without transaction costs and then add realistic slippage and trading costs and you will see significant alpha deterioration. Redo the analysis but then add a set of rules that account for turnover and costs to reduce the number of trades, and you can add back alpha. Of course, you will never get back to the original theoretical performance without any trading costs, but the number may still look attractive. There is no free lunch and there is no hidden lunch. Perhaps the easiest place to find alpha is through optimizing for costs. 

While cost analysis was not the main goal of the paper, "Does Trend Following Still Work on Stocks" it may be the key takeaway. The model is simply based on looking for new highs for stocks with an ATR stop exit strategy. There are no special features, but it does work until we add reasonable cost assumptions. Nevertheless, simple rule changes can get the strategy back on the right track through minimizing turn-over. Along with entry and exit signals, place turnover constraints into the model.

Scaling volatility - not just the square root rule

 

You look at daily volatility and you want to turn it into an annual number. That is easy, just multiply by the square root of 252 and you are done. Well, we sort of know that this may not be exact, but we like the idea that there can be a quick adjustment, yet investor should understand what the risks are with making this simple transformation. See, "Converting 1-day volatility to h-day volatility: scaling by square root of h is worse than you think".

Whether you can scale volatility is based on the assumption that the daily returns are iid; that is, the series is independent and identically distributed random variables. If there is a trend component or mean-reversion, then the series is not iid and the scaling rule will not give you an appropriate estimate. In simple terms, if there is mean reversion than actual volatility will be lower than scaled volatility. The idea that actual volatility will be different from scaled volatility has been used as a test of market efficiency. 

In the simple case, if some time series follows a GARCH process, then the scaling will not work. For example, if a series is GARCH (1,1), then volatility today will be related to past volatility and past errors. The memory will change the scale effect. This was explicitly modeled by Drost-Nijman who show that a short-term GARCH will scale to a GARCH process. You cannot avoid the memory. 

The long and short of what this math says that temporal aggregation of a GARCH process may cause volatility fluctuations to decline while the scaling rule will magnify volatility fluctuations. Because you may get different quality volatility forecasts through scaling, it seems that if you want to look at longer volatility, then calculate the longer volatility and if possible, avoid scaling.

Market reactions: It is always about the surprises

Markets are driven by macro data, specifically, the macro data surprises. If the data surprises to the upside there will be a corresponding positive reaction by markets. Similarly, if there is surprise negative news, the markets will decline. These surprise events have been embedded in surprise indices developed by banks and Bloomberg. 

Unfortunately, the creation of these indices often mixes different types of data, so it is hard to link the surprise index with market behavior. The above chart suggest that the survey and business cycle indicators are showing a marked negative bias. I would place more stock in survey data given its timeliness and close link with consumer and business behavior. Generally, after a strong showing post the election, the surprise index has turned negative and suggest that any euphoria embedded in the new media should be taken cautiously. 

As a coincident indicator especially for bonds, the surprise index should be watched especially when there is a switch from positive to negative and at extremes. This current data suggests that the move toward higher long-term bonds may be coming to a peak.