The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

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A deep learning framework for financial time series using stacked autoencoders and long-short term memory

Deep learning networks are applied to stock market analysis and prediction.A comprehensive analysis with different data representation methods is offered.Five-minute intraday data from the Korean KOSPI stock market is used.The network applied to residuals of autoregressive model improves prediction.Covariance estimation for market structure analysis is improved with the network. We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methodsprincipal component analysis, autoencoder, and the restricted Boltzmann machineon the networks overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.

Deep learning networks for stock market analysis and prediction

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

We propose a survey of soft computing techniques applied to financial market.We surveyed several primary studies proposed in the literature.A framework for building an intelligent trading system was proposed.Future directions of this research field are discussed. Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field.

Computational Intelligence and Financial Markets

Natural language processing (NLP), or the pragmatic research perspective of computational linguistics, has become increasingly powerful due to data availability and various techniques developed in the past decade. This increasing capability makes it possible to capture sentiments more accurately and semantics in a more nuanced way. Naturally, many applications are starting to seek improvements by adopting cutting-edge NLP techniques. Financial forecasting is no exception. As a result, articles that leverage NLP techniques to predict financial markets are fast accumulating, gradually establishing the research field of natural language based financial forecasting (NLFF), or from the application perspective, stock market prediction. This review article clarifies the scope of NLFF research by ordering and structuring techniques and applications from related work. The survey also aims to increase the understanding of progress and hotspots in NLFF, and bring about discussions across many different disciplines.

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Natural language based financial forecasting: a survey

There are many researches on forecasting time series for building trading systems for financial markets. Some of these studies have shown that it is possible to obtain satisfactory results, thereby contradicting the theory of Efficient Markets Hypothesis (EMH) that suggests that prices are randomly generated over time. This paper proposes an intelligent system based on historical closing prices that uses technical analysis, the Artificial Bee Colony Algorithm (ABC), a selection of past values (lags), nearest neighbor classification (k-NN) and its variation, the Adaptative Classification and Nearest Neighbor (A-k-NN). A very important step for time series prediction is the correct selection of the past observations (lags). Our method uses this strategy since it uses the k-NN and A-k-NN to decide on the buy and seIl points, combined with the ABC algorithm which is used to search for the best parameter settings of system and a good set of lags. This paper compares the results obtained by the proposed method with the buy and hold strategy and with other work that performed similar experiments with the same trading model and the same stocks. The key measure for performance comparison is the profitability in the analyzed period. The proposed method generates much larger profits compared to the other method and to the buy and hold strategy. Our method outperforms the other methods in thirteen out of the fifteen stocks tested, minimizing the risk of market ex pos ure.

Automatic method for stock trading combining technical analysis and the Artificial Bee Colony Algorithm

Financial time series represent the stock prices over time and exhibit behavior similar to a data stream. Many works report on the use of data mining techniques to predict the future direction of stock prices and to discover patterns in the time series data to provide decision support for trading operations. Traditional optimization methods do not take into account the possibility that the function to be optimized, namely, the final financial balance for operations considering some stock, may have multiple peaks, i.e., be represented by multimodal functions. However, multimodality is a known feature of real-world financial time series optimization problems. To deal with this issue, this article proposes the PAA-MS-IDPSO-V approach (Piecewise Aggregate Approximation - Multi-Swarm of Improved Self-adaptive Particle Swarm Optimization with Validation). The proposed method aims to find patterns in financial time series to support investment decisions. The approach uses multi-swarms to obtain a better particle initialization for the final optimization phase since it aims to tackle multimodal problems. Furthermore, it uses a validation set with early stopping to avoid overfitting. The patterns discovered by the method are used together with investment rules to support decisions and thus help investors to maximize the profit in their operations in the stock market. The experiments reported in this paper compare the results obtained by the proposed model with the Buy-and-Hold, PAA-IDPSO approaches and another approach found in the literature. We report on experiments conducted with S&P100 index stocks and using the Friedman Non-Parametric Test with the Nemenyi post-hoc Test both with 95% confidence level. The results show that the proposed model outperformed the competing methods and was able to considerably reduce the variance for all stocks.

Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation

Particle Swarm Optimization algorithm (PSO), when applied to problems with continuous variables, presents results with better quality at a lower computational cost when compared to the Genetic Algorithm (GA). Thus, the PSO becomes a very useful method to be applied with investment strategies used to optimize profit from operations made in the stock market, since investors seek quick and profitable results for their decision making. In this context, the Symbolic Aggregate Approximation (SAX) and Piecewise Aggregate Approximation (PAA) are time series representation techniques that, when used with optimization algorithms like PSO or GA, can help investors discover hidden and relevant patterns in financial time series data. The SAX uses discrete variables to represent their values, whereas PAA uses continuous values. Thus, this paper proposes the PAA-PSO technique, which combines the PAA with the optimization of PSO for discovery of patterns that will be used with a formulated investment strategy in order to maximize the profit from operations made in the stock market. Experiments that compare the proposed method to the results of the SAX-GA technique, which combines the techniques of SAX and GA in their investment strategy, are reported.

Rodrigo C. Brasileiro

Papers

Computational Intelligence and Financial Markets

Automatic method for stock trading combining technical analysis and the Artificial Bee Colony Algorithm

Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation

A PAA-PSO technique for investment strategies in the financial market