Skip to main content
SpringerLink
Log in

FAST: Fundamental Analysis Support for Financial Statements. Using semantics for trading recommendations

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

Trading systems are tools to aid financial analysts in the investment process in companies. This process is highly complex because a big number of variables take part in it. Furthermore, huge sets of data must be taken into account to perform a grounded investment, making the process even more complicated. In this paper we present a real trading system that has been developed using semantic technologies. These cutting-edge technologies are very useful in this context because they enable the definition of schemes that can be used for storing financial information, which, in turn, can be easily accessed and queried. Additionally, the inference capabilities of the existing reasoning engines enable the generation of a set of rules supporting this investment analysis process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abarbanell, J. S., & Bushee, B. J. (1997). Fundamental analysis, future earnings, and stock prices. Journal of Accounting Research, 35(1), 1–24.

    Article  Google Scholar 

  • Anastasakis, L., & Mort, N. (2009). Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach. Expert Systems with Applications, 36(10), 12001–12011.

    Article  Google Scholar 

  • Broekstra, K., Kampman, A., Van Harmelen, F. (2002). Sesame: A generic architecture for storing and querying RDF and RDF schema. The Semantic Web—ISWC 2002. 2342, 54–68

  • Castells, P., Foncillas, B., Lara, R., Rico, M., & Alonso, J. L. (2004). Semantic web technologies for economic and financial information management. The Semantic Web: Research and Applications, 3053, 473–487.

    Article  Google Scholar 

  • Chavarnakul, T., & Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34(2), 1004–1017.

    Article  Google Scholar 

  • Chen, J. S., & Liao, B. P. (2007). Piecewise nonlinear goal-directed CPPI strategy. Expert Systems with Applications, 33(4), 857–869.

    Article  Google Scholar 

  • Chen, Y., Mabu, S., Shimada, S., & Hirasawa, K. (2009). A genetic network programming with learning approach for enhanced stock trading model. Expert Systems with Applications, 36(10), 12537–12546.

    Article  Google Scholar 

  • Fernandez Garcia, M. E., De la Cal Marin, E. A., & Quiroga Garcia, R. (2010). Improving return using risk-return adjustment and incremental training in technical trading rules with GAPs. Applied Intelligence, 33(2), 93–106.

    Article  Google Scholar 

  • Fernandez, P., & Yzaguirre, J. (1995). IBEX 35: Análisis e investigaciones. In: (Eds.), Barcelona: Ediciones Internacionales Universitarias

  • Fox, M. S., Barbuceanu, M., Gruninger, M., & Lin, J. (1998). An organizational ontology for enterprise modeling. Simulating organizations: Computational models of institutions and groups (pp. 131–152). Cambridge: MIT Press.

    Google Scholar 

  • Gomez, J. M., García-Sanchez, F., Valencia-Garcia, R., Toma, I., Garcia-Moreno, C. (2009). SONAR: A semantically empowered financial search engine. International work-conference on the interplay between natural and artificial computation

  • Haarslev, V., & Möller, R. (2003). Racer: An OWL reasoning agent for the semantic web. Proceedings of the International Workshop on Applications, Products and Services of Web-based Support Systems

  • Horrocks, I., Patel-Schneider, P. F., Boley H., et al. (2004). SWRL: A semantic web rule language combining OWL and RuleML. W3C Member Submission

  • Jang, M., & Sohn, J. C. (2004). Bossam: An extended rule engine for OWL Inferencing. Rules and Rule Markup Languages for the Semantic Web, 3323, 128–138.

    Article  Google Scholar 

  • Kim, K. J. (2004). Toward global optimization of case-based reasoning systems for financial forecasting. Applied Intelligence, 21, 239–249.

    Article  Google Scholar 

  • Kovalerchuk, B., & Vityaev, E. (2000). Data mining in finance: Advances in relational and hybrid methods. Kluwer Academic

  • Lee, S. J., Ahn, J. J., Oh, K. J., & Kim, T. Y. (2010). Using rough set to support investment strategies of real-time trading in futures market. Applied Intelligence, 32(3), 364–377.

    Article  Google Scholar 

  • Lo, A., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithm, statistical inference, and empirical implementation. Journal of Finance, 55(4), 1705–1765.

    Article  Google Scholar 

  • Losada, A. S., Bas, J. L., Bellido, S., Contreras, J., Benjamins, R., Gomez, J. M. (2005). Data, information and process integration with semantic web services

  • Majhi, R., Panda, G., Majhib, B., & Sahoo, G. (2009). Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Systems with Applications, 36(6), 10097–10104.

    Article  Google Scholar 

  • Majhi, R., Panda, G., & Sahoo, G. (2009). Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Systems with Applications, 36(3), 6800–6808.

    Article  Google Scholar 

  • Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7, 77–91.

    Google Scholar 

  • Markowitz, H. (1999). The early history of portfolio theory. Financial Analysts Journal, 55(4), 1600–1960.

    Article  Google Scholar 

  • McGuinness, D. L., & Harmelen, F. V. (2004). OWL web ontology language overview. W3C Recommendation

  • Mochón, A., Quintana, D., Sáez, Y., & Isasi, P. (2008). Soft computing techniques applied to finance. Applied Intelligence, 29(2), 111–115.

    Article  Google Scholar 

  • Modigliani, F., & Modigliani, L. (1997). Risk-adjusted performance. Journal of Portfolio Management, 23, 45–54.

    Article  Google Scholar 

  • Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783.

    Article  Google Scholar 

  • Motik, B., & Studer, R. (2005). KAON2—a scalable reasoning tool for the semantic web. European Semantic Web Conference

  • Mullins, D. W. (1982). Does the capital asset pricing model work? Harvard Business Review, 105–113

  • Partridge, C., & Stefanova, M. (2001). A synthesis of state of the art enterprise ontologies. Lessons Learned. The BORO Program, LADSEB CNR

  • Qian, B., & Rasheed, K. (2006). Stock market prediction with multiple classifiers. Applied Intelligence, 26, 25–33.

    Article  Google Scholar 

  • Quah, T. S. (2009). DJIA stock selection assisted by neural network. Expert Systems with Applications, 35(1/2), 50–58.

    Google Scholar 

  • Rodríguez-González, A., García-Crespo, Á., Colomo-Palacios, R., Guildrís-Iglesias, F., & Gómez-Berbís, J. M. (2011). CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator. Expert Systems with Applications, 38(9), 11489–11500.

    Article  Google Scholar 

  • Roy, A. D. (1952). Safety first and the holding of assets. Econometrica, 20(3), 431–449.

    Article  Google Scholar 

  • Schumaker, R. P., & Chen, S. (2009). Textual analysis of stock market prediction using breaking financial news. ACM Transactions on Information Systems, 27(2)

  • Sharpe, W. F. (1966). Mutual fund performance. Journal of Business, 39(1), 119–138.

    Article  Google Scholar 

  • Shearer, R., Motik, B., Horrocks, I. (2008). HermiT: A highly-efficient OWL reasoner. In: A. Ruttenberg, U. Sattler, C. Dolbear (Ed.), Proc. of the 5th Int. Workshop on OWL: Experiences and Directions (OWLED 2008 EU)

  • Sirin, E., Parsia, B., Cuenca-Grau, B., Kalyanpur, A., & Katz, Y. (2007). Pellet: A practical OWL-DL reasoner. Web Semantics: Science, Services and Agents on the World Wide Web, 5(2), 51–53.

    Article  Google Scholar 

  • Sitte, R., & Sitte, J. (2002). Neural networks approach to the random walk dilemma of financial time series. Applied Intelligence, 16(3), 163–171.

    Article  Google Scholar 

  • Standfield, K. (2005). Intangible finance standards: Advances in fundamental analysis & technical analysis. Elsevier Academic Press

  • Vanstone, B., & Finnie, G. (2009). An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications, 36(3), 6668–6680.

    Article  Google Scholar 

  • Vanstone, B., & Tan, C. N. W. (2005). Artificial neural networks in financial trading. In: M. Khosrow-Pour (Ed.), Encyclopedia of information science and technology. Idea Group

  • Wang, Y. F. (2003). Mining stock prices using fuzzy rough set system. Expert Systems with Applications, 24(1), 13–23.

    Article  Google Scholar 

  • Wen, Q., Yang, Z., Song, Y., & Jia, P. (2010). Automatic stock decision support system based on box theory and SVM algorithm. Expert Systems with Applications, 37(2), 1015–1022.

    Article  Google Scholar 

  • XBRL International. (2009). XBRL: eXtensible business reporting language. Retrieved June 19, 2009, from XBRL International Web site: http://www.xbrl.org

  • Yoda, M. (1994). Predicting the Tokyo stock market. In: G. J. Deboeck (Ed.), Trading on the edge (pp. 66–79). Wiley (1994)

Download references

Acknowledgements

This work is supported by the Spanish Ministry of Science and Innovation under the project TRAZAMED (IPT-090000-2010-007).

Author information

Authors and Affiliations

  1. Computer Science Department, Universidad Carlos III de Madrid, Av. Universidad 30, Leganés, 28911, Madrid, Spain

    Alejandro Rodríguez-González, Ricardo Colomo-Palacios, Fernando Guldris-Iglesias, Juan Miguel Gómez-Berbís & Angel García-Crespo

Authors
  1. Alejandro Rodríguez-González
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ricardo Colomo-Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fernando Guldris-Iglesias
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan Miguel Gómez-Berbís
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Angel García-Crespo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alejandro Rodríguez-González.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rodríguez-González, A., Colomo-Palacios, R., Guldris-Iglesias, F. et al. FAST: Fundamental Analysis Support for Financial Statements. Using semantics for trading recommendations. Inf Syst Front 14, 999–1017 (2012). https://doi.org/10.1007/s10796-011-9321-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-011-9321-1

Keywords

Search

Navigation