Stock Price Research Articles

Pengaruh Earning Per Share, Return on Equity dan Price to Book Value Terhadap Harga Saham Periode 2019-2021 (Studi Kasus pada Perusahaan Sub Sektor Otomotif dan Komponenya di BEI)

The phenomenon that occurred during the Covid-19 pandemic that spread in Indonesia in early 2020 has an impact on all aspects of life, including the capital market. The spread of Covid-19 in Indonesia has caused fear for investors so that many of them choose to leave risky assets. This affects the movement of the Composite Stock Price Index (IHSG). The problem in this study aims to determine the effect of EPS (Earning Per Share), ROE (Return on Equity) and PBV (Price to Book Value) on stock prices in automotive companies listed on the IDX for the period 2019 - 2021. The type of research used is quantitative research with secondary data sources. The population in this study is automotive companies listed on the Indonesia Stock Exchange for the period 2019 - 2021 as many as 15 companies. Sampling using purposive sampling technique as many as 10 companies that meet the criteria. Data analysis techniques using multiple linear regression analysis techniques. The results of the study indicate that EPS can influence stock prices, and ROE also influences stock prices, and PBV influences company value. Simultaneously, the variables EPS (Earning Per Share), ROE (Return on Equity) and PBV (Price to Book Value) influence stock prices.

Impacts of CEO-employee pay disparity on investor behavior and market dynamics: Evidence from laboratory asset markets

This study explores the impacts of CEO-to-employee pay disparity on investor behavior and market dynamics using an experimental methodology. We employed two laboratory asset markets with identical company valuations but differing CEO-employee pay ratios. Our findings reveal that a higher pay ratio leads investors to quote higher prices for the company's stock. This effect is driven by two conflicting mechanisms of social preference: inequality aversion, which motivates investors who value fairness to quote lower prices for stocks of companies with high pay ratios, and social comparison, where the high pay ratio prompts investors to compare their wealth with that of CEOs, thus becoming more risk-seeking and quoting higher prices. Moreover, higher pay ratios contribute to increased stock prices, fostering larger market bubbles, enhancing the dispersion of investor opinions, boosting trading volumes, and escalating market volatility. This study enriches our understanding of how asset markets react to CEO-to-employee pay disparities, providing valuable insights for policymakers and market participants.

Portfolio Optimization Based on Rolling Window Using 5 Stocks

This paper aims to explore a method of constructing dynamically adjusted investment portfolios through the combination of rolling windows and mean-variance models. The construction of investment portfolios plays a crucial role in financial markets by effectively diversifying risks and achieving stable investment returns. This study analyzed adjusted closing price data from five stocks (AAPL, JPM, JNJ, XOM, and PG), sourced from Yahoo Finance. The rolling window method was employed to predict future stock prices and construct investment portfolios based on these predictions. This study calculated annualized average return and covariance matrix for each window. The mean-variance model and optimization algorithms were then used to determine the optimal weights for daily portfolio composition. The results demonstrate the high accuracy of the rolling window method in predicting short-term stock prices. Notably, Procter & Gamble (PG) and Johnson & Johnson (JNJ) exhibited the lowest prediction errors. Investment portfolios based on rolling window predictions performed well over a certain period. Nevertheless, the S&P 500 index did not show a substantial disadvantage compared to their long-term performance. This research provides dynamic portfolio optimization methods of practical value for investors in financial markets.

The Effect of GCG, CSR Disclosure, and Investment Decisions on Firm Value

Investors' opinion of a company's competence in resource management is known as firm value. Demand and supply factors in the stock market determine the firm's value, reflected in its stock price. According to investor opinions, a company's high worth is represented in both its present success and its prospects for the future. In general, a number of elements, including the stock market price, net income per share, the cost of capital, and assets and liabilities, define a company's worth. This research proposes examining the impact of managerial ownership, audit committee size, corporate social responsibility disclosure, and investment decisions on firm value. This is a quantitative descriptive study made applying purposive sampling. A total of 171 observations of property and real estate firms listed on IDX during three observation periods of 2020-2022. The data was analyzed using multiple linear regression. The study found that managerial ownership contributes positively to firm value, whereas investment decisions, corporate social responsibility disclosure, and audit committee size does not influence on firm value. This study emphasizes the importance of management ownership in efforts to raise a company's value by giving empirical data to support the application of the theory of agency and the theory of signaling in this research. It will also be expected to serve as input for the companies under review.

Impacts of blockchain cooperation project announcements on corporate stock market values: a loose coupling theory perspective

Purpose As a disruptive technology, blockchain technology brings new opportunities and challenges to operations management. We aim to examine the influences of blockchain cooperation project announcements (BCPAs) on firms’ stock market reactions in an emerging market. From 2016 to 2021, a total of 113 BCPAs of listed firms from the Chinese A-share market are selected as samples.Design/methodology/approach This study is based on the loose coupling theory and uses the event study method and probit regression analysis.Findings We find BCPAs positively affect the firm’s stock price on the day they are released. Compared with vertical BCPAs in a supply chain, horizontal BCPAs exert a more positive market reaction. Moreover, a BCPA with a partner within a shorter geographical distance exerts a more positive influence on market reaction. Contrary to the intuition of the decentralized blockchain feature that one-to-many cooperation leads to better benefits, one-to-one BCPAs exert a more positive effect on market reaction than one-to-many BCPAs. We further find that (1) industry type has a certain impact on cooperation mode selection, and (2) manufacturing firms are more inclined to choose one-to-one cooperation than those in service industry.Originality/value We focus on the impact of blockchain cooperative announcements and additionally use the probit regression models to analyze the influencing factors of cooperation mode selection and find the critical role of the industry type, which complements the existing empirical research on blockchain announcements and is conducive to provide decision-making reference for managers.

Portfolio Optimization Using LSTM for Five Selected Stocks

Portfolio construction is very critical in the financial markets as it assists investors in achieving maximum returns with minimum risks through diversification. In this paper, Long Short-Term Memory (LSTM) models are employed to predict stock prices and optimize investment portfolios. Historical adjusted closing prices from Yahoo Finance for the period from January 1, 2023, to March 1, 2024, were used to predict the future stock prices of Apple Inc. (AAPL), Microsoft Corporation (MSFT), BlackRock Inc. (BLK), JPMorgan Chase & Co. (JPM), and Tesla Inc. (TSLA) using LSTM models. Portfolios were constructed using mean-variance optimization based on these predictions, and Monte Carlo simulations were employed to capture the uncertainty around the estimates. The results are treasured for investors in financial markets as they spotlight the limitations and capability risks of depending totally on gadget learning predictions for portfolio optimization. This emphasizes the want for a greater comprehensive method to investment decision-making that considers more than one factors and methodologies.

Review of machine learning with sentimental analysis method for cross-model stock price prediction

Stock market prediction has been a popular area of research for years. Machine learning, as a fast-developing popular algorithm, is applied to stock market prediction by many previous researchers to better solve the time series involving problems over the changing prices. Different from traditional machine learning algorithms that focus on stock prices only, this paper gives a brief description and review of stock price predictions models that contain sentimental analysis over the recent paper works. Different from the prices in number format, sentimental analysis is more based on textual information extraction and mining, converting them into usable input to pass to a prediction model. This extends the prediction model's takeable input domain and strengthens the accuracy. To better classify the differences between the models, discussion and introduction are given based on different model types about whether they are traditional type or deep neural network embedded. Even though traditional types of models are more popular for sentimental analysis and neural networks perform better in prediction tasks, traditional methods are relatively easy to build or train with more explainability, compared to deep learning models suitable for larger data sets.

Government decisions and macroeconomic stability: Fiscal policies and financial market fluctuations

Since the 2008 Financial crisis, macroeconomic and financial markets' stabilisation policies relied mainly on monetary actions, reducing the ECB's margin of manoeuvre and calling for a major role for fiscal authorities. We investigate the impact of government decisions on financial markets for 11 Eurozone economies (from 1995 to 2021), in the very short run, using an event study, finding a positive (negative) reaction to fiscal expansion (consolidation) announcements and in the short/medium-run, with a Bayesian TVP-FAVAR, identifying cross-country heterogeneities; stronger (weaker) public finances show a direct (indirect) relation between fiscal policies and stock prices and an indirect (direct) one with 10-year bond yield.

Comparative analysis of machine learning and traditional models for financial option pricing

This study conducts a comparative analysis of traditional and machine learning models for financial option pricing, using historical stock prices and interest rates data. Traditional models such as the Black-Scholes, Heston, Merton Jump-Diffusion, and GARCH are evaluated against machine learning models including Multi-Layer Perceptrons (MLPs) and Long Short-Term Memory (LSTM) networks. The analysis employs performance metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R value. Results indicate that the GARCH model excels in predictive accuracy due to its ability to capture volatility clustering, while machine learning models, especially the Tuned Neural Network, demonstrate superior flexibility and adaptability in managing complex non-linear relationships in financial data. Traditional models, although theoretically robust, show limitations under varying market conditions. The study underscores the potential of hybrid approaches combining traditional and machine learning techniques to leverage their respective strengths for more accurate and reliable option pricing. Future research directions include exploring advanced machine learning architectures and improving model transparency through explainable AI.

Does Dividend Policy Affect Stock Price Volatility? Evidence from Egypt

Does Dividend Policy Affect Stock Price Volatility? Evidence from Egypt

Integrating LSTM Networks with Mean-Variance Optimization for Enhanced Portfolio Construction: An Empirical Study on UK Stock Market

Portfolio optimization has long been a central theme in finance. With ongoing advancements in machine learning, there is a significant opportunity to integrate predictive methods into portfolio optimization. This paper proposes leveraging Long Short-Term Memory (LSTM) networks alongside the established Mean-Variance (MV) optimization framework to construct optimal portfolios. These portfolios aim to help financial investors effectively manage and mitigate risk while maximizing returns. The study meticulously screened the leading stocks of 12 prominent UK companies listed on the London Stock Exchange (LSE), known for their influence and visibility. Initially, the study applies the LSTM networks to predict stock price volatility and integrates these predictions into the MV model to allocate portfolio weights effectively. To underscore the superiority of the proposed approach, the study compares cumulative returns from portfolios optimized for maximum and minimum variance Sharpe ratios with real data against the FTSE100 index over the same period. The results demonstrate that the portfolio optimized with the maximum Sharpe ratio significantly outperforms both the conservative minimum variance portfolio and the FTSE100 index. This empirical evidence underscores the practical value of integrating advanced machine learning techniques with established financial theory, enabling more innovative and efficient investment strategies to meet the evolving needs of financial investors.

Harnessing machine learning for enhanced quantitative financial strategies: Applications and case studies

Machine learning algorithms have long been an essential tool for quantitative finance, as they play an important role in the asset pricing, portfolio optimisation and financial risk management. This work discusses how to apply Random Forest, Support Vector Machine (SVM), and Neural Networks to further develop and enhance the quantitative investment research. With automatic feature selection, reconstruction of high-dimensional data and resist against over-fitting, Random Forest has been an efficient ensemble approach with remarkable prediction performance. For this reason, Random Forest is leveraged for the stock price prediction. SVM, particularly known for its robustness in high-dimensional classification task, has a non-linear optimisation ability that can be employed to capture the inherent non-linear dependencies in options prices. Neural Networks, as a learning approach capable of discovering complex relationships, is employed for the portfolio optimisation, with a promise for further improving the risk-adjusted portfolio return. The work is conducted through practical case studies to simulate how Random Forest, SVM and Neural Networks enable us to further improve the financial performance, and to illustrate its advantage, disadvantage and practical application in quantitative finance.

Financial Performance Measurement Using the Value Added Method to Determine the Effect on Stock Prices

One of the Indonesian government's policies since 2015 has been to increase development through its developed infrastructure, by increasing its budget for this sector from year to year. However, companies listed in the engineering and construction subsector on the Indonesian Stock Exchange, both state-owned enterprises and private companies, experienced losses so it could be said that their financial performance was poor. This study intends to evaluate the financial performance engineering and construction sub-sector companies using the Economic Value Added (EVA), Market Value Added (MVA), Refined Economic Value Added (REVA), Financial Value Added (FVA), and Shareholder Value Added (SVA) in 2018 – 2023 and determine their influence on stock prices. This type of research is quantitative descriptive research using E-Views 12 with a population 26 companies and a sample size 16 companies. Based on the results the calculations and analysis, it stated that EVA, MVA, REVA, FVA, SVA and stock prices engineering and construction companies, both state-owned and private, during the 2018 - 2023 period obtained an average of negative amounts. The research results show that the financial performance variables using value added have an influence on stock prices with influence of 74.57% while the rest is influenced by other variables not examined in this research. Partially only MVA and SVA have an influence on stock prices because based on the probability test using E-Views 12 it appears that only MVA and SVA produce results below the alpha. The suggestion for this research is that company management should pay attention to the company's financial performance, especially the MVA and SVA variables and further researcher can make comparative research between all value-added variables.

Pengaruh Return On Asset, Return On Equity, dan Earning Per Share terhadap Harga Saham

Capital markets help companies to finance their businesses. Investors invest in capital markets with the expectation of profit. Investors invest in capital markets with the expectation of profit. Investors consider financial performance in the form of financial metrics when investing capital. The purpose of this study is to examine the impact of Return on Assets (ROA), Return on Equity (ROE), and Earnings per Share (EPS) on stock prices of service sector companies listed on the Indonesian Stock Exchange (IDX). is to judge The period 2018-2021 is listed. The sample consists of 25 manufacturing companies selected using a targeted sampling method based on annual securities report data. Data were analyzed using multiple regression analysis using Eviews 10 for Windows. The results of this study show that the Return on Assets (ROA) and Return on Equity (ROE) variables have a negative impact and are insignificant. Earnings per share (EPS) has a positive impact and significant on stock prices

Synergies between price and life history enhance extinction risk in open‐access fisheries

AbstractThere have been few documented extinctions of fished species, but many bioeconomic models predict that open‐access incentives make extinction possible. Open‐access multi‐species fisheries can cause species' extinction if other, faster‐growing species maintain profits at fatal effort levels. Even target species can be profitably harvested to extinction if their prices rise sufficiently as they are depleted. Here, we explore interactions between these potential extinction mechanisms by modelling an open‐access multi‐species fishery with one or multiple fleets exploiting two species, each with different growth rates, ex‐vessel prices, and price dynamics. Increases in the strong stock's (the stock with higher productivity relative to fishing susceptibility) price as it is depleted increase the range of conditions under which the weak stock can be driven extinct and shrinks the range of bioeconomic parameters in which both species can coexist under open‐access. Catch hyperstability – whereby species become easier to catch as they are depleted – makes the weak stock weaker as it is depleted and further narrows the scope for coexistence. Fleet diversity in targeting ability can prevent weak stock extinction, as competition or switching balances species abundances. With few documented global fished‐species extinctions, our results raise important questions, which we discuss. Is the apparent lack of extinctions largely due to management? Are more species in lightly‐managed fisheries threatened with extinction than previously acknowledged? Have more extinctions than we realize already happened in data‐ and management‐poor fisheries? Or have fishes' high fecundity and the oceans' vastness provided protection against extinction that is uncaptured by existing theoretical models?

Regulating Carry Trades: Evidence from Foreign Currency Borrowing of Corporations in India

Abstract We establish that macroprudential controls limiting capital flows can curb risks arising from foreign currency borrowing by corporates in emerging markets. Firm-level data show that Indian firms tend to issue more foreign currency debt when the interest rate differential between India and the United States is higher. This “carry trade” relationship, however, breaks down once regulators institute more stringent interest rate caps on borrowing; in response, riskier borrowers cut issuance most. Prior to adoption of this macroprudential measure, stock price exposure of issuers to currency risk rises after issuance, as witnessed during the “taper tantrum” episode of 2013; this source of vulnerability is nullified by the measure, as confirmed during the October 2018 oil price shock and the COVID-19 outbreak. We find no evidence of the policy’s efficacy being undermined by leakage or regulatory arbitrage.

ANALISIS PENGARUH RETURN ON ASSET (ROA) DAN NET PROFIT MARGIN (NPM) TERHADAP HARGA SAHAM PADA PERBANKAN YANG TERDAFTAR DI LQ45

This research was conducted with the aim of examining the influence of Return On Assets (ROA), Net Proft Margin (NPM) on share prices in banks registered in LQ45 for the 2019 - 2022 period. The data used in this research is based on secondary data in the form of quantitative data. The research instrument used to measure this research uses a table listing the variable indicators Return On Assets (ROA), Net Profit Margin (NPM), and Price Earning Ratio (PER). The population of this research is all banks registered in LQ45 for 28 years, namely from 2019-2022. The samples in this research are 4 years each, so the total sample in this research is 20 years, namely from 2019 to 2022. The sampling technique used in this research is a non-probability sampling technique. The data collection techniques used in this research are documentation and literature study. The documentation intended in this research is financial reports and annual reports for banks registered in LQ45. The data analysis technique in this research is using the classic Assumption Test, Multiple Linear Regression Test, t Test, and F Test. The research results show that the variables Return On Assets (ROA) and Net Profit Margin (NPM) have no effect on stock prices (PER).

Trading Momentum in the U.S. Crude Oil Futures Market

This paper investigates if the Rate of Change (ROC), as a popular measure of momentum, can serve as a reliable technical analysis indicator to improve stock price prediction for U.S. West Texas Intermediate (WTI) crude oil futures market. The methodology centers on the application of the ROC and/or Moving Average (MA) price crossover/cross under strategies as a trading system. End of month futures prices of West Texas Intermediate (WTI) crude oil prices are collected for the period 28th May 2004 – 30th April 2024. The performance of the trading system is measured using both the Sharpe and Sortino ratios, thereby adjusting for total and downside risks. The model is also benchmarked against the naïve buy-and-hold strategy. Overall findings suggest a ROC based on 14-month periods outperform other momentum-based indicators, including when combined with price-crossover moving average strategies, and the naïve buy-and-hold strategy. After adjusting for the negative returns, the downside risk or semi-deviation amounted to 8.5%, and a Sortino value of 4.58. The Sortino value is however biased due to the 295% return witnessed in 2009. Findings have some vital policy implications for regulatory bodies and traders in the WTI crude oil energy futures market.

Analyzing the Selective Stock Price Index Using Fractionally Integrated and Heteroskedastic Models

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile’s stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China’s economic slowdown in 2012 and the subprime crisis in 2008.

Information flow and investment cash flow sensitivity

ABSTRACT In this paper, we examine whether firms with greater information flow, i.e. which reveal more firm-specific information through their idiosyncratic stock price movements, are associated with lower investment cash flow sensitivity. Using a sample of 450 French non-financial firms over the period 2006–2022, we find results consistent with the idea that greater information flow decreases information asymmetry, thus allowing firms to carry out investments more efficiently as they depend less on the availability of internal cash flows.