Agent-based computational investing recommender system

Authors:
Mona Taghavi;Kaveh Bakhtiyari;Edgar Scavino
Affiliations:
Universiti Kebangsaan Malaysia (The National University of Malaysia), Bangi, Malaysia;Universiti Kebangsaan Malaysia (The National University of Malaysia) / University of Duisburg-Essen, Bangi / Duisburg, Malaysia;Universiti Kebangsaan Malaysia (The National University of Malaysia), Bangi, Malaysia
Venue:
Proceedings of the 7th ACM conference on Recommender systems
Year:
2013

Citing 8
Cited 0

Using collaborative filtering to weave an information tapestry

Communications of the ACM - Special issue on information filtering
Collecting user access patterns for building user profiles and collaborative filtering

IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
An adaptive stock tracker for personalized trading advice

Proceedings of the 8th international conference on Intelligent user interfaces
A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick

Expert Systems with Applications: An International Journal
Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

Expert Systems with Applications: An International Journal
Diffusion dynamics of open source software: An agent-based computational economics (ACE) approach

Decision Support Systems
Implicit: a multi-agent recommendation system for web search

Autonomous Agents and Multi-Agent Systems
Information technology outsourcing, knowledge transfer, and firm productivity: an empirical analysis

MIS Quarterly

Quantified Score

Hi-index	0.00

Visualization

Abstract

The fast development of computing and communication has reformed the financial markets' dynamics. Nowadays many people are investing and trading stocks through online channels and having access to real-time market information efficiently. There are more opportunities to lose or make money with all the stocks information available throughout the World; however, one should spend a lot of effort and time to follow those stocks and the available instant information. This paper presents a preliminary regarding a multi-agent recommender system for computational investing. This system utilizes a hybrid filtering technique to adaptively recommend the most profitable stocks at the right time according to investor's personal favour. The hybrid technique includes collaborative and content-based filtering. The content-based model uses investor preferences, influencing macro-economic factors, stocks profiles and the predicted trend to tailor to its advices. The collaborative filter assesses the investor pairs' investing behaviours and actions that are proficient in economic market to recommend the similar ones to the target investor.