Currency Exchange Rate Forecasting with Neural Networks

This is the published version. Copyright De GruyterThis paper presents the prediction of foreign currency exchange rates using artificial neural networks. Since neural networks can generalize from past experience, they represent a significant advancement over traditional trading systems, which require a knowledgeable expert to define trading rules to represent market dynamics. It is practically impossible to expect that one expert can devise trading rules that account for, and accurately reflect, volatile and rapidly changing market conditions. With neural networks, a trader may use the predictive information alone or with other available analytical tools to fit the trading style, risk propensity, and capitalization. Numerous factors affect the foreign exchange market, as they will be described in this paper. The neural network will help minimize these factors by simply giving an estimated exchange rate for a future day (given its previous knowledge gained from extensive training). Because the field of financial forecasting is too large, the scope in this paper is narrowed to the foreign exchange market, specifically the value of the Japanese Yen against the United States Dollar, two of the most important currencies in the foreign exchange market

Mixture of Poisson distributions to model discrete stock price changes.

An application of a mixture of Poisson distributions is proposed to model the discrete changes in stock price based on the minimum price movement known as `tick-size\u27. The parameters are estimated using the Expectation-Maximization (EM) algorithm with a constant mixing probability as well as mixing probabilities which depend on order size. The model is evaluated using simulations and real data. Both the simulated and real data show reasonable estimates. Several adjustments are made to the model implementation to improve the efficiency with user written codes for the Newton Raphson algorithm and also implementing one of the most recent versions of the EM algorithm (PEM). Both the improvements show an exponentially increasing efficiency to the implementation. Further a Clustered Signed model is proposed to use summarized data to reduce the amount of data to be used in the model implementation using the discrete order sizes and the signs of the discrete stock price changes. The clustered model provided a significant time efficiency. A parametric bootstrap procedure is also considered to assess the significance of the order size on the mixing probabilities. The results show that the use of a variable mixture probability, which depends on the order size, is more appropriate for the model. The methods are illustrated with data from simulations and real data from Federal Express

Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures

In this work we present a non-parametric online market regime detection method for multidimensional data structures using a path-wise two-sample test derived from a maximum mean discrepancy-based similarity metric on path space that uses rough path signatures as a feature map. The latter similarity metric has been developed and applied as a discriminator in recent generative models for small data environments, and has been optimised here to the setting where the size of new incoming data is particularly small, for faster reactivity. On the same principles, we also present a path-wise method for regime clustering which extends our previous work. The presented regime clustering techniques were designed as ex-ante market analysis tools that can identify periods of approximatively similar market activity, but the new results also apply to path-wise, high dimensional-, and to non-Markovian settings as well as to data structures that exhibit autocorrelation. We demonstrate our clustering tools on easily verifiable synthetic datasets of increasing complexity, and also show how the outlined regime detection techniques can be used as fast on-line automatic regime change detectors or as outlier detection tools, including a fully automated pipeline. Finally, we apply the fine-tuned algorithms to real-world historical data including high-dimensional baskets of equities and the recent price evolution of crypto assets, and we show that our methodology swiftly and accurately indicated historical periods of market turmoil.Comment: 65 pages, 52 figure

Return predictability and its implications for portfolio selection

This thesis inquires into a range of issues in return predictability and its implications. First, the thesis investigates estimation bias in predictive regressions. This research stresses the importance of accounting for the bias when studying predictability. To tackle the problem of biased estimation, a general and convenient method based on the jackknife technique is proposed. The proposed method reduces the bias for both single- and multiple-regressor models and for both short- and long-horizon regressions. Compared with the existing bias-reduction methods in the literature, the proposed method is more stable, robust and flexible. More importantly, it can successfully reduce the estimation bias in long-horizon regressions, whereas the existing bias-reduction methods in the literature cease to work. The effectiveness of the proposed method is demonstrated by simulations and empirical estimates of common predictive models in finance. Empirical results show that the significant predictive variables under ordinary least squares become insignificant after adjusting for the finite-sample bias. These results cast doubt on conclusions drawn in earlier studies on the return predictability by these variables. Next, this thesis examines the predictability of return distributions. It provides detailed insights into predictability of the entire stock and bond return distributions in a quantile regression framework. The difficulty experienced in establishing predictability of the conditional mean through lagged predictor variables does not imply that other parts of the return distribution cannot be predicted. Indeed, many variables are found to have significant but heterogenous effects on the return distributions of stocks and bonds. The thesis establishes a quantile-copula framework for modelling conditional joint return distributions. This framework hinges on quantile regression for marginal return distributions and a copula for the return dependence structure. The framework is shown to be flexible and general enough to model a joint distribution while, at the same time, capturing any non-Gaussian characteristics in both marginal and joint returns. The thesis then explores the implications of return distribution predictability for portfolio selection. A distribution-based framework for portfolio selection is developed which consists of the joint return distribution modelled by the quantile-copula approach and an objective function accommodating higher-order moments. Threshold-accepting optimisation technique is used for obtaining optimal allocation weights. This proposed framework extends traditional moment-based portfolio selection in order to utilise the whole predicted return distribution. The last part of the thesis studies nonlinear dynamics of cross-sectional stock returns using classification and regression trees (CART). The CART models are demonstrated to be a valuable alternative to linear regression analysis in identifying primary drivers of the stock returns. Moreover, a novel hybrid approach combining CART and logistic regression is proposed. This hybrid approach takes advantage of the strengths in both CART and linear parametric models. An empirical application to cross-sectional stock return prediction shows that the hybrid approach captures return dynamics better than either a standalone CART or a logistic model

Predictive intraday correlations in stable and volatile market environments:Evidence from deep learning

Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers. Lastly, we show that our model's performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008.Comment: 15 pages, 6 figures, preprint submitted to Physica

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book

Reading the Market

Americans pay famously close attention to "the market," obsessively watching trends, patterns, and swings and looking for clues in every fluctuation. In Reading the Market, Peter Knight explores the Gilded Age origins and development of this peculiar interest. He tracks the historic shift in market operations from local to national while examining how present-day ideas about the nature of markets are tied to past genres of financial representation.Drawing on the late nineteenth-century explosion of art, literature, and media, which sought to dramatize the workings of the stock market for a wide audience, Knight shows how ordinary Americans became both emotionally and financially invested in the market. He analyzes popular investment manuals, brokers’ newsletters, newspaper columns, magazine articles, illustrations, and cartoons. He also introduces readers to fiction featuring financial tricksters, which was characterized by themes of personal trust and insider information. The book reveals how the popular culture of the period shaped the very idea of the market as a self-regulating mechanism by making the impersonal abstractions of high finance personal and concrete.From the rise of ticker-tape technology to the development of conspiracy theories, Reading the Market argues that commentary on the Stock Exchange between 1870 and 1915 changed how Americans understood finance—and explains what our pervasive interest in Wall Street says about us now

AN INVESTIGATION INTO THE FACTORS DETERMINING RUMINANT LIVESTOCK DISTRIBUTION IN THE FAR SOUTH WEST

Major changes are taking place in all sectors of the livestock and meat producing industries from farm to consumer which impinge on the processes and pattems of livestock distribution from farm to slaughter. These changes are identified and described. A farm business survey of lowland beef and sheep finishers was undertaken, prior to the 2001 Foot and Mouth outbreak, to gain a better understanding of farm business behaviour in order to model the farm business strategies in relation to aggregate livestock channel utilisation. Statistically robust and predictive models using a number of derived latent strategic variables, distilling marketing and business orientations, were used in an adapted multivariate approach. Group profiling confirmed consistency with the cluster profiles. Results show that both lowland beef and sheep producers can be statistically classified into three distinct strategic groups. The marketing approaches that farm businesses use vary according to group membership. For lowland beef producers these are described as selling orientation, buyer focus and differentiation strategies. Sellers view beef production as a minor enterprise to provide supplementary farm income, but fail to meet procurement requirements and are limited to channel utilisation. Buyer focus are production orientated, understand distribution, have good market knowledge and meet procurement standards. Differentiators have similar attributes to buyer focus, but are more likely to differentiate and add value and actively seek markets to which they can sell. Lowland sheep producer strategies are described as opportunist, production and differentiation. Opportunists have similar attributes to sellers, and fail to meet or understand procurement requirements. Producers are as production orientated as buyer focus, but have poorer market and distribution knowledge and tend to focus primarily on production concerns. Differentiators, as with beef finishers, are more likely to differentiate and add value and actively seek markets to which they can sell. The developed typologies reveal that farm business marketing behaviour changes according to group membership and this has a significant influence on aggregate channel utilisation within the Far South West. For some fanners it would appear that channel utilisation is predetermined

INVESTMENT AND TRADING

The goal of this project was to create a successful trading strategy for use in the forex market and create a positive track record which could be used to launch a money management company. Several different trading strategies were considered, and were subsequently tested both through live trading and through programming automated trading robots. Additionally, other programs were created along the way, both to aid in manual trading and in data gathering and processing. With our performance history we then sought out possibilities for launching a money management company. Such strategies and examinations of the markets illuminate both the benefits and detriments of several potential trading philosophies, and provide a solid background for the beginning trader