Author: shyam

Factor MAX

The paper Factor MAX and Predictable Factor Returns from Liyao Wang and Ming Zeng presents a twist on momentum investing that goes long the factor that had the largest single-day return in the previous month. It is distinct from factor momentum goes long the factor that had the largest return over a specific formation period.

We have been running factor and model momentum for a while now with mixed results so we decided to have a look at this new strategy in the Indian long-only context.

tl;dr: not so hot!

We selected the NIFTY500 factor indices: LOW VOLATILITY 50 TR, MOMENTUM 50 TR, QUALITY 50 TR and VALUE 50 TR to compare Factor MAX vs. Factor Momentum. Factor Momentum out-performed Factor MAX.

The problem with using a single day’s performance to select a factor is that more volatile factors get picked more often. Here’s a plot of the monthly active factor between the two strategies.

Quality and Low-volatility factors do not jump around every day. Hence, their low representation in Factor MAX. You could use volatility adjusted returns to paper over this. However, we felt that went against the main thrust of the paper that investors systematically under-react to factor-level news embedded in these extreme returns, creating exploitable return predictability.

We ran the same backtest over a subset of our momentum and value models. Factor Momentum bested Factor MAX here as well.

If you want to DIY Factor Momentum based on this backtest, you can do so with cheap index funds:

  • Nippon India Nifty 500 Quality 50
  • Nippon India Nifty 500 Low Volatility 50
  • Nippon India Nifty 500 Momentum 50
  • Axis Nifty500 Value 50

Code and charts on github.

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Winning with Market-cap ETFs

The Parag Parikh stable of funds attract a lot of attention because they are good story-tellers. In their Flexi-cap Fund, they are simultaneously placing concentrated bets on US and Indian equities, hedging, arbitraging, selling cover-calls, making cash allocation calls, and so on. And they talk about it a lot.

All this activity should surely result in superior performance?

We had written a couple of notes around this back in 2015 and 2019. Our concern revolved around return-attribution. When you are doing so many things, how do we know if you are actually good at any one of them?

If you look at returns since the first note came out, they under-perform the MIDCAP 150 index.

Not that there weren’t years where they out-performed. However, given all that activity, is this all they could do?

In our second note, we had mentioned that you could, technically, replace the fund with a midcap and S&P 500 index ETF in a 65-35 ratio. So, from that point on, if you were to construct such a portfolio, it would beat the fund as well.

It is not that their stock picks are bad. If you analyze the Indian equity portion of their portfolio over time, their stock picks, on average, has delivered 2% over the midcap index during the holding period.

And while digging through this, we noticed that there is alpha in keeping track of stocks that they have exited.

There is a decent skew in favor of entries but not as much as exits.

It is often said that exiting a position is tougher than entering it. In that sense, the fund managers have displayed good skill.

Tracking the current portfolio may not yield much. For example, if you look at positions held for more than 12 months, excess returns are distributed across the spectrum.

Our suggestion is that you can treat the fund as a research project for your own edification, but when it comes to deploying your own capital, you can stick with market-cap ETFs and index funds.

Code and charts on github.

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Performance & Flows

Our previous post examined how index providers and asset managers launch “hot” thematic/sectoral indices and funds to capitalize on stories. Who can blame them? Money always flows in to assets with strong recent performance (this is the very basis of momentum strategies). Take gold, for example.

Fund flows have a near perfect correlation with performance.

Flows into gold funds is nothing compared to what happened in thematic funds.

If investors were rational, flows would be predictable. However, that is not nearly the case.

The problem with lumpy flows in to hot assets is that once the price action cools down, the funds are trapped. Investors tend to feel the emotional pain of a loss about twice as intensely as the joy of an equivalent gain. So, they wait for the next cycle to exit.

If you look the cumulative flows into Sectoral/Thematic funds, there’s a large reservoir of capital that will look for an exit when these funds come back up to par.

Flows follow performance. And if the asset is illiquid enough, performance will then overshoot flows to form a spiral.

Map the terrain. Understand the landscape before making your move.

Code and charts on github.

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Index and Funds

Index funds and ETFs proved most naysayers wrong and finally took off post-COVID. Now, we are dealing with a problem of plenty.

The number of indices and index funds have skyrocketed with the vast majority of AUM concentrated in large-cap market-weighted indices.

As everything in investing, it is always better to wait for things to settle down before committing capital. Index post-launch returns tend to disappoint.

And these numbers are worse for index funds.

While investors win by having low-cost access to a wide range of strategies and sectors, they can still lose by rushing in to “hot” launches. Patience pays.

Charts and code on github.

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Understanding Futures Rollover Cost

What is the difference between buying gold through an ETF over buying the front-month futures contract and constantly rolling it over?

When you buy physical gold, there is a cost of carry involved (funding rate + storage), plus an ETF will charge an asset management fee.

Futures also have a similar cost of carry plus a rollover cost. At expiry, the price at which you sell the expiring contract and buy the next month contract is not the same. The differential is the rollover cost.

Typically, the farther you go out on the futures termstructure, the higher the premium to spot – the commodity needs to be financed and stored for longer. This is called contango.

Currently, gold futures (GC) traded on COMEX has the following termstructure:

However, sometimes, the demand for near delivery is much higher than future delivery. This typically happens during a supply shock. When the near expiry futures trade at a premium to later expiries, the termstructure is said to be in backwardation.

Currently, oil futures (CL) traded on NYMEX has the following termstructure:

During contango, rolling over long futures incurs a positive rollover cost, negative otherwise.

For Gold Minis (GOLDM) traded on the MCX, the historical rollover cost at expiry has fluctuated within a wide band:

What this means for our analysis is that if we merely lined up the closing prices of the front-month contract and calculated returns, we will be off by ~2.5% (not considering brokerage, fees and CTT):

So, to answer the question we posed at the beginning of this post, GOLDBEES or GOLDM?

GOLDBEES, definitely.

Previously: Investing in Gold

Charts and code on github.

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