Predicting LSE Security Price Movements with Twitter Sentiment

Social Market Analytics, Inc. (SMA) aggregates the intentions of professional investors as expressed on Twitter & StockTwits and publishes a series of metrics that describes the current conversation relative to historical benchmarks.  Our data is a leading indicator of price movement both positive and negative.

There is unique predictive information in unstructured content.  Social Market Analytics use AI and Machine Learning techniques developed over the last eight years to convert this unstructured content into data suitable for quantitative analysis. This opens a whole new area of big data analysis.

Social Market Analytics (SMA) calculates predictive sentiment on the entire US equity universe, Currencies, Commodities, Crypto currencies, ETF’s and custom sources.   This blog is about the predictive nature of our LSE security universe.  We calculate our custom metrics on the top 1000 market cap securities listed on the LSE.  Our LSE data starts on 1/1/2016. Below is a cumulative quintile distribution of returns based on our S-Score metrics.  Our S-Score is effectively a Z-Score comparing 24-hour sentiment based on the Tweets of professional investors compared to a 20-day baseline.   Prediction periods vary per asset class and baseline. Longer baseline comparisons lead to longer prediction periods.

Stocks with abnormally positive conversations typically outperform their peers and stocks with abnormally negative conversations typically underperform their peers.  As expected conversations with normal positive or negative tones perform like the overall market.

Below is a typical quintile chart for the LSE 1000 universe tracked from post Brexit to 8/31/2018. The spread between the top and bottom quintiles is 10% annualized.   Sharpe and Sortino ratios are in the table below that.  To learn more or request a historical data set contact SMA with any questions ContactUS@SocialMarketAnalytics.com

LSEQuintiles 1

LSE Quintiles2

Social Market Analytics Sentiment Data for Forex Trading

Social Market Analytics (SMA)  provides real-time sentiment data for equities (North America & LSE), commodities, foreign exchange, Crypto Currencies and ETF’s.

In this blog I am going to explore a trading system using the SMA Twitter based sentiment data to trade a basket of: EURUSD, EURGBP, GBPJPY, GBPUSD ,USDCAD ,USDCHF ,USDJPY.

We will explore two straight forward trading systems:

  • Forex Sentiment RSI: Daily Long/Short Strategy
  • SMA S-Score Based Currency Selection Model

RSI Calculation Methodology 

CurrencyBlog 1

This strategy is a single-factor model solely based on adjusting daily weights according to 3-Day Sentiment RSI on the 7 of the highest daily volume Forex pairs. It is long-short with the assumption that tails act with similar magnitude.

  • Long/Short
    1. RSI >= 50, Long
    2. RSI < 50, Short
  • 50% Long & 50% Short Asset Allocation
    1. Long weights are calculated using only longs
    2. Short weights are calculated using only shorts
  • Daily weight adjusted following:
    1. separately for the long side and the short side

 

currencyBlog2

The strategy significantly improves returns compared to an equal weighted baseline.  Sharpe and Sortino ratios are statistically significant:

  • Sharpe Ratio:
    • 2.77 Jan 03, 2017 to July 19, 2018
    • 3.40 YTD
  • Sortino Ratio:
    • 5.40 Jan 03, 2017 to July 19, 2018
    • 7.46 YTD

The volatility of each leg of the strategy is either kept stable or decreased in comparison with the baseline.

SMA S-Score Based Currency Selection Model

This daily trading strategy is based on the S-Score at 09:10:00 EST and executing a 24-hour hold based on these values at 09:15:00 EST. We find consistency across execution times.  The goal is to assess sentiment and take make a directional trade in agreeance, given that the sentiment falls at least 1 standard deviation from the 20-day mean.

Equal weighted based on standard deviation criteria:

– Long: S-Score > 1

– Short: S-Score < -1

– Baseline: Equal Weighted Portfolio of the 7 Currency pair

Long and short legs are capped at 50% of the daily portfolio, even on the occurrence of an outlier day where all pairs are long, or all pairs are short.

currencyBlog3

 

The strategy drastically improves returns compared an equal weighted baseline.  Up to 40% cumulative over a 19-month period with a consistent annual rate of return.

  • Sharpe Ratio:
    • 2.56 Jan 03, 2017 to July 19, 2018
    • 3.56 YTD
  • Sortino Ratio:
    • 4.93 Jan 03, 2017 to July 19, 2018
    • 7.72 YTD

These are straight forward strategies that illustrate the predictive nature of our dataset.  Twitter and StockTwits based factors.  To learn more about how Social Market Analytics sentiment data can help your trading please contact us at contactus@Socialmarketanalytics.com or Doug Hopkins @ (312) 788-2621.