STOCK MARKET PREDICTION: USING ECONOMETRIC MODELS AND NEURAL NETWORKS

2021 ◽  
pp. 134-139
Author(s):  
Parichay Pothepalli
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Mean Squared Error ◽  
Econometric Models ◽  
Paper Machine ◽  
Squared Error ◽  
Recursive Neural Networks ◽  
Stock Market Trading

Stock market trading involves buying and selling of shares or stocks, which represents ownership of business. This research paper will focus on capturing the algorithmic trading based on historical data and compare present day algorithms to nd the best t model to understand the underlying patterns in stock market trading. A comparative analysis of closing stock price for 12 companies from three different sectors has been considered to understand the efcacy of the models in order to predict the future stock prices with minimal errors. Stock market was earlier predicted using traditional econometric models like the ARIMA and SARIMA, however, in this paper, Machine Learning, a part of Articial Intelligence will be incorporated in the stock data collected from Yahoo Finance to train models and provide predictions/decisions without being explicitly programmed to do so. Models such as OLS, SARIMA, Convolutional Neural Networks and Recursive Neural Networks (LSTM) will also be used to analyze the historical stock data and will be compared for accuracy using testing parameters like Mean Squared Error (MSE).

Predicting Stock Price Using Artificial Neural Networks

2019 ◽  
Vol 118 (8) ◽  
pp. 96-117
Author(s):  
Dr. Nigama. K ◽  
Dr. R Alamelu ◽  
Dr. S. Selvabaskar ◽  
Dr. K.G. Prasanna Sivagami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Explicit Knowledge ◽  
Stock Exchange ◽  
Market Price ◽  
Financial Investment ◽  
Artificial Neural

Stock market facilitates the economic activities that contribute to a nation’s growth and prosperity. This is viewed as one of the lucrative avenues for financial investment. Although the stock market is a thrilling and potential opportunity to grow one’s money, it brings along with it certain challenges, because, there is no universal rule that suggests profitable investments.  Investors, corporate and advisors employ several techniques like fundamental and technical analysis, trend analysis and other analysis to suggest stocks that will give best yields but such tools are neither consistent nor foolproof in the prediction of stock prices. But human exertions to convert the tacit knowledge into explicit knowledge has never found any alternate. More, the uncertainties, more the efforts to know them with certainty.  Digital economy with its advanced technological tools aids the pursuit of not only understanding uncertainties but also predicting the future with maximum precision. The most prominent techniques in the technological realm includes the usage of artificial neural networks (ANNs) and Genetic Algorithms. This paper discusses the stock prices forecasting ability of Bombay stock exchange trend using genetically evolved neural networks, the input being the closing price of the previous five years and output being the price for the next day. Risk (Standard deviation), Average Return, variance and Market price are chosen as indicators of the performance. The objective of this study is to give an overview of the application of artificial neural network in predicting stock market.

Macroeconomic Factors and Pakistani Equity Market”

2004 ◽  
Vol 43 (4II) ◽  
pp. 619-637 ◽  
Author(s):  
Muhammad Nishat ◽  
Rozina Shaheen
Keyword(s):  
Stock Market ◽  
Industrial Production ◽  
Stock Prices ◽  
Stock Price ◽  
Consumer Price Index ◽  
Reverse Causality ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Equilibrium Relationships

This paper analyzes long-term equilibrium relationships between a group of macroeconomic variables and the Karachi Stock Exchange Index. The macroeconomic variables are represented by the industrial production index, the consumer price index, M1, and the value of an investment earning the money market rate. We employ a vector error correction model to explore such relationships during 1973:1 to 2004:4. We found that these five variables are cointegrated and two long-term equilibrium relationships exist among these variables. Our results indicated a "causal" relationship between the stock market and the economy. Analysis of our results indicates that industrial production is the largest positive determinant of Pakistani stock prices, while inflation is the largest negative determinant of stock prices in Pakistan. We found that while macroeconomic variables Granger-caused stock price movements, the reverse causality was observed in case of industrial production and stock prices. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock market and changes in the real economy are relatively short.

scholarly journals Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Ding Ding ◽  
Chong Guan ◽  
Calvin M. L. Chan ◽  
Wenting Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Digital Transformation ◽  
Google Trends ◽  
Building Stock ◽  
Market Sentiment ◽  
Price Performance ◽  
Novel Coronavirus ◽  
The Impact

Abstract As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

scholarly journals The Impact of COVID-19 on the Stock Price of Socially Responsible Enterprises: An Empirical Study in Taiwan Stock Market

2021 ◽  
Vol 18 (4) ◽  
pp. 1398
Author(s):  
Kuo-Jung Lee ◽  
Su-Lien Lu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Recovery Phase ◽  
Socially Responsible ◽  
Cumulative Impact ◽  
Study Approach ◽  
Price Performance ◽  
The Impact

This study examines the impact of the COVID-19 outbreak on the Taiwan stock market and investigates whether companies with a commitment to corporate social responsibility (CSR) were less affected. This study uses a selection of companies provided by CommonWealth magazine to classify the listed companies in Taiwan as CSR and non-CSR companies. The event study approach is applied to examine the change in the stock prices of CSR companies after the first COVID-19 outbreak in Taiwan. The empirical results indicate that the stock prices of all companies generated significantly negative abnormal returns and negative cumulative abnormal returns after the outbreak. Compared with all companies and with non-CSR companies, CSR companies were less affected by the outbreak; their stock prices were relatively resistant to the fall and they recovered faster. In addition, the cumulative impact of the COVID-19 on the stock prices of CSR companies is smaller than that of non-CSR companies on both short- and long-term bases. However, the stock price performance of non-CSR companies was not weaker than that of CSR companies during times when the impact of the pandemic was lower or during the price recovery phase.

EVOLUTION OF SHANGHAI STOCK MARKET BASED ON MAXIMAL SPANNING TREES

2012 ◽  
Vol 27 (03) ◽  
pp. 1350022 ◽  
Author(s):  
CHUNXIA YANG ◽  
YING SHEN ◽  
BINGYING XIA
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Spanning Tree ◽  
Stock Price ◽  
Spanning Trees ◽  
Turning Points ◽  
Single Step ◽  
P Value ◽  
Power Law Distribution ◽  
Structure Variation

In this paper, using a moving window to scan through every stock price time series over a period from 2 January 2001 to 11 March 2011 and mutual information to measure the statistical interdependence between stock prices, we construct a corresponding weighted network for 501 Shanghai stocks in every given window. Next, we extract its maximal spanning tree and understand the structure variation of Shanghai stock market by analyzing the average path length, the influence of the center node and the p-value for every maximal spanning tree. A further analysis of the structure properties of maximal spanning trees over different periods of Shanghai stock market is carried out. All the obtained results indicate that the periods around 8 August 2005, 17 October 2007 and 25 December 2008 are turning points of Shanghai stock market, at turning points, the topology structure of the maximal spanning tree changes obviously: the degree of separation between nodes increases; the structure becomes looser; the influence of the center node gets smaller, and the degree distribution of the maximal spanning tree is no longer a power-law distribution. Lastly, we give an analysis of the variations of the single-step and multi-step survival ratios for all maximal spanning trees and find that two stocks are closely bonded and hard to be broken in a short term, on the contrary, no pair of stocks remains closely bonded for a long time.

scholarly journals Disposition Effect and Return Predictability in the Tunisian Stock Market

2017 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Cheïma Hmida ◽  
Ramzi Boussaidi
Keyword(s):  
Stock Market ◽  
Behavioral Finance ◽  
Stock Prices ◽  
Stock Price ◽  
Return Predictability ◽  
Capital Gains ◽  
Disposition Effect ◽  
Individual Investors ◽  
Long Run ◽  
Return Reversal

The behavioral finance literature has documented that individual investors tend to sell winning stocks more quickly than losing stocks, a phenomenon known as the disposition effect, and that such a behavior has an impact on stock prices. We examined this effect in the Tunisian stock market using the unrealized capital gains/losses of Grinblatt & Han (2005) to measure the disposition effect. We find that the Tunisian investors exhibit a disposition effect in the long-run horizon but not in the short and the intermediate horizons. Moreover, the disposition effect predicts a stock price continuation (momentum) for the whole sample. However this impact varies from an industry to another. It predicts a momentum for “manufacturing” but a return reversal for “financial” and “services”.

scholarly journals Is Abnormally Large Volume a Clue?

2016 ◽  
Vol 8 (9) ◽  
pp. 226
Author(s):  
Tsung-Hsun Lu ◽  
Jun-De Lee
Keyword(s):  
Stock Market ◽  
Large Volume ◽  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Sensitive Analysis ◽  
Support Level ◽  
Out Of Sample ◽  
Sample Test ◽  
Abnormal Trading Volume

This paper investigates whether abnormal trading volume provides information about future movements in stock prices. Utilizing data from the Taiwan 50 Index from October 29, 2002 to December 31, 2013, the researchers employ trading volume rather than stock price to test the principles of resistance and support level employed by technical analysis. The empirical results suggest that abnormal trading volume provides profitable information for investors in the Taiwan stock market. An out-of-sample test and a sensitive analysis are conducted for the robustness of the results.

scholarly journals The Effect of The Match Results on Bali United’s Stock Price Movements

2020 ◽  
Vol 3 (1) ◽  
pp. 26
Author(s):  
Agung Novianto Margarena ◽  
Arian Agung Prasetiyawan
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
European Countries ◽  
Football Club ◽  
Study Results ◽  
Price Movements ◽  
Positive Effect ◽  
Stock Price Movements

This study was conducted due to differences in the study results inseveral countries related to the effect of the match results on stockmovements. Dimic et. al (2019) stated the match results effect themovement of stock prices, while Mishra & Smyth (2010) stated thevice versa. Then, Floros (2014) put forward different results throughthe study of four clubs in four European countries. Thus, this studyreexamines the effect of the match results on the stock pricemovement of Bali United. Moreover, Bali United is the first SoutheastAsian football club to be listed on the stock market. This study uses aquantitative method with a sample of 31 Bali United’s matches afterlisted on the stock market. The data were analyzed using simple linearregression with SPSS 21 with either won, drawn or lost match resultsrepresented by goal margins. The stock price movements arerepresented by stock prices after the results of the match. It was foundthat the results of the match had a positive effect on the stockmovement of Bali United

scholarly journals The effects of dividend policy to stock prices of listed companies in Vietnam stock market

2020 ◽  
Author(s):  
Thị Lam Hồ ◽  
Thùy Phương Trâm Hồ
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Dividend Policy ◽  
Corporate Value ◽  
Business Managers ◽  
Dividend Payment ◽  
Share Prices ◽  
Finance Management ◽  
The Impact

Dividend policy is one of the most important policies in corporate finance management. Understanding the impact of dividend policy on the distribution of profits, corporate value and thus on the stock price is important for business managers to make policies and for investors to make investment decisions. This study is conducted to evaluate the impact of dividend policy on share prices for companies listed on Vietnam’s stock market in the period from 2010 to 2018, based on the availability of continuous dividend payment data. Using the FGLS method with panel data of 100 companies listed on the HoSE and HNX, we find evidence of the impact of dividend policy on stock prices, supporting supports the bird in the hand and the signal detection theories. The findings of this study help to suggest a few recommendations for business managers and investors.

scholarly journals IntelliFin: Advanced Stock Prediction using Hybrid ML and LSTM Model with Financial Indicators powered by Sentiment Determination using NLP

2020 ◽  
Vol 9 (5) ◽  
pp. 428-433
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Majority Voting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Financial History ◽  
The Right

Stock Trading has been one of the most important parts of the financial world for decades. People investing in the share market analyze the financial history of a corporation, the news related to it and study huge amounts of data so as to predict its stock price trend. The right investment i.e. buying and selling a company stock at the right time leads to monetary benefits and can make one a millionaire overnight. The stock market is an extremely fluctuating platform wherein data is produced in humongous quantities and is influenced by numerous disparate factors such as socio-political issues, financial activities like splits and dividends, news as well as rumors. This work proposes a novel system “IntelliFin” to predict the share market trend. The system uses the various stock market technical indicators along with the company's historical market data trends to predict the share prices. The system employs the sentiment determination of a company's financial and socio-political news for a more accurate prediction. This system is implemented using two models. The first is a hybrid LSTM model optimized by an ADAM optimizer. The other is a hybrid ML model which integrates a Support Vector Regressor, K-Nearest Neighbor classifier, an RF classifier and a Linear Regressor using a Majority Voting algorithm. Both models employ a sentiment analyzer to account for the news impacting the stock prices which is powered by NLP. The models are trained continuously using Reinforcement Learning implemented by the Q-Learning Algorithm to increase the consistency and accuracy. The project aims to support the inexperienced investors, who don't have enough experience in investing in the stock market and help them maximize their profit and minimize or eliminate the losses. The developed system will also serve as a tool for professional investors to help and aid their decision making.

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