A Stock Prices Prediction Approach Via Neural Network by Several Investor Indicators

The prediction of stock indicators such as prices, trends and market indices is the focus of researchers. However, stock market has the characteristics of high noise and non-linearity. Generally, linear algorithms are not good for predicting stock market indicators. Therefore, BP neural network, a model suitable for nonlinear task, is widely used in stock market forecasting. However, many BP neural network prediction models are only based on historical stock quantitative data, and do not consider the impact of investor behavior on the stock market. Therefore, based on historical stock data and quantitative data of investor behavior of ten selected Chinese stocks, this paper trains a three-layer BP neural network to predict the stock prices such as the highest price ,the opening price ,the closing price, the lowest price in a short term. And then, the model that incorporates the investor behavior indicator is compared with the model that is not added. The results show that investor behavior indicators can improve the accuracy and generalization of the stock price forecasting model effectively, especially when the model based on stock quantitative data has a poor prediction accuracy on the test set.

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Applications of neural network based methods on stock market prediction: survey

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.6.10070 ◽

2018 ◽

Vol 7 (2.6) ◽

pp. 71 ◽

Cited By ~ 1

Author(s):

Avilasa Mohapatra ◽

Smruti Rekha Das ◽

Kaberi Das ◽

Debahuti Mishra

Keyword(s):

Neural Network ◽

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Prediction Models ◽

Optimization Techniques ◽

Stock Market Prediction ◽

Stock Price Prediction ◽

Statistical Measures ◽

Financial Application

Financial forecasting is one of the domineering fields of research, where investor’s money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann’s development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

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

Frontiers of Business Research in China ◽

10.1186/s11782-020-00089-z ◽

2020 ◽

Vol 14 (1) ◽

Cited By ~ 2

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.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18041398 ◽

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.

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

Tạp chí Nghiên cứu Tài chính - Marketing ◽

10.52932/jfm.vi60.5 ◽

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.

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Short-Term Wind Power Dynamic Prediction Based on GA-BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.986-987.524 ◽

2014 ◽

Vol 986-987 ◽

pp. 524-528 ◽

Cited By ~ 2

Author(s):

Ting Jing Ke ◽

Min You Chen ◽

Huan Luo

Keyword(s):

Neural Network ◽

Prediction Model ◽

Wind Power ◽

Bp Neural Network ◽

Prediction Models ◽

Model Parameters ◽

Short Term ◽

Dynamic Prediction ◽

Power Dynamic ◽

Prediction Approach

This paper proposes a short-term wind power dynamic prediction model based on GA-BP neural network. Different from conventional prediction models, the proposed approach incorporates a prediction error adjusting strategy into neural network based prediction model to realize the function of model parameters self-adjusting, thus increase the prediction accuracy. Genetic algorithm is used to optimize the parameters of BP neural network. The wind power prediction results from different models with and without error adjusting strategy are compared. The comparative results show that the proposed dynamic prediction approach can provide more accurate wind power forecasting.

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Life-cycle asset allocation and the peso problem: does ambiguity aversion matter?

Review of Behavioral Finance ◽

10.1108/rbf-11-2020-0297 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Nuno Silva

Keyword(s):

Life Cycle ◽

Stock Market ◽

Asset Allocation ◽

Stock Prices ◽

Stock Price ◽

Ambiguity Aversion ◽

Content Type ◽

Portfolio Decisions ◽

State Of The Economy ◽

The Impact

PurposeThe study aims to show that ambiguity aversion exerts a non-negligible effect on the investors' decisions, especially due to the possibility of sharp declines in stock prices.Design/methodology/approachThe vast majority of previous studies on life-cycle consumption and asset allocation assume that the equity premium is constant. This study evaluates the impact of rare disasters that shift the stock market to a low return state on investors' consumption and portfolio decisions. The author assumes that investors are averse to ambiguity relative to the current state of the economy and must incur a per period cost to participate in the stock market and solve their optimal consumption and asset allocation problem using dynamic programming.FindingsThe results show that most young investors choose not to invest in stocks because they have low accumulated wealth and the potential return from their stock market investments would not cover the participation costs. Furthermore, ambiguity-averse investors hold considerably fewer stocks throughout their lifetime than ambiguity-neutral ones. The fraction of wealth invested in stocks over the typical consumer's life is hump-shaped: it is low for a young individual, peaks at his early 30s and then decreases until his retirement age.Originality/valueTo the best of the author’s knowledge, this is the first study that assesses the impact of negative stock price jumps on the optimal portfolio of an ambiguity-averse investor.

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Impact on Stock Price by the Inclusion to and Exclusion from CNX Nifty Index

Global Business Review ◽

10.1177/097215091101300103 ◽

2012 ◽

Vol 13 (1) ◽

pp. 39-50 ◽

Cited By ~ 5

Author(s):

M. Selvam ◽

G. Indhumathi ◽

J. Lydia

Keyword(s):

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Research Study ◽

Stock Exchange ◽

Valuation Effect ◽

Index Maintenance ◽

Indian Stock Market ◽

The Impact ◽

Indian Perspective

Changes in an index are a regular phenomenon and they take place due to the inclusion and exclusion of stocks from the index. The inclusion or exclusion of stocks creates great impact on the value of the firm. However, these changes are simply a short-lived event with no permanent valuation effect. The present research study analyzed the impact of the inclusion into and exclusion of certain stocks from National Stock Exchange (NSE) S&P CNX Nifty index with Indian perspective. The study provides evidence on whether the announcements of Nifty index maintenance committee have any information content. This will also demonstrate the efficiency of Indian stock market with particular reference to NSE. The study revealed that on an average, no permanent effects were observed on stock prices. It is also found from the study that the NSE reacted unfavourably to the inclusion and exclusion of stocks and it is impossible to earn any excess returns where the particular stocks are included or excluded from the index.

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Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

Sustainability ◽

10.3390/su13031011 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1011

Author(s):

Seung Hwan Jeong ◽

Hee Soo Lee ◽

Hyun Nam ◽

Kyong Joo Oh

Keyword(s):

Genetic Algorithm ◽

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Trading Volume ◽

Weighted Average ◽

Market Liquidity ◽

Average Price ◽

Volume Data ◽

The Impact

Research on stock market prediction has been actively conducted over time. Pertaining to investment, stock prices and trading volume are important indicators. While extensive research on stocks has focused on predicting stock prices, not much focus has been applied to predicting trading volume. The extensive trading volume by large institutions, such as pension funds, has a great impact on the market liquidity. To reduce the impact on the stock market, it is essential for large institutions to correctly predict the intraday trading volume using the volume weighted average price (VWAP) method. In this study, we predict the intraday trading volume using various methods to properly conduct VWAP trading. With the trading volume data of the Korean stock price index 200 (KOSPI 200) futures index from December 2006 to September 2020, we predicted the trading volume using dynamic time warping (DTW) and a genetic algorithm (GA). The empirical results show that the model using the simple average of the trading volume during the optimal period constructed by GA achieved the best performance. As a result of this study, we expect that large institutions will perform more appropriate VWAP trading in a sustainable manner, leading the stock market to be revitalized by enhanced liquidity. In this sense, the model proposed in this paper would contribute to creating efficient stock markets and help to achieve sustainable economic growth.

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Short term stock price forecasting with application of neural network

10.32920/ryerson.14668224 ◽

2021 ◽

Author(s):

Jiayin Liu

Keyword(s):

Neural Network ◽

Stock Market ◽

Bp Neural Network ◽

Stock Price ◽

Principal Component ◽

Price Forecasting ◽

Short Term ◽

Stock Price Forecasting ◽

Price Forecast ◽

Short Term Prediction

With the world’s rapid economic growth and the expansion of stock market, it produced a large amount of valuable data information. That data become an important investors in stock investment analysis subject Thorough analysis the short-term stock price forecast problem and comparing a variety of stock price forecasting method, on the basis of BP neural network (BPNN) [1] and principal component analysis (PCA）[2] and genetic algorithm and the feasibility of short-term prediction of stock price .BP neural network can use the study of historical stock market data, find out the inherent law of development and change of the stock market, so as to realize the future stock price data changes over a period of time.

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Stock Price Prediction Using LSTM on Indian Share Market

10.29007/qgcz ◽

2019 ◽

Author(s):

Achyut Ghosh ◽

Soumik Bose ◽

Giridhar Maji ◽

Narayan Debnath ◽

Soumya Sen

Keyword(s):

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Short Term Memory ◽

Investor Sentiment ◽

Spot Price ◽

Physical Factors ◽

Stock Price Prediction ◽

The Impact ◽

A Company

Predicting stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors,etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. We investigate data analysis as a game changer in this domain.As per efficient market theory when all information related to a company and stock market events are instantly available to all stakeholders/market investors, then the effects of those events already embed themselves in the stock price. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trend. ML techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. We propose a framework using LSTM (Long Short- Term Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company.

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