Finance Engineering

The possibility to acquire on the Internet large amounts of financial data is today reshaping the processes of trading, investment research, and risk management. Due to the broad availability of this information, it is now possible to apply analysis techniques that are new to the financial industry, whereas traditional signal processing techniques (e.g. moving averages and regression approaches) have had limited success in predicting markets, due to the dynamic behavior of the markets. The financial industry is currently dominated by a handful of workhorse a few historical models, such as the Capital Asset Pricing Model, CAPM, and the Black-Scholes options pricing model (and all related models).

Quantitative financial analysis is instead mainly based on econometrics methodology. Nonetheless, prediction of stock market trends has been an area of great interest both to those who wish to profit by trading stocks in the stock market and for researchers attempting to uncover the information hidden in the stock market data. In financial risk management, signal processing is growing tremendously in interest. Financial risk management is a set of tools that firms and individuals use to manage risk exposures. The goal is to avoid risks that are not profitable so that more risks that are advantageous can be taken on. Among the several types of risks, the two basic ones are unsystematic risk and systematic risk. Unsystematic risk affects specific agents or industries and can be reduced by diversification. Investors who choose to bear it receive no compensation in the form of higher expected returns for doing so. The number of both traders and brokers is ever increasing, and it is expected that the number of amateur traders will be always higher than the number of experienced traders. The competition among the broker platforms on the market is already strong. In an evolving business where the customer satisfaction is the key to the success, only the managers able to provide on-line trading platforms with more efficient data processing algorithms can scoop the competitors

  1. F. Benedetto , G. Giunta, L. Mastroeni - "A Computational Method for Predicting the Entropy of Energy Market Time Series", Notes in Economics and Mathematical Systems (Springer), in press, 2014.
  2. F. Benedetto, G. Giunta, L. Mastroeni - "Signal Processing for Financial Markets: Trends, Opportunities, and Associated Risks", in "Encyclopedia of Information science and Technology", IGI Global Publisher, in press July 2014. DOI: 10.4018/978-1-4666-5888-2.ch.723. ISBN: 978-1-46665888-2.
  3. F. Benedetto, G. Giunta, L. Mastroeni - "A computational method for predicting the entropy of energy market time series", 11th Int. Conf. on Computational Management Science (CMS 2014), 29-31 May 2014, Lisbon, Portugal.
  4. F. Benedetto, G. Giunta - "Communications Services Applied to Business: A Simple Algorithm for Personal Trading", 24th IEEE Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC'13), 8-11 Sept. 2013, London, UK.
  5. G. Giunta, F. Benedetto - "Empirical Case Study of Binary Options Trading: an Interdisciplinary Application of Telecommunications Methodology to Financial Economics", Journal of Interdisciplinary Telecommunications and Networking (IJITN), vol. 4, no. 4, pp. 54-63, 2012.
  6. F. Benedetto, G. Giunta, A. Neri - "A Bayesian Business Model for Video-Call Billing for End-to-End QoS Provision" - IEEE Trans. on Vehicular Technology , vol. 58, no. 2, pp. 836 - 842, Feb. 2009. DOI:10.1109/TVT.2008.925316
  7. F. Benedetto, G. Giunta - "A Business Model for QoS Assessment in Mobile Wireless Networks" - in "4G Mobile & Wireless Communications Technologies", S. Kyriazakos, J. Soldatos and G. Karetsos, editors, published byRiver Publishers September 2008, ISBN: 978-87-92329-02-8.