Rui Ferreira Neves

Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition

The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. The presented paper proposes a new approach, based on Intelligent Computation, in particular genetic algorithms, which aims to manage a financial portfolio by using technical analysis indicators (EMA, HMA, ROC, RSI, MACD, TSI, OBV). In order to validate the developed solution an extensive evaluation was performed, comparing the designed strategy against the market itself and several other investment methodologies, such as Buy and Hold and a purely random strategy. The time span (2003–2009) employed to test the approach allowed the performance evaluation under distinct market conditions, culminating with the most recent financial crash. The results are promising since the approach clearly beats the remaining approaches during the recent market crash.

Using Attack Injection to Discover New Vulnerabilities

Due to our increasing reliance on computer systems, security incidents and their causes are important problems that need to be addressed. To contribute to this objective, the paper describes a new tool for the discovery of security vulnerabilities on network connected servers. The AJECT tool uses a specification of the servers communication protocol to automatically generate a large number of attacks accordingly to some predefined test classes. Then, while it performs these attacks through the network, it monitors the behavior of the server both from a client perspective and inside the target machine. The observation of an incorrect behavior indicates a successful attack and the potential existence of a vulnerability. To demonstrate the usefulness of this approach, a considerable number of experiments were carried out with several IMAP servers. The results show that AJECT can discover several kinds of vulnerabilities, including a previously unknown vulnerability

Vulnerability Discovery with Attack Injection

The increasing reliance put on networked computer systems demands higher levels of dependability. This is even more relevant as new threats and forms of attack are constantly being revealed, compromising the security of systems. This paper addresses this problem by presenting an attack injection methodology for the automatic discovery of vulnerabilities in software components. The proposed methodology, implemented in AJECT, follows an approach similar to hackers and security analysts to discover vulnerabilities in network-connected servers. AJECT uses a specification of the servers communication protocol and predefined test case generation algorithms to automatically create a large number of attacks. Then, while it injects these attacks through the network, it monitors the execution of the server in the target system and the responses returned to the clients. The observation of an unexpected behavior suggests the presence of a vulnerability that was triggered by some particular attack (or group of attacks). This attack can then be used to reproduce the anomaly and to assist the removal of the error. To assess the usefulness of this approach, several attack injection campaigns were performed with 16 publicly available POP and IMAP servers. The results show that AJECT could effectively be used to locate vulnerabilities, even on well-known servers tested throughout the years.

A multi-objective routing algorithm for Wireless Multimedia Sensor Networks

Graphical abstractDisplay Omitted HighlightsA new multi-objective approach for the routing problem in Wireless Multimedia Sensor Networks (WMSNs) is proposed.Classical approximations optimize a single objective or Quality of Service (QoS) parameter.Classical approximations do not take into account the conflicting nature of QoS parameters which leads to sub-optimal solutions.The proposed approach takes into account multiple QoS requirements such as delay and the Expected Transmission Count (ETX).The case studies applying the proposed approach shows clear improvements on the QoS routing solutions. In this paper, a new multi-objective approach for the routing problem in Wireless Multimedia Sensor Networks (WMSNs) is proposed. It takes into account Quality of Service (QoS) requirements such as delay and the Expected Transmission Count (ETX). Classical approximations optimize a single objective or QoS parameter, not taking into account the conflicting nature of these parameters which leads to sub-optimal solutions. The case studies applying the proposed approach show clear improvements on the QoS routing solutions. For example, in terms of delay, the approximate mean improvement ratios obtained for scenarios 1 and 2 were of 15 and 28 times, respectively.

A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniques

This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions.

A hybrid approach to portfolio composition based on fundamental and technical indicators

This work proposes a multi-objective GA to efficiently manage a stock portfolio.Two objectives the return and the risk (variance of returns) are used to optimize the models.To select the best companies the algorithm uses fundamental financial ratios.To find the best entry point the algorithm uses technical indicators.The results found outperform the main indexes with lower volatility. This paper describes a new approach to portfolio management using stocks. The investment models tested incorporate a fundamental and technical approach using financial ratios and technical indicators. A Multi-Objective Evolutionary Algorithms (MOEA) with two objectives, the return and the risk, are used to optimize the models. Three different chromosomes are used for representing different investment models with real constraints equivalent to the ones faced by portfolio managers. To validate the present solution three case studies are presented for the S&P 500 for the period June 2010 until 2014. Simulations demonstrate that the stock selection based on financial ratios can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. The increase of fundamental indicators enhances the quality of the chromosomes found by the MOEA, and the results of real simulations become more precise. Some of the best chromosomes found by the algorithms invest in stocks with high return on equity (ROE), in conjunction with high rate of growth of the net income and a high profit margin. To obtain stocks with high valuation potential, it is necessary to choose companies with a lower or average market capitalization, low PER, high rates of revenue growth and high operating leverage.

Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel

This paper describes a new computational finance approach. This approach combines pattern recognition techniques with an evolutionary computation kernel applied to financial markets time series in order to optimize trading strategies. Moreover, for pattern matching a template-based approach is used in order to describe the desired trading patterns. The parameters for the pattern templates, as well as, for the decision making rules are optimized using a genetic algorithm kernel. The approach was tested considering actual data series and presents a robust profitable trading strategy which clearly beats the market, S&P 500 index, reducing the investment risk significantly.

Company event popularity for financial markets using Twitter and sentiment analysis

The work proposes an Event Popularity Algorithm for Financial Trading.The approach is based on sentiment analysis to the social network Twitter.Planning and performing a financial community for the extraction of analyzed tweets.The events are focused on the thirty companies that compose the Dow Jones index. The growing number of Twitter users makes it a valuable source of information to study what is happening right now. Users often use Twitter to report real-life events. Here we are only interested in following the financial community. This paper focuses on detecting events popularity through sentiment analysis of tweets published by the financial community on the Twitter universe. The detection of events popularity on Twitter makes this a non-trivial task due to noisy content that often are the tweets. This work aims to filter out all the noisy tweets in order to analyze only the tweets that influence the financial market, more specifically the thirty companies that compose the Dow Jones Average. To perform these tasks, in this paper it is proposed a methodology that starts from the financial community of Twitter and then filters the collected tweets, makes the sentiment analysis of the tweets and finally detects the important events in the life of companies.

Using GAs to balance technical indicators on stock picking for financial portfolio composition

The building of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. This subject is becoming popular among computer scientists which try to adapt known Intelligent Computation techniques to the markets domain. The presented paper proposes a potential system, based on those techniques, which aims to generate a profitable portfolio by using technical analysis indicators. In order to validate the designed application we performed a comparison against the Buy & Hold strategy and a purely random one. The preliminary results are promising once; the developed approach easily beats the remaining methodologies during Bull Market periods.

A new SAX-GA methodology applied to investment strategies optimization

This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions.