scholarly journals Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Qiaoyu Wang ◽  
Kai Kang ◽  
Zhihan Zhang ◽  
Demou Cao
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Financial Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Polynomial Regression ◽  
Time Series Prediction ◽  
Machine Learning Algorithms ◽  
Trend Prediction ◽  
Lstm Network

Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

scholarly journals Stock Market Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3628-3632
Author(s):  
Prof. Gowrishankar B S
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction ◽  
The Future ◽  
Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

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.

scholarly journals APPLYING MACHINE LEARNING MODELS IN STOCK MARKET PREDICTION

2020 ◽  
pp. 395-398
Author(s):  
Vignesh CK
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Support Vector ◽  
Company Stock ◽  
The Future ◽  
Future Value ◽  
Past Data

This paper deals with the techniques of attempting to calculate the future value of a company stock or any other financial instrument which is being traded in a stock exchange. This prediction plays a great role in many financing and investing decisions. This calculation can be done by Machine learning by training a model to identify the trend from past data in order to predict the future. The main topic of study here will be the comparative analysis of the SVM and LTSM algorithms. KEYWORDS: Machine learning, Stock price, Stock market, Support vector machine, neural network, long short term memory.

scholarly journals Prediction of Option Price using Ensemble of Machine Learning Algorithms for Indian Stock Market

2019 ◽  
Vol 8 (2) ◽  
pp. 3231-3241
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Learning Algorithm ◽  
Option Price ◽  
Machine Learning Algorithms ◽  
Parametric Models ◽  
Accuracy Of Prediction ◽  
Indian Stock Market

The non-deterministic behavior of stock market creates ambiguities for buyers. The situation of ambiguities always finds the loss of user financial assets. The variations of price make a very difficult task to predict the option price. For the prediction of option used various non-parametric models such as artificial neural network, machine learning, and deep neural network. The accuracy of prediction is always a challenging task of for individual model and hybrid model. The variation gap of hypothesis value and predicted value reflects the nature of stock market. In this paper use the bagging method of machine learning for the prediction of option price. The bagging process merge different machine learning algorithm and reduce the variation gap of stock price.

Application of Machine Learning Algorithms in Stock Market Prediction

2020 ◽  
pp. 153-180
Author(s):  
Sumit Kumar ◽  
Sanlap Acharya
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Better Than

The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM.

scholarly journals Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 4 ◽  
Author(s):  
Francesco Rundo ◽  
Francesca Trenta ◽  
Agatino Luigi Di Stallo ◽  
Sebastiano Battiato
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Ad Hoc ◽  
Trend Prediction ◽  
Trading System ◽  
Trading Systems ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Historical Data Analysis

Stock market prediction and trading has attracted the effort of many researchers in several scientific areas because it is a challenging task due to the high complexity of the market. More investors put their effort to the development of a systematic approach, i.e., the so called “Trading System (TS)” for stocks pricing and trend prediction. The introduction of the Trading On-Line (TOL) has significantly improved the overall number of daily transactions on the stock market with the consequent increasing of the market complexity and liquidity. One of the most main consequence of the TOL is the “automatic trading”, i.e., an ad-hoc algorithmic robot able to automatically analyze a lot of financial data with target to open/close several trading operations in such reduced time for increasing the profitability of the trading system. When the number of such automatic operations increase significantly, the trading approach is known as High Frequency Trading (HFT). In this context, recently, the usage of machine learning has improved the robustness of the trading systems including HFT sector. The authors propose an innovative approach based on usage of ad-hoc machine learning approach, starting from historical data analysis, is able to perform careful stock price prediction. The stock price prediction accuracy is further improved by using adaptive correction based on the hypothesis that stock price formation is regulated by Markov stochastic propriety. The validation results applied to such shares and financial instruments confirms the robustness and effectiveness of the proposed automatic trading algorithm.

Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques

2019 ◽  
Vol 6 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Jai Prakash Verma ◽  
Sudeep Tanwar ◽  
Sanjay Garg ◽  
Ishit Gandhi ◽  
Nikita H. Bachani
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Time Window ◽  
Stock Exchange ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Trend Prediction ◽  
Stock Market Forecasting ◽  
Learning Techniques

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. In order to conduct these processes, a real-time dataset has been obtained from the Indian stock market. This article learns the model from Indian National Stock Exchange (NSE) data obtained from Yahoo API to forecast stock prices and targets to make a profit over time. In this article, two separate algorithms and methodologies are analyzed to forecast stock market trends and iteratively improve the model to achieve higher accuracy. Results are showing that the proposed pattern-based customized algorithm is more accurate (10 to 15%) as compared to other two machine learning techniques, which are also increased as the time window increases.

scholarly journals Future Trend Prediction in Share Market with Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3978-3981
Author(s):  
Prof. Kanchan Mahajan
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Life Span ◽  
Learning Algorithms ◽  
Future Trend ◽  
Machine Learning Algorithms ◽  
Trend Prediction ◽  
Stock Market Prediction ◽  
The Future

In Stock Market Prediction, the point is to estimate the future worth of the monetary loads of an organization. The new pattern in securities exchange forecast advances is the utilization of AI which makes expectations dependent on the upsides of current financial exchange lists via preparing on their past qualities. AI itself utilizes various models to make expectation simpler and credible. The thought centers on the utilization of dissimilar Machine learning algorithms to anticipate stock qualities. Variables considered are open, close, low, high and volume. The principal thing we have considered is the dataset of the securities exchange costs from earlier year. The dataset was pre-handled and adjusted for genuine examination. What's more, the proposed thought inspects the utilization of the forecast framework in verifiable settings and issues related with the accuracy of the general qualities given. The thought additionally portrays AI model to foresee the life span of the stock in a serious market. The effective forecast of the stock will be an extraordinary resource for the securities exchange establishments and will give genuine answers for the issues that stock financial backers face.

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