Stock Market Prices Research Articles

Deep Neural Network Model for Hurst Exponent: Learning from R/S Analysis

This paper proposes a deep neural network (DNN) model to estimate the Hurst exponent, a crucial parameter in modelling stock market price movements driven by fractional geometric Brownian motion. We randomly selected 446 indices from the S&P 500 and extracted their price movements over the last 2010 trading days. Using the rescaled range (R/S) analysis and the detrended fluctuation analysis (DFA), we computed the Hurst exponent and related parameters, which serve as the target parameters in the DNN architecture. The DNN model demonstrated remarkable learning capabilities, making accurate predictions even with small sample sizes. This addresses a limitation of R/S analysis, known for biased estimates in such instances. The significance of this model lies in its ability, once trained, to rapidly estimate the Hurst exponent, providing results in a small fraction of a second.

A causality investigation into stock prices and macroeconomic indicators in the Indian stock market

Background of the study The systematic impact of macroeconomic variables on stock market returns makes it crucial to comprehend the link between macroeconomic variables and the stock market. Stock prices are closely linked to macroeconomic indicators, a crucial aspect for investors, policymakers, and researchers in emerging markets like India, influencing investment decisions and policy formulation. Methods The autoregressive distributed lag (ARDL) model was used in this study to examine the causal links between specific macroeconomic factors and Indian stock prices from April 2009 to March 2023. Results The outcomes of the research suggest that macroeconomic variables exert influence on the Indian stock market, across the short and long term. Moreover, the results of the paired Granger causality test suggest that the domestic macroeconomic variables possess predictive significance for stock prices in the Indian stock market. Conclusion The study reveals that macroeconomic variables significantly impact the Indian stock market, highlighting the need for investors and portfolio managers to monitor these conditions to optimize returns and mitigate risks. The Reserve Bank of India should maintain an optimal money supply to prevent inflation and exchange rate fluctuations, while bolstering the export sector and facilitating imports through initiatives like Atma-nirbhar Bharat Abhiyan and Make in India. Policies focusing on productivity, infrastructure, and a favourable business environment are also crucial. Therefore, it is crucial for investors and portfolio managers to consistently analyse the current macroeconomic conditions in order to maximize their profits and minimize risks. This research has extensive significance for comprehending the intricate connections between the stock market and macroeconomic issues.

Measuring Impact of Market Sentiment on Stock Prices

This study considers the impact of investor sentiment on stock market prices, it is when something beyond traditional economic factors, such as emotions or perceptions that play dominating role in driving psychological and real pricing. This behaviour in investor sentiment often leads to irrational decision making, always influenced by news, social media and the movements of markets. This will make stocks price more volatile. Through sentiment analysis, historical stock data and behavioural finance models the study examines times when there were significant movements in the market. These trends suggest that happy days can cause major prices to rise (along with the sun) and bad news can punch holes in a report even if business hasn't really looked any worse. The regulators and the investors should understand that sentiment can improve the efficiency of the stock price-prediction algorithms as well as bring anomalies to light in the market. The study of market sentiment and its effect on the stock price. In the given paper of research, it is easy to notice the impact of interaction between investor sentiment and stock price.

Estimating systemic risk for non-listed Euro-area banks

SRISK is a measure of a firms' systemic risk contribution that is computed using its listed stock market price. SRISK measurement is extended and applied to firms that do not have listed equity. A mapping from balance sheet characteristics to SRISK for listed firms is applied to SRISK for unlisted European banks. The mapping is validated by comparing SRISK measures for unlisted banks with their losses in European bank stress-testing.

A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework

Stock market prediction is an interesting and complex problem that has recently been in the limelight, thanks to the significant accuracy achieved by deep learning models. However, a complete platform with prediction and risk analysis ability is unavailable. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model integrates two different approaches successfully to improve accuracy significantly. The model inputs two sources – a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles. The traditional problem of stock price prediction is dealt with using LSTMs on individual stock prices. At the same time, the confidence is represented by a risk value calculated intelligently using XGBoost and LSTM output. We have deployed natural language processing techniques for performing sentiment and subjectivity analyses, which are then used to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. It removes the hassle for investors to research the project or company they want to invest in and provides all relevant analysis and data.

Research On the Impact of Stock Market Price Fluctuations on Corporate Financing Costs

The fluctuation of stock market prices is one of the research areas that has attracted much attention in the current financial market. As the core of the stock market, price fluctuations not only affect investors’ investment returns, but also increase the financing costs of enterprises. This article delves into the factors that affect stock market price fluctuations, analyzes the impact of stock market price fluctuations on corporate financing costs, and further proposes corresponding suggestions to promote sustainable development of enterprises and stable operation of financial markets.

The research on the impact of short-term cross-border capital flows on stock market prices in China

This paper examines the long-term dynamic impact of short-term cross-border capital flows on the Shanghai Composite Index and the CSI 300 Index using monthly data from 2010 to 2020 and a Vector Autoregression (VAR) model. With the financial opening and the internationalization of the renminbi in China, the stock market has gradually liberalized its management of foreign capital. As a result, the A-share market has become a target for passive capital inflows from overseas investors. This has further intensified the volatility of asset prices and posed challenges to China's cross-border capital regulation. According to the study, short-term cross-border capital flows have a negligible impact. However, a positive correlation appears between 4 to 9 months, followed by a significant negative impact in the 10th month, reflecting the oscillatory risks that cross-border capital flows pose to China's stock prices. This finding reflects how Chinese capital controls have limited the short-term negative impact of cross-border capital flight on stock prices. The paper recommends strengthening the regulation of short-term cross-border capital, cautiously advancing the financial opening system, and actively improving the stock market system.

Dividend mispricing: Evidence from all‐stock merger deals

AbstractStock prices of targets in all‐stock merger deals should reflect acquirer share values, net of expected dividend payments before deal completion. This setting provides a unique opportunity to examine whether market prices of target stocks reflect the price reducing impact of impending acquirer dividends. We find evidence that target stock returns on the acquirer ex‐day are negatively related to the size of the dividend payments, indicating an incomplete adjustment on the last cum‐day. However, given the size of typical quarterly dividends, the magnitude of the estimated 30% incomplete adjustment to the dividend does not represent a viable arbitrage opportunity. Examining a longer window, we find that roughly 70% of the acquirer dividend is incorporated into the target stock price in the period two days after the dividend announcement to the ex‐day. Overall, our results are consistent with target stock prices adjusting slowly over time to reflect impending acquirer dividends.

Predicting the stock market prices using a machine learning-based framework during crisis periods

Predicting the stock market prices using a machine learning-based framework during crisis periods

Development of multi-forecasting model using Monte Carlo simulation coupled with wavelet denoising-ARIMA model

The analysis and prediction of stock market prices are crucial areas of research due to their complex, chaotic, and nonlinear features. As a result, making significant gains in stock market investments is a crucial task. However, expert and intelligent modeling techniques can help in achieving positive stock market returns. In this study, we use the Monte Carlo (MC) simulation method to generate multiple future values of the time series of closing prices of a particular stock of BSE using a combination of wavelet denoising and the autoregressive integrated moving average (ARIMA) model. The multiple future realizations of stock prices produced by the Monte Carlo (MC) simulation can help minimize risk and uncertainty in stock market investments. Firstly, we use wavelet analysis to detect significant noise levels in the time series at each scale in discrete wavelet decomposition, which is then eliminated by an appropriate wavelet denoising method. Next, the time series of denoised stock prices is fitted with a suitable ARIMA model, and the future values are obtained using this model. The future values of the denoised time series are simulated using MC simulation. The results of the study show that simulated forecasts obtained by MC simulation using the integrated wavelet-denoising-ARIMA model become more accurate with increasing simulation count than by applying a single ARIMA model to noisy stock price series. It has also been observed that MC simulation reduces the standard error of estimates to one half when the number of simulations is quadrupled.

An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data

Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms are utilized, significantly improving the accuracy of price trend predictions and sentiment analysis. Technical indicators from stock market data are processed using the Bi-LSTM model to predict future stock prices. Concurrently, sentiment data from the Economic Times is pre-processed through Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and pre-trained transformer models to extract key sentiment scores. The ABC algorithm enhances textual feature selection, while the Firefly algorithm reduces skewness in the error distribution of technical indicators, thereby improving the alignment between forecasted and actual stock prices. The fusion mechanism integrates predicted price movements with sentiment analysis to provide actionable insights for stock selection. The decision-making framework classifies stocks based on the consistency between price movements and sentiment: stocks predicted to rise with positive sentiment are labelled as "Sure Selection"; rising prices with negative sentiment are categorized as "Dicey Selection"; declining prices with positive sentiment result in a "Risky Selection"; and negative sentiment with falling prices trigger an "Avoid Selection". The proposed approach achieved a prediction accuracy of 67.00% for the Bi-LSTM model, with a precision of 0.714, recall of 0.695, and an F1 score of 0.677, demonstrating the model's robustness. The mean absolute error (MAE) for the Bi-LSTM model was 4.30, indicating strong predictive performance. The combined use of ABC and Firefly algorithms optimizes both technical and sentiment features, significantly contributing to the overall performance of the prediction model. By fusing multiple data sources and employing advanced optimization techniques, this methodology offers a unique and effective solution for stock market prediction and decision-making

Time series forecasting using transformer neural network

Time series forecasting is a vital component of data science, giving essential insights that help decision-makers to predict future trends across a number of sectors. This paper focuses on projecting complicated stock market price dynamics, weather data variations, and hourly traffic occupancy rates. The Informer-Based Time Series Forecasting (IBTSF) model, which employs a transformer-based architecture to capture long-term relationships and give probabilistic predictions, is proposed.This model is assessed against Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model to benchmark its performance. Simulation findings demonstrate that the IBTSF model considerably outperforms other models across all datasets. For traffic data, the IBTSF model achieves a Mean Squared Error (MSE) of 10.1 and a Mean Absolute Scaled Error (MASE) of 1.69, compared to other model's larger mistakes. In stock data forecasting, the IBTSF model reports an MSE of 8.04 and a MASE of 1.53, again outperforming other models. Similarly, using meteorological data, the IBTSF model obtains an MSE of 86.12 and a MASE of 0.7, whereas other models fall short. These results underline the IBTSF model's resilience and accuracy in capturing subtle patterns and relationships within time series data, giving precise insights that benefit decision-making processes.

Unveiling the Dynamics: Exploring the Relationship between Emerging Stock Market Prices and Macroeconomic Factors through ARDL Analysis

This study investigates the interaction between stock prices and prices in other markets using a panel ARDL model. Analyzing data from January 2004 to December 2022 for 19 emerging market countries, the study explores the impacts of gold prices, exchange rates, inflation rates, and interest rates on stock prices. The results indicate a long-term negative relationship, except for gold, between the variables. The effects are generally insignificant in the short run, except for gold's negative impact. The global financial crisis of 2008 negatively affected emerging market stock markets in both the short and long term. In the short run, the COVID-19 pandemic negatively impacted stock market returns, which turned positive in the long run. This study highlights the importance of sound monetary policies focused on price stability and fiscal policies aimed at reducing government dominance in financial markets to foster long-term growth in stock markets.

Enhancing Agricultural Commodity Price Forecasting Using Generative Models: A Deep Learning Approach

Predicting stock market prices is a critical yet challenging task in finance. Technical analysis, a widely used methodology in investment theory, involves forecasting price movements by analyzing historical market data. Recently, deep learning has gained prominence for its exceptional ability to process complex data, making it a popular tool for financial applications such as stock prediction, portfolio optimization, financial information analysis, and trade execution strategies. In this study, we propose a novel deep learning architecture that integrates a Generative Adversarial Network (GAN) with a Convolutional Neural Network (CNN) as the discriminator and Gated Recurrent Units (GRU) as the generator. This framework generates distributions of daily stock prices through adversarial learning to predict stock closing prices. Using daily trading data from Ruchi Soya Industries Limited, an empirical analysis was performed across a broad time frame. Results show that the proposed GAN-based architecture significantly outperforms traditional deep learning models, including GRU, LSTM, and Bi-LSTM, in predicting stock closing prices. These findings demonstrate the potential of this novel approach for improving stock price forecasting accuracy.

Constructos del agua, mercancía y valor

The work revisits certain categories within social sciences to analyze water through two primary lenses. The first lens views water as a social construct, involving various communal, social, and institutional actors. This perspective has historical roots extending back to the liberal era of the early 20th century and continues through the post-war period of 'developmentalism.' During this time, water resources were seen as a public service managed by municipalities, communities, and, following the establishment of the Sistema Nacional de Acueductos y Alcantarillados (SNAA, known as AyA) in 1961, by a centralized state institution. Secondly, the study examines the transition of water from a 'use value' (satisfying a need) to an 'exchange value.' This analysis employs the concept of 'elasticity' of the commodity, representing a kind of expansive long wave embedded in the international political debates of the sixties and seventies. These debates involved various social movements and American business elites, who perceived the concept of 'free enterprise' as under threat. This resulted in 'authoritarian liberalism,' which has had lasting effects. In the present era, a 'total market' logic prevails, where water markets, transnational corporations, and stock market pricing determine the distribution of this resource on the planet through supply and demand laws.

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.

The impact of foreign institutional investors on the information content of Chinese stock prices

This paper examines the impact of foreign institutional investors on the information content of stock prices in China's capital market. Using A-share listed companies in Shanghai and Shenzhen from 2005 to 2021 as the research sample, the study finds that institutional investors play a “leading role” in increasing the information content of the market in both the short and long term. Furthermore, the information content of domestic and foreign institutional investors is complementary, with foreign institutional investors more effective in promoting the increase of information content in the Chinese stock market than their domestic counterparts. These findings provide new empirical evidence on how to enhance the effectiveness of China's stock market by further opening to foreign institutional investors and increasing the price information content.

Measuring ESG risk premia with contingent claims

We propose a contingent claims approach for estimating ESG risk premia from market information and market participants' decisions. To this end, we infer the asset value dynamics via the structural model of Merton [1974, “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates.” Journal of Finance 29: 449–470.] for a large panel of S&P 500 firms using an estimation algorithm that utilizes the information embedded in stock market prices, CDS spreads, and default probabilities. We find a statistically significant relationship between the ESG score and the volatility and drift terms of the asset value process, suggesting that ESG factors are structurally connected to the value of the firm. We establish a mapping between ESG scores and the cost of equity and debt as implied by firm's contingent claims, and derive estimates of the ESG risk premium across different ESG and leverage profiles. In addition, we break down the ESG risk premia by industry, and demonstrate how practitioners can adjust the weighed average cost of capital of ESG laggard firms for valuation and decision making purposes.

Research on Impact of Investor Sentiment on Chinese Stock Market: Review and Prospect

Over time, investor behavior has become more complicated due to the unpredictability of the Chinese stock market. Major research has found that investor sentiment swings have a huge impact on the market. Therefore, investors need to observe and understand how investor sentiment affects the market in the right way. Much of the past literature has used textual data mining and event analysis to measure the impact of investor sentiment and its impact on stock market returns, pricing, and volatility. Several articles have concluded that investor sentiment is highly responsive to the Chinese stock market. Another phenomenon that has emerged on various forums and social media is that people's fear of the stock market is spreading. The results of event analysis can prove that the impact of events such as the lifting of the ban on restricted shares and the listing of companies will be very obvious and have an impact on investor sentiment. An important implication of this study is that in addition to the dominant role of Granger causality in the causality that produces negative returns, negative investor sentiment is the main factor determining the overall behavior of the stock market. When bad news breaks, investors release their pent-up emotions for a short period, resulting in a violent reaction. In addition, market sentiment can also affect trading volumes and stock prices. This has put pressure on the market. In the end, this paper expands the future research direction from the two aspects of communication mechanism and quantitative analysis.

Can ChatGPT Predict Stock Market Price Movements?

Can ChatGPT Predict Stock Market Price Movements?