Event Recognition Strategies
               applied in the Mercurio Project

    Davide Azzalini1 , Fabio Azzalini1 Davide Greco2 , Mirjana Mazuran1 , and
                                 Letizia Tanca1
                               1
                              Politecnico di Milano
               {davide.azzalini|fabio.azzalini}@mail.polimi.it
                  {mirjana.mazuran|letizia.tanca}@polimi.it
                            2
                              info@davidegreco.it



       Abstract. Mercurio is a project currently investigated at Politecnico di
       Milano whose aim is to support the decision-making process of financial
       investors. Mercurio identifies relevant events both from financial news
       articles and financial indexes and uses sequential pattern mining to pre-
       dict exceptional events given their past occurrences and relationships
       with other events. The process of event recognition, both from textual
       and numerical data sources, is crucial to successfully reach the goals.


   Investors constantly read financial news and analyze financial indexes, using
their knowledge and experience to predict market events and make profitable
investments. Mercurio [1, 2] aims at supporting this process by automatically
extracting, from data freely available on the Web, events that influence and
shake the market. Event recognition strategies are applied to both textual and
numerical financial information.

Textual data sources. Mercurio monitors Italian financial data sources such
as Sole 24 Ore 3 , Corriere della Sera 4 , Radiocor 5 , etc. These data are processed
according to different strategies:

Semantic recognition. Events are recognized through semantic rules that formal-
    ize the knowledge of our domain expert. Rules define relationships between
    sentence structures and events; they are designed to capture meanings that
    go beyond the sole natural language processing since they recognize “hid-
    den” information inside the news, e.g. financial newspapers, usually, publish
    interviews when requested by a company: why would a company want to
    be interviewed? It seems that interviews are often published for reassuring
    investors in times of crisis.
Classification. Often, data information sources specify, for each article, one or
    more categories, possibly hierarchical, it belongs to, e.g. articles about bal-
    ance, merge& acquisition, etc. These categories are mostly general-purpose
3
  http://www.ilsole24ore.com/
4
  http://www.corriere.it/economia/
5
  http://www.radiocor.ilsole24ore.com/
    and hard to use in our context in a significant way. Mercurio employs a
    domain-specific classification of articles, manually performed by our domain
    expert on a training set of articles, then automatically derived by the system
    for the remaining articles.
Communication style. Over time, companies develop a certain communication
    style characterized by the amount of published news, the different sources
    dealing with it, the diversity of reported topics, etc.. It is interesting to
    discover events where a company breaks its expected communication trend,
    e.g. an out-of-the-blue article breaking a “long” communication silence.
Article summarization. Long articles are hardly read by investors who, instead
    of reading the whole news, usually skim through its content or trust little
    more than title and introduction. Mercurio applies summarization techniques
    to provide investors with only the most relevant information. It ranks each
    sentence in the article according to its position in the text (at the beginning,
    end, etc.) and to its content (domain-dependent stop-words as well as sig-
    nificant expressions are kept into account) and constructs a summary that
    contains only the most informative sentences.

Numerical data sources. Mercurio gathers stock prices from Yahoo! Finance 6
and employs technical analysis [3] techniques to determine significant events.
Stock events. Mercurio identifies changes in stock using simple moving aver-
    ages (sma) of different day lengths. For up/down trends and congestions,
    sma10, sma20 and sma40 are used, e.g., if the price is above sma10; sma10
    is above sma20 and sma20 is above sma40 there is an ongoing uptrend. To
    find up/down jumps, Mercurio analyzes how sma3 and sma5 change in a
    time-window of a week. All stock changes are found a posteriori and then
    used for training.
Candlestick patterns. Japanese candlestick charts 7 are used to represent stock
    prices at possibly different aggregation levels; specific candlestick patterns
    predict particular market movements. Mercurio uses these patterns in combi-
    nation with stock events to increment the precision of the event recognition.
    Moreover, we are investigating the use of patterns at different levels of ag-
    gregation, e.g. a candlesticks representing days or weeks.

References
1. S. Marchesini, M. Morlacchi Bonfanti, M. Mazuran, D. Greco, L. Tanca: Mercurio:
   predicting financial events from expert knowledge and financial news. SEBD 2016:
   174-181.
2. M. Brambilla, D. Greco, S. Marchesini, L. Marconi, M. Mazuran, M. Morlacchi
   Bonfanti, A. Negrini, L. Tanca: Forecasting Out-of-the-Ordinary Financial Events.
   FINREC 2016: 11-15.
3. J. J. Murphy: Technical Analysis of the Financial Markets: A Comprehensive Guide
   to Trading Methods and Applications. New York Institute of Finance, 1999.

6
    https://finance.yahoo.com/
7
    https://en.wikipedia.org/wiki/Candlestick_pattern