Profitable Investment Decisions Research Articles

Comprehensive evaluation of digital technology's effects on the supply chain of prefabricated construction

PurposeDigital technology, which is regarded as a prominent and transformational force in modern society, encompasses a wide variety of technology that utilize digital data to process, store and transfer various types of information. Digital technologies have continually been introduced as cutting-edge information tools in order to achieve effective management of vast information that arises from the prefabrication supply chain. However, without a sufficient performance evaluation, drawbacks of technology investment, such as financial losses and ineffective resource allocation, keep occurring, which hinders the widespread implementation of digital technologies. This study demonstrates a comprehensive evaluation of digital technologies’ effects on the prefabrication supply chain based on multi-criteria decision analysis (MCDA) theory.Design/methodology/approachSpecifically, the targeted digital technologies and project constraints were first identified through a systematic literature review. The effects of the digital technologies were then scored using a questionnaire survey. The TOPSIS model was established to quantitatively rank the effectiveness of selected digital technologies.FindingsOverall, BIM technology shone out in the rankings and is regarded as the most beneficial digital solution by multi-stakeholders to the existing constraints, such as working efficiency. Collaboration patterns between different stakeholders and technology integration trend were also indicated.Originality/valueCompared with existing outcomes, this study specifically focused on examining the effects of digital technologies on the prefabrication supply chain, the most significant link in the process for prefabricated structures. New findings indicate the overall performance that considered both multi-stakeholders’ preferences and project constraints. The quantitative evaluation presents a comprehensive understanding of digital technologies’ effects, enabling industrial participants to reach well-informed, strategic and profitable investment decisions.

Analysis of Fundamental Factors Affecting Islamic Bank Share Prices in Indonesia

While investing in the stock market can generate profits, it can also generate losses. Understanding the impact of certain important components on stock prices can help investors make more profitable investment decisions. The study investigated the key components that influenced the price of sharia bank Indonesian shares. Secondary data used for research. The information comes from monthly financial statements compiled from June 2018 to December 2020. The study looked at the impact of the shock and long-term response of each variable on the stock price using the Vector Error Correction Model (VECM). In the long run, PER and ROE variables have a positive effect on the stock price, but EPS has a negative effect. Unlike other variables, DER and PBV do not affect the stock price. The main factor is the EPS, which has a big and positive impact on the stock price in the short term. DER and PER have a very negative impact on the value of stocks. PBV has a negative but non-significant impact, while ROE has a positive but not significant impact. Keywords: Fundamental Factor, Stock Price, Vector Error Correction Model

The Effect of Financial Ratios on Stock Returns: By Mediation of Price Earning Ratio

This study was conducted to analyze financial ratios effects towards Stock Returns in which the mediating variable is Price Earning Ratio (PER). The financial ratios are Debt to Equity Ratio (DER), Price to Book Value (PBV), and Return on Equity (ROE). This study uses a population of 65 real estate and property enterprises appeared in Indonesia Stock Exchange (IDX). This research’s samples are 36 companies registered in 2018-2020 IDX list. The data analysis technique uses Partial Least Square. The results showed (1) ROE and DER did not directly affect Stock Returns, (2) PBV directly affected Stock Returns, (3) ROE had an effect on Stock Returns partially mediated by the PER, (4) PBV and PER had no effect on Stock Returns mediated by the PER. The implications of the research result in investors needing to know the right time to make profitable investment decisions by analyzing the company's financial ratios to Stock Returns, which are an indicator of the level of profit earned by investors.

Pengaruh Return On Asset, Return On Equity, dan Price Earning To Ratio Terhadap Harga Saham Syariah Berasarkan Closing Price Pada Perusahaan yang Terdaftar di Jakarta Islamic Index Periode Tahun 2020-2022

Investing in the capital market, investors need accurate information, so they are not trapped in adverse conditions. One of the information that can be used by investors in assessing a company is financial statements. For investors, financial statements are useful for determining the best and profitable investment decisions. Based on the analysis of financial statements, investors can find out the comparison of the intrinsic value of the company's shares compared to the market price of the company's shares. To measure stock prices, it can be known from the influence of financial ratios on stock prices. Financial ratios are designed to show the relationship between financial statement estimates. This study aims to determine the effect of Return On Asset, Return On Equity ((ROE), and Price Earning to Ratio on Sharia Stock Prices based on Closing Prices in Companies listed on the Jakarta Islamic Index (JII) for the 2020-2022 period. The research method is quantitative research, technical data analysis using descriptive statistics. The source of research data is secondary data in the form of financial statements and stock prices from companies listed on the Jakarta Islamic Index (JII) for the 2020-2022 period using purposive sampling techniques. Secondary data processing using SPSS analysis technique version 23. Hypothesis testing in this study used multiple linear regression tests. Based on the results of the study shows that Return On Asset partially does not affect Islamic stock prices, Return On Equity partially has a positive and significant effect on Islamic stock prices, and Price Earning to Ratio partially does not affect Islamic stock prices. Return On Asset, Return On Equity, Price Earning to Ratio simultaneously have a positive and significant effect on Islamic stock prices in companies listed on the Jakarta Islamic Index (JII) for the 2020-2022 period.

Pengaruh EPS, ROE, dan ROA terhadap Harga Saham Perusahaan Consumer Goods Periode 2015-2020

Prospective investors must do a financial ratio analysis to determine the best and most profitable investment decisions. In theory, the value of Earning Per Share (EPS), Return on Equity (ROE), and Return On Assets (ROA) increases and will increase stock prices. The identification results show that when the EPS, ROE, and ROA of consumer goods companies have increased EPS, ROE, ROA, and Stock Price, the stock prices of consumer goods companies have decreased. This shows that there needs to be more theory and data. Based on this, this study aims to examine and analyze the effect of EPS, ROE, and ROA on the stock price of Consumer Goods companies partially or simultaneously. This type of research is quantitative research using secondary data. There is also data collection using a documentation study. The data used in this study is in the form of annual reports of Consumer Goods companies listed on the Indonesia Stock Exchange (IDX) for the 2015-2020 period. The analytical method used is panel data regression analysis with the stages of panel data regression model estimation, panel data regression model selection, classical assumption test, and significance test; the software used is Eviews 9.0. The results of this study indicate that the EPS variable partially has a positive effect on stock prices. ROE variable partially does not affect stock prices. The ROA variable partially has a negative effect on the Stock Price. The EPS, ROE, and ROA variables simultaneously significantly affect the Stock Price. Adjusted R-squared has a value of 77.135%. This shows that EPS, ROE, and ROA in this study affect 77.135% of stock prices, while other variables outside this study influence 22.86%.

Using Machine Learning To Predict Future Stock Prices

The volatile nature of financial markets presents a formidable challenge in accurately forecasting equity returns and stock prices. Artificial intelligence (AI) and machine learning are increasingly being employed to predict future stock prices and reduce risks, enabling more profitable investment decisions. 
 In this paper, we employed a few AI regression and classification models to tackle the challenge of predicting future stock prices These models utilized a historical time-series dataset of the opening and closing prices of stock over the last five years to generate predictions that were eventually compared with actual prices The models were evaluated based on their regression or classification score, measured as a percent of accuracy. Among the models tested the MLP classification and linear regression models were the most effective predictors.

Herding as behavior investing: A bibliometric analysis

<p>Behavioral investing seeks to understand and exploit psychological biases in financial decision-making. It is based on the idea that investors are often influenced by psychological factors such as emotions, heuristics, and social norms, which can lead to systematic errors in judgment. These biases can cause investors to make irrational or suboptimal decisions, which can have negative consequences for their financial well-being. Behavioral investors aim to identify these biases to make more rational and profitable investment decisions. This research using bibliometric analysis from 246 article was published in a reputable (SCOPUS), peer-reviewed journal that is included in an index of scholarly literature. Bibliometric analysis is a method of analyzing and quantifying the characteristics of a set of publications, to study the trends and patterns in the existing research on herding behavior. Based on the research reviewed, it appears that herding behavior is a complex phenomenon that is influenced by various factors, including psychological biases, environmental conditions, and market conditions. It is often studied in the context of behavioral finance and investor behavior, and is relevant to the study of market efficiency and capital markets.</p><p> </p>

Natural Gas Flaring Management System: A Novel Tool for Sustainable Gas Flaring Reduction in Nigeria

The use of hydrocarbon fuels increases with population growth and rising standards of living, and so does natural gas flaring. Natural gas flaring is both a waste of natural resources and a violation of Nigeria’s energy policy for sustainable development through natural gas conservation. However, it remains the most cost-efficient and effective associated natural gas (ANG) management option in developing countries such as Nigeria. The World Bank’s initiative to eliminate routine gas flaring by 2030 has increased the need to limit or eliminate routine gas flaring. Often, studies on natural gas utilisation techniques fail to consider the lack of practical tools that integrate economic, technical, and regulatory factors into a gas flaring management framework, and the intricacies of existing tools, which often come at the expense of simplicity to achieve real-time information output. This paper aims to establish a framework and ANG management tool to reduce regular gas flaring in Nigeria. This research established a management framework (using a flowchart decision tree) and models to provide a user-friendly ANG flaring tool (using a MATLAB graphical front end user interface with back-end ASPEN HYSYS thermodynamic models). This was combined with techno-economic models for liquefied natural gas, gas-to-methanol, and gas-to-wire ANG utilisation options. The tool was then tested with data obtained from Fields Y and X in the Niger Delta region of Nigeria. The results, considering both economic and technical factors, showed that the choice of liquefied natural gas for Field Y was best due to its proximity to the pipeline infrastructure and its cost-effectiveness, and the availability of a high-demand LNG market for that area. For Field X, gas-to-wire was best due to its proximity to the electrical grid and high electricity requirements for that area. Additional geographical profiles in West Africa and ANG utilisation alternatives were recommended for further investigation. This paper developed and validated a one-of-a-kind ANG flaring management tool that incorporates techno-economic analysis of selected ANG utilisation options to assist operators and investors in making more profitable investment decisions.

Investigating self-efficacy and behavioural bias on investment decisions

The determinants of irrational decisions on the stock market are found in numerous empirical studies. However, self-efficacy and behavioural biases have a sturdy influence on stock market investment decisions. Behavioural biases are formed with heuristics, prospect theory and herding effect concerning stock market investments. Self-efficacy is independent of behavioural biases but is closely connected with controlling behavioural intentions in decision-making. The research was conducted to find the influence of self-efficacy and behavioural biases in the decision of stock market investment. The study was conducted with 250 individual investors and applied the SEM technique. Findings indicated that heuristics had a positive relationship with behavioural biases, but behavioural biases reported a negative relationship with the herding effect and prospect theory. Heuristics were mostly developed on the intrinsic strength of individual investors; therefore, investors believe heuristics will be a better decision-making tool than prospect theory or the herding effect. Prospect theory is shaped and influenced by regret aversion, loss aversion, self-control and mental accounting. Financial literacy, risk tolerance, and peer support profoundly develop the self-efficacy of investors to make profitable investment decisions. Self-efficacy is formed by risk tolerance, financial literacy and peer support in the stock market investment decision and identified the evidence of individual investors not making rational decisions and facing one or more behavioural biases and self-efficacy factors. The study finds the combined effect of behavioural biases and self-efficacy in stock market investment decisions, which have significant implications among individual investors, particularly in emerging markets.

Maximizing portfolio profitability during a cryptocurrency downtrend: A Bitcoin Blockchain transaction-based approach

The volatile and unpredictable nature of the cryptocurrency market makes it particularly challenging to make profitable investment decisions. different machine learning-based techniques have been employed for forecasting cryptocurrency value. However, although some works have addressed incorporating the Blockchain transactions’ data into the analysis, none of them has provided a hybrid solution, including features obtained through complex network modeling. In this paper, we investigated the use of machine learning and complex network techniques to improve the profitability of a cryptocurrency portfolio during a downtrend period. We extracted features through a complex network-building methodology based on the Bitcoin blockchain transactions, merged them with the historical cryptocurrency values, and generated the predictions using different machine-learning models. The results indicated that incorporating complex network features improved the performance in retaining the initial capital at the end of the experiment, leading to an increment of 7.09% and 4.33% for the CNN and LSTM models, respectively. Our findings suggest that the proposed method enhanced the performance of cryptocurrency investment strategies during downtrend periods.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature. The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends’ prices by using STIs. The proposed model has implemented the Deep Learning (DL) model to establish the concept of Correlation-Tensor. The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange (NSE) – India, a Long Short Term Memory (LSTM) is used. The datasets encompassed the trading days from the 17 of Nov 2008 to the 15 of Nov 2018. This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends. The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one. The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%, 56.25%, and 57.95% on the datasets of HDFC, Yes Bank, and SBI, respectively. Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models

Financial markets exhibit alternating periods of rising and falling prices. Stock traders seeking to make profitable investment decisions have to account for those trends, where the goal is to accurately predict switches from bullish to bearish markets and vice versa. Popular tools for modelling financial time series are hidden Markov models, where a latent state process is used to explicitly model switches among different market regimes. In their basic form, however, hidden Markov models are not capable of capturing both short- and long-term trends, which can lead to a misinterpretation of short-term price fluctuations as changes in the long-term trend. In this article, we demonstrate how hierarchical hidden Markov models can be used to draw a comprehensive picture of market behaviour, which can contribute to the development of more sophisticated trading strategies. The feasibility of the suggested approach is illustrated in two real-data applications, where we model data from the Deutscher Aktienindex and the Deutsche Bank stock. The proposed methodology is implemented in the R package fHMM, which is available on CRAN.

The impact of index‐insured loans on credit market participation and risk‐taking

AbstractIndex‐insured loans offer considerable advantages over standalone insurance policies for improving farmers’ access to agricultural credit. However, research on demand for such products and their impact on profitable investment decisions has been limited and conflicting. In this article, we investigate the impact of index insurance on demand for credit and investment decisions using a lab‐in‐the‐field experiment conducted in rural Tanzania. We find that index insurance increases demand for credit and high‐risk high‐return investments.

A Machine Learning Approach for the Short-term Reversal Strategy

The short-term reversal effect is a pervasive and persistent phenomenon in worldwide financial markets that has been found to generate abnormal returns not explainable by traditional asset pricing models. In contrast to the linear model employed in most studies on the short-term reversal, this article aims to establish a nonlinear framework to study the reversal anomaly, by using machine learning approaches. Machine learning methods including Random Forest, Adaptive Boosting, Gradient Boosted Decision Trees and extreme Gradient Boosting, are employed to test the profitability of the short-term strategy in the US and Chinese stock markets. Significant outperformances with extremely high Sharpe ratio, moderate kurtosis, and positive skewness are found, showing remarkable classification efficiency of the machine learning models and their applicability to various markets. Further studies reveal that the strategy returns can be weakened with the extension of the holding period. Notably, by comparing the performances of machine learning with our newly developed linear reversal strategy, the nonlinear methods are proved to be capable of providing a diversified model predictability with improved classification accuracy. Our research indicates the significant potential of machine learning in resolving the stock return and feature relationship, which can be helpful for quantitative traders to make profitable investment decisions.

A New Quantitative Predictability-Based Method of Risk Measurement to Make Profitable Investment Decisions in Financial Markets

The ultimate goal of investment in financial markets is to achieve the maximum expected rate of return. Risk is one of the most important factors in making profitable investment decisions. Therefore, in the literature, many efforts have been made to define, model, and determine the effect of it in making financial decisions. The first and the most fundamental attempt to quantitatively model the risk and involve it in making investment decisions is related to Markowitz’s studies. The results of these studies, besides many financial applications, have been the basis for other riskbased studies. Such methods have been investors’ main instrument in developing profitable investment strategies. However, despite scientific and practical advantages, none of them consider the predictability of under-study systems. While the predictability logically has a non-strict inverse relationship with the risk. In this paper, a new method for measuring risk based on the predictability is presented to eliminate this disadvantage. In the proposed method, Markowitz methods have been developed by entering predictability. Accordingly, the proposed method, besides the advantages of Markowitz methods, enters predictability in risk measurement. In order to evaluate the performance of the proposed method in risk measurement and investment decisions, in comparison to other methods, data from twenty companies listed on the Tehran Stock Exchange were used. The empirical results indicate the efficiency of the proposed method to make financial decisions in comparison with other methods.

Analisis Dependensi Faktor Makroekonomi terhadap Tingkat Harga Emas Dunia dengan Pendekatan Copula

Gold is a precious metal that used many times as an alternative investment. Before investing, every investor requires relevant information to make profitable investment decisions. Relevant information can be obtained by looking at the dependency relationship between variables. In identifying the relationship between variables, a Copula approach could be used, since it is not tight against the assumption of normality, which is common in macroeconomic variables. Copula used were Archimedean Copula family, such as Clayton, Frank, and Gumbel. The results of this study indicated that the Archimedean Copula of the Frank family is the best Copula models to explain the structure of dependencies between gold and each composite stock price index and exchange rate, with each parameter obtained were 2.286 and -2.2390, respectively, while Clayton Copula family was the best Copula models to explain the structure of dependencies between gold and oil, with parameter obtained was 3.4090.

Predicting the Stock Market Index Using Stochastic Time Series Arima Modelling: The Sample of BSE and NSE

Stock market is basically volatile and the prediction of its movement will be more useful to the stock traders to design their trading strategies. An intelligent forecasting will certainly abet to yield significant profits. Many important models have been proposed in the economics and finance literature for improving the prediction accuracy and this task has been carried out through the modelling based on time series analysis. The main aim of this paper is to check the stationarity in time series data and predicting the direction of change in stock market index using the stochastic time series ARIMA modelling. The best fit ARIMA (0,1,0) model was chosen for forecasting the values of time series, viz., BSE_CLOSE and NSE_CLOSE by considering the smallest values of AIC, BIC, RMSE, MAE, MAPE, Standard Error of Regression, and the relatively high Adjusted R2 values. Using this best fitted model, the predictions were made for the period ranging from 7th January, 2018 to 3rd June, 2018 (22 expected values) using the weekly data ranging from 6th January, 2014 to 31st December, 2017 (187 observed values). The results obtained from the study confirmed the prospective of ARIMA model to forecast the future time series in short-run and would assist the investing community in making the profitable investment decisions.

Predicting the Stock Market Index using Stochastic Time Series ARIMA Modelling: The Sample of BSE and NSE

The stock market is basically volatile, and the prediction of its movement will be more useful to the stock traders to design their trading strategies. An intelligent forecasting will certainly abet to yield significant profits. Many important models have been proposed in the economics and finance literature for improving the prediction accuracy, and this task has been carried out through modelling based on time-series analysis. The main aim of this paper was to check the stationarity in time series data and predicting the direction of change in stock market index using the stochastic time series ARIMA modelling. The best fit ARIMA (0,1,0) model was chosen for forecasting the values of time series, that is, BSE_CLOSE and NSE_CLOSE by considering the smallest values of AIC, BIC, RMSE, MAE, MAPE, standard error of regression, and the relatively high adjusted R 2 values. Using this best fitted model, the predictions were made for the period ranging from January 7, 2018 to June 3, 2018 (22 expected values) using the weekly data ranging from January 6, 2014 to December 31, 2017 (187 observed values). The results obtained from the study confirmed the prospectives of ARIMA model to forecast the future time series in short-run and would assist the investing community in making profitable investment decisions.

Stock Market Returns Under Macroeconomic Conditions: An Empirical Evidence from BRIC Economies

Financial markets continuously incorporate information about macroeconomic events and monetary policy in order to make profitable investment decisions, and these are reflected in stock returns. In fact, financial markets have no perfect information, they do behave in forward looking manner, in that they monitor for new information that could affect the profitable investment decisions. The main objective of our paper is to explore the sensitivity of stock market returns to macroeconomic environments in Brazil, Russia, India, and China. In order to achieve the objective, the authors utilized data for major macroeconomic factors namely exchange rate, inflation rate, interest rate and oil price for the sample period starting from May 2007 to April 2017. We utilized OLS estimation technique to estimate the empirical models of our study. The findings of show no significant relationship between respective exchange rate, inflation rate, interest rate and oil price on market returns of either BRIC economy. However, the regression analysis reveals insignificant positive relationship of exchange rate, inflation rate and interest rate with stock market returns while oil prices has insignificant negative relationship. This suggests influence of other domestic and international macroeconomic factors on stock market returns. Furthermore, in the collective panel regression model of BRIC economies, we found that inflation rate has significant influence on stock market returns of BRIC economies. The findings of study help investors to know how specific stock performs to take profitable investment decision. It also assists policy makers to enhance and monitor monetary policy and helps managers in risk management. Keywords: Macroeconomic indicators, Stock market returns, OLS JEL classification : E31, E44, G1 DOI : 10.7176/JESD/10-1-07

ANALISIS LAPORAN KEUANGAN SEBAGAI DASAR PENILAIAN KINERJA PADA PERUSAHAAN SEMEN YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2015-2016

Company's financial performance can be known from the aspect of financial ratios, such as Liquidity, Solvency, Profitability, and Activity ratios. The development of a company, can be seen and can be compared through the company's financial performance. This is certainly useful for investors, creditors, and owners to make profitable investment decisions. This study aims to assess the financial performance of PT. Semen Indonesia Tbk, PT. Indocement Tunggal PrakasaTbk, and PT HolcimIndonesia by using financial ratio analysis. This study uses secondary data from Indonesia Stock Exchange. The result of research shows there are differences of financial performance between PT. Semen Indonesia Tbk, PT. Indocement Tunggal PrakasaTbk, and PT Holcim Indonesia. Suggestions for the Management Party should further improve the performance of total asset management, so that efficient corporate finance will be better.Keywords: Liquidity, Solvency, Profitability, Activity, and Financial Performance