Machine Learning Methods to Exploit the Predictive Power of Open, High, Low, Close (OHLC) Data

Novel machine learning techniques are developed for the prediction of financial markets, with a combination of supervised, unsupervised and Bayesian optimisation machine learning methods shown able to give a predictive power rarely previously observed. A new data mining technique named Deep Candlestick Mining (DCM) is proposed that is able to discover highly predictive dataset specific candlestick patterns (arrangements of open, high, low, close (OHLC) aggregated price data structures) which significantly outperform traditional candlestick patterns. The power that OHLC features can provide is further investigated, using LSTM RNNs and XGBoost trees, in the prediction of a mid-price directional change, defined here as the mid-point between either the open and close or high and low of an OHLC bar. This target variable has been overlooked in the literature, which is surprising given the relative ease of predicting it, significantly in excess of noisier financial quantities. However, the true value of this quantity is only known upon the period's ending – i.e. it is an after-the-fact observation. To make use of and enhance the remarkable predictability of the mid-price directional change, multi-period predictions are investigated by training many LSTM RNNs (XGBoost trees being used to identify powerful OHLC input feature combinations), over different time horizons, to construct a Bayesian optimised trend prediction ensemble. This fusion of long-, medium- and short-term information results in a model capable of predicting market trend direction to greater than 70% better than random. A trading strategy is constructed to demonstrate how this predictive power can be used by exploiting an artefact of the LSTM RNN training process which allows the trading system to size and place trades in accordance with the ensemble's predictive certainty

25 Years of IIF Time Series Forecasting: A Selective Review

We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.Accuracy measures; ARCH model; ARIMA model; Combining; Count data; Densities; Exponential smoothing; Kalman Filter; Long memory; Multivariate; Neural nets; Nonlinearity; Prediction intervals; Regime switching models; Robustness; Seasonality; State space; Structural models; Transfer function; Univariate; VAR.

Deep Learning of the Order Flow for Modelling Price Formation

The objective of this thesis is to apply deep learning to order flow data in novel ways, in order to improve price prediction models, and thus improve on current deep price formation models. A survey of previous work in the deep modelling of price formation revealed the importance of utilising the order flow for the deep learning of price formation had previously been over looked. Previous work in the statistical modelling of the price formation process in contrast has always focused on order flow data. To demonstrate the advantage of utilising order flow data for learning deep price formation models, the thesis first benchmarks order flow trained Recurrent Neural Networks (RNNs), against methods used in previous work for predicting directional mid-price movements. To further improve the price modelling capability of the RNN, a novel deep mixture model extension to the model architecture is then proposed. This extension provides a more realistically uncertain prediction of the mid-price, and also jointly models the direction and size of the mid-price movements. Experiments conducted showed that this novel architecture resulted in an improved model compared to common benchmarks. Lastly, a novel application of Generative Adversarial Networks (GANs) was introduced for generative modelling of the order flow sequences that induce the mid-price movements. Experiments are presented that show the GAN model is able to generate more realistic sequences than a well-known benchmark model. Also, the mid-price time-series resulting from the SeqGAN generated order flow is able to better reproduce the statistical behaviour of the real mid-price time-series

Artificial Neural Networks: A Financial Tool As Applied in the Australian Market

Aim. In the elderly subjects metabolic syndrome (MetS) seems to be associated with low levels of circulating protective soluble receptor for advanced glycation end products (sRAGE). This secondary study aimed to answer whether this phenomenon is manifested from early childhood. Methods. 73 mothers and their 77 infants (4-to-12-months of age) were included in the study. Mothers were classified according to the presence of MetS components as negative (n = 32), those with pre-MetS (insulin resistance + 1 sign of MetS, n = 27) and overt MetS (n = 14). sRAGE and carboxymethyllysine (CML) were determined in the mothers and the infants. Results. Mothers with pre- and overt MetS displayed lower sRAGE levels, while in their children only a trend towards decline was observed. sRAGE levels significantly and inversely correlated with insulin sensitivity and BMI/body weight. No difference in CML levels across the groups was observed. Conclusions. Metabolic syndrome is associated with decreased levels of sRAGE in the mothers and a tendency towards decline of sRAGE in their offspring. Infants of mothers with MetS maintain normoglycemia on the account of higher insulin levels. Keywords: metabolic syndrome, mother-child pairs, QUICKI, sRAGE, insulin resistance, CML.Мета. У пацієнтів похилого віку розвиток метаболічного синдрому (МетС) асоційований з низьким рівнем циркулюючого розчинного рецептора для кінцевих продуктів повного глікозилювання (sRAGE). Мета цієї роботи полягала у пошуку відповіді на питання, чи проявляється таке явище у ранньому дитинстві? Методи. Досліджено 73 матері і 77 дітей віком від чотирьох до 12 місяців. Залежно від присутності компонентів МетС матерів розділили на три групи: негативна (n = 32) – без компонентів МетС; з початковою стадією МетС (резистентність до інсуліну + одна ознака МетС, n = 27) та з явно вираженим МетС (n = 14). У матерів і дітей визначали рівень концентрації sRAGE і карбоксиметиллізину (КМЛ). Результати. У матерів з початковою та явно вираженою стадіями МетС встановлено нижчий рівень sRAGE порівняно з їхніми дітьми, у яких спостерігали лише тенденцію до його падіння. Кількість sRAGE корелює з чутливістю до інсуліну та показником BM (індекс маси тіла) I /маса тіла. Різниці в концентрації КМЛ по групах не знайдено. Висновки. Метаболічний синдром пов'язаний із зниженням рівня sRAGE у матерів. Показано тенденцию до зменшення кількості sRAGE у їхніх дітей. Нормоглікемія у дітей, у матерів яких визначено МетС, підтримується вищим рівнем інсуліну. Ключові слова: метаболічний синдром, пара мати–дитина, QUICKI, sRAGE, резистентність до інсуліну, карбоксиметиллізин.Цель. У пациентов пожилого возраста развитие метаболического синдрома (МетС) ассоциировано с низким уровнем циркулирующего растворимого рецептора для конечных продуктов полного гликозилирования (sRAGE). Цель этой работы состояла в поиске ответа на вопрос, проявляется ли данное явление в раннем детстве? Методы. Исследованы 73 матери и 77 детей в возрасте от четырех до 12 месяцев. В зависимости от присутствия компонентов МетС матерей разделили на три группы: отрицательная (n = 32) – без компонентов МетС; с начальной стадией МетС (резистентность к инсулину + один признак МетС, n = 27) и с явно выраженным МетС (n = 14). У матерей и детей определяли уровень концентрации sRAGE и карбоксиметиллизина (КМЛ). Результаты. У матерей с начальной и явно выраженной стадиями МетС выявлен более низкий уровень sRAGE, в то время как у их детей наблюдалась лишь тенденция к его снижению. Количество sRAGEкоррелирует с чувствительностью к инсулину и показателем BM (индекс массы тела) I/масса тела. Разницы в концентрации КМЛ по группам не установлено. Выводы. Метаболический синдром связан со снижением уровня sRAGE у матерей. Показана тенденция к уменьшению количества sRAGE у их детей. Нормогликемия у детей, у матерей которых обнаружен МетС, поддерживается более высоким уровнем инсулина. Ключевые слова: метаболический синдром, пара мать–ребенок, QUICKI, sRAGE, резистентность к инсулину, карбоксиметиллизин

Non Linear Modelling of Financial Data Using Topologically Evolved Neural Network Committees

Most of artificial neural network modelling methods are difficult to use as maximising or minimising an objective function in a non-linear context involves complex optimisation algorithms. Problems related to the efficiency of these algorithms are often mixed with the difficulty of the a priori estimation of a network's fixed topology for a specific problem making it even harder to appreciate the real power of neural networks. In this thesis, we propose a method that overcomes these issues by using genetic algorithms to optimise a network's weights and topology, simultaneously. The proposed method searches for virtually any kind of network whether it is a simple feed forward, recurrent, or even an adaptive network. When the data is high dimensional, modelling its often sophisticated behaviour is a very complex task that requires the optimisation of thousands of parameters. To enable optimisation techniques to overpass their limitations or failure, practitioners use methods to reduce the dimensionality of the data space. However, some of these methods are forced to make unrealistic assumptions when applied to non-linear data while others are very complex and require a priori knowledge of the intrinsic dimension of the system which is usually unknown and very difficult to estimate. The proposed method is non-linear and reduces the dimensionality of the input space without any information on the system's intrinsic dimension. This is achieved by first searching in a low dimensional space of simple networks, and gradually making them more complex as the search progresses by elaborating on existing solutions. The high dimensional space of the final solution is only encountered at the very end of the search. This increases the system's efficiency by guaranteeing that the network becomes no more complex than necessary. The modelling performance of the system is further improved by searching not only for one network as the ideal solution to a specific problem, but a combination of networks. These committces of networks are formed by combining a diverse selection of network species from a population of networks derived by the proposed method. This approach automatically exploits the strengths and weaknesses of each member of the committee while avoiding having all members giving the same bad judgements at the same time. In this thesis, the proposed method is used in the context of non-linear modelling of high-dimensional financial data. Experimental results are'encouraging as both robustness and complexity are concerned.Imperial Users onl

DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS

During the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty

Machine learning in stock indices trading and pairs trading

This thesis focuses on two fields of machine learning in quantitative trading. The first field uses machine learning to forecast financial time series (Chapters 2 and 3), and then builds a simple trading strategy based on the forecast results. The second (Chapter 4) applies machine learning to optimize decision-making for pairs trading. In Chapter 2, a hybrid Support Vector Machine (SVM) model is proposed and applied to the task of forecasting the daily returns of five popular stock indices in the world, including the S&P500, NKY, CAC, FTSE100 and DAX. The trading application covers the 1997 Asian financial crisis and 2007-2008 global financial crisis. The originality of this work is that the Binary Gravity Search Algorithm (BGSA) is utilized, in order to optimize the parameters and inputs of SVM. The results show that the forecasts made by this model are significantly better than the Random Walk (RW), SVM, best predictors and Buy-and-Hold. The average accuracy of BGSA-SVM for five stock indices is 52.6%-53.1%. The performance of the BGSA-SVM model is not affected by the market crisis, which shows the robustness of this model. In general, this study proves that a profitable trading strategy based on BGSA-SVM prediction can be realized in a real stock market. Chapter 3 focuses on the application of Artificial Neural Networks (ANNs) in forecasting stock indices. It applies the Multi-layer Perceptron (MLP), Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) neural network to the task of forecasting and trading FTSE100 and INDU indices. The forecasting accuracy and trading performances of MLP, CNN and LSTM are compared under the binary classifications architecture and eight classifications architecture. Then, Chapter 3 combines the forecasts of three ANNs (MLP, CNN and LSTM) by Simple Average, Granger-Ramanathan’s Regression Approach (GRR) and the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, this chapter uses different leverage ratios in trading according to the different daily forecasting probability to improve the trading performance. In Chapter 3, the statistical and trading performances are estimated throughout the period 2000-2018. LSTM slightly outperforms MLP and CNN in terms of average accuracy and average annualized returns. The combination methods do not present improved empirical evidence. Trading using different leverage ratios improves the annualized average return, while the volatility increases. Chapter 4 uses five pairs trading strategies to conduct in-sample training and backtesting on 35 commodities in the major commodity markets from 1980 to 2018. The Distance Method (DIM) and the Co-integration Approach (CA) are used for pairs formation. The Simple Thresholds (ST) strategy, Genetic Algorithm (GA) and Deep Reinforcement Learning (DRL) are employed to determine trading actions. Traditional DIM-ST, CA-ST and CA-DIM-ST are used as benchmark models. The GA is used to optimize the trading thresholds in ST strategy, which is called the CA-GA-ST strategy. Chapter 4 proposes a novel DRL structure for determining trading actions, which replaces the ST decision method. This novel DRL structure is then combined with CA and called the CA-DRL trading strategy. The average annualized returns of the traditional DIM-ST, CA-ST and CA-DIM-ST methods are close to zero. CA-GA-ST uses GA to optimize searches for thresholds. GA selects a smaller range of thresholds, which improves the in-sample performance. However, the average out-of-sample performance only improves slightly, with an average annual return of 1.84% but an increased risk. CA-DRL strategy uses CA to select pairs and then employs DRL to trade the pairs, providing a satisfactory trading performance: the average annualized return reaches 12.49%; the Sharpe Ratio reaches 1.853. Thus, the CA-DRL trading strategy is significantly superior to traditional methods and to CA-GA-ST

PROGNOSIS - Historical Pattern Matching for Economic Forecasting and Trading

In recent years financial markets have become complex environments that continuously change and they change quickly. The strong link between the continuous change in the markets and the danger of losing money when trading in them, has made financial studies a domain that concentrates increasing scientific and business attention. In this context, the development of computational techniques that can monitor recent financial events can process them according to their similarity with historical data recordings, and can support financial decision making, is a challenging problem. In this work, the principal idea for tackling this problem is the integration of 'current' market information as derived from the market's recent past and historical information. A robust technique which is based on flexible pattern matching, segmented data representations, time warping, and time series embedding dimension measures is proposed. Complementary time series derived features, concerning trend structures, temporal considerations and statistical measures are systematically combined in this technique. All these components have been integrated into a software package, which I called PROGNOSIS, that can selectively monitor its application and allows systematic evaluation in terms of financial forecasting and trading performance. In addition, two other topics are discussed in this thesis. Firstly, in chapter 3, a neural network, that is known as the Growing Neural Gas network, is employed for financial forecasting and trading. To my knowledge, this network has never been applied before to financial problems. Based on this a neural network forecasting and trading benchmark was constructed for comparison purposes. Secondly, a novel method of approaching the well established co-integraton theory is proposed in the last chapter of the thesis. This method enhances the co-integration theory by integrating into it local time relations between two time series. These local time dependencies are identified using dynamic time warping. The hypothesis that is tested is that local time shifts, delays, shrinks or stretches, if identified, may help to reveal co-integrating movement between the two time series. I called this type of co-integration time-warped co-integration. To this end, the time-warped co-integration framework is presented as an error correction model and it is tested on arbitrage trading opportunities within PROGNOSIS