An Improved BPNN Algorithm Based on Deep Learning Technology to analyze the Market Risks of A+H Shares

The backpropagation neural network (BPNN) algorithm of artificial intelligence (AI) is utilized to predict A+H shares price for helping investors reduce the risk of stock investment. First, the genetic algorithm (GA) is used to optimize BPNN, and a model that can predict multi-day stock prices is established. Then, the Principal Component Analysis (PCA) algorithm is introduced to improve the GA-BP model, aiming to provide a practical approach for analyzing the market risks of the A+H shares. The experimental results show that for A shares, the model has the best prediction effect on the price of Bank of China (BC), and the average prediction errors of opening price, maximum price, minimum price, as well as closing price are 0.0236, 0.0262, 0.0294 and 0.0339, respectively. For H shares, the model constructed has the best effect on the price prediction of China Merchants Bank (CMB). The average prediction errors of opening price, maximum price, minimum price and closing price are 0.0276, 0.0422, 0.0194 and 0.0619, respectively.

What Do We Learn from Daily Leaders and Laggards in Stock Investment? Do They Help Outperform the Market Average?

Investors generally believe that rising stocks are more likely to maintain their trend and rise going forward, whereas the losing stocks look more price attractive. This belief can lead the investors to expect that they can outperform the average market by trading the stocks purely based on the price movements. However, this research finds that this simple trading strategy does not effectively outperform the market. Nonetheless, we find five sectors of rising stocks and three sectors of declining stocks that outperform the average market in this limited study.

Stock Investment Decision: The Effects of Personal Factors and Moderating Role of Years of Service

This paper aims to elaborate stock investment decision and to examine the impact of five influential factors as independent variables and the influence of years of investment as mediating variable. This paper is based on empirical study which involved 286 individual investors in Indonesia Stock Exchange using data from Riri et.al (2020). Structural equation modelling approach was used for estimating relationship between influential factors (e.g., personal financial needs, overconfidence, advocate recommendation, social relevance, and self or firm image) on stock investment decisions. The result found that decision on stock investment is determined by social relevance, overconfidence, personal financial need, and advocate recommendation significantly and positively. Years of Investment has played moderating role on relationship between for advocate recommendation and personal with stock investment decisions. Years of Investment is moderating variable to become a novelty this paper.

Behavioral Factors Influencing Stock Investment Decision of Individuals

Purpose: The purpose of this study is to identify the behavioral factors influencing individual investors’ decisions and to analyze the relationship between these factors and investment decision performance. Design/Methodology/Approach: The tested variables were: Anchoring bias, Gambler’s Fallacy, Overconfidence bias, Availability and Representativeness bias from heuristics factor, Mental Accounting, Loss and Regret Aversion from prospect factor, and Market variables and Herding factors. The study employed exploratory and confirmatory factor analysis. In addition, structural equation modeling is applied for the testing of the hypotheses. Findings: Prospect behavioral factor is seen to have negative correlation to investment performance. Herding, Market variables and Heuristic (including overconfidence and anchoring bias) are found to have positive correlation to investment performance. Implications: To cope with intense competition among the competitors in Nepali stock market, this study provides strong evidence herding and heuristic approach that have positive indication to investment performance

The Accuracy of Balance Model in Predicting Stock Investment During The Covid-19 Pandemic on LQ 45 Index

In doing investment, an investor certainly avoids risk; thus, the investor needs a model in making predictions to forecast the return of shares. There are two models to predict this: Capital Asset Pricing Capital (CAPM) and Arbitrage Pricing Theory (APT). The purpose of this study is to find out which models are more accurate in determining investment options, especially during the Covid-19 pandemic in companies that are included in the LQ 45 Index group. The population in this study is 50 companies listed in LQ 45 from February 2020 - July 2021. The sampling technique used in this study is purposive sampling. The data used in this study will be processed through Ms.Excel and SPSS Version 21. The data analysis techniques used in this study are the Basic Assumption Test consisting of Normality Test and Homogeneity Test, Mean Absolute Deviation (MAD), and hypothesis testing consisting of independent t-test samples. The results in this study show that Model is accurate in predicting stock returns in the Covid-19 pandemic is a CAPM model this is because the value of MAD CAPM is smaller than mad APT. Furthermore, independent t-test samples showed that H0 was rejected which meant that there was a difference in accuracy between CAPM and APT in calculating the return of LQ 45 shares. The implication of this study are expected to provide references to investors and potential investors as a source of information in decision making to make investments in this pandemic period.

ANALISIS TEKNIKAL DENGAN MENGGUNAKAN MOVING AVERAGE CONVERGENCE-DIVERGENCE DAN RELATIVE STRENGTH INDEX PADA SAHAM PERBANKAN

ABSTRACT Stock investment is an investment that has a high risk. An investor needs to do an investment analysis before deciding to invest. Investment analysis can be carried out using both fundamental and technical approaches. Technical analysis is often an option because it is fast and easy to apply. This study aims to examine the level of differences in the use of technical analysis with the moving average convergence-divergence (MACD) method and the relative strength index (RSI) as a means of making stock investment decisions. The research method used in this research is the descriptive analysis method. This research was conducted on a group of banking stocks that are included in LQ45. The results showed that there was no difference between the price of the buy signal and the sell signal before and after using the MACD and RSI methods. The results also show that there is no difference between the buy signal and the sell signal between MACD and RSI. Therefore, it can be stated that for the same object and period, the MACD and RSI methods produce the same investment decisions (buy signal and sell signal). Keywords: technical analysis, MACD, RSI, buy signal, sell signal ABSTRAK Investasi saham merupakanjenis investasi yang memiliki resiko tinggi. Seorang investor perlu melakukan analisis investasi sebelum memutuskan untuk berinvestasi. Analisis investasi dapat dilakukan dengan menggunakan pendekatan fundamental dan teknikal. Analisis teknikal seringkali menjadi pilihan karena cepat dan mudah diterapkan. Penelitian ini bertujuan untuk menguji tingkat perbedaan penggunaan analisa teknikal dengan metode moving average convergence-divergence (MACD) dan relative strength index (RSI) sebagai alat pengambilan keputusan investasi saham. Metode penelitian yang digunakan dalam penelitian ini adalah metode analisis deskriptif. Penelitian ini dilakukan pada sekelompok saham perbankan yang termasuk dalam LQ45. Hasil penelitian menunjukkan bahwa tidak ada perbedaan harga antara sinyal beli dan sinyal jual sebelum dan sesudah menggunakan metode MACD maupun RSI. Hasil penelitian juga menunjukkan bahwa tidak ada perbedaan antara sinyal beli dan sinyal jual antara MACD dan RSI. Dengan demikian dapat dikatakan bahwa untuk objek dan periode yang sama, metode MACD dan RSI menghasilkan keputusan investasi yang sama (sinyal beli dan sinyal jual). Kata kunci: analisa teknikal, MACD, RSI, sinyal beli, sinyal jual

THE IMPACT OF DIVIDEND POLICY, FINANCIAL DISTRESS RISK AND CORPORATE GOVERNANCE TOWARD STOCK PRICE

Stock investment is emerging in this era. The market on stock investment will be predicted to grow every year. Stock price is defined by offer and bid price. With this term use in Indonesia Stock Exchange, it makes people hard to guess on what will be the opening price tomorrow as asking price and bidding price need to reach an agreement. This research will investigate the impact of Dividend Policy, Financial Distress Risk, and Corporate Governance as the independent variables toward Stock Price. This research is conducted by using quantitative research method using secondary data that were taken from the LQ45 companies which are listed in Indonesian Stock Exchange with the population of 61 companies. The samples are obtained using purposive sampling method. The total sample is 42 companies from the year 2017-2019. The data analysis is using multiple linear regression analysis. Based on the results of research and analysis by using SPSS 25 indicate that: Dividend per share (dividend policy) and corporate governance have significant impact toward stock price while dividend payout ratio (dividend policy) and distress risk does not have significant impact toward stock price. It is concluded that the impact of dividend policy, financial distress risk and corporate governance is only 29.2% as the rest is impacted by other variables.

Strategic Unification of Artificial Intelligence in Foreign Direct Investment Application Forms

A foreign direct investment (FDI) is a very popular method of investing overseas but different from a stock investment in a foreign company. It could be purchasing of an interest in a company by an investor located outside its borders and in most cases, governments pay special interest on them. This is a business decision to acquire a substantial stake in a foreign business or to buy it outright as to expand its operations to a new region. Embedding artificial intelligence (AI) across the business requires significant investment and a change in overall approach. It is highly constructive and productive transformation that should be planned professionally, applied systematically, and managed strategically. AI drives meaningful value to business through better decision-making and consumer-facing applications. The general perception about filling a FDI application is a cumbersome job. Some countries manage this stage very methodically and investors always give priority for them as they can commence the production/business activities within a short period. Those countries who fail to gain this competitive advantage tend to lose the FDI opportunities even if they own various other advantages of resources to attract investors. This paper attempts to evaluate the potential of embedding a strategic unification of artificial intelligence in the application forms used to fill by investors at the time of starting foreign direct investment projects.

Implementation of GASING Stock Gamification to Increase Financial Literacy for High School Students

Financial inclusion played an essential role in increasing the nation's welfare. Therefore, it is crucial to increase financial literacy since the literacy index in Indonesia is still 38,03%. Stock market investment had the lowest contribution toward the level of literacy index. The advancement of ICT and the penetration of internet users support the spreading of financial information among the young generation. Due to its low literacy rate, Indonesians are still vulnerable to false financial information, leading to investment fraud. Gamification is chosen as an alternative method in taking advantage of interactive game development that can easily download via smartphone. The gamification system design, which is named GASING, gives the younger generation adequate information related to secure financial investment. The target game user of GASING is high-school students. The feature of the GASING that offer attractive UI/UX design was expected to increase the knowledge of high-school students in learning stock investment. The usage of GASING gamification was also expected to increase more young generation participation in the Indonesian capital market while simultaneously increasing their knowledge, skills, and confidence in the stock market.

Stock market investment and different behavioural patterns: an exploratory study

PurposeThe purpose of the research is to gain an understanding about how stock market investors impact various behavioural personality traits in various consumer groups with differing levels of motivation and capacity to absorb emerging stock market data.Design/methodology/approachThe research has used structural equation modelling (SEM) to test the validity of the theoretical model.FindingsThe current paper is the first study that uses stock market data from an emerging economy to examine the relationship between stock market investment and different behavioural patterns such as stock market attachment, trust, satisfaction and loyalty. The authors observe the presence of direct positive relationships between stock market investment and different behavioural personality traits. Moreover, the authors also observe that stock market attachment can be seen as an intermediary variable between stock investment involvement and satisfaction. The empirical findings also suggest the presence of indirect relationships between stock investment involvement and satisfaction and between stock market attachment and loyalty. The authors find that the indirect relationship between stock market attachment and loyalty occurs when the level of satisfaction is higher. Therefore, satisfaction appears to facilitate the relationship between stock market attachment and loyalty.Research limitations/implicationsOne major limitation of the study is data availability. More specifically, the study was conducted with customers of eight different banks in the province of Eskisehir, Turkey. From the 250 questionnaires distributed, 173 were returned, yielding a response rate of 69.2%.Practical implicationsBy identifying the trait characteristics of segments of stock market participants relative to their propensity to invest in stocks, it is possible to tailor messages that influence people to invest for the long term.Originality/valueThe paper deploys stock market data from an emerging economy to investigate the relationship between stock market investment and different surface traits such as stock market attachment, trust, satisfaction and loyalty. To the best of the authors' knowledge the current paper is the first such study.