A Hybrid Neural Network for Stock Price Direction Forecasting

The volatility of stock markets makes them notoriously difficult to predict and is the reason that many investors sell out at the wrong time. Contrary to the efficient market hypothesis (EMH) and the random walk theory, contribution to the study of machine learning models for stock price forecasting has shown evidence of stock markets predictability with varying degrees of success. Contemporary approaches have sought to use a hybrid of convolutional neural network (CNN) for its feature extraction capabilities and long short-term memory (LSTM) neural network for its time series prediction. This comparative study aims to determine the predictability of stock price movements by using a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) neural network, a standalone LSTM neural network, a random forest model, and support vectors machines (SVM) model. Specifically, the study seeks to explore the predictive ability using stock price data, technical indicators, and foreignexchange (FX) rates transformed into deterministic trend signals as features for a hybrid CNN-LSTM neural network. This paper additionally considered including news article sentiment scores relating to stocks as part of the training dataset, but significant correlation was not found. In this study, the predictive ability is the accuracy of predicting the direction a stock price moves not the actual price. The experiment results suggest that a hybrid CNN-LSTM model can achieve around 60% accuracy trained with deterministic trend signals for stock trend prediction. This accuracy has higher than the accuracy of LSTM, random forest, and SVM. On this basis, one can conclude that the hybrid neural network model is superior to standalone LSTM, random forest, and SVM for stock price trend prediction

Assessing Market Expectations on Exchange Rates and Inflation: A Pilot Forecasting System for Bulgaria

Econometric forecasting models typically perform bad in volatile environments as they are often present in economies in transition. Since forecasts of key macroeconomic variable are inevitable as guidelines for economic policy, one might alternatively make attempts at measuring market participants’ expectations or conduct surveys. However, often financial markets are underdeveloped and regular surveys are unavailable in transition countries. In this paper we propose to conduct experimental stock markets to reveal market participants’ expectations. W? present the results fr?m a series of pilot markets conducted in Bulgaria throughout 2002 indicating that the method could be useful especially for transition countries.http://deepblue.lib.umich.edu/bitstream/2027.42/40145/3/wp759.pd

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201

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