Stock Trading Research Articles

De-centring the interface. Towards the integrated study of interfacial relations

ABSTRACT Digital interfaces have become the primary way people relate to technology. While tangible and graphical user interfaces have long attracted scholarly attention, a variety of different interfaces connect cityscapes, homes, and brains with the digital realm. These connections may be lifesaving for some and a vehicle for invasive surveillance for others. Interfaces are thus also sites where layered relations, both digital and analogue, become translated, contested, and maintained. Furthermore, interfaces lie at the heart of new forms of machine-to-machine interaction and coordination. Software interfaces such as Application Programming Interfaces, have come to play a pivotal role in shaping platform economies, or automating entire sectors such as stock trading. Despite their ubiquity and variety, interfaces are largely reduced to a user-facing device, for example, a screen or a keyboard. In this article, we endeavour to go beyond this narrow notion of the interface. Drawing on an emerging body of literature that takes interfaces as the starting point for studies of the digital, we lay the groundwork for the integrated study of interfaces that conceptualises them as processual, distributed, and constitutively political. From this perspective, we focus not on what interfaces are but what they do. This shift towards processes of interfacing expands the scope of interface studies beyond the user-machine relation and towards the body-machine and machine-to-machine relation. Ultimately, we contend that interface studies must account for the layering of these interfacial relations.

National differences in gambling-driven stock trading behavior: evidence from a simulated trading game

AbstractThere are significant differences in real-life gambling behavior between Germany and Italy. Contemporary theories suggest that these differences in individual preferences would translate to differences in trading behavior. We elicited trading behavior from participants of both Italian and German language groups using a stylized hold-or-trade scenario distributed via a survey. Additionally, we collected data on their self-stated gambling preferences. Our findings confirm the existence of large and significant differences in gambling consumption, problem gambling, and trading behavior between the two nationalities. Furthermore, we observed that gambling preferences explain most of the national differences. This suggests that established factors of financial decision-making play a more prominent role in explaining national differences in trading behavior than any latent cultural factors.

Exploring the principal factors driving the volatility structure of stock trade levels in New Zealand: a Covid-19 perspective

This paper explores factors that drive the volatility structure of New Zealand stock trade levels, with the aim of understanding unusual volatility of stock trade levels that may pose a risk to the critical operations of a trading venue. Weekly number of trades from 2014–2024 for the sampled stocks were examined within principal axis factoring (PAF) and GARCH models. The results showed that three factors accounted for around 60% of volatility in the number of stock trades over the sampled period; they were driven primarily by electricity generation stocks, consumer discretionary stocks, and commercial property stocks respectively. During the sampled period, two subperiods of market shock showed unusual levels of volatility in all principal factors, with the second subperiod, which coincided with the Covid-19 outbreak, being particularly notable. An implication of the study is that given the volatilities of stock trade levels are little studied in the literature, the findings of this study could be a useful complement to existing studies examining stock price volatility for the purposes of mitigating unusual stock trading volatility.

Effect of Perceived Technology Acceptance on Online Stock Trading Behavior: An Empirical Analysis

This study investigates the influence of perceived technology acceptance on the online stock trading behavior of retail investors. The objective of this research is to examine the predictors of online stock trading behavior and their impact. Using a regression model, the findings reveal that perceived usefulness, perceived ease of use, and perceived benefit significantly contribute to and promote online stock trading behavior. The standardized coefficients indicate a strong positive relationship between perceived usefulness (β = 0.838), perceived ease of use (β = 0.727), and perceived benefit (β = 0.783) with online stock trading behavior. The results suggest that retail investors are more inclined to engage in online stock trading when they perceive technology as useful, easy to use, and beneficial. This study underscores the pivotal role of technological applications in shaping the behavior of retail investors in the online stock trading environment, shedding light on the factors that drive participation in this increasingly digitized investment landscape.

Technical indicator empowered intelligent strategies to predict stock trading signals

Technical analysis is widely employed in stock trading, relying on popular indicators such as MACD, DMI, KST etc. to predict stock trends. Despite their common use, these lagging indicators can occasionally generate misleading signals. In the literature, machine learning researchers developed many intelligent strategies for predicting stock trading signals using these indicators as inputs. However, significant differences exist in how these indicators are applied by technical analysts and machine learning experts. Building on this knowledge, this study developed intelligent stock trading signal prediction strategies using MACD, DMI, and KST indicators, and implemented these strategies with LSTM and GRU networks due to their ability to manage long-term dependencies and maintain context. The proposed intelligent trading strategies were assessed using ARR, SR, and win rate metrics, based on historical trading data from 18 stocks—six each from NEPSE, BSE, and NYSE—leading to four key insights. (1) For predicting stock trading signals, a 5-day lookback period is optimal for intelligent strategies based on MACD and DMI, while a 10-day period is best for the KST-based strategy. (2) Intelligent trading strategies implemented with GRU networks demonstrated superior performance compared to those implemented with LSTM. (3) The intelligent trading strategies based on MACD, DMI, and KST indicators outperform their peer classical stock trading methods. (4) Among the three proposed intelligent strategies, the MACD-based approach is found to be the safest and most effective.

Government procurement interventions as an economic and legal instrument to ensure a balance of interests of agricultural market participants

The sustainability of the agricultural sector of the economy is traditionally associated with a set of government support measures, among which public procurement interventions occupy a significant place. The article focuses on the importance of government procurement interventions for price regulation in the agricultural products market, which helps to ensure a balance of interests of its participants.The mechanism of public procurement transactions is considered, the problems of legal regulation are identified, including the lack of transparency in the selection of a procurement agent to the state intervention fund, the lack of a regulatory period for the start of public procurement transactions, and the difficulty of access to stock trading. Proposals have been formulated to improve the legal regulation of this sphere of relations.

Valuation and Returns on Stock Return Volatility

ABSTRACT This paper provides an accounting-based valuation model that predicts that cross-sectional variation in firm-level returns to investments in both stock and stock return volatility are related to cross-sectional variation in firm-level fundamentals. The model predicts that expected stock returns have a positive quadratic relation with stock return variance and a negative quadratic relation with gains to trading in stock return variance. Consistent with these predictions, firms with high model-implied expected stock returns have high future stock return variance, and the relation is roughly quadratic. In contrast, firms with high expected stock returns have low future returns to trading in stock return variance through option contracts because these firms have high option-implied variance relative to future realized variance, i.e., low variance risk premia (VRP). The study provides a framework for using fundamentals for trading in individual stocks and options. JEL Classifications: G12; G14; G17.

Nexus Between Stock Return and Market Volatility in Indian Perspective

This research seeks to explore the volatility patterns for the SENSEX index to figure out the characteristics of volatility in Indian stock trading and to investigate the relationship between returns and volatility of the Indian market for the last ten years. The research adds to the existing knowledge about stock market fluctuations, their implications for investors, and their impact on developing the country's economy. The study involves using daily returns data from BSE SENSEX from 01 Jan 2014 to 31 Dec 2023. Daily closing prices are obtained from the official website of BSE, and returns are calculated based on these prices. The Dickey-Fuller and Phillips-perron are taken to make the time series static. ARCH, GARCH, and GARCH-M tests are utilized to capture volatility clustering, return and volatility relationship. The results reveal that Fluctuations in the Indian stock market, particularly shown in the SENSEX index of the BSE, were highest in the year 2020. Findings suggest that GARCH-M does not show any relation between expected returns and market volatility. The risk-premium parameter is positive but statistically insignificant. If you want to hedge against risk, the risk premium is not very high. It means that taking risks does not give you more returns. Durbin Watson's value of 1.981401 further supports the model's suitability.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Analisa Keandalan Instrumentasi Pada Pulp Dryer Unit Menggunakan Metode Reliability Centered Maintenance (RCM) di PT. Indah Kiat Pulp and Paper Perawang

<p><strong>PT. Indah Kiat Pulp and Paper Perawang, a backup of Sinarmas, is one of the biggest companies concurring to the Indonesia Stock Trade. One of the most critical units in the production process is the pulp dryer unit, with an estimated production capacity of 657 tons per day. Based on the data obtained, the pulp dryer unit frequently experiences failures that disrupt the production process. This ponder utilizes the RCM (Reliability Centered Maintenance) strategy to decide which gadgets within the mash dryer unit are most inclined to issues by calculating the RPN (Risk Priority Number) for each component, assessing the unwavering quality level of these gadgets, and giving proposals for an ideal support plan. The think about comes about appear the RPN values for mash dryer instrumented from most elevated to least as takes after: temperature <em>sensor</em> at 392, press transmitter at 288, level transmitter at 280, stream transmitter at 252, solenoid valve at 252, consistency transmitter at 245, and on/off valve at 210. The unwavering quality values for each instrumented component that did not meet the edge set by the SII (Indonesian Industrial Standards), which is 0.7, demonstrate the require for support activities. The recommended maintenance schedule for the instrumentation is as follows: temperature <em>sensor</em> at 252 days, press transmitter at 352 days, level transmitter at 352 days, flow transmitter at 352 days, solenoid valve at 352 days, consistency transmitter at 293 days, and on/off valve at 440 days.<em></em></strong></p><p><strong><em>Keywords</em></strong> – <em>FMEA, Inst</em><em>rumentation</em><em>, </em><em>Reliability</em><em>, Pulp Dryer, RCM.</em></p>

CNN-BiLSTM-Attention Based Stock Price Prediction and Quantitative Investment Strategy

With the rapid development of technology and the increasing maturity of financial markets, stock price prediction has become a hot topic and an important trend in the financial field. However, the stock market has complexity and volatility, in order to reduce the investment risk and ensure the maximization of benefits, selecting the optimal stock to predict the stock price and formulating the quantitative trading strategy are extremely important issues for financial academics and investors. For the research of stock price prediction and quantitative trading, firstly, the quantitative stock selection model combining XGBoost and multi-factor stock selection model is constructed, and four stocks are screened out, and then the CNN-BiLSTM-Attention model is proposed to predict the stock price trend of the selected stocks, and it is found from the prediction results of the four stocks that the prediction accuracies all reach more than 95%, which is higher than that of the single model prediction. accuracy and passed the validity test of the fitted model. Secondly, based on the quantitative investment portfolio given in the above analysis and the quantitative stock trading strategy of MACD, the prediction results are verified, and the total return of most stocks based on the strategy is higher than 40% at the highest, and the loss is controlled within 10%, which indicates that the trading strategy in this paper is effective and feasible. The study concludes that greater returns can be obtained by calculating the total returns of stock portfolios based on different portfolio approaches.

Prediction of stock prices with automated reinforced learning algorithms

AbstractPredicting stock price movements remains a major challenge in time series analysis. Despite extensive research on various machine learning techniques, few models have consistently achieved success in automated stock trading. One of the main challenges in stock price forecasting is that the optimal model changes over time due to market dynamics. This paper aims to predict stock prices using automated reinforcement learning algorithms and to analyse their efficiency compared with conventional methods. We automate DQN models and their variants, known for their adaptability, by continuously retraining them using recent data to capture market dynamics. We demonstrate that our dynamic models improve the accuracy of predicting the directions of various DAX stocks from 50.00% to approximately 60.00%, compared with conventional methods. Additionally, we conclude that dynamic models should be updated in response to shifts rather than at fixed intervals.

Online Portfolio Based on Trend Trading Strategy Considering Investor Sentiment Using Text Analysis

Intelligent online portfolios have become an important research topic in the field of quantitative finance. This paper proposes an online portfolio based on trend trading strategy using fuzzy logic technology analysis method and considering investor sentiment. Firstly, the paper uses SVM classification algorithm to analyze stock comment text data in online forums and constructs investor sentiment indicators. Secondly, the paper transforms the heuristic algorithm of technical trading into corresponding fuzzy IF-THEN rules and combines them into a fuzzy investment decision system. Thirdly, the paper constructs a new online portfolio based on trend trading strategy. Finally, the paper uses the text data of investor reviews and stock trading data on the internet for empirical analysis to illustrate the effectiveness of the online portfolio strategy constructed. The results indicate that online portfolios constructed based on trend trading strategies can achieve higher performance than benchmark strategies while considering transaction costs.

DeepInvesting: Stock market predictions with a sequence-oriented BiLSTM stacked model – A dataset case study of AMZN

Intelligent forecasters are now being considered in the stock market, providing essential insights and strategic guidance to investors and traders by presenting analytical tools and predictive models, thus enabling informed decision-making and mitigating financial risks in this dynamic market. The importance of intelligent analyzers in stock trading routines is considered in this work, where DeepInvesting, a multimodal deep learning model tailored for stock price prediction, is introduced. Employing a Sequence-Oriented, Long-Term Dependent (SoLTD) architecture featuring Bidirectional Long Short-Term Memory (BiLSTM) networks, DeepInvesting is applied to essential features of the Amazon Corp. (AMZN) market dataset, gathered from Yahoo Finance, including Closing, Opening, High, Low, Volume, and Adj Close prices. Exceptional performance in forecasting Closing, Opening, High, Low, and Adj Close prices is demonstrated, with minimal Mean Absolute Percentage Error (MAPE) and Root Mean Squared Percentage Error (RMSPE) scores, coupled with high R-squared (R2) values, manifesting a robust fit to the data, as well as computational complexity, and Rates Per Second (RPS) metrics in comparison to other models of KNN, LSTM, RNN, CNN, and ANN. Finally, challenges in the accurate prediction of trading volumes are identified, highlighting an area for future enhancement.

A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes.

The Impact Of Daftar Efek Syariah (DES) Announcement To The Asii’s Abnormal Returns And Trading Volume

DES announcement can be an important information for investors in making investment decisions. Islamic investors have to sell their stock when their stocks are excluded from DES. On the other hand, investors who want to invest in Islamic stocks will buy stocks that are included in the DES. The purpose of this study is to examine the impact of ASII stock reentry and exit from and into DES to the abnormal return and trading volume activity. This study uses secondary data obtained from investing.com. The data used in this study are daily stock closing prices, IHSG index and daily stock trading volume. This study follows event study methods. The first event window used in this study is 7 days before November,23,2020 and 7 days after. The second event window used is 7 days before May 24, 2023 and 7 days after. Descriptive statistic and Wilxocon sign ranks is used to analysis the data. The results showed that there was no significant difference in abnormal returns both at the time of ASII exit and reentry. However, trading volume of ASII stock significant difference before and after exit announcement. But when ASII stock reentry to the DES there was no significant difference.

Emotional intelligence and behavioral biases on millennial stock trading decisions: a case study of Bibit investors

Emotional intelligence and behavioral biases on millennial stock trading decisions: a case study of Bibit investors

Unveiling the drivers of satisfaction in mobile trading: Contextual mining of retail investor experience through BERTopic and generative AI

The proliferation of mobile stock trading has introduced various apps with distinct features, emphasizing the need to understand users’ evaluations after adopting the service. This study explores the determinants of retail investors’ satisfaction with mobile stock trading services by employing an advanced textual analysis of customer reviews for four leading trading applications. We utilized Bidirectional Encoder Representations from Transformers (BERT) based Topic modeling (BERTopic modeling) to identify key topics within customer reviews and used the results as input for generative AI to discern the theme and sentiment of each topic. Based on Service Quality (SERVQUAL) theory, topics are categorized into key quality dimensions: functionality, usability, information quality, customer service, and system quality. Regression models were employed to assess the impact of the quality dimensions on investor satisfaction, revealing positive feedback on usability, information quality, and service quality as primary enhancers of satisfaction. In contrast, negative feedback on service quality, system quality, and functionality was identified as the primary inhibitor of satisfaction. This study explores how the influence of each quality dimension varies among different types of brokers (full-service vs. online-only brokerages). Finally, we propose a visualization tool called Topic Rating Impact and Frequency Analysis (TRIFA), which is designed to categorize topics based on their frequency of occurrence and impact on satisfaction. This tool aids in identifying the strengths and areas for improvement in services by effectively visualizing the results of text review analysis. This research not only deepens our understanding of the quality dimensions of mobile financial services but also offers valuable insights for service providers by suggesting predictive models that could help increase customer retention.

DETERMINAN VOLATILITAS HARGA SAHAM PADA INDEKS IDX30 (STUDI PADA TAHUN 2018-2021)

The purpose of this study is to determine the factors that influence the volatility of the IDX30 company’s stock prices. Independent variable used are Stock Trading Volume, dividend policy, leverage, firms size, and asset growth. This study using the report data from companies listed in IDX in the Index IDX30 between 2018-2021. Data analyzed using data panel regression analysis. The result from the analysis found out if variable dividend yield, firm size, and growth asset has no significant effects on Stock Price Volatility. Meanwhile, trading volume and leverage have a significant effect on stock price volatility.

Stock Market Prediction in Nepali Stock Market using Machine Learning Model

The stock market, a key driver of the global economy, presents a formidable challenge for accurate prediction due to its intricate, chaotic, and dynamic nature. This study explores machine learning approaches, specifically comparing three prediction models—Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The analysis incorporates historical stock trading data, including open, high, low, and close prices, and technical analysis indicators such as moving average, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Commodity Channel Index (CCI) so on. The evaluation focuses on the performance of these models in predicting future trends in stock prices, utilizing data from the Nepal Stock market spanning from 2013 to 2023. The selected stocks for analysis include two stock NIC Asia Bank Limited and, NMB Bank Limited, two index NEPSE and Hydropower index. Through rigorous assessment, it is determined that LSTM demonstrates superior overall performance compared to GRU and BPNN. This research contributes valuable insights into the effectiveness of machine learning models in forecasting stock market trends, with implications for investors and financial practitioners.

A study on the efficacy of an attention mechanism-based strategy planning method for stock trading portfolios

In order to take full advantage of the fact that there is a large amount of information and data available in the field of stock trading, this paper develops a new type of stock trading strategy, which reduces investment risk. A stock trading portfolio strategy planning method based on an attention mechanism, as disclosed in the present invention, relates to the field of stock trading technology. The introduction of an attention mechanism allows the strategy to focus on those stocks with higher evaluation coefficients, thereby improving the selectivity and accuracy of the strategy and thus the effectiveness of the portfolio strategy. Furthermore, based on the prediction results of the deep spatio-temporal neural network model, it is possible to determine the stock's short-term trends and identify the optimal trading points. This information can be utilized as a reference for investors, assisting them in making more informed decisions regarding the timing of buying and selling, improving the success rate and yield of trading, enabling them to swiftly and accurately assess the performance of each stock, facilitating timely adjustments to their portfolios, and enabling them to devise the optimal trading strategy and timing for each stock, enhancing the accuracy and efficiency of their trading decisions.