Stock Trading Research Articles

Psychological profile and investment decisions

We conduct a study to test whether psychological factors influence stock trading behavior in a sample of 176 individuals. Using a trading simulation game, we combined financial data with demographic information, psychological traits, and risk attitudes. Our findings indicate that conscientiousness is a significant factor in explaining higher trading volume and greater risk-taking. Furthermore, demographics and risk attitude measures influenced individual investment choices. These results demonstrate that financial decision-making and the frequency of stock trading are influenced not only by our attitudes toward risk but also by our level of conscientiousness. These findings have important implications for modeling decisions under risk and providing financial advice.

ANALYSIS OF DIFFERENCE IN STOCK PRICE, MARKET CAPITALIZATION, AND TRADING VOLUME ACTIVITY BEFORE AND DURING COVID-19

The aim for this study is to find whether if there is a difference of stock price, market capitalization, and trading volume before and during COVID-19 pandemic on agriculture and mining sector. The data used for this research is taken from January 1st until April 30th 2020. The sampling technique used on this study was purposive sampling and processed using SPSS 25. The result shows that there are a difference between stock price before and during COVID-19, market capitalization before and during COVID-19, and trading volume before and during COVID-19 on both agriculture and mining sector.

ANALYSIS OF DIFFERENCE IN STOCK PRICE, MARKET CAPITALIZATION, AND TRADING VOLUME ACTIVITY BEFORE AND DURING COVID-19

The aim for this study is to find whether if there is a difference of stock price, market capitalization, and trading volume before and during COVID-19 pandemic on agriculture and mining sector. The data used for this research is taken from January 1st until April 30th 2020. The sampling technique used on this study was purposive sampling and processed using SPSS 25. The result shows that there are a difference between stock price before and during COVID-19, market capitalization before and during COVID-19, and trading volume before and during COVID-19 on both agriculture and mining sector.

THE IMPACT OF COVID-19, MARKET CAPITALIZATION, STOCK TRADING VOLUME, AND LARGE-SCALE SOCIAL RESTRICTIONS ON STOCK RETURNS

The purpose of this research is to determine the effect of Covid-19, market capitalization, stock trading volume and large-scale social restrictions on stock returns. This research uses agriculture companies and property, real estate and building construction companies listed in Indonesian Stock Exchange from March 2, 2020 to December 31, 2020. The Covid-19 measured by growth of positive confirmed cases. The results of research in the agriculture sector show that growth of positive confirmed cases has a negative significant influence on stock return, market capitalization has no significant influence on stock return, stock trading volume and large-scale social restrictions have a positive significant influence on stock return. The results of research in the property, real estate and building construction sector show that growth of positive confirmed cases has no significant influence on stock return, market capitalization has a negative significant influence on stock return, stock trading volume and large-scale social restrictions have a positive significant influence on stock return.

THE IMPACT OF COVID-19, MARKET CAPITALIZATION, STOCK TRADING VOLUME, AND LARGE-SCALE SOCIAL RESTRICTIONS ON STOCK RETURNS

The purpose of this research is to determine the effect of Covid-19, market capitalization, stock trading volume and large-scale social restrictions on stock returns. This research uses agriculture companies and property, real estate and building construction companies listed in Indonesian Stock Exchange from March 2, 2020 to December 31, 2020. The Covid-19 measured by growth of positive confirmed cases. The results of research in the agriculture sector show that growth of positive confirmed cases has a negative significant influence on stock return, market capitalization has no significant influence on stock return, stock trading volume and large-scale social restrictions have a positive significant influence on stock return. The results of research in the property, real estate and building construction sector show that growth of positive confirmed cases has no significant influence on stock return, market capitalization has a negative significant influence on stock return, stock trading volume and large-scale social restrictions have a positive significant influence on stock return.

Risk perception and adoption of digital innovation in mobile stock trading

AbstractAn increase in retail investors, trading platforms, and smartphones is facilitating digital transformation in stock trading. Investments in mobile‐centered digitalization by trading firms are rapidly becoming a source of competitive advantage. Distinctive psychological aspects involving risks, make stock trading a unique individual decision‐making behavior. The objective of the present research is to examine the determinants of retail investors' behavioral intentions (BI) for mobile stock trading (MoST). Across two studies using a mixed method approach we empirically test a framework comprising cognitive risk‐mitigation factors, perceived risk (PR), perceived financial cost (PFC), and technology acceptance model (TAM) constructs. The results indicate information quality, privacy, and security protection to mitigate the risk perception involved in trading on mobile platforms. Further, we report the mediation of PR in the relationship between the cognitive risk‐mitigating factors and BIs to adopt MoST. Moreover, we find PFC to moderate PR and the TAM constructs' relationship with BIs to adopt MoST. Our research has important theoretical contributions and managerial implications to understand digital adoption behavior by retail investors for MoST platforms.

Study on Knowledge of investors and impact of Advisory services on investment output in Future & options

Derivative trading was launched in India on NSE as well as BSE in the year 2000 and since then it has been witnessed that trading in Derivative segment is growing at a very high speed. If we look at the data for the last 10 years i,e. from 2012-13 to 2021-2022, the turnover of trading in options and Futures on Stock & index has moved from 31533003.96 cr to 1695233134.47cr (source: NSE). It has also been observed that nowadays most of the traders who were trading in equity segment only have started trading in future and options segment. The F&O contracts are used by the investors for variety of purpose such as hedging, speculation, arbitrage, profit maximization etc. It has also been observed that nowadays most of the individuals, who have little knowledge about stock market and trading, are entering into options and future trades and some of them are taking the services of some advisors for reaping maximum profit out of it. As per the current study conducted by SEBI, it has been observed that total number of traders who traded from the top 10 brokers in Indian in F&O segments has increased to 45.2 lakhs in the FY 22 from the 7.1 lakhs in the FY 2019. It shows that there is a significant increase of around 500% in the FY 2022 as compared to the FY 2019. The current study has been conducted to understand the awareness level and perception of investors towards making investment in future and options. The data has been collected from 102 respondents of Jammu & Kashmir who are actively involved in F&O trading through Non-random purposive sampling technique. After an in-depth review of literature, a comprehensive questionnaire was developed to collect the data that can be analyzed by using the relevant statistical tools to achieve the objectives of the study. The data shows that 76% of the respondents were aware about the F&O contracts available in the market and around 70% have knowledge about the concept of expiry in future and options. Regarding preference of investment in future & options only 50% of the investors are there who prefer to make investment in future & options. It has also been found that majority of the investors have gone for taking the help of financial advisors for making investment in future and options and chi-square analysis shows that there exists a significant relationship between investment output of the investors and advisory services taken by the investors

Analysis of Investor Rationality Towards Stock Price Index and Optimal Portfolio in Go Public Company Shares on The Indonesian Stock Exchange

This study aims to determine the existence of investor rationality in choosing optimal stocks and portfolios by using a single index model on company shares listed on the IDX in 2018. The type of research used is analytic research. This analytic study aims to draw general conclusions and prove hypotheses about the average difference of two independent samples. The data used are secondary data obtained from information released by the Indonesia Stock Exchange including the daily closing stock price and the daily closing stock price index of listed companies. The sample selection method used is purposive sampling, where the sample selection is done on the basis of the researchers' consideration. The number of samples used in this study were 30 samples. The results showed that there was investor rationality towards optimal ownership of shares and portfolios by using a single index model on stocks listed on the Indonesia Stock Exchange during the weakening period of the rupiah in 2018 (t count value of 2.16 or greater than t value of α of 2.13). This can be seen from the difference in the average stock trading volume where the average trading volume of the optimal portfolio candidate shares is greater than the average trading volume of shares not the optimal portfolio candidate.
 Keywords : Investor Rationality, Share Ownership and Optimal Portfolio

Analisis Pengaruh Penjualan dan Perputaran Piutang Terhadap Profitabilitas Pada Perusahaan Manufaktur Yang Terdaftar di Bursa Efek Indonesia Tahun 2017-2021

The reason for this exploration is to find out what deals and records receivable turnover mean for benefit in assembling organizations that are recorded on the Indonesia Stock Trade from 2017 to 2021. With deals and records receivable turnover filling in as the free factors and productivity as proxied by Net revenue filling in as the reliant variable, this study utilizes a quantitative exploration approach. The data wellsprings of this assessment are discretionary and fundamental data. This study utilized a purposive examining system to choose 27 assembling organizations and 135 information tests from among the organizations that were recorded on the Indonesia Stock Trade somewhere in the range of 2017 and 2019. Numerous direct relapse was the method of information examination used in this study. The results showed that somewhat bargains on a very basic level influence efficiency and records receivable turnover moreover through and through influence benefit. Benefit is influenced at the same time by deals and records receivable turnover.”

Optimal execution with liquidity risk in a diffusive order book market

We study the optimal order placement strategy with the presence of a liquidity cost. In this problem, a stock trader wishes to clear her large inventory by a predetermined time horizon T. A trader uses both limit and market orders, and a large market order faces an adverse price movement caused by the liquidity risk. First, we study a single period model where the trader places a limit order and/or a market order at the beginning. We show the behavior of optimal amount of market order, [Formula: see text], and optimal placement of limit order, [Formula: see text], under different market conditions. Next, we extend it to a multi-period model, where the trader makes sequential decisions of limit and market orders at multiple time points.

Comparison Random Forest Regression and Linear Regression For Forecasting BBCA Stock Price

Stock trading is a popular financial instrument worldwide. In Indonesia, the stock market is known as the Indonesia Stock Exchange (BEI), and one actively traded stock is PT Bank Central Asia (BBCA). However, predicting stock price movements is challenging due to various influencing factors. Investors use fundamental and technical analyses for decision-making, but results often vary. Machine learning, particularly random forest regression and linear regression algorithms, can be used for stock price forecasting. In this paper, we compares these two machine learning methods to forecast BBCA stock prices, aiming to provide more accurate and effective solutions for investor's investment and trading decisions. The evaluation results of cross-validation mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for linear regression were 0.12848, 0.35807, 0.29570, and 0.0036%, respectively, while for random forest regression were 27473.76, 158.04, 142.70, and 1.7153%. These findings indicate that linear regression outperforms in forecasting performance.

IMPLEMENTASI GRIDSEARCHCV PADA SUPPORT VECTOR REGRESSION (SVR) UNTUK PERAMALAN HARGA SAHAM

Stock is a sign of the capital participation of a person or authority in a company (PT). PT Anabatic Technologies Tbk (ATIC) is one of the service providers and IT consultants that is included in the technology sector, which is a new sector in the IDX-IC classification. ATIC stock trading was temporarily suspended due to a significant increase in cumulative prices. This indicates that stock prices tend to be volatile and non-linear. The Support Vector Regression (SVR) method can be used to predict stock prices. SVR is able to solve non-linear data problems by using kernel functions so it can overcome overfitting problems and will give good performance. The SVR problem is difficult to determine the optimal hyperparameters, so this research uses grid search cross validation (GridSearchCV). In this research, ATIC’s daily closing price data was used with 1007 training data and 252 testing data. The results show that the best model is SVR with a linear kernel and the hyperparameters used are Cost and epsilon . The linear kernel SVR model produces a MSE of 0,001237173; SMAPE of 0,1167301; and = 0,9206643

Research on Stock Trading Strategies Based on Moving Average Technical Indicators

This study examines the application of the moving average indicator in stock trading to identify the most probable timing for profitable buying and selling of stocks. The trading data of Guizhou Maotai stock from August 27, 2020, to October 28, 2022, was selected. The conclusion drawn is that using a 5-day moving average for the short-term trend and a 20-day moving average for the long-term trend yields favorable results.The achievable profit amounts to 15,146.700000 RMB.

PENGARUH VOLUME PERDAGANGAN SAHAM, TINGKAT INFLASI DAN NILAI TUKAR TERHADAP VOLATILITAS HARGA SAHAM SUB SEKTOR BATU BARA YANG TERDAFTAR DI BURSA EFEK INDONESIA

<em>This study aims to analyze the effect of stock trading volume, inflation rate and exchange rate on the volatility of stock prices in the coal, sub-sector listed on the Indonesia Stock Exchange. The population of this study is companies listed in the coal, sub-sector on the IDX for the 2011-2020 period. The sampling method used purposive sampling. The results showed that partially the volume of stock trading and the exchange rate had a significant positive effect on stock price volatility, while the inflation rate variable had a significant negative effect on stock price volatility. Trade volume, inflation rate and exchange rate simultaneously have a significant effect on stock price volatility</em>

Quantitative Trading of Stocks Based on TD3 Algorithm

Intelligent and efficient stock analysis can help investors and institutions judge stock trends, improve investment returns, and avoid investment risks. The use of deep reinforcement learning methods to process stock data and provide investment recommendations has important research value. This question proposes to use the TD3 algorithm to implement a deep reinforcement learning model, introduce commonly used stock technical indicators, design reward and action functions, and use them to backtest recent stock trading data. Finally, comparing it with the moving average strategy and other deep reinforcement learning models, it was found that in the past decade of historical data, the annualized rate of the moving average strategy was 10% -15%, while the annualized rate of the TD3 algorithm was 23% -25%. This indicates that the TD3 algorithm can help investors or institutions make judgments and effectively improve investment returns.

Normality Test of NEPSE Sensitive Index

A return from the stock market is assumed to follow the random walk hypothesis. An investor is concerned about the risk and returns of the fund in which he or, she has invested. A risk-averse investor develops a portfolio with a secured return followed with a perfect prediction. Hence, a normality test for the market return is an easier way to diversify and eliminate risk. Thus, this paper tests the normality of the returns from the ‘blue-chip’ stocks in the context of NEPSE. A NEPSE Sensitive Index is an index calculated based on the trade of stocks for large, capital-based Nepalese listed companies with better earning histories and higher book values. The NEPSE sensitive index and NEPSE sensitive float index are tested for normality. The paper followed the data visualization technique, descriptive statistics, and test statistics to test the normality and find out the parameters of normal distribution. The paper found that the daily return from both indices best fit a normal distribution. Hence, two basic statistical parameters from the returns, i.e., the mean and standard deviation, are sufficient to estimate future trend for ‘blue-chip’ stock return in Nepal. The governing body should develop a new index that would better represent the market movement in the coming days that would be beneficial to predict the return movement from NEPSE.

THE REACTION OF INDONESIAN CAPITAL MARKET TO THE RUSSIAN INVASION ON UKRAINE (EVENT STUDY ON COMPANIES LISTED IN THE FOOD AND BEVERAGE SECTOR OF THE INDONESIA STOCK EXCHANGE)

The capital market is an important entity due to its role as an economic indicator for a country. The more important the existence of the capital market is to a country's economy, the more sensitive the market is to available information. This makes an event or incident capable of causing a market reaction, especially if the event can affect the economy and global conditions. On February 24, 2022, the Russian Federation invaded Ukraine, causing a surge in world food commodity prices, especialy wheat. The purpose of this research is to determine the Indonesian capital market's reaction to the Russian invasion of Ukraine, measured by abnormal return, trading volume activity, and market capitalization differences before and after the event. The observation period was conducted for 30 days before and after the event. This study is quantitative and uses comparative methods. The sampling method used is purposive sampling, which gives a research sample of 47 companies in the food and beverage sub-sector of the Indonesian Stock Exchange. The results of this study show that the Russian invasion of Ukraine did not contain information that caused the market to react, as evidenced by the lack of significant differences in abnormal return and market capitalization before and after the invasion. However, the Russian invasion of Ukraine contained information that caused market participants to react in stock trading, as evidenced by significant differences in the trading volume activity variable. Keywords: event study, abnormal return, trading volume activity, market capitalization

EMD-BiLSTM Stock Price Trend Forecasting Model based on Investor Sentiment

The movement of stock prices is the focus of investors' attention in the stock market, so stock price trend prediction has always been a hot topic in quantitative investment research. Traditional machine learning forecasting models are difficult to process nonlinear, high-frequency, high-noise stock price time series, which makes the prediction accuracy of stock price trends low. In order to improve the prediction accuracy, according to the temporal characteristics of stock price data, it is proposed to use a combination of empirical mode decomposition (EMD), investor sentiment and two-way long short-term memory neural network to predict the rise and fall of stock prices. Firstly, the empirical mode decomposition algorithm is used to extract the characteristics of the stock price time series on different time scales, and the investor sentiment indicators of the text from the close of the previous stock trading day to the opening of the next trading day are extracted by constructing a financial sentiment dictionary, and finally the EMD-BiLSTM model is used to predict the rise and fall of the next index trading day. Experiments on the dataset of stock price series show that the optimized BiLSTM model has strong predictive ability for the trend of consumer sector indexes.

Ameliorated graph sample and aggregate network and convolutional neural network for stock trading decisions

Both graph neural network and convolutional neural network (CNN) perform well in predicting the trading signals of time series data. However, these models often encounter challenges in profitability due to either inadequate information or algorithmic limitations. A model AG-MCNN is proposed by combining ameliorated graph sample and aggregate network (GraphSAGE) and multi-CNN for stock trading decisions. Ameliorated GraphSAGE (AG) is used to learn graph embedding in daily market network constructed from comprehensive market information, with neighbors having higher correlation exerting stronger influence on the target node. Moreover, a more reasonable graph modeling approach is adopted to preserve the important features of time series while filtering out noise. Multi-scale time series and comments with sentiment information are collected to analyze short-term and long-term trends. The short-term features of the weighted graph are extracted by a dual concatenated CNN. The pre-trained model is fine-tuned based on transfer learning to address the issue of insufficient sentiment information in the long-term trend. Experimental results demonstrate that AG-MCNN achieves an average profit rate of 68.4094%, compared to the highest value of 46.1091% for comparison models and 18.2234% for the buy-and-hold strategy. AG-MCNN’s high level of profitability in stock investments makes it a promising choice for investors.

When financial advice rocks the market

We document the impact of recommendations by a hugely influential Chilean pension advisor, H&L, on pension investments by individuals, domestic stock market outcomes, and pension fund manager investment strategies. Following H&L's retirement portfolio recommendations, pension investors shifted amounts that, in a week, often exceeded 100% of monthly domestic stock trading volume. The market believed the recommendations—domestic stock prices responded, but they did so without trade: the massive portfolio shifts did not generate abnormally-high trading volume. To accommodate H&L's impact, pension managers adjusted portfolio compositions, making them more similar, and shifted holdings of liquid assets rather than domestic stocks.