scholarly journals Machine Learning Model Approaches for Price Prediction in Coffee Market using Linear Regression, XGB, and LSTM Techniques

2021 ◽  
pp. 10-48
Author(s):  
Tesyon Korjo Hwase ◽  
Abdul Joseph Fofanah
Keyword(s):  
Comparative Analysis ◽  
Linear Regression ◽  
Stock Market ◽  
Future Price ◽  
Android Application ◽  
Commodity Exchange ◽  
Price Prediction ◽  
Prediction Algorithms ◽  
Short Period ◽  
The Future

Investors and other business persons have a desire to know about the future market price because, if the investors know about the future price of a certain commodity or stock it will help them to make appropriate business decisions and they can also get profit out of their investment. There are many previous researches that has been done on stock market predictions but there is no related research that has been done on Ethiopia commodity exchange (ECX). Performing future price prediction with better accuracy and performing comparative analysis between the algorithms for two of Ethiopia commodity exchange (ECX) items which are Coffee and Sesame as the research key objectives. Three different types of prediction algorithms to predict the future price, such as Linear Regression (LR), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM) was utilized. There are limited researches worked on price prediction of ECX items specifically, the idea of the price prediction on different Stock markets like New York stock market Exchange and other commodity market items prediction in order to develop our research in ECX was presented. The study apart from predicting the future price, comparative analysis was implemented between the prediction algorithms that we used based on their performance. Two different datasets from ECX: coffee and sesame were used. The reason for the utilization of these datasets is, the commodity items are the largest export items in Ethiopia which makes them very important for Ethiopian economy, and the different datasets helps us to get the advantage of evaluating the algorithms with different number of datasets, since sesame dataset has 7205 instances and coffee dataset has 1540 instances and both of them has 11 attributes. We build an android application in order two implement our algorithms on mobile applications and see if it is possible to implement the prediction algorithms on mobile platforms and make it easy and accessible to users. We call this mobile application Ethiopia Coffee Prices Predictor (ECPP). This application will be used to display the prediction result of Ethiopia Coffee price for short period and it is designed in the way to be user friendly. The programming environment used to implement the prediction algorithms is python, java programming language to design our android application and we used PHP to implement the API, and finally we used MySQL database in order to store information’s online and make them accessible everywhere.

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.

scholarly journals Future Stock Market Prediction Using Machine Learning

2021 ◽  
pp. 553-557
Author(s):  
Raghavendra Likhite ◽  
Gowardhan Mahajan ◽  
Samadhan Padulkar ◽  
Suraj Kakani ◽  
P. T. Suradkar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Stock Market ◽  
Support Vector ◽  
Stock Market Prediction ◽  
Highly Effective ◽  
The Future

Stock market prediction using machine learning is highly effective to predict the future prices of the stock with minimum investment. This paper proposes the system that will predict the future prices of the stock of different companies this prediction will help a investor to take decisions to maximize profits. This paper shows that by using different techniques like support vector, LSTM, linear regression future prices of the stock can be effectively predicted.

scholarly journals Indian Stock-Market Prediction using Stacked LSTM AND Multi-Layered Perceptron

2020 ◽  
Vol 9 (3) ◽  
pp. 1051-1055
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Future Research ◽  
Future Price ◽  
Data Set ◽  
Stock Market Prediction ◽  
The Future ◽  
Future Value ◽  
Indian Stock Market

We aim to construe the Stacked Long–Short term memory (LSTM) and Multi-layered perceptron intended for the NSE-Stock Market prediction. Stock market prediction can be instrumental in determining the future value of a company stock.It is imperative to say that a successful prediction of a stock's future price could yield significant profit which would be beneficial for those who invested in the pipeline of things including stock market prediction. The model uses the information pertaining to the stocks and contemplates the previous model accuracy to innovate the approach used in our paper. The experimental evaluation is based on the historical data set of National Stock Exchange (NSE). The proposed approach aims to provide models like Stacked LSTM and MLP which perform better than its contemporaries which have been achieved to a certain extent. This can be verified by the results embedded in the paper . The future research can be focused on adding more variables to the model by fetching live data from the internet as well as improving model by selecting more critical factors by ensemble learning.

scholarly journals Stock Price Prediction using Linear Regression in Machine Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 1382-1385 ◽  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Stock Price ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Past ◽  
Stock Value ◽  
The Future ◽  
The Cost

Forecast of financial exchange has been an alluring subject to the stock representatives and the specialists from different fields. Stock value forecast is dependably a dominating objective for each speculator which encourages them to realizing the future costs thinking about the past records. There have been various examinations to foresee the cost of the loads of a specific organization utilizing AI method. In this paper we would utilize straight relapse to foresee the stock cost of the organization.

scholarly journals Assessing the Impact of COVID-19 Pandemic on the Stock and Commodity Markets Performance and Sustainability: A Comparative Analysis of South Asian Countries

2021 ◽  
Vol 13 (10) ◽  
pp. 5669
Author(s):  
Farhan Ahmed ◽  
Aamir Aijaz Syed ◽  
Muhammad Abdul Kamal ◽  
Maria de las Nieves López-García ◽  
Jose Pedro Ramos-Requena ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Stock Market ◽  
South Asian ◽  
Oil Prices ◽  
Commodity Markets ◽  
Commodity Market ◽  
Asian Countries ◽  
Short Period ◽  
The Impact ◽  
Second Wave

COVID-19 is certainly the first sustainability crisis of the 21st century. The paper examines the impact of COVID-19 on the Indian stock and commodity markets during the different phases of lockdown. In addition, the effect of COVID-19 on the Indian stock and commodity markets during the first and second waves of the COVID-19 spread was compared. A comparative analysis of the stock market performances and sustainability of selected South Asian countries is also included in the study, which covers the lockdown period as well as the time frame of the first and second waves of COVID-19 spread. To examine the above relationship, the conventional Welch test, heteroskedastic independent t-test, and the GMM multivariate analysis is employed, on the stock return, gold prices, and oil prices. The findings conclude that during the different phases of lockdown in India, COVID-19 has a negative and significant impact on oil prices and stock market performance. However, in terms of gold prices, the effect is positive and significant. The results of the first wave of COVID-19 infection also corroborate with the above findings. However, the results are contradictory during the second wave of coronavirus infection. Furthermore, the study also substantiates that COVID-19 has significantly affected the stock market performances of selected South Asian countries. However, the impact on the stock market performances was only for a short period and it diminished in the second wave of COVID-19 spread in all the selected South Asian countries. The findings contribute to the research on the stock and commodity market impact of a pandemic by providing empirical evidence that COVID-19 has spill-over effects on stock markets and commodity market performances. This result also helps investors in assessing the trends of the stock and commodity markets during the pandemic outbreak.

scholarly journals The usage of gold and the investment analysis based on gold rate in India

2019 ◽  
Vol 9 (5) ◽  
pp. 4296
Author(s):  
Vanitha S ◽  
Saravanakumar K
Keyword(s):  
Comparative Analysis ◽  
Stock Market ◽  
Crude Oil ◽  
Mutual Fund ◽  
Oil Price ◽  
Investment Analysis ◽  
Crude Oil Price ◽  
Investment Pattern ◽  
Indian Context ◽  
The Future

Gold is one of the main commodities where the customers invest their money comparatively with bank for better interest. In the Indian context people purchase gold for their children’s marriages for later period. The investment in gold is better suits for easy conversion into money with quickest possible time from the bank and gold merchants. The appreciation or depreciation of gold based on other investment options like fixed deposit, provident fund, international crude oil price, stock market, mutual fund etc. The comparative analysis of gold with other investment options give an edge to the customer to clearly understand the investment pattern for their hard-earned money expected to give good returns in the future.

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