Reinforcement Learning in Stock Trading

Using machine learning techniques in financial markets, particularly in stock trading, attracts a lot of attention from both academia and practitioners in recent years. Researchers have studied different supervised and unsupervised learning techniques to either predict stock price movement or make decisions in the market. In this paper we study the usage of reinforcement learning techniques in stock trading. We evaluate the approach on real-world stock dataset. We compare the deep reinforcement learning approach with state-of-the-art supervised deep learning prediction in real-world data. Given the nature of the market where the true parameters will never be revealed, we believe that the reinforcement learning has a lot of potential in decision-making for stock trading

Prediction of Stocks and Stock Price using Artificial Intelligence : A Bibliometric Study using Scopus Database

Prediction of stocks and the prices of the stock is one of the most crucial points of discussion amongst the researchers and analysts in the financial domain to date. Every stakeholder and most importantly the investor desires to earn higher profit for his investment in the market and try to use several different strategies to invest their money. There are numerous methods to predict and analyse the movement of the stock prices. They are broadly divided into – statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms like – SVMs, CNNs, LSTMs, RNNs , etc. This bibliometric study focusses on the study based primarily on the Scopus database. We have considered important keywords, authors, citations along with the correlations between the co-appearing authors, source titles and keywords with the use of network diagrams for visualisation. On the basis of this paper, we conclude that there is ample opportunity for research in the domain of financial market

Stock Prediction with Random Forests and Long Short-term Memory

Machine learning as a popular computer science area has been promoted and developed for more than two decades. It has been applied in many fields in our life, like domestic products such as Alexa from Amazon, photographic products such as Mavic from Dji and so many other areas. This report represents an interesting way to apply machine learning and deep learning technologies on the stock market. We explore multiple approaches, including Long Short-Term Memory (LSTM), a type of Artificial Recurrent Neural Networks (RNN) architectures, and Random Forests (RF), a type of ensemble learning methods. The goal of this report is to use real historical data from the stock market to train our models, and to show reports about the prediction of future returns for picked stocks

Adaptive Grey Wolf Optimization Technique for Stock Index Price Prediction on Recurring Neural Network Variants

In this paper, we propose a Long short-term memory (LSTM) and Adaptive Grey Wolf Optimization (GWO)--based hybrid model for predicting the stock prices of the Major Indian stock indices, i.e., Sensex. The LSTM is an advanced neural network that handles uncertain, nonlinear, and sequential data. The challenges are its weight and bias optimization. The classical backpropagation has issues of dangling on local minima or overfitting the dataset. Thus, we propose a GWO-based hybrid approach to evolve the weights and biases of the LSTM and the dense layers. We have made the GWO more robust by introducing an approach to improve the best possible solution by using the optimal ranking of the wolves. The proposed model combines the GWO with Adam Optimizer to train the LSTM. Apart from the LSTM, we have also implemented the Adaptive GWO on other variants of Recurring Neural Networks (RNN) like LSTM, Bi-Directional LSTM, Gated Recurrent Units (GRU), and Bi-Directional GRU and computed the corresponding results. The Adaptive GWO here evolves the initial weights and biases of the above-discussed neural networks. In this research, we have also compared the forecasting efficiency of our proposed work with a particle-warm optimization (PSO) based hybrid LSTM model, simple Grey-wolf Optimization (GWO), and Adaptive PSO. According to the experimental findings, the suggested model has effectively used the best initial weights, and its results are the best overall

Portfolio optimization: from markowitz to machine learning

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementIn the past few decades, substantial progress has been made in portfolio optimization, especially with the emergence of machine learning. Therefore, it is essential to find the models that not only achieve the best results but also simplify the process. This project aims to demonstrate that to achieve optimal portfolios cannot be based only on traditional statistical methods. Therefore the Random Forest regression model, a machine learning model, was chosen to predict stock prices to complement the Markowitz model, a classical portfolio selection model. To evaluate the efficacy of the modified model compared to the classical model the following methodology was adopted: data was collected (from 2012 to 2019 from 10 companies and it was divided in 15 periods) and treated; some common technical indicators were extracted; one stock price was predicted per period; expected returns and partially estimated volatility were derived from the predictions and introduced in the classical model; 15 portfolios were constructed by each model; and finally, a performance analysis was conducted. The results obtained show that the 1-day predictions were quite accurate, almost 90%, and the modified model’s portfolios’ outperformed the classical model’s portfolios for most periods analyzed

An Ensemble Classifier for Stock Trend Prediction Using Sentence-Level Chinese News Sentiment and Technical Indicators

In the financial market, predicting stock trends based on stock market news is a challenging task, and researchers are devoted to developing forecasting models. From the existing literature, the performance of the forecasting model is better when news sentiment and technical analysis are considered than when only one of them is used. However, analyzing news sentiment for trend forecasting is a difficult task, especially for Chinese news, because it is unstructured data and extracting the most important features is difficult. Moreover, positive or negative news does not always affect stock prices in a certain way. Therefore, in this paper, we propose an approach to build an ensemble classifier using sentiment in Chinese news at sentence level and technical indicators to predict stock trends. In the training stages, we first divide each news item into a set of sentences. TextRank and word2vec are then used to generate a predefined number of key sentences. The sentiment scores of these key sentences are computed using the given financial lexicon. The sentiment values of the key phrases, the three values of the technical indicators and the stock trend label are merged as a training instance. Based on the sentiment values of the key sets, the corpora are divided into positive and negative news datasets. The two datasets formed are then used to build positive and negative stock trend prediction models using the support vector machine. To increase the reliability of the prediction model, a third classifier is created using the Bollinger Bands. These three classifiers are combined to form an ensemble classifier. In the testing phase, a voting mechanism is used with the trained ensemble classifier to make the final decision based on the trading signals generated by the three classifiers. Finally, experiments were conducted on five years of news and stock prices of one company to show the effectiveness of the proposed approach, and results show that the accuracy and P / L ratio of the proposed approach are 61% and 4.0821 are better than the existing approach

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization

Structure-oriented prediction in complex networks

Complex systems are extremely hard to predict due to its highly nonlinear interactions and rich emergent properties. Thanks to the rapid development of network science, our understanding of the structure of real complex systems and the dynamics on them has been remarkably deepened, which meanwhile largely stimulates the growth of effective prediction approaches on these systems. In this article, we aim to review different network-related prediction problems, summarize and classify relevant prediction methods, analyze their advantages and disadvantages, and point out the forefront as well as critical challenges of the field