scholarly journals Improved AdaboostM1 for Stock Price Prediction Using Multi-layer Perceptron to Integrate Weak Learners

2021 ◽  
Author(s):  
Rebwar M. Nabi ◽  
Soran AB.M. Saeed ◽  
Rania Azad M. San Ahmed
Keyword(s):  
Stock Market ◽  
Multilayer Perceptron ◽  
Stock Price ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Data Availability ◽  
Economic Gain ◽  
Market Prices ◽  
Stock Price Prediction ◽  
Price Prediction

Investment in the stock market is currently very popular due to its economic gain. Therefore, numerous researchers and academicians work is focused on financial time series prediction due to its data availability and profitability. Based on the literature it can be seen that various versions of the AdaboostM1 algorithm have been applied in the stock market either by tuning the algorithm parameters or attempting various base learners but the accuracy has not yet reached to favorable and reliable level. Therefore, this study proposes an improved version of AdaboostM1(ADA), which is implemented in the Waikato Environment for Knowledge Analysis(WEKA) to predict stock market prices based on historical data. The improved AdaBoostM1 integrates the set of Multilayer Perceptron (MLP) predictors instead of using DecisionStumps, which is normally being applied. The enhanced AdaBoostM1 is named Adaboost with Multilayer Perceptron (ADA-MLP). As the result, the ADA-MLP was found to be outperforming the original ADA by 1.52%, in which the ADA-MLP achieved the CA of 100% on average while the ADA achieved 98.48%. Furthermore, the ADA-MLP was al

scholarly journals A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Zhongcui Li ◽  
Norbert Herencsar ◽  
Bay Vo ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Financial Management ◽  
Stock Price ◽  
Short Term Memory ◽  
Prediction Algorithm ◽  
Leading Indicators ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Wealth Management

AbstractIn today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

scholarly journals Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction

2020 ◽  
Author(s):  
Qianggang Ding ◽  
Sifan Wu ◽  
Hao Sun ◽  
Jiadong Guo ◽  
Jian Guo
Keyword(s):  
Financial Markets ◽  
Stock Price ◽  
Financial Time Series ◽  
Multi Scale ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Movement Prediction ◽  
Novel Approach ◽  
Hierarchical Features

Predicting the price movement of finance securities like stocks is an important but challenging task, due to the uncertainty of financial markets. In this paper, we propose a novel approach based on the Transformer to tackle the stock movement prediction task. Furthermore, we present several enhancements for the proposed basic Transformer. Firstly, we propose a Multi-Scale Gaussian Prior to enhance the locality of Transformer. Secondly, we develop an Orthogonal Regularization to avoid learning redundant heads in the multi-head self-attention mechanism. Thirdly, we design a Trading Gap Splitter for Transformer to learn hierarchical features of high-frequency finance data. Compared with other popular recurrent neural networks such as LSTM, the proposed method has the advantage to mine extremely long-term dependencies from financial time series. Experimental results show our proposed models outperform several competitive methods in stock price prediction tasks for the NASDAQ exchange market and the China A-shares market.

scholarly journals Failure in Stock Price Prediction: A Comparison bettwen the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes of Existing Machine Learning Algorithms

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Ping Zhang ◽  
Jia-Yao Yang ◽  
Hao Zhu ◽  
Yue-Jie Hou ◽  
Yi Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stock Price ◽  
Financial Time Series ◽  
Learning Algorithms ◽  
Shape Feature ◽  
Curve Shape ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial Time

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

scholarly journals Multi Layer Feed Forward Neural Network Knowledge Base to Future Stock Market Prediction

2019 ◽  
Vol 8 (11S) ◽  
pp. 1061-1075 ◽  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Feed Forward Neural Network ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Past ◽  
Knowledge Set ◽  
Neural Network Prediction ◽  
Network Prediction

Stock price prediction is always a most challenging task. Artificial Neural Network prediction clears the stock price prediction challenge by forming the training set. By using the past information as the network input, one can predict the expected output of the network. In order to predict the expected result as the accurate we add multi-layer perceptron to the knowledge set we formed from the past historical data available in the nifty NSE and Sensex BSE. This paper proves that proposing the learning knowledge set using multilayer neural network will predict the accurate closing price of future stock in stock market.

scholarly journals Stock Market Prediction Using Twitter Sentiment Analysis

2021 ◽  
pp. 265-270
Author(s):  
Padmanayana ◽  
Varsha ◽  
Bhavya K
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Market Price ◽  
Future Price ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Media Platform ◽  
The Impact

Stock market prediction is an important topic in ?nancial engineering especially since new techniques and approaches on this matter are gaining value constantly. In this project, we investigate the impact of sentiment expressed through Twitter tweets on stock price prediction. Twitter is the social media platform which provides a free platform for each individual to express their thoughts publicly. Specifically, we fetch the live twitter tweets of the particular company using the API. All the stop words, special characters are extracted from the dataset. The filtered data is used for sentiment analysis using Naïve bayes classifier. Thus, the tweets are classified into positive, negative and neutral tweets. To predict the stock price, the stock dataset is fetched from yahoo finance API. The stock data along with the tweets data are given as input to the machine learning model to obtain the result. XGBoost classifier is used as a model to predict the stock market price. The obtained prediction value is compared with the actual stock market value. The effectiveness of the proposed project on stock price prediction is demonstrated through experiments on several companies like Apple, Amazon, Microsoft using live twitter data and daily stock data. The goal of the project is to use historical stock data in conjunction with sentiment analysis of news headlines and Twitter posts, to predict the future price of a stock of interest. The headlines were obtained by scraping the website, FinViz, while tweets were taken using Tweepy. Both were analyzed using the Vader Sentiment Analyzer.

Extreme learning machine for stock price prediction

2021 ◽  
pp. 002072092098467
Author(s):  
Fangzhao Zhang
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Extreme Learning Machine ◽  
Stock Price ◽  
Market Performance ◽  
Market Analysis ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Stock Market Performance ◽  
Learning Machine

Stock market performance prediction has always been a hit research topic and is attractive due to its strong potential to generate financial profit. Being able to predict future stock price in a relatively accurate way forms a significant task of stock market analysis. Different mechanisms from fundamental analysis to statistical modeling have been deployed to study stock market performance and various factors from fundamental factors, technical factors to market sentiments are also incorporated in the stock price prediction task. However, due to the chaotic stock market performance, which is close to random walk, and the difficulty in discerning influential factors, predicting stock price faces a lot of challenges. In recent years, fast development in fields such as machine learning has offered new ways to look at this task. In this paper, we employ Extreme Learning Machine (ELM) algorithm, a recent modification of traditional feedforward neural network with single hidden layer, whose learning speed is greatly improved based on solid mathematical background and capability to circumvent problems such as local minimum is also enhanced, to construct an ELM combination model to study stock market performance and predict stock price. A comparison between the predicted output and the real data is carried out to test the feasibility of applying ELM model to stock market analysis. The result indicates that ELM model is desirable for predicting stock price variation trend while some inaccuracy exists in the prediction of peak values, which may require further model modification. Overall, by applying the machine learning model ELM to predict stock price and generating desirable outcome, this paper both contributes to offering a new way to investigate stock market performance and enlarging the field deployment of ELM model as well.

scholarly journals STOCK PRICE PREDICTION USING MARKOV CHAINS ANALYSIS WITH VARYING STATE SPACE ON DATA FROM THE CZECH REPUBLIC

2021 ◽  
Vol 24 (4) ◽  
pp. 142-155
Author(s):  
Milan Svoboda ◽  
Pavla Říhová
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Markov Chains ◽  
State Space ◽  
Stock Price ◽  
Business Models ◽  
Short Term ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Transaction Fees

The article describes empirical research that deals with short-term stock price prediction. The aim of this study is to use this prediction to create successful business models. A business model that outperforms the stock market, represented by the Buy and Hold strategy, is considered to be successful. A stochastic model based on Markov chains analysis with varying state space is used for short-term stock price prediction. The varying state spate is defined based on multiples of the moving standard deviation. A total of 80 state space models were calculated for the moving standard deviation with 5-step lengths from 10 to 30 in combination with the standard deviation multiples from 0.5 to 2.0 with the step of 0.1. The efficiency of the business models was verified for 3 long-term, liquid stocks of the Czech stock market, namely the stocks of KB, CEZ, and O2 within a 14-year period – from the beginning of 2006 to the end of 2019. Business models perform best when they use a state space defined on the length of a moving standard deviation between 15 and 30 in combination with multiples of the standard deviation between 1.1 and 1.2. Business models based on these parameters outperform the passive Buy and Hold strategy. In fact, they outperform the Buy and Hold strategy for both the entire period under review and the yielded five-year periods (including transaction fees). The only exception is the five-year periods covering 2015 for O2 stocks. After the end of the uncertainty period caused by unclear intentions of the new majority stockholder, the stock price rose sharply. These results are in conflict with the efficient markets theory and suggest that in the period under review, the Czech stock market was not effective in any form.

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