Although the 130/30 strategy is an attractive way to increase returns, the structure of the fund means that extra risks are generated in surprising areas. This is especially the case for those seeking to pursue the strategy in Asia. It has implications not only for the administration of the fund but also for the methods employed to manage it.

A 130/30 structure is better than a long-only portfolio in two respects, which we can call the ‘direct effect’ and the ‘indirect effects’.The direct effect refers to the fact that if we do not allow any short selling in the portfolio, we are unable to exploit all available information, because we cannot take bets on negative alphas. That is to say, if I have a view that a stock XYZ will do badly, I could profit more by short selling (if I don’t already own it in my portfolio), than by doing underweighting or zero-weighting. This ‘direct effect’ is the first limitation of long-only funds.

Second, an indirect effect of no short constraints grows out of the desire to stay fully invested. In any active portfolio, overweights require underweights. That is, we finance active positive positions by taking offsetting active negative positions. In a long-only portfolio we are restricted to some maximum negative position we can take. This restriction then constrains our ability to finance active overweight positions due to a scarcity of active underweight positions. Removing the ‘no short’ constraint allows the investor to express proper underweight positions. For example, if a manager can short a stock, then what might have been a 0.5% bet against the benchmark by not owning the stock can be expressed as a 5% short against the benchmark. Which, if correct, can generate extra alpha.

Since the short enabled portion (30/30) is operated as a ‘beta zero’ - ie, market neutral - position, the strategy amplifies returns to manager skills to generate a higher alpha relative to the performance benchmark. And it does so without taking additional market risk. Therefore, the strategy has two key reasons why it is a better structure than the long-only structure.

A key motivation to use 130/30 is to increase the opportunity set by increasing transfer coefficient - ie, managers with skill can more fully exploit sell ideas. It is possible to do this without increasing risk, but this depends on the correlation between longs and shorts and the underlying stock characteristics. There is more opportunity to get things right - or wrong! Risk would have to be assessed on a case-by-case basis.

130/30 can be seen as the best of two worlds:

a long-only portfolio to which a small hedged portfolio has been added - it is a combination of a 100% long exposure, 30% of short selling and an added 30% of long exposure coming from investing the proceeds of shorting.

it is a ‘beta one’ product, as it is 100% net long, and the 30/30 component is ‘beta zero’. It allows the manager to add symmetrical long and short positions that increase the proportion of stock-specific risk, rather than market risk.

When we look at the overall 130/30 risk structure, because we are trying to stay market neutral in the 30/30 portion, it is the additional stock-specific risk that matters. This has interesting implications for the choice of which stocks to short and the methods to be employed. I argue that the most theoretically correct way to do this is to employ quantitative techniques to short a diversified basket of medium to small cap stocks. Let me clarify by use of the following example, using the MSCI Asia Pacific ex Japan as our benchmark and then approaching the index from the point of view of a 130/30 portfolio manager.

First, let us look at the distribution of size within the benchmark and rank stocks based on percentage weight in the index. We see the familiar result shown in the figure.

There are some extremely large companies. The largest weight in the index is 3.43%. If we want to short this stock then, given its large weight, we must have a very high conviction view on it. And high conviction views are harder to maintain on well-covered large caps, where market inefficiencies are less.

However, moving further down the index, the problem reverses itself. There is no need to develop a really strong negative view on, say, stock number 40, as this is only 0.5% of the market. Going further down the index this story becomes more convincing.


he point is that risk is present because the portfolio manager is unable to short large cap stocks without a high conviction view and therefore naturally is inclined to short smaller cap stocks. As a consequence of his inclination, the fund tends to have an increasing negative exposure to small cap stocks. It is easier, given less than perfect information, to implement a large number of short positions in small cap stocks. Also, as we see from the distribution of the benchmark, there are more such stocks available.

A further reason that small stocks are preferred shorting targets is that because the long-only portion of the fund is likely to hold large weightings in large cap stocks, shorting those same stocks in the 30/30 portion just has the effect of reducing the weight in the long-only portfolio and doing so in an inefficient way. Shorting large caps is often, therefore, an inefficient way of adding alpha. After all, if you want to reduce your position in a large cap stock, just sell it!


n operational concern that creeps in with shorting is that it is operationally and legally complex strategy to handle. Shorting requires a prime broker, because in many markets standard custodians cannot hold short positions. Establishing (and, to a lesser extent, maintaining) a prime brokerage relationship is a complicated undertaking. Proper systems needed to be placed before any such shorting is implemented. Type of systems and requirements needed are monitoring lending rates and fees, negotiating a contract between the prime broker and fund manager, obtaining short locates, booking trades with prime brokers, reconciling cash on daily basis and meeting compliance reporting etc.

And the key problem here is that, in Asia Pacific markets, many brokers may not have access to the smaller stocks to short them. Operationally, it requires a large number of these relationships with prime brokers. One critical consideration when dealing with a prime broker is credit risk. Excess collateral kept with the prime broker, along with any gains generated on the investor’s positions held by the prime broker, could be lost if the contract is not structured properly and the prime broker becomes insolvent.

One can also face short squeeze or a stock recall. That is when the lender decides to take the stock back before the contract expires. Such a request means one has to liquidate the short position earlier than expected, which might not have been of any material gain. Short squeeze can become surprisingly expensive when you include the cost of unwinding a short position.

Though this can happen in large caps, all of these are magnified when the underlying stock is a small cap and the broker backing it may also be a smaller, local broker with lower capital.

Shorting also has significant portfolio monitoring demands as well, including dividends and stock borrowing cost. Dividends must be accounted for because they have to be paid to the lender of the stock.


tock borrowing costs and the short rebate must be monitored as well, because leverage in 130/30 structures is not the same as leveraging by borrowing money. It is the alpha source which is being leveraged. This can demand higher transaction costs particularly higher transaction costs in small and illiquid stocks. And the differences in such costs can be significant. The table shows indicative costs of shorting stocks in regional markets.

130/30 definitely has potential for adding alpha by incorporating additional information not available to the long-only traditional fund manager.

However, because it tends to incorporate a small cap bias, in practical terms the technique requires monitoring and shorting a diverse portfolio of smaller capitalisation stocks.

This type of structure is perhaps far more suited to a quantitative type of portfolio construction process than a traditional one. Quantitative processes are more adept at handling a large number of small positions and monitoring the complex risk relations between them.

Azim Alvi is a quantitative analyst covering equities and derivatives in Asia Pacific with Mirae Asset Global Investment Management. In his current role he focuses on adding value to the investment process by applying quantitative techniques to stock selection, portfolio construction and asset allocation. Before joining the firm, Azim was a quantitative analyst at Goldman Sachs Asset Management. Prior to that he held a quantitative analyst role at ING Investment Management Australia.