Stock Trading Research Articles

Good Corporate Governance Mechanism Effect and Green Accounting on Environmental Performance

Environmental performance is a company's relationship with the environment in terms of how it uses resources, how organizational processes affect the environment, how products and services affect the environment, how well products are processed, and how well it complies with work environment regulations. Manufacturing sector industrial companies are companies that have a major role as environmental pollution reason. It has been proven that there have been several chemical manufacturing cases of businesses whose surroundings are polluted. The goal of this study is to learn how institutional ownership, managerial ownership, independent commissioner, and green accounting effect environmental performance in from 2018 to 2021, manufacturing companies were listed on IDX. This research uses all data on manufacturers who are consistently recorded on Indonesia Stock Trade and become Appropriate members during 2018-2021 period. Purposive sampling technique used resulted 33 company’s sample. Using panel data regression model, collected data is processed using EViews 12. The findings of this study indicate that independent commissioners have a positive impact on environmental performance. In addition, managerial ownership has no effect on environmental performance, institutional ownership and green accounting have an effect on environmental performance.

Retail ETF investing

AbstractUsing marketable order flow data, we analyze key characteristics of aggregate retail exchange‐traded fund (ETF) investing from 2010 to 2021, including allocations, holding period and investment performance. Retail traders allocate 12% more dollar volume to leveraged and inverse ETFs versus nonretail traders. Retail ETF trades distinctly increase with prior ETF returns, in contrast to contrarian stock trading. Estimated ETF holding periods are longer for retail investors versus nonretail. Finally, retail and nonretail ETF trades perform similarly over hypothetical holding periods up to one quarter. Overall, we provide policy‐relevant insights into retail investing behaviours, which have been the subject of recent concern.

Enhancing daily stock trading with a novel fuzzy indicator: Performance analysis using Z-number based fuzzy TOPSIS method

In the dynamic realm of financial markets, developing effective strategies for stock exchange transactions is paramount. This research addresses this critical need by introducing a pioneering indicator for daily stock trading, leveraging a robust fuzzy inference system (FIS). The indicator ingeniously integrates key technical indicators including Moving Average Convergence and Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator (SO), and On-Balance-Volume (OBV). The FIS is meticulously constructed based on expert opinions and gleaned fuzzy rules. The fuzzified values then serve as inputs to the FIS, which in turn generates signals indicating optimal actions: buy, hold, or sell stocks. To validate the importance of the FIS, a carefully curated selection of stocks from the NASDAQ stock exchange is employed for experimentation. To prove the efficiency of the FIS, the technical indicators and the fuzzy risk-adjusted returns are considered as alternatives and criteria, respectively. A novel Z-number-based technique for order preference by similarity to the ideal solution (TOPSIS) method is used to rank the technical indicators and the FIS. The comparative results unequivocally demonstrate that the developed FIS surpasses existing indicators, yielding superior returns.

An Empirical Bayes Approach to Controlling the False Discovery Exceedance

In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the false discovery proportion (FDP) is highly variable. We develop an empirical Bayes approach to control the FDX. We show that, for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to the FDX constraint. We propose a data-driven FDX procedure that uses carefully designed computational shortcuts to emulate the oracle rule. We investigate the empirical performance of the proposed method using both simulated and real data and study the merits of FDX control through an application for identifying abnormal stock trading strategies.

DIGITAL BANKING STOCK PERFORMANCE: THE IMPACT OF COVID-19

The Covid-19 pandemic has catalyzed a dramatic change in entire banking operations. However, not a few banks have transformed into digital banks to continue providing top services to customers. This study aims to identify Indonesia's banking market reaction to the Covid-19 pandemic especially for digital banking industries. The market reaction is seen from the movement in stock prices and stock trading volumes. The research was conducted using qualitative research methods. The sample is PT Bank Jago Tbk. (ARTO) with observation period from January 2020 to December 2021. The results of this study indicate that the market reacts to information entering the market quickly. The speed of information entering the market also accelerates market reactions that occur. The market reacts negatively to negative events and reacts positively to positive events. In addition to confirming the efficient market theory, the results of this study also confirm the signal theory. The president's announcement of the first case of Covid-19 in Indonesia is a bad signal for investors regarding cash flow and the company's future. This study also found that stock prices and volume did not only react to Covid- 19 conditions but also corporate actions. Several corporate actions that have an impact on price movements and volume of ARTO stocks are the issuance of rights issues, changes in the company's business processes, and the entry of the new investors. Based on the results of this study, investors in the stock market can pay attention to certain events that can provide buy or sell signals.

Decoding the Magic Formula: Navigating the Complex World of Stock Trading

Stock trading is complicated and risky. Investors' conditions, aspirations, and viewpoints shape their journeys. This article examines stock trading and investing, focusing on Joel Greenblatt's "The Little Book That Beats the Market" and "Magic Formula" investment technique. Greenblatt's book argues that stock trading is better than saving. The "Magic Formula" focuses on return on capital and earnings yield and is simple but effective. The Magic Formula helps investors find cheap, high-quality firms by evaluating them by these two ratios. The Magic Formula, like every investment approach, has limitations. Its success depends on the investor's goals, risk tolerance, and situation. The Magic Formula works for some investors but not others. This emphasizes the need of personalizing one's financial approach and knowing that one size does not fit all. Greenblatt also stresses the significance of knowing and believing in the Magic Formula, even when it seems to be failing. He believes that effective investing requires both picking the correct companies and sticking to your strategy, even when it's hard. This emphasizes the significance of patience and discipline in investing, which are often neglected but essential for long-term success. The Magic Formula is one of several investment techniques and theories, each with its own assumptions, methods, and risk profiles. From buy-and-hold to complicated quantitative models, these methods cover it all. Investors' goals, risk tolerance, and circumstances should determine their strategy. In conclusion, stock trading demands cautious navigation and intelligent decision-making. Joel Greenblatt's Magic Formula helps guide investors. Its effectiveness depends on the investor and is not a one-size-fits-all solution. Thus, each individual must establish their own stock trading success route with the correct knowledge, method, and mindset.

Benchmarking Methodology of Banks Based on Financial Sustainability Using CRITIC and RAFSI Techniques

This study aims to determine the influence of financial risks on the financial sustainability of Iraq banks, in contrast considering the moderator role of financial expertise and the risk of directors’ riskiest directorships. Moreover, the investment attractiveness has also been accounted for specifying the mediating impact between the moderator variables and financial sustainability. Robust random effect model was employed to investigate the determinants of financial sustainability for the listed banks on the Iraqi stock trade and the Iraqi financial marketplace. In addition, multicriteria decision-making techniques were utilized to rank the banks from the best to worst based on financial sustainability. This study has examined 19 Iraqi banks over 14 years from 2007 to 2020 (266 observations), during the development of the Iraqi corporate governance cod. The findings indicate that financial risk has a negative influence on the financial sustainability of Iraq. Furthermore, the link between financial risks and financial sustainability is fully mediated by investment attractiveness. In this context, financial expertise and the relinquishing of directors’ riskiest directorships have an interactive role in the link among investment attractiveness and financial sustainability indicators. Multi-criteria decision making (MCDM) results revealed that the best bank in terms of financial sustainability is BTRI and the worst bank is BNOR. The findings enable managers to develop strategies to mitigate financial risk in phases and prepare for future unpredicted risks. Hence, the results provided an insight to practitioners regarding the best banks in terms of financial sustainability in order to invest and deal with them.

The Impact of Procuring Per Offer, Turnover of Absolute Resource, Return on Value and Debt Asset Ratio on Offer Value of Maufactur Area Recorded on the Indonesia Stock Trade Period 2019 - 2022

The value of the money is going to decrease because of inflation, so we have to invest in an investment asset. The investment tool explored in this research is c-stock which uses financial ratios to determine whether financial ratios are correlated with stock prices in order to identify capital intensive companies. This research uses ratios such as EPS TATO ROE and asset or debt ratio to provide profitability. This study has been carried out in the case of manufacturing companies listed on IDX, using Eviews version 10 as a data processing tool. The primary sampling method was applied in this study, as it is the standard of sampling. From 2019 to 2022 the samples were six companies with research data and therefore 32 observation reports have been collected. Because of the review, it is known that (1) Profit per share emphatically affects stock costs. The meaning of 00154 demonstrates this so H1 is acknowledged. ( 2) The complete resource turnover rate essentially affects the stock cost as confirmed by the meaning of 01040 so H2 is dismissed. ( 3) Return on value emphatically affects stock cost ( ) which is demonstrated at the importance level 00390 so H3 is acknowledged. ( 4) The obligation to resource rate affects share costs, this is validated by a critical worth of0.0719 so H4 is dismissed;( 5) accomplished contemporaneous Experimental outcomes calculation results F of3.103040 with probability0.031819 can be closed in the event that all factors have a huge impact contemporaneously on costs of stock. The aftereffects of the retrogression examination show the adjusted worth( r2) of0.213441, implies that varieties in Profit per Offer, Advancement of All out Resource, Return on Value, and Obligation to implies rate can make sense of the21.34 of changes in stock value, the remaining78.66 is made sense of not analyzed by different factors in this review.

Influence of Macroeconomics Factors, Profitability, and Stock Trading Volume on Stock Prices in The Years 2019-2021

The Covid-19 pandemic, which struck Indonesia in March 2020, impacted various sectors, including the capital market. The capital market, which primarily served as a source of funding for investors, was also affected. This research was designed to examine the impact of economic elements on the share prices of manufacturing sectors amidst the Covid-19. The aspects under examination encompassed exchange rate, inflation, probability, and stock trading volume. This research employed data from all total of 171 manufacturing industry firms listed on the Indonesia Stock Exchange (IDX) over the period from 2019 to 2021 . The research method used is a quantitative method. The total sampling technique used resulted in a sample of 59 (fifty-nine) companies that met the research criteria. The method of data analysis employed is panel data regression analysis, utilizing the Random Effect Model (REM) estimation, which is tested using Eviews 12. The findings indicate that profitability significantly influences stock prices, while exchange rate, inflation and stock trading volume do not have a significant effect on the stock prices of manufacturing companies during 2019-2021. Based on the simultaneous test result obtained, exchange rate, inflation, profitability, and stock trading volume have a significant effect on stock prices. 

Activity of informed traders and stock returns

PurposeRecent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.Design/methodology/approachThe authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.FindingsThe authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).Originality/valueThe authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.

Reaksi Pasar Modal Sebelum Dan Sesudah Munculnya Financial Technology (Fintech) Peer To Peer (P2P) Lending Pada Bank Yang Terdaftar Di Bursa Efek Indonesia

The development of innovation in the financial sector, namely fintech, fintech is an abbreviation of financial technology or financial technology. The existence of financial technology has made one of the fastest growing business sectors in the technology sector. With the development of technology and the increase in start-up companies, the development of fintech in Indonesia. Finance in Indonesia has shifted due to a format that can produce solutions and make it easier for consumers, namely the combination of economy and technology (fintech). The purpose of this study is to determine whether there are differences in trading volume activity and abnormal returns on the cooperation between banks listed on the Indonesia Stock Exchange and peer to peer P2P lending financial technology. This study uses secondary data from third-party publications with a total sample of 4 companies. The data analysis technique used is the even study data analysis, normality test and paired t-test hypothesis testing. Based on the research results, there is no significant difference in the volume of stock trading activity (trading volume activity) before and after the collaboration between peer to peer lending financial technology and banks listed on the Indonesia Stock Exchange, there is no significant difference in abnormal returns before and after the existence of cooperation between financial technology peer to peer lending to banks listed on the Indonesia Stock Exchange.

An integrated approach of TAM and TPB with financial literacy and perceived risk for influence on online trading intention

PurposeThis paper aims to propose a decision-making framework by investigating the impact of perceived risk and computer self-efficacy on the intention to use online stock trading. Furthermore, it demonstrates the mediation effect of attitude and perceived risk as well as the moderating effect of financial literacy.Design/methodology/approachAn integration of two popular models, technology acceptance model (TAM) and theory of planned behaviour (TPB), is used to provide a sound theoretical base and enhance the understanding of investors’ behaviour towards online trading platforms. The proposed hypothesised model was examined using structural equation modelling.FindingsThe results obtained from this study indicate that all variables, except subjective norms, had a significant impact on investors’ intention to trade online. Perceived risk was found to be a partial mediator between computer self-efficacy and the intention of investors. Finally, financial literacy was also found as a significant moderator for online trading intention of investors.Practical implicationsThis study shows the significance of using the TAM and TPB together to provide a comprehensive understanding of the factors that influence an investor’s behaviour in adopting and using technology for online trading. The hybrid approach of TAM and TPB could be considered for a more nuanced and complete understanding of technology adoption and usage in risky affairs like investment decisions. Again, the significant moderating role of financial literacy provides a lance to look into the scope for improvements in investment decision-makings.Originality/valueThe paper develops an assessment framework for analysing the variables based on the hybrid approach for online trading intention in the context of a developing country.

PENGARUH PROFITABILITAS, LEVERAGE DAN PRODUKTIVITAS TERHADAP PERINGKAT OBLIGASI PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BEI PERIODE TAHUN 2020-2022

The stock exchange is the market where trading in stocks, bonds and other financial instruments takes place. The stock exchange is a place where companies can issue shares to obtain funds from investors, and investors can buy shares as a form of company ownership and benefit from changes in share prices. This study aims to measure how significant the influence of Profitability, Leverage and productivity is in determining bond ratings in manufacturing companies in 2020-2022. The sample in this study is a manufacturing company that issues bonds and is rated by PEFINDO (Indonesian Rating Agency), uses the rupiah currency in presenting the company's financial statements, and publishes complete audited financial statements. The data source used comes from the official website of the Indonesia Stock Exchange (IDX), namely www.idx.co.id and the bond rating database accessed from PTPEFINDO (www.pefindo.com). The conclusion of this study is that profitability has a positive effect on bond ratings, leverage has a negative effect on bond ratings and productivity has no effect on bond ratings.
 
 Keywords: Profitabilitas, Obligasi, Leverage, Produktifitas

Stock Price Forecasts Based on KNN and LSTM

Every stock trader wants to successfully predict the price or trend of a stock in order to make a profit because stock price forecasts provide investors, traders, and financial professionals with signals about potential price movements, which can help them make more informed decisions about buying, selling, or holding stocks. This article selects the four largest stocks in the U.S. stock market by market capitalization: Google, Apple, Microsoft, and Amazon, and predicts their closing prices from 2013 to 2023. First, K-Nearest Neighbors (KNN) model is established for the closing price sequence after the first-order difference. Then a two-layer LSTM model is constructed to visualize the prediction results of the two models, and RMSE is calculated respectively. Comparing the prediction results of the two models, LSTM has a better prediction effect on the data set used in this paper. This paper finds that the LSTM model can capture the crucial time dependencies and relationships in financial time series data, which are essential for stock price prediction. Therefore, the LSTM model can often be used when predicting stocks in the future.

A novel Deep Reinforcement Learning based automated stock trading system using cascaded LSTM networks

Deep Reinforcement Learning (DRL) algorithms have been increasingly used to construct stock trading strategies, but they often face performance challenges when applied to financial data with low signal-to-noise ratios and unevenness, as these methods were originally designed for the gaming community. To address this issue, we propose a DRL-based stock trading system that leverages Cascaded Long Short-Term Memory (CLSTM-PPO Model) to capture the hidden information in the daily stock data. Our model adopts a cascaded structure with two stages of carefully designed deep LSTM networks: it uses one LSTM to extract the time-series features from a sequence of daily stock data in the first stage, and then the features extracted are fed to the agent in the reinforcement learning algorithm for training, while the actor and the critic in the agent also use a LSTM network. We conduct experiments on stock market datasets from four major indices: the Dow Jones Industrial index (DJI) in the US, the Shanghai Stock Exchange 50 (SSE50) in China, S&P BSE Sensex Index (SENSEX) in India, and the Financial Times Stock Exchange 100 (FTSE100) in the UK. We compare our model with several benchmark models, including: (i) a model based on a buy-and-hold strategy; (ii) a Proximal Policy Optimization (PPO) model with Multilayer Perceptron (MLP) policy; (iii) some up-to-date models like the MLP model, LSTM model, Light Gradient Boosting Machine (LGBM) model, and histogram-based gradient boosting model; and (iv) an ensemble strategy model. The experimental results show that our model outperforms the baseline models in several key metrics, such as cumulative returns, maximum earning rate, and average profitability per trade. The improvements range from 5% to 52%, depending on the metric and the stock index. This indicates that our proposed method is a promising way to build an automated stock trading system.

LEGAL ANALYSIS OF INTERNATIONAL EXPERIENCE IN THE PROCESS OF DIGITALIZATION OF ASSETS

This article explores the essence of international experience in digital imaging, the need to regulate different types of assets, approaches to legal regulation, prospects for the legal regulation of assets in the digital economy of the Republic of Uzbekistan. The strategy for this growth must be determined by the private sector, directed by the government, analyzed by civil society and academia through the lens of private international law. The main purpose of the article is to expose the unclear jurisdictions, conflicting laws, and fragmented oversight that create barriers to the accountable management of traditional cross-border finance that are missing in decentralized networks. The authority to regulate stock trading remains contested among national and subnational regulators, resulting in duplicative compliance efforts that are estimated to cost investors large sums of money.

Soft Regulatory Capture and Supervisory Independence: A Case-Study on Wirecard

623The traditional view on regulatory capture focuses on capture as a distortion of public purpose through a malicious relationship, corruption and possible collusion between the regulator and the industry (hard capture). This paper argues that regulatory capture can arise from political and institutional conditions which do not allow or favour the supervisory independence of authorities from both the industry and the government (soft capture). This paper’s argument is illustrated through a case-study on the German Federal Financial Supervisory Authority’s (BaFin) handlining of the Wirecard AG case. The basis for the analysis are the findings from the Committee of Inquiry of the German Bundestag and the European Securities and Markets Authority (ESMA) Fast Track Peer Review (FTPR) through three lines of inquiry: (1) lack of balance sheet control; (2) the short selling ban; and (3) Wirecard AG’s stock trading by BaFin’s employees. This paper concludes that BaFin was not hard captured in the Wirecard AG case as de facto influence cannot be proven. Instead, its de jure dependency vis-à-vis the MoF (as implicitly endorsed by German law) might have contributed to a case of soft regulatory capture – especially in the aspect of the short selling ban. The paper then analyses the reforms enacted by Germany and promoted by Europe in post-Wirecard case.

Decision Making Process through Technical Analysis in New York Stock Exchange (NYSE) Stock Trading (Case Study: Shares of Companies Supporting the Genocide War in Palestine for the Period November 2022 – November 2023)

This research aims to carry out technical analysis for the decision-making process in stock price movements listed on the New York Stock Exchange (NYSE). With a focus on company shares linked to support for the genocidal conflict in Palestine. The role of finance in the context of this conflict has been the subject of widespread debate and controversy. Therefore, technical analysis is considered a tool that can provide an objective view of market behavior, while still considering the social and humanitarian impact of companies involved in support of genocide. This research uses four technical analysis tools including Moving Average Coverage Divergence (MACD), Stochastic, Relative Strength Index (RSI) and Bollinger Bands, this aims to see the level of effectiveness of each of these tools in reading stock movements.

THE IMPACT OF COVID-19 OUTBREAKS ON INDONESIA’S AUTOMOTIVE AND PAPER INDUSTRY STOCK RETURN

This study was carried out to identify the effect of COVID-19, Large-Scale Social Restriction policies, market capitalization, and stock trading volume on the company's stock returns from 2 March 2020 to 30 December 2020. The subjects in this study are companies in the automotive and component industries as well as the pulp and paper industry which are listed on the Indonesia Stock Exchange (IDX) during 2020. This study uses 3 theories, namely the Signaling Theory, Efficient Market Hypothesis Theory, and Black Swan Theory. Panel data regression analysis was used in the study, using EViews 9 program and Microsoft Excel to process all data. Results show that growth of COVID-19 positive cases and market capitalization have a significant negative effect on the company's stock return. The other results show that the increasing of LSSR (Large-Scale Social Restriction) policies and stock trading volumes have an impact on increasing company’s stock returns, while the growth of death cases of COVID-19 has no impact on increasing or decreasing company’s stock returns.

An effective approach for predicting daily stock trading decisions using fuzzy inference systems

An effective approach for predicting daily stock trading decisions using fuzzy inference systems