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Machine Learning Stock Market Prediction Studies: Review and Research Directions
Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. A systematic literature review methodology is used to identify relevant peer-reviewed journal articles from the past twenty years and categorize studies that have similar methods and contexts. Four categories emerge: artificial neural network studies, support vector machine studies, studies using genetic algorithms combined with other techniques, and studies using hybrid or other artificial intelligence approaches. Studies in each category are reviewed to identify common findings, unique findings, limitations, and areas that need further investigation. The final section provides overall conclusions and directions for future research
An empirical methodology for developing stockmarket trading systems using artificial neural networks
A holistic auto-configurable ensemble machine learning strategy for financial trading
Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions
Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network
It is a challenging problem to predict trends of futures prices with
traditional econometric models as one needs to consider not only futures'
historical data but also correlations among different futures. Spatial-temporal
graph neural networks (STGNNs) have great advantages in dealing with such kind
of spatial-temporal data. However, we cannot directly apply STGNNs to
high-frequency future data because future investors have to consider both the
long-term and short-term characteristics when doing decision-making. To capture
both the long-term and short-term features, we exploit more label information
by designing four heterogeneous tasks: price regression, price moving average
regression, price gap regression (within a short interval), and change-point
detection, which involve both long-term and short-term scenes. To make full use
of these labels, we train our model in a continual manner. Traditional
continual GNNs define the gradient of prices as the parameter important to
overcome catastrophic forgetting (CF). Unfortunately, the losses of the four
heterogeneous tasks lie in different spaces. Hence it is improper to calculate
the parameter importance with their losses. We propose to calculate parameter
importance with mutual information between original observations and the
extracted features. The empirical results based on 49 commodity futures
demonstrate that our model has higher prediction performance on capturing
long-term or short-term dynamic change
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