Automated Trading Systems VS Manual Trading in Forex Exchange Market

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementIn the recent decades, automated trading has been widely used in Forex and Money Markets, as well as in financial markets. This auto trading provided substantial benefits to transaction efficiency. Many trading robots have been created to substitute humans, capable of simulating trading strategies and continuously making profits. Nevertheless, programs cannot reproduce all human behaviour and most robots are over-sensitive, therefore, it is difficult to have the same results as human traders. The study focuses on evaluating the trading machines sensitivity and effectiveness. The economic markets can benefit from the machine in several ways, through continuous operation, increasing diversification, short/term trading opportunities and by forecasting opportunities e. g. currency price changes. The further investigation indicates that the majority of forex trading robots are profitable, in fact, there is a great tendency for curve-fitting or data-mining. There are some impressive robots out there; of course, these systems maintain an advantage and successfully manage risk. The best ones are more about position sizing and cutting losses quickly and less about high win rates. The greater the sensitivity the greater the trading opportunities, but this decreases the performance. This research will contain interviews with experts that will validate the study

ROBUST DECISION SUPPORT SYSTEMS WITH MATRIX FORECASTS AND SHARED LAYER PERCEPTRONS FOR FINANCE AND OTHER APPLICATIONS

The recent financial crisis showed the need for more robust decision support systems. In this paper, we introduce a novel type of recurrent artificial neural network, the shared layer perceptron, which allows forecasts that are robust by design. This is achieved by not over-fitting to a specific variable. An entire market is forecast. By training not one, but many networks, we obtain a distribution of outcomes. Further, multi-step forecasts are possible. Our system uses hidden states to model internal dynamics. This allows the network to build a memory and hardens it against external shocks. Using a single shared weight matrix offers the possibility of interpreting system output. An often cited disadvantage of neural networks, the black box character, is not an issue with our approach. We focus on two case studies: determining value at risk and transaction decision support. We also present other applications, including load forecast in electricity networks

Conceptualizing possibilities of artificial intelligence in furtherance of the banking sector : an effective tool for improving customer relationship, customer service and public relations

Purpose: In many developing countries, the agricultural sector has been seen as a major sector that should drive economic development and industrialization because of its importance in the provision of food for the increasing population, the supply of raw material to the growing industrial sector, generation of foreign exchange earnings, creation of employment opportunities, and provision of market for the product of the industrial sector. This study therefore investigates the causal linkage between agricultural financing and agricultural output growth in Nigeria. Design/Methodology/Approach: The data were mainly sourced from Central Bank of Nigeria statistical bulletins and World Bank Economic Indicators and the study adopted the Pairwise Granger Causality test. Findings: The result showed that there was no causal linkage between agricultural financing and agricultural output growth within the period under review. Practical Implications: With these findings it is therefore imperative for Nigeria to take more careful look into why agricultural financing has not made significant impact on agricultural output growth. There should exist massive education and enlightenment of farmers to know the different sources of agricultural financing available. When such funds are accessed, it should be properly monitored to ensure efficient utilization in order to increase agricultural output. Originality/Value: The study adds to literature on agricultural financing in Nigeria and it has serious implications for agricultural output growth and other areas of the economy. The findings of this study is novel and it is a pointer to the government to more proactive in ensuring that the agricultural sector is well financed and monitored in order to increase agricultural productivity.peer-reviewe

A Study of the EUR/USD Exchange Rate and EUR Call Options

International business has grown rapidly in recent years as companies seek to take advantage of expanding market and supply chain opportunities. As companies enter into contracts to take advantage of engineering, production, and cost reduction capabilities of the global supply chain, they may be creating a foreign currency exchange rate risk. The purpose of this quantitative study was to endeavor to develop a multiple linear regression EUR/USD forecasting methodology for companies to use when determining when to use currency call options for managing currency risk in accounts payable. The study examined the 60-day EUR/USD exchange rate fluctuation with the conclusion that the variability of the EUR/USD over 60-days does pose financial risk to a company. Multiple linear regression models were created using historical exchange rate data, interest rate data, and Brent crude oil price data. The multiple linear regression models using historical data were not statistically significant in predicting the directional movement of the EUR/USD. This finding aligns with the weak form of the efficient market hypothesis. The study also found that using currency call options to hedge this 60-day exchange rate risk resulted in an overall financial loss as compared to no hedging. The findings suggest that historical data alone cannot be used to predict future EUR/USD directional movements and that purchasing call options to hedge the risk results in a net financial loss. The study did not address the financial benefits of the use of hedging to smooth financial results

Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

Forecasting monthly airline passenger numbers with small datasets using feature engineering and a modified principal component analysis

In this study, a machine learning approach based on time series models, different feature engineering, feature extraction, and feature derivation is proposed to improve air passenger forecasting. Different types of datasets were created to extract new features from the core data. An experiment was undertaken with artificial neural networks to test the performance of neurons in the hidden layer, to optimise the dimensions of all layers and to obtain an optimal choice of connection weights – thus the nonlinear optimisation problem could be solved directly. A method of tuning deep learning models using H2O (which is a feature-rich, open source machine learning platform known for its R and Spark integration and its ease of use) is also proposed, where the trained network model is built from samples of selected features from the dataset in order to ensure diversity of the samples and to improve training. A successful application of deep learning requires setting numerous parameters in order to achieve greater model accuracy. The number of hidden layers and the number of neurons, are key parameters in each layer of such a network. Hyper-parameter, grid search, and random hyper-parameter approaches aid in setting these important parameters. Moreover, a new ensemble strategy is suggested that shows potential to optimise parameter settings and hence save more computational resources throughout the tuning process of the models. The main objective, besides improving the performance metric, is to obtain a distribution on some hold-out datasets that resemble the original distribution of the training data. Particular attention is focused on creating a modified version of Principal Component Analysis (PCA) using a different correlation matrix – obtained by a different correlation coefficient based on kinetic energy to derive new features. The data were collected from several airline datasets to build a deep prediction model for forecasting airline passenger numbers. Preliminary experiments show that fine-tuning provides an efficient approach for tuning the ultimate number of hidden layers and the number of neurons in each layer when compared with the grid search method. Similarly, the results show that the modified version of PCA is more effective in data dimension reduction, classes reparability, and classification accuracy than using traditional PCA.</div

Advanced neural networks : finance, forecast, and other applications

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