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

Yield Curves and Macro Variables Interactions and Predictions

This research is based on the yield curves and five macro variables, namely equity indices, FX rates, central banks’ policy rates, inflation rates and the GDP growth rates, for nine different markets, from different geographical regions. Our aim was to identify common trends in yield curves and macro variables behaviors, from two perspectives: the interaction and predictive power of the variables. Firstly, we studied the interaction between yield curves and macro variables based on: Granger Causality, Impulse Response Function and Variance Decomposition. Afterwards, we predicted yield curves based on ANN Regression Multitask learning, and lastly, we predicted our five macro variables based on three different ANN Classifiers, in order to generalize and present results that are not specific to a country, or region, or model. The most persistence trend, amongst the variables, was the association between the GDP, inflation, policy rate and the Level. Based on Multitask learning, we achieved a 1-mnth average yield curves prediction accuracy of 80.2% for all yield maturities and studied markets. Additionally, we found out that increasing the hidden nodes led to overfitting the data, hence, we recommend the use of a simple neural network architecture. Furthermore, we designed a model that computes the optimum number of hidden nodes based on: the number of input/output nodes and forecasted months ahead. The Independent Variable Contribution analysis increased the weight of Slope on average for all markets. Weighted KNN caused a deterioration in the prediction accuracy of macro variables, and K of KNN increased with the horizon forecasted. In terms of predictive power of the variables, the yield curve on its own had predictive powers over long term equity markets, and the policy rate seemed to be affected by macro variables in the short term. Furthermore, the inflation and GDP were dominated by their own past values