Country Rotation Based on Regulatory Alerts Sentiment

Abstract

In this tutorial, we test four new alternative data strategies that base their trading decisions on the US Regulatory Alerts dataset. The first strategy capitalizes on movement in the healthcare sector in response to announcements from the U.S. Food and Drug Administration (FDA). The second strategy captures momentum in the Bitcoin-USD trading pair in reaction to new Crypto regulations. The third strategy seeks to exploit trading patterns in the SPY that form from specific regulatory alerts. The fourth strategy is a country rotation strategy that uses a novel natural language processing (NLP) approach to detect the sentiment of country exchange-traded funds (ETFs) without introducing look-ahead bias. The results show that all four strategies outperform their respective benchmarks.

Background

NLP is a subfield of artificial intelligence that strives to process unstructured text and understand its meaning. In most NLP trading strategies, the developer provides a set of pre-selected phrases and their sentiment scores, which usually introduces look-ahead bias into the strategy. In this algorithm, we circumvent this error by assigning sentiment scores to words on-the-fly based on how they impact the future returns of the security.

Country rotation is a strategy where we move capital among a set of countries in an effort to outperform the overall market. The idea is that each country's ETF is an independent security and we can forecast the future financial performance of each country based on the sentiment of their regulatory alerts. If we can adjust the exposure of our portfolio to align with the public sentiment of each country, we can position our portfolio to benefit from positive and negative regulatory changes while diversifying on an international scale.

Method

Let’s review how we can implement these four strategies with the LEAN algorithmic trading engine.

Strategy #1 - FDA Announcements

To implement this strategy, we check the title of the regulatory alert articles everyday. If an article title contains the “FDA” acronym, we buy the healthcare sector ETF, XLV. Otherwise, we short XLV.

Strategy #2 - Crypto Announcements

To implement this strategy, we read the title and summary of the regulatory alert articles everyday. If an article contains the word “Crypto” and the BTCUSD trading pair is trending up, we buy BTCUSD. Otherwise, we short BTCUSD.

Strategy #3 - Trading Patterns from Individual Alert Types

To implement this strategy, we fit a model every Sunday that uses the AlertType of each regulatory alert to predict the expected returns of SPY over the following 24 hours. Each day, we make predictions for the future returns of SPY. If the aggregated prediction is positive, we buy SPY. Otherwise, we short SPY.

Strategy #4 - NLP Country Rotation

To implement this strategy, we first subscribe to US Regulatory Alerts.

self.dataset_symbol = self.AddData(RegalyticsRegulatoryArticles, "REG").Symbol

Next, we gather a set of country ETFs. To avoid selection bias, we include as many countries in the universe as possible. To get the ETF that represents each country, we opened the ETF Country Exposure Tool on the ETF DB website, selected each country that’s included in the US Regulatory Alerts dataset, and then chose the ETF with the largest weight. The following table shows the ETFs in the universe:

Country	Ticker	Description
Argentina	ARGT	Global X MSCI Argentina ETF
Australia	EWA	iShares MSCI-Australia ETF
Austria	EWO	iShares MSCI Austria ETF
Belgium	EWK	iShares MSCI Belgium ETF
Brazil	EWZS	iShares MSCI Brazil Small-Cap ETF
Canada	FLCA	Franklin FTSE Canada ETF
China	CHIU	Global X MSCI China Utilities ETF
Colombia	GXG	Global X MSCI Colombia ETF
Croatia	FM	iShares MSCI Frontier and Select EM ETF
Cyprus	SIL	Global X Silver Miners ETF
Czech Republic	NLR	VanEck Uranium+Nuclear Energy ETF
Denmark	EDEN	iShares MSCI Denmark ETF
Egypt	EGPT	VanEck Egypt Index ETF
Estonia	FM	iShares MSCI Frontier and Select EM ETF
Finland	EFNL	iShares MSCI Finland ETF
France	FLFR	Franklin FTSE France ETF
Germany	FLGR	Franklin FTSE Germany ETF
Greece	GREK	Global X MSCI Greece ETF
Hong Kong	EWH	iShares MSCI Hong Kong ETF
Hungary	CRAK	VanEck Oil Refiners ETF
Indonesia	EIDO	iShares MSCI Indonesia ETF
Ireland	EIRL	iShares MSCI Ireland ETF
Israel	EIS	iShares MSCI Israel ETF
Italy	FLIY	Franklin FTSE Italy ETF
Japan	DFJ	WisdomTree Japan SmallCap Dividend Fund
Libya	BRF	VanEck Brazil Small-Cap ETF
Luxembourg	SLX	VanEck Steel ETF
Malaysia	EWM	iShares MSCI Malaysia ETF
Malta	BETZ	Roundhill Sports Betting & iGaming ETF
Mexico	FLMX	Franklin FTSE Mexico ETF
Netherlands	EWN	iShares MSCI Netherlands ETF
Norway	NORW	Global X MSCI Norway ETF
Pakistan	PAK	Global X MSCI Pakistan ETF
Poland	EPOL	iShares MSCI Poland ETF
Portugal	PGAL	Global X MSCI Portugal ETF
Qatar	QAT	iShares MSCI Qatar ETF
Republic of the Philippines	EPHE	iShares MSCI Philippines ETF
Romania	FM	iShares MSCI Frontier and Select EM ETF
Russia	FLRU	Franklin FTSE Russia ETF
Saudi Arabia	FLSA	Franklin FTSE Saudi Arabia ETF
Singapore	EWS	iShares MSCI Singapore ETF
South Korea	FLKR	Franklin FTSE South Korea ETF
Spain	EWP	iShares MSCI Spain ETF
Sri Lanka	FM	iShares MSCI Frontier and Select EM ETF
Sweden	EWD	iShares MSCI Sweden ETF
Switzerland	EWL	iShares MSCI Switzerland ETF
Taiwan	FLTW	Franklin FTSE Taiwan ETF
Thailand	THD	iShares MSCI Thailand ETF
The Bahamas	RNSC	Furst Trust Small Cap US Equity Select ETF
Turkey	TUR	iShares MSCI Turkey ETF
Ukraine	TLTE	FlexShares Morningstar Emerging Markets Factor Tilt Index
United Arab Emirates	UAE	iShares MSCI UAE ETF
United Kingdom	EWUS	iShares MSCI United Kingdom Small-Cap ETF
United States	SPY	SPDR S&P 500 ETF
Vietnam	VNAM	Global X MSCI Vietnam ETF

Using the ETFs in the preceding table, we create a static universe of country ETFs.

self.etf_by_country = {}
for country, etf_ticker in etf_by_country.items():
    self.etf_by_country[country] = CountryETF(country, self.AddEquity(etf_ticker, Resolution.Daily).Symbol)

To ensure the algorithm is fit using the most recent regulatory alerts, we train a model at the beginning of the algorithm and we schedule training sessions to re-fit the model at the end of every month.

self.Train(self.DateRules.MonthEnd(), self.TimeRules.At(23,0), self.train_model)
self.train_model()

The train_model method makes history requests to gather the daily returns of each country ETF and all the regulatory alerts over the trailing 90 days. Each regulatory alert can be tagged with the name of the country from which it’s released, so we classify the regulatory alerts into their respective countries and then train a unique model for each country. To train the model, we use the following procedure:

1. Get the words of each article title.
2. Tokenize the title and drop filler words like “the”, “a”, and “an”.
3. Create a dictionary that maps each word to the expected future return of the country ETF over the following 24 hours.

Each day, we then parse all the regulatory alerts to find the sentiment of each country. For each country that has a positive sentiment, we enter a long position. For each country that has a negative sentiment, we enter a short position. We allocate an equal portion of the portfolio to each country ETF and scale the position sizes to achieve 1x leverage.

Results

We backtested each strategy using all the data from the US Regulatory Alerts dataset, which currently spans from August 2021 to February 2023.

Strategy #1 - FDA Announcements

This strategy achieved a 0.828 Sharpe ratio. It outperforms buy-and-hold with the XLV healthcare sector ETF, which results in a 0.161 Sharpe ratio over the same time period. To reproduce our results, backtest this algorithm.

Strategy #2 - Crypto Announcements

This strategy achieved up to a 1.744 Sharpe ratio. It can outperform buy-and-hold on Bitcoin over the same time period, but the strategy is sensitive to changes in the lookback window. To reproduce our results, backtest this algorithm.

Strategy #3 - Trading Patterns from Individual Alert Types

This strategy achieved a 0.223 Sharpe ratio. It outperforms buy-and-hold with the SPY ETF, which results in a -0.024 Sharpe ratio over the same time period. To reproduce our results, backtest this algorithm

Strategy #4 - NLP Country Rotation

This strategy achieved a 0.362 Sharpe ratio. It outperforms the following benchmarks:

• Buy-and-hold with the SPY ETF, which results in a -0.024 Sharpe ratio over the same time period.
• Buy-and-hold with the iShares MSCI World ETF, URTH, which results in a -0.312 Sharpe ratio.
• An equal-weighted portfolio of all the country ETFs, which results in a -0.224 Sharpe ratio.

During the backtest period, the algorithm rebalanced the portfolio 281 times. The algorithm determined the United States had the most negative regulatory alerts, shorting the US 232 times during the 281 rebalances (83%). The algorithm determined Portugal had the most positive regulatory alerts, longing Portugal 148 times during the 281 rebalances (53%).

To reproduce our results, run the following algorithm.

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