Tax loss alpha is getting big

 

There has been an increase in stories about tax alpha and how this has become a big thing in the hedge fund industry. Hedge funds are not tax effciency. The active trading in many funds generates positive returns, but capital gains may be limited, so returns are generally treated as ordinary income. Managed futures will have some tax advanatges, but the general case is that invetsors should compare after-tax returns across strategies. 

The question is who should be generating the tax alpha - the manager or the investor. The answer is to look at some combination of both, There is the old adage by Buffet about the two rules of asset management: Rule 1 protect principal, and rule 2, follow rule 1. 

Of course, the top priority is for any hedge fund is generate return, yet, tax efficicny should be a goal that can provide improved returns without significnat risk. For those who have SMAs, the tax efficiency can be achieved by the investor and viewed more holistically. Wash sales, tax loss harvesting, and forms of tax defferral can all help reduce tax drag. As more "retail" investors get involved in hedge funds, the issue of tax efficiency will come to the forefront. 

Friction needs to be managed

Friction is like transaction costs. For business economists, this is often overlooked or treated as an afterthought, yet solving the transaction-cost problem is the critical driver of most business structures. The Friction Project, by two Stanford business professors, makes an interesting observation on the impact of friction on business success. What is interesting is that sometimes it is important to reduce friction to make decisions easier. Still, there are also times when friction, slowing things down, and making decisions harder can be beneficial.

The job of a manager is to cut through friction, another way of making businesses more efficient. For example, make memos shorter. Make meetings more focused. Friction adds to the grind of impediments to increasing productivity. Look for frictions and then cut them. Friction can also be used as a tool to make it more difficult to switch or decide. Yet, frictions may mask bigger problems. 

Transaction costs often seem so abstract, so I like the descriptive word “friction” as a better way to identify impediments to getting things done.

Managing the hard things - there is help from Ben Horowitz

 

Just read The Hard Thing About Hard Things by Ben Horowitz, a management book about experiences as an entrepreneur at tech start-ups. I often pick up these books for a quick read, hoping there may be a nugget or two on how to improve as a manager. The first part was not impressive, but Horwitz then goes on to offer practical advice on numerous topics managers face. How do you hire? How do you fire? How do you promote? How do you motivate? 

These practical tips are very good and can be implemented by any manager. Are these tips easy to use? No, making hard decisions is not easy, but Ben provided a guide on how one person has addressed these issues. The usual business management book will talk about cases, but readers want specifics like how to deal with well-defined problems. This book delivers. Be direct, be truthful, and do not try to avoid the hard decisions. 

The problem of bimodality and deep mometum

 

Momentum is considered one of the financial factors that shows consistency throughout time. Other factors come and go, but not so with momentum. This does not mean that momentum will work at all times. Additionally, momentum is subject to significant risks. It depends on the regime, with momentum profits greater during an expansion than during a recession. There is also a greater likelihood of crash risk with momentum trades. This is the premium investors are paid to hold assets showing momentum. Finally, it is found that momentum exhibits a bimodal distribution of relative returns. Past winners may be likely to persist, but there is also a greater likelihood of becoming losers. This U-shaped distribution creates specific risks when holding momentum stocks.

The paper, "Bimodality Everywhere: International Evidence of Deep Momentum,” explores this specific momentum feature by using a deep momentum technique (RET) to mitigate or exploit it. Deep momentum is a two-step process: first, neural networks generate probabilities of future return declines; second, stock performance is based on returns or the Sharpe ratio to form long-short portfolios. It is shown that using specific machine learning techniques yields significant improvements over traditional methods for forming momentum portfolios. 

There is a lot going on with this paper, so the devil is in the details, but it shows that machine learning can be used to improve over a simple momentum strategy.