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Trading in financial markets involves significant risk of loss which can exceed deposits and may not be suitable for all investors.
Before trading, please ensure that you fully understand the risks involved
Trading in financial markets involves significant risk of loss which can exceed deposits and may not be suitable for all investors. Before trading, please ensure that you fully understand the risks involved

Wednesday, October 07, 2020

Fang + Russell 2000

by Century Financial in Investment Insights

Fang + Russell 2000
Fang + Russell 2000

*This performance is only observed with historical backtests and not traded by the company.

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The product and investment ideas do not consider the risk profile and financial position of the recipient and may not be suitable for everyone. Trading in financial markets involves a significant risk of loss, which can exceed deposits. Please read the complete disclaimer carefully.

Equity market across the globe, especially the US, have staged a strong come-back from their March lows as the Central banks pumped ample liquidity into the markets and the economic activity continued to gain traction.

However, with extreme elections uncertainty round the corner and growing anxiety about severe second wave of covid-19 in the winter months, investors now have a tricky decision to make. On one end, investors fear missing out the dip while on the other they risk losing their capital. In the current perplexing environment, pair trading strategy can allow investors to participate in the market and keep a check on the losses.

However, it is important to note that Trading pairs is not a risk-free strategy. The difficulty comes when prices of the two securities move contrary to the positions taken resulting in losses. Thus, it is important to adhere to strict risk management rules when dealing with such adverse situations.

Strategy

The figures in the table have been derived by undertaking long positions in the US FANG Plus futures while simultaneously shorting Russell 2000 Index (US small cap) with equal exposure on both sides. For instance: if $500K worth US FANG Plus futures are bought, then simultaneously $500K worth US small cap 2000 have to be sold.

Over the past 23 quarters, starting from Jan 2015, this strategy would have resulted in total profits of +160% (with 2x leverage) with 18 quarters providing positive return. (78% hit ratio). This year itself the US Fang Plus index has outperformed the Small cap index by more than 60%.

The benefits of trading the US Fang+ index in pair is that it would help limit the drawdown in case of a sharp correction in markets. The max drawdown on a naked long US Fang+ position would have been 39% on a daily closing basis, while the max drawdown on the pair strategy was 16% on a daily closing basis.

US Fang Plus Quarterly Return US Small Cap 2000 quarterly return Long: US Fang Plus Short: US Small Cap 2000 Max Drawdown on Naked US Fang Plus trade Max Drawdown on Pair trade
3/31/2015 6% 4% 2%
6/30/2015 8% 0% 8% 0% 0%
9/30/2015 -3% -12% 9% 3% 0%
12/31/2015 17% 3% 14% 0% 0%
3/31/2016 -4% -2% -2% 4% 2%
6/30/2016 1% 3% -3% 3% 5%
9/30/2016 19% 9% 10% 0% 0%
12/30/2016 0% 8% -8% 0% 8%
3/31/2017 14% 2% 12% 0% 0%
6/30/2017 15% 2% 13% 0% 0%
9/29/2017 12% 5% 6% 0% 0%
12/29/2017 8% 3% 5% 0% 0%
3/30/2018 10% 0% 10% 0% 0%
6/29/2018 18% 7% 10% 0% 0%
9/28/2018 -3% 3% -7% 3% 7%
12/31/2018 -20% -21% 1% 23% 6%
3/29/2019 18% 14% 4% 5% 2%
6/28/2019 -3% 2% -5% 8% 7%
9/30/2019 0% -3% 3% 8% 4%
12/31/2019 22% 10% 13% 0% 0%
3/31/2020 -4% -31% 27% 4% 0%
6/30/2020 38% 25% 13% 0% 0%
9/30/2020 31% 5% 27% 0% 0%
Total Return 162%
Strategy Rationale

The fundamental idea behind the strategy is that well established top U.S. tech stocks will tend to outperform the U.S. small cap companies over the long run and the hedged positions will try and minimize the losses in case of downward trending markets.

While many areas of the economy are currently losing a vast majority of their revenue such as travel and hospitality, non-essential retail, offline entertainment or live events, the tech has remained resilient in a pandemic as online shopping, streaming, gaming, social media, digital payment, cloud services etc found every reason to surge in this in this work-at-home and shop-at-home economy. Not only are big tech companies reaping record profits, but the pandemic has exacerbated the significance of their services to the wider economy. Large-cap tech stocks are expected to continue to outperform, because that’s where the cash flow is in this work & shop at home economy. That impact will likely only increase further in the future, even in a post-pandemic world since that world will categorically be transformed by an acceleration toward automation and tech-enabled services. This crisis has proven that tech is indispensable, and it should only speed up the transition to more tech. As such, there’s good reason to believe that the sector will keep up better than the rest.

*Past performance is not indicative of and does not guarantee future results. Trading in markets may involve loss of capital

Risks & Assumptions

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The strategy might suffer from look-ahead bias which occurs due to the use of information or data in a study or simulation that would not have been known or available during the period being analyzed. This can lead to inaccurate results in the study or simulation.
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Future price movements may not be exactly the same as the historical price movements and this could lead to variation in performance.
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Testing can sometimes lead to over-optimization. This is a condition where performance results are tuned so high to the past they are no longer as accurate in the future.
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The model assumes no slippages in trading. Slippage refers to the difference between the expected price of a trade and the price at which the trade is actually executed.
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Drawdowns in actual trading can be higher than the tested system and losses could be significant in the event of leverage.
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Unforeseen events can lead to variation in performance from the tested trading strategy.
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The tested result has been computed with price feeds available from Bloomberg.
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The testing environment has not considered transaction or any other costs.
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Trading indicators used for the purpose of testing has been provided by Bloomberg.
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The strategy might suffer from data mining fallacy, selection bias and backfill bias.

Data Source: Bloomberg

Arun Leslie John
Chief Market Analyst

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