Stock market forecasting is one of the biggest challenges in the financial market since its time series has a complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. However, due to computing development, an intelligent model can help investors and professional analysts reduce the risk of their investments. As Deep Learning models have been extensively studied in recent years, several studies have explored these techniques to predict stock prices using historical data and technical indicators. However, as the objective is to generate forecasts for the financial market, it is essential to validate the model through profitability metrics and model performance. Therefore, this systematic review focuses on Deep Learning models implemented for stock market forecasting using technical analysis. Discussions were made based on four main points of view: predictor techniques, trading strategies, profitability metrics, and risk management. This study showed that the LSTM technique is widely applied in this scenario (73.5%). This work significant contribution is to highlight some limitations found in the literature, such as only 35.3% of the studies analysed profitability, and only two articles implemented risk management. Therefore, despite the widely explored theme, there are still interesting open areas for research and development.

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Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

Stock performance prediction plays an important role in determining the appropriate timing of buying or selling a stock in the development of a trading system. However, precise stock price prediction is challenging because of the complexity of the internal structure of the stock price system and the diversity of external factors. Although research on forecasting stock prices has been conducted continuously, there are few examples of the successful use of stock price forecasting models to develop effective trading systems. Inspired by the process of human stock traders looking for trading opportunities, we propose a deep learning framework based on a hybrid convolutional recurrent neural network (HCRNN) to predict the important trading points (IPs) that are more likely to be followed by a significant stock price rise to capture potential high-margin opportunities. In the HCRNN model, the convolutional neural network (CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) approach learns the long-term temporal dependencies to improve stock performance prediction. Comprehensive experiments on real stock market data prove the effectiveness of our proposed framework. Our proposed method ITPP-HCRNN achieves an annualized return that is 278.46% more than that of the market.

Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network

The prediction of stock price has always been a main challenge. The time series of stock price tends to exhibit very strong nonlinear characteristics. In recent years, with the rapid development of deep learning, the ability to automatically extract nonlinear features has significantly attracted scholars’ attention. However, the majority of the relevant studies have concentrated on prediction of the changes of stock market based on the data of the specific stock (e.g., transaction data, financial data, etc.), while those studies ignored the interaction between stocks of different industries, especially the interaction between the stocks of upstream enterprises and downstream enterprises in the industrial chain. This paper aims to propose a combination of transfer learning of industrial chain information and deep learning models, including multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), for stock market prediction. These models are used to predict the trend of the 379 stock market indices by industry in China, and the DM test was employed for validation of the prediction results. It can be concluded that RNNs are not necessarily such an optimal choice for the prediction when dealing with specific time series data, and it could be justified by using the local interpretable model-agnostic explanations (LIME) algorithm. Hence, the MLP was selected to effectively improve the accuracy of the prediction of the stock market indices based on the transfer learning of industrial chain information. The investment strategy is constructed according to the prediction results, and the yield of maturity exceeds that of the buy-and-hold strategy.

Jointly modeling transfer learning of industrial chain information and deep learning for stock prediction

The prediction of stock price has always been a main challenge. The time series of stock price tends to exhibit very strong nonlinear characteristics. In recent years, with the rapid development of deep learning, the ability to automatically extract nonlinear features has significantly attracted scholars’ attention. However, the majority of the relevant studies have concentrated on prediction of the changes of stock market based on the data of the specific stock (e.g., transaction data, financial data, etc.), while those studies ignored the interaction between stocks of different industries, especially the interaction between the stocks of upstream enterprises and downstream enterprises in the industrial chain. This paper aims to propose a combination of transfer learning of industrial chain information and deep learning models, including multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), for stock market prediction. These models are used to predict the trend of the 379 stock market indices by industry in China, and the DM test was employed for validation of the prediction results. It can be concluded that RNNs are not necessarily such an optimal choice for the prediction when dealing with specific time series data, and it could be justified by using the local interpretable model-agnostic explanations (LIME) algorithm. Hence, the MLP was selected to effectively improve the accuracy of the prediction of the stock market indices based on the transfer learning of industrial chain information. The investment strategy is constructed according to the prediction results, and the yield of maturity exceeds that of the buy-and-hold strategy. 

4차 산업혁명시대의 인공지능 기술은 의학, 공학, 금융 등 다양한 산업분야에서 큰 영향을 미치고 있다. 특히, 인공지능에 기반한 로보어드바이저의 등장으로 금융시장과 금융산업은 혁신을 도모하고 있다. 로보어드바이저는 낮은 수수료와 높은 편리성을 바탕으로 기존의 자산관리서비스 시장에 빠르게 진입하고 있으며, 스타트업과 기존 금융회사 모두 로보어드바이저 시장에 큰 관심을 두고 있다. 그러나 국내 로보어드바이저 시장의 경우 미국과 유럽시장에서의 성공사례와는 달리, 비대면 일임계약에 대한 제약과 같은 규제와 비효율적인 제도운영으로 인하여 지속적인 성장이 불투명하다. 따라서, 본 연구에서는 국내외 로보어드바이저 기업의 현황과 사례를 살펴보고, 로보어드바이저 시장에 대한 개괄적인 비용구조분석을 통해 개선방안에 대하여 고찰하고 정책 방향을 제시한다. 본 연구에서 분석한 비용 구조분석은 로보어드바이저 산업이 시장에 먼저 진입한 기업이 우위를 차지하게 되는 비용구조로 되어있다면, 효과적인 정책의 부재는 소수 기업이 시장을 과점하는 상황을 초래하고 장기적인 성장을 저해할 수 있음을 보인다. 현행규제를 검토하고 잠재력 있는 신규 진입기업에 대한 지원정책을 강화하되, 기업들 간에 가격의 인하에만 집중하는 과도한 경쟁이 아닌 첨단서비스와 기술혁신에 따른 경쟁체제를 유도해야 한다.

금융시장 로보어드바이저 산업에 대한 고찰: 현황과 개선방안

In Korea, because of the high interest in stock investment, many researchers have attempted to predict stock prices using deep learning. Studies to predict stock prices have been continuously conducted. However, the type of stock data that is suitable for deep learning has not been established, and it has not been confirmed that the developed stock prediction model can actually result in a profit. To date, designing a good deep learning model depends on how well the user can extract the features that represent all the characteristics of the training data. Among the various available features for training and test data, we determined that the use of event binary features can make stock price prediction models perform better. An event binary feature refers to a 0 or 1 value describing whether an indicator is satisfied (1) or not (0) for any given day and stock. We proposed and compared a stock price prediction model with three different feature combinations to verify the importance of binary features. As a result, we derived a prediction model that defeated the market (KOSPI and KODAQ (KOSPI (Korea Composite Stock Price Index) and KOSDAQ (Korean Securities Dealers Automated Quotations) is Korean stock indices)). The results suggest that deep learning is suitable for stock price prediction.

https://www.mdpi.com/2076-3417/10/5/1597/pdf

Yoojeong Song

Papers

Importance of Event Binary Features in Stock Price Prediction