Largecaps look better as smallcaps price in strong growth: Franklin Templeton’s Arihant Jain

Largecap stocks may be better placed than their mid- and small-cap peers as investors have already priced in strong earnings growth in the broader market, according to Arihant Jain, portfolio manager for SIF and multi-factor funds at Franklin Templeton India.

While mid- and small-cap stocks are factoring in 20% to 30% earnings growth, large-caps are being valued against more modest 10% to 12% expectations. That leaves greater room for earnings upgrades and a valuation re-rating in large-caps, Jain said. Edited excerpts from a chat:

The Sapphire Equity Long-Short SIF can hold 75–100% in long positions and short up to 25%. What is the current long-short positioning, and what specific signals would make you deploy the full shorting limit?


Our asset-allocation model determines our long and short exposure. Leverage is not allowed in India. Globally, long-short strategies often operate on a 150-50 model—150% long and 50% short, resulting in net equity exposure of 100%. In India, we decide how much to be long and short without leverage.

We use a macroeconomic and technical model to assess whether the market is bullish, bearish or volatile. If the market is on a positive trajectory, we may be 100% long. If we see stress or an opportunity in a particular sector, we may take technical short positions. Our net equity exposure can range from 60% to 100%.

Our multi-factor model selects stocks using four factors: quality, valuation, price momentum and earnings momentum. We have developed our own factor definitions on the Mosaic platform, calculate a score for each company and use a portfolio-construction tool to determine sector and market-cap allocation.

Quality is subjective. Our definition differs from the factor indices of NSE, BSE or MSCI because we want to control and continuously upgrade it. One of our quality factors is innovation: we assess spending on research and development and branding because such companies may eventually deliver stronger sales growth.

For valuation, we focus more on enterprise value than market capitalisation because enterprise value also captures debt. For momentum, we assess price as well as earnings. Price is historical, while earnings momentum provides information about the future. We look at EPS-revision growth to identify surprises. If a company is growing at 40%, that may already be priced in. But a change from 40% to 42% or 38% is new information, which we try to capture.

The short book uses the same framework to identify the weakest companies.

We were 100% long in July because domestic macroeconomic signals were bullish. Credit growth was above 15% to 20%, and earnings growth was between 10% and 30% across large-, mid- and small-cap companies. We were not seeing negative macro signals. Technically, the market was neutral, so we consciously chose to be 100% long.

Since launching the fund, have you taken any short positions?

Very small ones. Over a five- to seven-year horizon, short positions may not always add value. In a bull market, a stock that merely underperforms the benchmark may not generate a short-side return. The stock needs to deliver an absolute negative return

The universe available for shorting is also relatively small. Futures and options are available in around 200 stocks, although these represent roughly 80% to 85% of the market by capitalisation. Having all 500 stocks available for shorting would help, but that is not the current situation.

The strategy is not designed to maintain short positions at all times. In a stressed or bearish market, or during periods of high volatility, we can increase short exposure to protect the downside. The upside will primarily come from equity exposure.

The market has been broadly range-bound for two years. While domestic flows have protected the downside and new supply has restricted the upside. With foreign investors returning and a large IPO pipeline absorbing capital, is it becoming harder to generate alpha?

Arihant Jain: It depends on where a fund is positioned. The market was broadly flat in 2018-19, but that did not prevent every fund manager from generating alpha. Over a five- to seven-year period, there may be two or three flat years. That does not mean the market will remain flat going forward.

We are positive on earnings growth. We expect the second quarter to be strong, with analysts projecting 20% to 30% growth in mid- and small-caps. If earnings growth remains strong, market capitalisation will eventually move in the same direction. Ultimately, the market is driven by earnings growth.

What is your market outlook? Will the market return to record highs, or will small- and mid-caps continue to lead?

Arihant Jain: It depends on earnings growth and the multiples available for that growth. Mid- and small-caps are already pricing in 20% to 30% earnings growth. Unless there is a positive surprise, much of that expectation is reflected in prices.

In large-caps, investors are expecting nominal earnings growth of around 10% to 12%. That leaves room for earnings estimates to be revised upwards and for valuation multiples to expand. I am not saying small- and mid-caps will necessarily de-rate, but on a risk-adjusted basis, large-caps may look better going forward.

Your multi-factor fund has around 80% exposure to large-caps. Is that a deliberate choice?

Arihant Jain: It is also a result of portfolio construction. The fund’s benchmark is the BSE 200, which is predominantly represented by large- and mid-cap stocks. We are generally comfortable holding 70 to 90 stocks; the fund currently has around 80.

What makes a multi-factor fund different from single-factor value or momentum funds?

Arihant Jain: Single-factor strategies carry higher drawdown risk. If a factor goes out of favour, the underperformance can be significant. We have seen a single factor underperform the benchmark by as much as 70 percentage points over four years—for example, while the Nifty 200 rose 72%, a single-factor strategy gained only 1%.

We use multi-factor investing as a core allocation. We assess a company from a 360-degree perspective, looking at its quality, valuation and momentum. We assign strategic weights to the factors and maintain exposure to all of them. We may take small tactical positions, but it is almost impossible to time when a factor will perform or crash.

Our approach is “and”, not “or”. We look for a company with good quality, reasonable valuation and momentum. Selecting the top companies separately on quality, valuation and momentum and then combining them may create a portfolio that appears diversified but is actually a combination of three correlated factor portfolios.

Which sectors look attractive to you at this stage?

Arihant Jain: Private banks could be an opportunity given their valuations and the credit environment. Metals may also be interesting, depending on how the current cycle develops.

How is a quantitative model different from an AI-driven model?

Arihant Jain: A quantitative model is algorithm-based. The efficiency of the algorithm and the experience of the team are important. Our team has been running quantitative strategies globally for more than 20 years, so we understand the risks, how to control them and how to upgrade the model.

You can broadly compare it with AI because both involve machine learning. However, we control both the inputs and the underlying algorithm. With a third-party AI system, you may control the inputs but not the algorithm.

If we have a sector view, we generally let the model drive 80% to 90% of the portfolio and take an active call on the remaining 10% to 20%. For example, if a corporate governance issue may take time for the model to capture, our experience can help us react earlier. This makes the strategy something between a passive fund and a pure active fund.

The model is continuously upgraded. Stock selection is important, but position sizing can sometimes be even more important. We focus on both.

Help us understand how your quant model works and how many factors you consider?

Arihant Jain: Globally, the team has more than 100 people. We also have a dedicated 15-member technology team maintaining the Mosaic platform. The models are refreshed daily, although the portfolio is generally rebalanced monthly.

In the multi-factor fund, we use more than 35 sub-factors. Quality, for example, contains more than 12 sub-factors, which are combined into a single quality score. The same applies to valuation, price momentum and earnings momentum.

We use macroeconomic data to help determine long and short exposure. I am evaluating alternative, higher-frequency data such as short-interest data and the put-call ratio, although nothing concrete has been added in the last year.

What is the typical churn ratio?

Arihant Jain: It is typically around 70% to 90% for our quantitative funds. Since we use a core approach based on quality, value and sentiment, a stock tends to remain unless something materially changes. The multi-factor approach generally results in more stable positions than a single-factor strategy.

How do you see the SIF category developing?

Arihant Jain: There is a structural gap between what regulations allow and the tools available to fund managers. Mutual funds can take positive positions, but they have limited tools when they are neutral or negative. An SIF provides another potential source of alpha and drawdown protection, thereby adding diversification.

The ability to take active short positions is a key differentiator. Mutual funds cannot undertake naked shorting. If we are negative about a theme or stock, we may hedge or avoid it, but we cannot take an active short position at scale. An SIF provides that additional tool and a potentially different source of returns.

Taxation is another advantage because it is similar to mutual-fund taxation. Derivatives have traditionally been treated as business income, which can attract a much higher tax rate.