scholarly journals Ensemble Stock Market Prediction using SVM,LSTM, and Linear Regression

2021 ◽  
Author(s):  
Amila Indika ◽  
Nethmal Warusamana ◽  
Erantha Welikala ◽  
Sampath Deegalla
Keyword(s):  
Linear Regression ◽  
Stock Price ◽  
Short Term Memory ◽  
Historical Data ◽  
Stock Exchange ◽  
Support Vector ◽  
Financial Forecasting ◽  
Stock Volatility ◽  
Stock Value ◽  
Under Performing

<div>Abstract: Stock forecasting is challenging because of stock volatility and dependability on external factors, such as economic, social, and political factors. This motivates investors to seek tools to identify stock trends to reap profits.</div><div><div>In this research, we compared several heterogeneous ensembles for financial forecasting, including averaging, weighted, stacking, and blending ensembles. In addition, we used a random forest regressor as the baseline.<br></div><div>Regression was used to predict the next day’s closing stock price. We used classification to label closing stock value as HIGH or LOW by comparing with the opening stock value of a particular company. We used Long Short Term Memory (LSTM) models, Linear Regression, and Support Vector Machines (SVM) as individual models. Further, we analyzed 10 years of historical data of the most active 20 companies of the NASDAQ stock exchange for implementing ensemble models.<br></div><div>In conclusion, experimental results depict blending ensembles perform the best out of compared ensembles in financial forecasting. Further, they reveal SVM is under-performing, LSTM outputs are satisfactory, while linear regression produced promising results.<br></div></div><div>Data: Data for this research was gathered from online available sources from the NASDAQ American stock exchange.</div><div>We gathered data for most active 20 companies and 10 years of historical data from 21st September 2019 backwards. We used 40044 data points in total.</div>

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.

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.

PENGARUH FREKUENSI PERDAGANGAN PADA SEKTOR PROPERTY DAN REAL ESTATE TERHADAP HARGA SAHAM

2019 ◽  
Vol 7 (02) ◽  
pp. 51
Author(s):  
Adri Wihananto
Keyword(s):  
Real Estate ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Trading ◽  
Trading Frequency ◽  
Important Thing ◽  
Know How ◽  
Stock Value ◽  
Measuring Frequency ◽  
Case Based

Trading frequency can be said as the implementation from trader of commerce. This case based on positive or negative trader reaction given by trader information.  Stock trading in BEI always fluctuate with price of volume value and frequency particularly. Frequency itself shows the company  involved or not. In trading frequency, if the indicator frequency it self shown the higher point, it means better. In spite of the most important thing is how the fluctuation or value conversion itself. On the frequencies we also could see which stocks is interested by the investor. When trading frequency high, it  may be create sense of interest from investors.The aim of this research, in order to know how far the effect of trading frequency (X) with stock value (Y) using cover stock value. The information used is begin 2008 with sample from twelve property and real estate companies. According to the research can be conclude from twelve companies in Indonesia Stock Exchange in 2008, 75 % of trading frequency samples doesn’t have signification degree between trading frequency and stock value. This case can be explained count on smaller than t tableEvaluation of this research is the trading measuring frequency at property sector and real estate not influence to stock priceKeywords : Trading Frequency, Stock Price 

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

scholarly journals Profitabilitas Terhadap Harga Saham Perusahaan Sektor Industri Dasar Dan Kimia

2019 ◽  
Vol 2 (1) ◽  
pp. 15
Author(s):  
Ajeng Septianti ◽  
Diah Yudhawati ◽  
Supramono Supramono
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Stock Price ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Sampling Technique ◽  
Profit Margin ◽  
Return On Equity ◽  
Insignificant Effect ◽  
Net Profit

The purpose of this research is to analyze the influence of Return On Equity (ROE), Net Profit Margin (NPM) to stock price in 2011-2017. This study uses secondary data, the sample in this study as many as 28 data derived from livestock feed companies listed on the Indonesia Stock Exchange from 2011 to 2017 and include complete financial report data. Sampling technique using purposive sampling technique or sampling based on certain considerations and criteria. The analytical method used is simple linear regression analysis and multiple linear regression analysis with first classical asusmsi test which includes normality test, heteroscedasticity test, autocorrelation test. The results of this study show that partially Return On Equity (ROE) has a positive and insignificant effect on price, Net Profit Margin (NPM) has a positive and insignificant effect on stock prices. Simultaneously Return On Equity (ROE) and Net Profit Margin (NPM) have positive and insignificant effect to stock price at company of basic industry and kumia subs of poultry feed listed on Indonesia Stock Exchange (BEI) year 2011- 2017.

PENGARUH IMPLEMENTASI GOOD CORPORATE GOVERNANCE TERHADAP KINERJA KEUANGAN PERUSAHAAN

2019 ◽  
Vol 14 (1) ◽  
pp. 47
Author(s):  
Bambang Purnomo Hediono ◽  
Insiwijati Prasetyaningsih
Keyword(s):  
Corporate Governance ◽  
Linear Regression ◽  
Financial Performance ◽  
Linear Regression Model ◽  
Stock Price ◽  
Stock Exchange ◽  
Operating Profit ◽  
Good Corporate Governance ◽  
Governance Index ◽  
Positive Effect

This study aims to examine the effect of Good Corporate Governance (GCG) implementation on  company,s financial performance. Sample size in this study were 16 companies listed on the Indonesia Stock Exchange. The Company’s Good Corporate Governance Index Score is based on ranking the SWA Governance Index. The analytical method used in this study uses a linear regression model. The results showed that GCG had a positive effect on corporate income, operating profit and post-tax profit. This shows that GCG has a positive effect on financial performance. Meanwhile, GCG  has no significant effect on stock price. Key Words: Good Corporate Governance (GCG), Financial Performance ABSTRAK Penelitian ini bertujuan untuk menguji pengaruh implementasi Good Corporate Governance (GCG) terhadap kinerja keuangan Perusahaan. Ukuran sampel dalam penelitian ini adalah 16 perusahaan yang terdaftar di Bursa Efek Indonesia. Skor Indek GCG Perusahaan mendasarkan pada perangkingan Indek Tata Kelola SWA.  Metode analisis yang digunakan dalam penelitian ini menggunakan model regresi  linier. Hasil penelitian menunjukkan bahwa GCG berpengaruh positif terhadap pendapatan perusahaan, laba operasional dan laba setelah pajak. Hal ini menunjukkan bahwa implementasi GCG berpengaruh positif terhadap kinerja keuangan. Sementara itu, GCG tidak berpengaruh signifikan terhadap kinerja harga saham.  Kata Kunci: Good Corporate Governance (GCG), Kinerja Keuangan

Patient visit forecasting in an emergency department using a deep neural network approach

Kybernetes ◽  
2019 ◽  
Vol 49 (9) ◽  
pp. 2335-2348 ◽  
Author(s):  
Milad Yousefi ◽  
Moslem Yousefi ◽  
Masood Fathi ◽  
Flavio S. Fogliatto
Keyword(s):  
Neural Network ◽  
Emergency Department ◽  
Linear Regression ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Network Approach ◽  
Content Type

Purpose This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days. Design/methodology/approach In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor. Findings Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression. Research limitations/implications The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications. Originality/value To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

scholarly journals Profitabilitas sebagai Pemoderasi Nilai Saham Perusahaan Industri Barang Konsumsi

2020 ◽  
Vol 30 (3) ◽  
pp. 785
Author(s):  
Hartono Hartono ◽  
Fiona Audrey ◽  
Widya Sari
Keyword(s):  
Consumer Goods ◽  
Stock Price ◽  
Sampling Method ◽  
Stock Exchange ◽  
Fixed Asset ◽  
Current Ratio ◽  
Stock Value ◽  
Asset Turnover ◽  
Inventory Turnover ◽  
Equity Ratio

This study aims to determine and analyze how the influence of Current Ratio, Inventory Turnover, Fixed Asset Turnover and Debt to Equity Ratio on Stock Price and Profitability as a moderating variable to consumer goods sector companies listed on the Indonesia Stock Exchange (IDX). Population in this study are 39 companies and 14 companies used as samples. This research uses purposive sampling method. The results of this study indicate that the Fixed Asset Ratio and Debt to Equity Ratio affects stock value. By using profitability as a moderator, Current Ratio and Debt to Equity Ratio affects the value of the stock. Keywords: Current Ratio (CR); Inventory Turnover (ITO);  Fixed Asset Turnover (FAT); Debt to Equity Ratio (DER); Stock Price.

scholarly journals Pengaruh Kurs, Suku Bunga, dan Dividen Pershare Terhadap Harga Saham Pada Sektor Perbankan yang Terdaftar di Bursa Efek Indonesia Tahun 2013– 2017

2018 ◽  
Vol 10 (1) ◽  
pp. 21-33
Author(s):  
Atika Riziqyani ◽  
Gunistiyo ◽  
Niken Wahyu C
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Interest Rate ◽  
Linear Regression ◽  
Stock Prices ◽  
Stock Price ◽  
Banking Sector ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Share Price

The effect of exchange rate, interest rate and dividend of share price on banking sector which is listed in Indonesia Stock Exchange year 2013-2017. Essay. Tegal: Faculty of Economics and Business Universitas Pancasakti Tegal,2018. The purpose of this study is to determine the ability of investors in considering stock prices in the banking sector in 2013-2017. Hypothesis in this research is 1) exchange rate effect on stock price. 2) interest rates affect the stock price. 3) dividend pershare effect on stock price. 4) exchange rate, interest rate and dividend pershare simultaneously affect the stock price. The population used in this study is a banking company that publishes stock prices listed on the Indonesia Stock Exchange in 2013-2017. The sample in this research are 21 banking companies. With technique of sampling using purposive sampling. The data in this research is quantitative data. Sources of data in this study are secondary sources obtained from the share price of an annual banking company published in Indonesia Stock Exchange period 2013-2017. Data collection techniques using documentation techniques. Data analysis method using descriptive statistic, classical assumption test, simple linear regression analysis, multiple linear regression analysis and coefficient of determination, then obtained the result of research that the exchange rate does not have a significant effect on stock prices, the interest rate does not significantly influence the stock price, against stock price, exchange rate, interest rate and dividend pershare have significant effect to stock price.

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