Using attribute construction to improve the predictability of a GP financial forecasting algorithm

Financial forecasting is an important area in computational finance. EDDIE 8 is an established Genetic Programming financial forecasting algorithm, which has successfully been applied to a number of international datasets. The purpose of this paper is to further increase the algorithm’s predictive performance, by improving its data space representation. In order to achieve this, we use attribute construction to create new (high-level) attributes from the original (low-level) attributes. To examine the effectiveness of the above method, we test the extended EDDIE’s predictive performance across 25 datasets and compare it to the performance of two previous EDDIE algorithms. Results show that the introduction of attribute construction benefits the algorithm, allowing EDDIE to explore the use of new attributes to improve its predictive accuracy

Risk Management using Model Predictive Control

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%

Risk Management using Model Predictive Control

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%

The 2014 Australia-China trade report

Examines the benefits of the Australia-China trading relationship at a household level and looks beyond the resources boom and exploring the growth other Australian industries are seeing with China. It also provides practical advice on how to do business in China from Australian businesses already successfully doing it. Executive summary This report provides close analysis of the impact of bilateral trade between Australia and China on Australia’s business and economic integration with global value chains. It also extends the findings of previous reports by evaluating the latest flow-on effects of Australia-China trade for the Australian economy right down to the household level. Commissioned by the Australia China Business Council, this report expands on prior versions of the “Benefits to Australian Households of Trade with China Report” which, since 2009, have tracked the benefits to ordinary Australian households from trade with China

A Case Study of Using Online Communities and Virtual Environment in Massively Multiplayer Role Playing Games (MMORPGs) as a Learning and Teaching Tool for Second Language Learners

Massively Multiplayer Online Role Playing Games (MMORPGs) create large virtual communities. Online gaming shows potential not just for entertaining, but also in education. This research investigates the use of commercial MMORPGs to support second language teaching. MMORPGs offer virtual safe spaces in which students can communicate by using their target second language with global players. Using a mix of ethnography and action research, this study explores the students’ experiences of language learning and performing while playing MMORPGs. The results show that the use of MMORPGs can facilitate language development by offering fun, informal, individualised and secure virtual spaces for students to practise their language with native and other second language speakers

Dynamic Generation of Investment Recommendations Using Grammatical Evolution

The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest

Dynamic generation of investment recommendations using grammatical evolution

The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest.The authors would like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin). This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3MXX), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

Using massively multiplayer online role playing games (MMORPGs) to support second language learning: Action research in the real and virtual world

Massively Multiplayer Online Role Playing Games (MMORPGs) create large virtual communities. Online gaming shows potential not just for entertaining, but also for education. The aim of this research project is to investigate the use of commercial MMORPGs to support second language teaching. MMORPGs offer a digital safe space in which students can communicate by using their target language with global players. This qualitative research based on ethnography and action research investigates the students’ experiences of language learning and performing while they play in the MMORPGs. Research was conducted in both the real and virtual worlds. In the real world the researcher observes the interaction with the MMORPGs by the students through actual discussion, and screen video captures while they are playing. In the virtual world, the researcher takes on the role of a character in the MMORPG enabling the researcher to get an inside point of view of the students and their own MMORPG characters. This latter approach also uses action research to allow the researcher to provide anonymous/private support to the students including in-game instruction, confidence building, and some support of language issues in a safe and friendly way. Using action research with MMORPGs in the real world facilitates a number of opportunities for learning and teaching including opportunities to practice language and individual and group experiences of communicating with other native/ second language speakers for the students. The researcher can also develop tutorial exercises and discussion for teaching plans based on the students’ experiences with the MMORPGs. The results from this research study demonstrate that MMORPGs offer a safe, fun, informal and effective learning space for supporting language teaching. Furthermore the use of MMORPGs help the students’ confidence in using their second language and provide additional benefits such as a better understanding of the culture and use of language in different contexts

A Framework for XML-based Integration of Data, Visualization and Analysis in a Biomedical Domain

Biomedical data are becoming increasingly complex and heterogeneous in nature. The data are stored in distributed information systems, using a variety of data models, and are processed by increasingly more complex tools that analyze and visualize them. We present in this paper our framework for integrating biomedical research data and tools into a unique Web front end. Our framework is applied to the University of Washington’s Human Brain Project. Specifically, we present solutions to four integration tasks: definition of complex mappings from relational sources to XML, distributed XQuery processing, generation of heterogeneous output formats, and the integration of heterogeneous data visualization and analysis tools