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- E-Finance Lab e.V. (8) (remove)
ALGORITHMIC DECISION MAKING PLAYS AN IMPORTANT ROLE IN FINANCIAL MARKETS. ONE SOURCE OF INFORMATION FOR SUCH ALGORITHMS IS THE SENTIMENT OF SOCIAL MEDIA MESSAGES AND NEWS ARTICLES CONCERNING A LISTED COMPANY. YET, CURRENT TOOLS DO NOT DISTINGUISH BETWEEN POPULAR AND LESS POPULAR NEWS AND IT IS UNCLEAR WHETHER METHODOLOGIES BASED ON DATA ANALYTICS CAN BE APPLIED ON SMALL DATASETS OF LESS POPULAR COMPANIES. THEREFORE, WE ANALYZE WHETHER THE IMPACT OF MEDIA SENTIMENT ON FINANCIAL MARKETS IS INFLUENCED BY TWO LEVELS OF INVESTOR ATTENTION AND WHETHER THIS IMPACTS ALGORITHMIC DECISION MAKING.
THE PROLIFERATION OF THE INTERNET HAS ENABLED PLATFORM INTERMEDIARIES TO CREATE TWO-SIDED MARKETS IN MANY INDUSTRIES. IN SUCH MARKETS, NETWORK EFFECTS OFTEN OCCUR WHICH CAN DIFFER FOR NEW AND EXISTING CUSTOMERS. THE AUTHORS DEVELOP AN INFLUX-OUTFLOW MODEL TO INVESTIGATE THE CONDITIONS UNDER WHICH THE ESTIMATION OF SAME-SIDE AND CROSS-SIDE NETWORK EFFECTS SHOULD DISTINGUISH BETWEEN ITS IMPACT ON THE NUMBER OF NEW CUSTOMERS (I.E., ACQUISITION) AND EXISTING CUSTOMERS (I.E., THEIR ACTIVITY).
NATURAL LANGUAGE (NL) IS A PROMISING ALTERNATIVE INTERFACE TO DATABASE MANAGEMENT SYSTEMS (DBMSs) BECAUSE IT ENABLES NON-TECHNICAL USERS TO FORMULATE COMPLEX QUESTIONS. RECENTLY, DEEP LEARNING HAS GAINED TRACTION FOR TRANSLATING NATURAL LANGUAGE TO SQL. HOWEVER, THE CORE PROBLEM WITH EXISTING DEEP LEARNING APPROACHES IS THAT THEY REQUIRE AN ENORMOUS AMOUNT OF MANUALLY CURATED TRAINING DATA IN ORDER TO PROVIDE ACCURATE TRANSLATIONS. WE PRESENT DBPAL THAT USES A NOVEL TRAINING PIPELINE TO LEARN NL2SQL INTERFACES WHICH SYNTHESIZES TRAINING DATA AND, THUS, DOES NOT RELY ON MANUALLY CURATED TRAINING DATA.
Economic value of data
(2020)
FIRMS COLLECT A LARGE AMOUNT OF DATA BY ENGAGING HEAVILY IN THE COLLECTION AND STORAGE OF ONLINE USER ACTIVITY VIA VARIOUS USER TRACKING TECHNOLOGIES. RECENT POLICY INITIATIVES AIM AT RESTRICTING THIS PRACTICE TO PROTECT CONSUMER PRIVACY. WE STUDY EMPIRICALLY THE CONSEQUENCES OF SUCH RESTRICTIONS FOR ONLINE PUBLISHERS, SUCH AS NEWS WEBSITES, BECAUSE THEY STRONGLY RELY ON REVENUES THAT ARE GENERATED BASED ON USER DATA. WE FIND A PRICE DECREASE OF CA. 30% FOR ONLINE ADS WHEN NO DATA FROM USER TRACKING IS AVAILABLE. THE POTENTIAL REVENUE LOSS COULD BE MORE THAN EUR 14 BILLION IN THE EU AND MORE THAN USD 27 BILLION IN THE US.