Tag: risk

Embracing Volatility

Market volatility is a feature, not a bug.

In the short run, market is a voting machine; in the long run, it’s a weighing machine. – Benjamin Graham

Price is a measure of Sentiment

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Early last year, when it became clear that the China Virus had spread all over the world and was getting millions sick, overwhelming healthcare systems everywhere, the markets tanked. In USD terms, Indian stocks were down more than 40% and the S&P was down more than 30%.

Then, a miracle cure was discovered and the markets quickly recovered.

Just kidding!

Governments and Central Banks everywhere did whatever they could to lift sentiment. And markets quickly followed.

Sentiment remains one of the least understood but the most important factor in investing. All prices are eventually tied to how optimistic or pessimistic investors are feeling about the future.

While Graham’s weighing machine might arrive in time for tenured investments, like bonds, that have a fixed maturity date, perpetual securities like stocks are always at the mercy of the voting machine, i.e., sentiment.

The Market sets the Price

For stocks, Price = multiple x earnings

For prices to go up, you don’t need earnings to go up. It is enough if multiples do.

The market doesn’t care why you are transacting. Only that you are. It doesn’t matter what your investment horizon is or the type of investor you are, all transactions take place in the market at a price set by it.

As an investor, you can be right about the company (direction of earnings) but wrong about the sentiment (direction of multiple) and can end up with a stock that goes nowhere in price for years and exit with no rewards for your effort.

No such thing as Buy-and-Hold Forever

It is the end of a “long-term” for a subset of investors everyday.

Investors usually save with a specific goal in mind. These goals tend to be time bound: retirement, kid’s education, etc. While their horizons can be “long” at the outset, it gradually shortens as the D-day arrives. Equities (and other high-risk securities) are constantly being sold and rotated into bonds (and other low-risk securities) set by a glide-path.

As much as professionals like to fantasize about long-term investing, the investors in their funds have bounded horizons. This is especially true for open-end funds.

Sentiment + Finite Holding Periods = Volatility

Finite holding periods create the need to transact. This makes it impossible to ignore sentiment. The two combine to create volatility.

It is easy to blame investor greed and fear for bad portfolio outcomes. We have all seen this sketch make the rounds:

Buy High, Sell Low: How To Free Yourself From The Madness

However, even if an investor overcomes the call of greed and fear, it is impossible to ignore time. This makes sentiment the prime determinant of investment outcomes.

If you think investors having longer time-horizons can ignore volatility. Think again. As the chart above illustrates, volatility is an equal opportunity hater.

Embrace and Extinguish

Volatility clusters. You have reasonably long periods of calm, then suddenly a lot of things “go wrong.” Markets gyrate and you feel that all hell has broken lose.

This leads investors to assume that periods of calm are normal and volatility is abnormal. But in markets, the reverse is true. Sudden shocks, volatility and jolts to sentiment are the norm. Calm periods are the anomaly.

Sentiments wax-and-wane. Multiples expand and contract. Markets melt-up and melt-down for no good reason.

The only time-tested way of reducing volatility is asset allocation. Invest in a basket of different assets (make sure that at least a few of them a not financialized) and accept the market for what it is.

Embrace volatility and extinguish it.

Looking for a sensible way to invest? Here’s how to get started.

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Volatility and Allocation

Think in terms of volatility buckets, not assets

This post is part of our series on diversification and asset allocation. Previously:

  1. Diversification and its Malcontents

  2. The Permanent Portfolio

  3. Sequence Risk and Asset Allocation

  4. Static vs. Tactical Allocation

  5. Tactical Allocation

The thrust of our previous posts on allocation was that Indian investors shouldn’t blindly copy strategies that worked well in the US. There are a lot of qualitative arguments to be made to support a India-dominant view for allocation strategies. In this post, we introduce a quantitative aspect to the discussion.

It is Volatility, Stupid!

In finance, more than any other field, it is very easy to get correlation and causation mixed up.

A man goes to the doctor and says, “Doctor, wherever I touch, it hurts.”
The doctor asks, “What do you mean?”
The man says, “When I touch my shoulder, it really hurts. When I touch my knee – OUCH! When I touch my forehead, it really, really hurts.”
The doctor says, “I know what’s wrong with you. You’ve broken your finger!”

There are no universal laws for an asset class that holds across geographies and economic systems. The reason why a 60/40 Portfolio “works” in the US has more to with the quantitative aspects of the assets being mixed than what they are called. US bonds have benefitted greatly from a 30 year slide in yields, benign inflation and a flight-to-safety bid. None of these hold true for Indian bonds. So, expecting a 60/40 Indian portfolio to behave like a 60/40 US portfolio just because you mixed the same assets together is idiotic.

The most import aspect while considering assets for diversification are their volatilities. Specifically, the correlation of their volatilities at their left tails.

To keep things simple, consider a 2 asset portfolio: Eq and X. Eq has some average return that will be held constant during this analysis. What changes is its standard deviation (aka, volatility.) X is a stable asset with zero volatility (think of it as a fixed deposit.) How does different allocations to Eq change portfolio returns and volatility?

  1. Low volatility is supportive of higher allocations

  2. Higher allocations to the higher volatility asset progressively reduces the predictability of portfolio returns

Volatility is Volatile

Asset return volatility is itself volatile.

The past performance of a diversified portfolio is based on the realized volatility of its components. However, volatility itself is unpredictable over long periods of time.

Take-away

While considering assets to diversify into, look at the volatility of the asset rather than what it is called.

Don’t expect the quantitative aspect of an asset class to transcend economic systems – different markets need different treatments.

All investing is forecasting. And all allocation is forecasting volatilities.

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Fat Tails

Introduction

Years of returns can get wiped out in a month in the markets. While investors mostly focus on the average, the tails end up dictating their actual returns. (Introduction)

Sampling and Measurement

Typically, a uniform sample is taken. The problem with this is it under-represents the tails. This leads to models that work on average but blow up on occasion. One way to overcome this problem is through stratified sampling. (Sampling)

Expected shortfall (ES) is a risk measure that can be used to estimate the loss during tail-events. (Measuring)

Acceptance

All assets have fat tails. It is a feature, not a bug. (Historical)

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Fat Tails, Everywhere

There is no asset free of extreme tail losses. If an asset produces any sort of return, it is going to be exposed to some sort of tail event.

One can try to find uncorrelated assets so that those losses don’t occur at the same time. However, correlations between asset returns are not stable – they change over time and behave quite erratically during market panics.

In the end, to be an investor is to accept the fact that large losses occasionally happen.

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Fat Tails, Expected Shortfall

No matter how you slice it, there is no escaping tail events in investing. It is the nature of the beast and every attempt you make eliminating the risk results in you giving up a significant portion of your returns. But given two investment opportunities, how do you go about figuring out which one is more susceptible to tail events?

Expected shortfall (ES) is a risk measure that can be used to estimate the loss during tail-events. The “expected shortfall at q% level” is the expected return on the portfolio in the worst q% of cases. ES estimates the risk of an investment in a conservative way, focusing on the less profitable outcomes. For high values of q it ignores the most profitable but unlikely possibilities, while for small values of q it focuses on the worst losses. Typically q is 5% and in formulae, p (= 100% – q) is often used as a substitute.

ES of Weekly Returns

Here’s a dilemma that most investors face: Mid-caps have given higher returns in the past compared to large-caps. But, how do their tail-risks compare?

Turns out, ES of the NIFTY MIDCAP 150 TR index is -6.73% vs. NIFTY 50 TR’s -5.65%. This is how much an investor would have lost in the worst 5% of weeks since 2011.

Sampling

In our previous post, we showed how strata-sampling can be used to make sure that you don’t end up ignoring tail-risk in your simulations. By definition, tail-events are rare. So, the differences are subtle.

Tactical Allocation

Reducing tail-risk is one of the biggest draws of tactical allocation. Anything that reduces deep drawdowns has the effect of keeping investors faithful to their investment process.

One way to setup a tactical allocation strategy is to use a Simple Moving Average (SMA) to decide between equity and bond allocations. Different SMA look-back periods will result in different levels of risk and reward. From an ES point of view, here’s how things for NIFTY shakes out:

Since 1999
Since 2010

Using an SMA and re-balancing weekly significantly reduces tail-risk.

How far back should you go?

The problem with tail-events is that there aren’t enough of them to build an effective model. There’s always a temptation to use as much data as possible so that these events find sufficient representation. However, markets evolve, regulatory structures change and past data stop being representative.

For example, if you run a tactical allocation back-test with all the data that is available, you’ll conclude that shorter the SMA, the better:

However, if you remove 2008 and its aftermath and look only are the data from 2011 onward, you get a different picture:

While metrics like ES and strategies like SMA are useful, the data that they are presented will give different results based on the regime that they are drawn from.

Risk management is a continuous process and cannot be reduced to single number.

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