scholarly journals Stock Market Prediction and Investment using Deep Reinforcement Learning- a Continuous Training Pipeline

2020 ◽  
Vol 10 (2) ◽  
pp. 93-98
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Language Processing ◽  
Learning Algorithms ◽  
Statistical Prediction ◽  
Experimental Result ◽  
Future Market ◽  
Gradient System ◽  
Continuous Training ◽  
Market Trends

Fluctuating nature of the stock market makes it too hard to predict the future market trends and where to invest. Hence, there is a need for a cross application backed by an ultramodern architecture. With the latest advancement in Deep Reinforcement Learning, successive practical problems can be modeled and solved with human level accuracy. In this paper, an agent-based Deep Deterministic Policy Gradient system is proposed to imitate professional trading strategies which is a state-of-the-art framework that can predict and make investment of customers money with high return. In addition to this, dealing with interday trading strategy, the proposed architecture is designed as a continuous training pipeline so that the model saved is up-to-date with the recent market trends by giving higher accuracy in prediction. The framework outperforms the base reinforcement learning algorithms and maximizes portfolio return. The experimental result shows how natural language processing and statistical prediction can help us to choose the trending stock based on news headlines and historical data so that model invests money only in the market which gives higher return. To evaluate the performance of the proposed method, comparison of our portfolio results was done with various other reinforcement learning algorithms by keeping the same configuration.

Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading

2022 ◽  
Vol 16 (4) ◽  
pp. 1-22
Author(s):  
Chang Liu ◽  
Jie Yan ◽  
Feiyue Guo ◽  
Min Guo
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Mean Square Error ◽  
Short Term Memory ◽  
The State ◽  
Machine Learning Algorithms ◽  
Future Market ◽  
Mean Square ◽  
Market Data ◽  
Market Trends

Although machine learning (ML) algorithms have been widely used in forecasting the trend of stock market indices, they failed to consider the following crucial aspects for market forecasting: (1) that investors’ emotions and attitudes toward future market trends have material impacts on market trend forecasting (2) the length of past market data should be dynamically adjusted according to the market status and (3) the transition of market statutes should be considered when forecasting market trends. In this study, we proposed an innovative ML method to forecast China's stock market trends by addressing the three issues above. Specifically, sentimental factors (see Appendix [1] for full trans) were first collected to measure investors’ emotions and attitudes. Then, a non-stationary Markov chain (NMC) model was used to capture dynamic transitions of market statutes. We choose the state-of-the-art (SOTA) method, namely, Bidirectional Encoder Representations from Transformers ( BERT ), to predict the state of the market at time t , and a long short-term memory ( LSTM ) model was used to estimate the varying length of past market data in market trend prediction, where the input of LSTM (the state of the market at time t ) was the output of BERT and probabilities for opening and closing of the gates in the LSTM model were based on outputs of the NMC model. Finally, the optimum parameters of the proposed algorithm were calculated using a reinforced learning-based deep Q-Network. Compared to existing forecasting methods, the proposed algorithm achieves better results with a forecasting accuracy of 61.77%, annualized return of 29.25%, and maximum losses of −8.29%. Furthermore, the proposed model achieved the lowest forecasting error: mean square error (0.095), root mean square error (0.0739), mean absolute error (0.104), and mean absolute percent error (15.1%). As a result, the proposed market forecasting model can help investors obtain more accurate market forecast information.

Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis

2019 ◽  
Vol 24 (15) ◽  
pp. 11019-11043 ◽  
Author(s):  
Wasiat Khan ◽  
Usman Malik ◽  
Mustansar Ali Ghazanfar ◽  
Muhammad Awais Azam ◽  
Khaled H. Alyoubi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Situation Analysis ◽  
Political Situation ◽  
Public Sentiment ◽  
Market Trends

scholarly journals Predicting Stock Market Trends using Hybrid SVM Model and LSTM with Sentiment Determination using Natural Language Processing

2019 ◽  
Vol 9 (1) ◽  
pp. 2870-2875
Keyword(s):  
Natural Language Processing ◽  
Stock Market ◽  
Natural Language ◽  
Language Processing ◽  
Stock Prices ◽  
Market Price ◽  
Majority Voting ◽  
Svm Model ◽  
Market Trends ◽  
The Right

In the financial world, stock trading is one of the most crucial activities. Investors make educated guesses to predict stock market trends by analyzing news, studying the company history, industrial history and a lot of other data. successfully predicting the stock market trends and investing in the right shares at the right time can maximize the investor’s profit or at least minimize the losses. Stock market price data is generated in huge volumes and is affected by various diverse factors. This work proposes two models to predict the stock market prices. The first model is an LSTM model that employs a backpropagation optimized LSTM network to forecast future stock prices. The second model is a hybrid model that combines an SVM model, KNN model and a Random Forest classifier using the Majority Voting algorithm to predict stock market trends. Both models have a sentiment analyzer to factor the news influencing the stock market using Natural Language Processing (NLP). The project aims to help investors who are new to the stock market and don’t possess sufficient knowledge to make share investments as well the experienced investors by predicting stock market trends.

scholarly journals A Systematic Study on Sentiment Analysis based on Text Mining and Deep learning for Predictions in Stock Market Trends Through Social and News Media Data

2021 ◽  
Vol 9 (10) ◽  
pp. 1589-1593
Author(s):  
Suvigya Jain
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Stock Market ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
News Media ◽  
Market Sentiment ◽  
Market Trends

Abstract: Stock Market has always been one of the most active fields of research, many companies and organizations have focused their research in trying to find better ways to predict market trends. The stock market has been the instrument to measure the performance of a company and many have tried to develop methods that reduce risk for the investors. Since, the implementation of concepts like Deep Learning and Natural Language Processing has been made possible due to modern computing there has been a revolution in forecasting market trends. Also, the democratization of knowledge related to companies made possible due to the internet has provided the stake holders a means to learn about assets they choose to invest in through news media and social media also stock trading has become easier due to apps like robin hood etc. Every company now a days has some kind of social media presence or is usually reported by news media. This presence can lead to the growth of the companies by creating positive sentiment and also many losses by creating negative sentiments due to some public events. Our goal in this paper is to study the influence of news media and social media on market trends using sentiment analysis. Keywords: Deep Learning, Natural Language Processing, Stock Market, Sentiment analysis

scholarly journals Importance of Machine Learning in Making Investment Decision in Stock Market

2021 ◽  
pp. 025609092110599
Author(s):  
Akhilesh Prasad ◽  
Arumugam Seetharaman
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Stock Market ◽  
Stock Price ◽  
Learning Algorithms ◽  
Technical Analysis ◽  
Processing Unit ◽  
Accuracy Score ◽  
Research Papers ◽  
Price Trends

Executive Summary Predicting stock trends in the financial market is always demanding but satisfying as well. With the growing power of computing and the recent development of graphics processing unit and tensor processing unit, analysts and researchers are applying advanced techniques such as machine learning techniques more and more to predict stock price trends. In recent years, researchers have developed several algorithms to predict stock trends. To assist investors interested in investing in the stock market, preferably for a short period, it has become necessary to review research papers dealing on machine learning and analyse the importance of their findings in the context of how stock price trends generate trading signals. In this article, to achieve the stated task, authors scrutinized more than 50 research papers focusing on various machine learning algorithms with varied levels of input variables and found that though the performance of models measured by root-mean-square error (RMSE) for regression and accuracy score for classification models varied greatly, long short-term memory (LSTM) model displayed higher accuracy amongst the machine and deep learning models reviewed. However, reinforcement learning algorithm performance measured by profitability and Sharpe ratio outperformed all. In general, traders can maximize their profits by using machine learning instead of using technical analysis. Technical analysis is very easy to implement, but the profit based on it can vanish too soon or making a profit using technical analysis is almost difficult because of its simplicity. Hence, studying machine, deep and reinforcement learning algorithms is vital for traders and investors. These findings were based on the literature review consolidated in the result section.

scholarly journals Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 150199-150212 ◽  
Author(s):  
Mojtaba Nabipour ◽  
Pooyan Nayyeri ◽  
Hamed Jabani ◽  
Shahab S. ◽  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Stock Market ◽  
Binary Data ◽  
Learning Algorithms ◽  
Market Trends

Methods for Determining Stock Market Trends

1946 ◽  
Vol 2 (3) ◽  
pp. 12-21
Author(s):  
Ralph Rotnem
Keyword(s):  
Stock Market ◽  
Market Trends

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

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