Stock Trading Research Articles

Sectoral Corporate Profits and Long‐Run Stock Return Volatility in the United States: A GARCH‐MIDAS Approach

ABSTRACTThis study aims to examine the usefulness of corporate profits in predicting the return volatility of sectoral stocks in the United States. We use a GARCH‐MIDAS approach to keep the datasets in their original frequencies. The results show a consistently positive slope coefficient across various sectoral stocks. This implies that higher profits lead to increased trading of stocks and, subsequently, a higher volatility in the long run than usual. Furthermore, the analysis also extends to predictability beyond the in‐sample. We find strong evidence that corporate profits can predict the out‐of‐sample long‐run return volatility of sectoral stocks in the United States. These findings are significant for investors and portfolio managers.

Application of Artificial Intelligence in the Securities Market

Artificial Intelligence (AI) involves creating machines that replicate human cognitive functions, enabling them to think and act similarly to people. In the context of stock trading, AI refers to the use of technology designed to buy and sell shares with increased precision and efficiency compared to human capabilities. AI-driven systems are currently employed to predict stock market trends and analyze trading patterns among investors and brokers. Prominent financial institutions on Wall Street, such as Goldman Sachs and Morgan Stanley, are increasingly adopting specialized AI solutions, including data mining, natural language processing, and self-learning algorithms. These tools operate with greater speed than everyday applications. Additionally, AI aids wealth management firms in continuously monitoring market movements and adjusting portfolios to achieve target profits. While AI can significantly reduce workload and provide real-time recommendations, it does not entirely eliminate the need for human oversight. Keywords: artificial intelligence, securities, trading, forecasting.

AI-Driven Optimization of Financial Quantitative Trading Algorithms and Enhancement of Market Forecasting Capabilities

Abstract: The application of artificial intelligence (AI) into financial markets has revolutionised quantitative trading and market forecasting by increasing the efficiency of algorithmic trading, improving the accuracy of market predictions and facilitating real-time market decisions. This paper will provide an overview of the application of Al in the financial markets focusing on the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) in optimizing the trading algorithms, specifically the capability of Al to process very high data points and complex relationships that other quantitative models are unable to capture. We will discuss trading algorithms such as XGBoost, deep neural networks such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs), how they can outperform traditional quantitative trading models and real-time decision making in stock price prediction, pattern recognition and trading strategy optimisation. We will also look at Al-enhanced predictive models that utilise deep learning and layered models, such as Natural language processing (NLP) sentiment analysis to capture the public sentiment in the market to forecast employing diverse datasets such as historical prices, market volatility, macroeconomic factors and social media sentiment to improve the forecasting accuracy. By going through several experiments and case studies, this paper will shed light on the impact of entrusting quantitative trading and market forecasting decisions to AI for improved performance and reduced errors. There are many challenges ahead but AI plays a constructive role in improving the trading strategies and forecasting market outcomes accurately.

The Influence of Digital Trading Applications on Financial Markets

The word "fintech" refers to a new technology that aims to make financial services better and more automated. Investment management, retail banking, artificial intelligence, block chains, mobile payments, stock trading, and other industries are increasingly included in the fintech sector. With today's high-tech electronic trading on the stock exchange, ordinary investors can purchase or sell shares in a matter of seconds, giving investors the opportunity to efficiently perform their research before investing in equities. This paper, thus, talks about the emergence of fintech, which has led to the development of various mobile trading applications like Zerodha, Grow, Upstox, Angel Broking, etc. These apps have become popular among young investors in recent years because of their low-cost broking services. In this study, both primary and secondary data have been used. Primary survey-based research has been used to know the experiences of people belonging to different age groups on trading apps, and secondary data for statistical figures and other factual points has been used from verified stock exchange data and websites of trading apps. This paper will also shed light on the various seamless services offered by such broking applications, which have drastically changed the way the market functions. Investors could easily browse through the available investments, order what they wanted, and manage their portfolio with a single tap from their phone, making adjustments right away. The paper will go on to explain how stock trading applications have affected the financial markets by encouraging an increasing number of people to participate in the process of constructing economies by trading in financial assets. The report can assist policymakers in determining the advantages and hazards of using these new technologies, as well as the repercussions of their use. It offers legislative recommendations that are meant to encourage the use of AI in finance while making sure that its application is consistent with fostering market integrity, financial stability, and competition while safeguarding financial consumers. To support and encourage the use of responsible AI, emerging dangers from the deployment of AI techniques need to be detected and mitigated.

Empowering Investors: Reducing Herd Bias through Financial Literacy and Self-reflective Thinking

Herd behavior, where investors mimic the trades of others rather than relying on their own information or analysis, is a widespread phenomenon that can lead to suboptimal investment decisions and market inefficiencies. Hence, understanding how to reduce this behavior is crucial for improving individual investment outcomes and sustainable market performance. Though the literature suggests that self-reflection and financial literacy have potential to mitigate herd bias, it is unclear as to how these two perspectives interact to minimize this irrational behavior. By drawing on insights from the adaptive market hypothesis, transformative learning theory, concept of bounded rationality and dual process theory, this study attempts to enhance the understanding of this phenomenon by introducing a conditional mediation model that promotes self-directed learning. It predicts that investors can reduce their herd bias through self-reflection on their past stock trading experiences, which they can practice by engaging intuitive logical thinking and strengthen further through their financial literacy. Since herding is generally predicted to be more prevalent in frontier stock markets, the study was conducted in a frontier market - the Colombo Stock Exchange. The data was obtained through a self-administered questionnaire from 253 active individual investors, and analyzed by applying the PROCESS procedure. The findings reveal the significance of self-reflection as a mediating variable across its antecedents, which provides strong evidence for its central role in reducing herd bias. Notably, the findings indicate that self-reflection has a stronger effect on reducing herd bias of investors with lower level of financial literacy. It implies that investors with low financial literacy are more susceptible to herd bias, and self-reflection facilitates them to recognize and minimize their herd bias. Accordingly, the study concludes that promoting self-reflection can empower individual investors to become more financially literate and thereby mitigate their herd bias.

The Effect of Accounting Profit, Operating Cash Flow, Return on Equity (ROE) and Dividends on Stock Trading Volume in Mining Companies Listed on the Indonesia Stock Exchange in 2020-2023

This study aims to test whether accounting profit, operating cash flow, return on equity and dividends affect the volume of stock trading in mining companies listed on the Indonesia Stock Exchange (IDX) for the period of 2020-2023. The sampling technique uses purposive sampling of 21 companies that meet the criteria. The method uses multiple linear regression analysis with the SPSS program. The results of the study show that partially operating cash flow and dividends have an influence on stock trading volume. Meanwhile, accounting profit and return on equity have a significant effect but show a negative influence on stock trading volume. Simultaneously, the results of the study stated that accounting profit, operating cash flow, return on equity and dividends have an effect on stock trading volume.

Users’ self-description on social media: a methodology to integrate labels and textual information

AbstractTo analyze how groups of people self-describe on social media, existing studies separately or partly exploited labels and textual self-descriptions. In this paper, we leveraged both sources of information about the users in an integrated procedure, by training a language model on a sample of text documents with balanced characteristics, and then, by using a list of both domain-specific and statistically relevant words as a guide, similarities between word and document representations were explored to analyze group differences in self-describing. Eventually, a bootstrap procedure was employed to assess the reliability of the results. The methodology proposed was applied on data from StockTwits platform, where people write a bio and declare their experience, approach, and primary holding period in trading. We found that groups of traders differ in semantics. In addition, the relationship between the approach in trading and the holding period still holds in self-descriptions, whereas the experience transversely influences bio writing, with professionals using words strictly specific to the domain of stock trading.

Insider Trading and Law Enforcement in the Capital Market

Illegal stock trading, or insider trading, has become a significant issue in the capital markets, both in Indonesia and the United States. This research aims to compare law enforcement related to insider trading between the two countries. This research uses a normative method with legislative and conceptual approaches. Data was collected through analysis of laws and regulations and case studies related to insider trading practices. The findings show that the regulations in the UUPM in Indonesia still use the fiduciary duty theory, which limits the scope of actions that can be sanctioned. In contrast, in the United States, the application of the fiduciary duty theory and misappropriation theory provides stronger legal protection and stricter sanctions against insider trading offenses. Although both countries have regulations prohibiting insider trading, the enforcement mechanism in the US is more comprehensive than in Indonesia. In the US, there are more legal precedents that clarify limits and sanctions, while in Indonesia, there is still a lack of implementation and understanding of insider trading. Enforcement of insider trading laws in the US is more effective than in Indonesia. The UUPM in Indonesia needs to be revised to integrate the misappropriation theory and strengthen sanctions against insider trading violations, in order to provide better protection to investors.

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

There 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.

Analisis Laporan Keuangan untuk Menilai Kinerja Keuangan Perusahaan Tahun 2018 – 2024

This research on plans to take a gander at monetary reports with respect to the improvement of exhibition of agribusiness organizations recorded on the Indonesia Stock Trade throughout recent years from 2018 to 2022 utilizing Current Ratio (CR), Quick Ratio (CR), Debt to Assets Ratio (DAR), Debt to Equity Ratio (DER) Net Profit Margin (NPM), Return On Assets (ROA),, Return On Equity (ROE), Total Assets Turnover (TATO) Fixed Assets Turnover (FATO),. Overall, the research reveals that agricultural companies listed on the Indonesia Stock Exchange have varying financial ratios, with some having very high ratios and others having very low ratios.

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.