Experiments 2018-11-12T06:35:21+00:00

Our recent experiments have been carried out according to the following process:

1. Input Data:

  • Market data (source: Thomson Reuters)
  • We cover European mainland countries, UK and US
  • Analysis for short and long term sovereign and corporate bonds
  • News and sentiment sources:
    • Macroeconomic news sentiment (source: Ravenpack)
    • Relevance and Sentiment included, economic news item concerning the issuing country
    • Social media sentiment (source: Stockpulse)

2. Calculate term structure from bonds of specific country

  • through the Svensson model calculate spread with (AAA) Eurobond from ECB

3. Development of sentiment-enhanced key figures

  • Analysis of country-specific macroeconomic news sentiment as well as firm-specific sentiment
  • Aggregation of intra-day sentiment to daily sentiment and impact values
  • Distinguish between positive and negative news, creation of daily impact scores with decay
  • Analysis of volume of news

4. Effect research:

  • inference of news events and bond prices
  • Sentiment impact evaluation
  • Integration of sentiments to spread and volatility prediction of Fixed Income products
  • Integration of sentiments to default probabilities and rating migration
  • Correlation: 87% of analysed spread time series, at least one news sentiment series showed significant correlation with the spread series

Example German bonds:

  • Create spread with benchmark (AAA ECB rate)
  • Correlation between sentiment series and spread series low but significant
  • Linear regression analysis points to most relevant regressors being “number of all news”, “positive sentiment”, negative sentiment”
  • Modelling closing yields through ARIMA model with external regressor
  • Empirical results for German bunds and bubills between 2007 and 2017
  • Bunds: 15 instruments with a maturity between 5 and 30 years
  • Bubills: 36 instruments with a maturity between 3 months and 2 years.
  • Model order: ARIMA(2,1,2) and ARIMA(1,1,1) chosen via AIC
  • Fit and Forecast without external regressor and with positive/ negative sentiment scores, impact values and news counts

– ARIMA Model set-up:

  • [M1] no external regressor
  • [M2] Volume of all news; All News Impact; Volume of positive news; Positive news impact
  • [M3] Volume of all news; All News Impact; Volume of negative news; Negative news impact
  • [M4] Volume of all news; All News Impact
  • [M5] Positive Impact; Negative Impact
  • [M6] Mean Positive Sentiment
  • [M7] All News Impact
  • [M8] Volume of all news

– Results for German bunds:

  • Best performing ARIMAX models are Model 2 (Volume of all news; All News Impact; Volume of positive news; Positive news impact) and Model 3 (Volume of all news; All News Impact; Volume of negative news; Negative news impact) for in-sample
  • Model 7 (All News Impact) performs best for out-of sample one-step ahead predictions.

– Results for German bubills

  • Best performing ARIMAX model is Model 3 followed closely by Model 4 (Volume of all news; All News Impact) for in- and out-of sample forecasts