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Title: Employing web-based information in financial decision-making
Author: Green, Lawrence
ISNI:       0000 0004 7234 2800
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2018
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This thesis, which is divided into three papers, explores the use of web-based information in financial decision-making and identifies how web information has improved forecasting. New online information is easily accessed and constantly available to the public, potentially enabling decision-makers to make decisions that are more accurate. The academic literature has proclaimed that the web has transformed decision-making but there is little understanding of how increased information availability and transparency can lead to improved forecasting accuracy and enhanced decision-making. The three empirical papers herein exemplify how web-based information can be employed in decision-making models related to financial markets and particularly, speculative markets, to show the added value of web-based information in decision-making models in a real-world setting. In order to understand how web-based information affects decision-making, this thesis is separated into three papers. The first paper explores how new geospatial information improved forecasting accuracy of performances of racehorses and how quickly unprecedented information derived from new Information Technology (IT) is discounted at the aggregate market level. The second paper shows how distance information, which is freely available and easily accessed over the internet, helps explain the decision-making behavior of experts and novices, highlighting how expect knowledge can be elicited from trainers to improve forecasting accuracy. The third paper examines how the sentiment in online news information affects individual-level decision-making behavior and performance. Taken together, the three papers provide empirical analysis exemplifying how online information can improve forecasting in the real world. The results of the three papers have important contributions to the literature. Paper 1 highlights market convergence with respect to geospatial information and the horse race betting market, showing that improved web-based information availability provides unprecedented information to improve forecasts and ultimately, how the market adapts to this information becoming efficient. Paper 2 identifies how distance information informs the behavior of distinct sub-groups of decision-makers (experts and novices) and, how the elicited knowledge from experts improves forecasting decisions for a limited time before the betting crowd discount such information. Finally, in contrast to the majority of literature on how market prices respond to online information, paper 3 isolates the effect of sentiment on individual behavior, showing how individuals act in a sentiment contrarian fashion providing fine-grained analysis of the effect of online information at the individual level. This thesis shows how improved access to online information improves forecasting abilities at various levels by showing how web-based information is discounted at the aggregate market level, how distance information informs expert and novices behavior, and how information affects individual behavior and performance. The web has transformed decision-making and this thesis exposes the benefit of web information to improve forecasting accuracy. Online information can improve forecasting and, the rate at which the information diffuses into financial markets is an important research area as new information becomes available and markets constantly adapt to such information.
Supervisor: Ma, Tiejun Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available