Market Movements Research Articles

Application of Sentiment Analysis to Explore the Connection Between Public Sentiment and Stock Price Movement by Python

This study aims to research the relationship between sentiments of public and the trend of the stock. Specially, it focuses on analyzing how positive or negative sentiment correlated to the trend of stocks. In methodology, this research investigates the predictive power of public sentiment on stock market trend by analyzing users comments on Reddit by utilizing Python Reddit API Wrapper (PRAW) to systematically web-scrape relevant financial discussion from a subreddit called stocks. After the data collecting, preprocessing step is conducted to removing noise and standardize text. Eventually, sentiment analysis is applied to analyze the sentiments of comments by using Natural Language Processing (NLP) techniques. This approach allows for an in-depth examination of the correlation between the sentiments expressed in Reddit comments and subsequent stock price fluctuations by comparing the outcome and the actual price movement. Eventually, through the comparison between the sentiment analysis result and actual stocks movement, the sentiment of public comments shares the same trend with the market movement.

Do Global Disruptive Events Induce Herding Behaviour during Upward and Downward Market Movements? The Evidence from Nordic and Baltic Stock Markets

Do Global Disruptive Events Induce Herding Behaviour during Upward and Downward Market Movements? The Evidence from Nordic and Baltic Stock Markets

Potential Predictors of Psychologically Based Stock Price Movements

Investment in stocks is increasingly dependent on artificial intelligence (AI), but the psychological and social factors that affect stock prices may not be fully covered by the measures currently used in AI training. Here, we search for additional measures that may improve AI predictions. We start by reviewing stock price movements that appear to be affected by social and psychological factors, drawing on stock market behaviour during the COVID-19 pandemic. A review of processes that are likely to produce such stock market movements follows: the disposition effect, momentum, and the response to information. These processes are then explained by regression to the mean, negativity bias, the availability mechanism, and information diffusion. Taking account of these processes and drawing on the consumer behaviour literature, we identify three factors which may not be covered by current AI training data that could affect stock prices: publicity in relation to capitalization, stock-holding penetration in relation to capitalization, and changes in the penetration of stock holding.

Asymmetric spot‐futures prices adjustments in Quebec grain markets

AbstractAsymmetric price transmission has been the subject of many studies in agricultural economics, but few has been said on Quebec grain market. This study uses threshold cointegration models to examine the dynamic relationship between Quebec spot and futures prices. Estimation results with daily, weekly, and monthly data from September 1994 to May 2022 are three‐fold. First, the main point to keep from cointegration tests is that futures prices and Quebec local grain prices are integrated but estimations with daily data are consistent to highlight a nonlinear long‐run relationship. Second, results indicate overall positive asymmetric for daily and weekly prices transmissions from futures to spot market. Third, the positive asymmetric prices transmissions shed light on the oligopoly structure of Quebec grain market and inefficiencies in the pricing mechanism which favor local grain sellers. Results with different data frequencies show that aggregated monthly prices data compared to more disaggregated data such as daily and weekly prices may lead to different results. Results imply that policymakers and market stakeholders could facilitate increased grain market competition. Grain users like Quebec hog producers need to better understand additional tools for their management of price risk like futures market and real time prices movement monitoring.

Emotion contagion and stock market: the role of online investor network in China

ABSTRACT Social media serves as a crucial channel for investors to acquire asset-related information while also facilitating emotional contagion through social connections. However, the association between the emotional contagion on social networks and asset prices remains unclear. We examine the relationship between market returns and disseminated emotion resulting from emotion contagion in a social network of more than 2.5 million investors in a finance-specific forum. Investors’ emotions are detected from their short-text posts based on a novel classification framework that leverages the traditional dictionary-based method and the Bidirectional Encoder Representations from Transformers (BERT). The potential of investors in emotional contagion is approximated in terms of a global metric of Collective Influence measured daily from a large-scale social network. We find that disseminated emotion is negatively associated with next-day returns, highlighting the importance of the network effect in reshaping the process of investors’ emotional contagion and subsequently influencing asset prices. Our findings suggest that understanding and monitoring emotional contagion in social networks can aid in predicting market movements, and policymakers can leverage these insights to enhance market stability by addressing investors’ psychological and social aspects.

Predicting Stock Market Trends using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data

This investigation aimed to utilize machine learning algorithms for predicting stock market movements in Iran. The study centered on three specific sectors - diversified finance, information technology (IT), and metals - within the Tehran Stock Exchange. Ten years of historical data were analyzed . Incorporating ten technical indicators. To achieve this goal, six machine learning models were deployed. Support Vector Regression (Linear)Support Vector Regression (RBF)Linear Regression, Random -Forests ,K-Nearest Neighbours (KNN) Decision Trees.

Exploring calendar effects: the impact of WASDE releases on grain futures market volatility

ABSTRACT The monthly issuance of the World Agriculture Supply and Demand Estimates (WASDE) by the United States Department of Agriculture (USDA) plays a pivotal role in shaping market dynamics through its comprehensive projections and assessments of agricultural commodities. This paper examines the influence of WASDE reports on the intraday volatility of grain futures. We analyse the calendar effect on volatility, focusing on the diurnal patterns on WASDE release days compared to other trading days. This study leverages high-frequency intraday data to explore the immediate and sustained market reactions to the reports, aiming to identify distinct volatility patterns and their implications for traders and market analysts. Our findings confirm the presence of unique diurnal volatility patterns on WASDE release days, significantly diverging from non-release days. This research contributes to the literature by providing refined insights into how public information releases affect market volatility, offering valuable perspectives for understanding and anticipating market movements in response to agricultural reports.

Conditional effects of higher order co-moments in asset pricing: Evidence from Borsa Istanbul

This paper explores how systematic higher order moments (co-skewness and co-kurtosis) are priced in Borsa Istanbul. We tested the significance of higher order co-moments and analyzed their contribution to the standard capital asset pricing model and the Fama and French (2015) 5-factor model. We used a two-stage method to analyze the weekly returns of beta and size-sorted portfolios and individual stocks over the sample period from June 22, 2007 to November 15, 2023. We also used models conditional on market movements. The findings reveal that co-skewness has statistically significant effects on portfolio returns in Borsa Istanbul, especially in up markets. We also present the statistically significant effects of co-kurtosis on individual stock returns in both up and down markets.

The Heston–Queue-Hawkes process: A new self-exciting jump–diffusion model for options pricing, and an extension of the COS method for discrete distributions

We propose a new self-exciting jump–diffusion process, the Heston–Queue-Hawkes (HQH) model, which integrates the well-known Heston model with the recently introduced Queue-Hawkes (Q-Hawkes) jump process. Similar to the Heston–Hawkes process (HH), the HQH model effectively captures both the slow and continuous evolution of prices, and the sudden and impactful market movements due to self-excitation and contagion. But a significant advantage of the HQH model is that its characteristic function is available in closed form, allowing for the efficient application of Fourier-based fast pricing algorithms, such as the COS method (which we extend to deal with discrete distributions). We also demonstrate that, by leveraging partial integrals of the characteristic function, which are explicitly known for the HQH process, we can reduce the dimensionality of the COS method, thereby decreasing its numerical complexity. Our numerical results for pricing European and Bermudan options indicate that the HQH model provides a broader range of volatility smiles compared to the Bates model, while maintaining a substantially lower computational burden than the HH process.

Beyond market anomalies: How heuristics and perceived efficiency shape investor behavior in developing markets

Cognitive biases often influence investor behavior in developing capital markets, leading to market anomalies and affecting overall market efficiency. With the increasing integration of global financial markets and the growing participation of retail investors, understanding these biases is more critical than ever. While market anomalies have been extensively studied in developed markets, their influence in developing economies remains under-explored. This study aims to examine the impact of heuristic biases on investment decisions, focusing on Nepal’s stock market. Structural Equation Modeling is used to assess how perceived market efficiency mediates the relationship between heuristic biases and investor behavior. Data were collected from purposively selected 403 active individual investors in Nepal Stock Exchange (NEPSE). The findings reveal that representative and overconfidence biases significantly and positively influence investment decisions and market efficiency. Specifically, investors exhibiting these biases are more likely to make confident and bold investment choices, believing in their ability to predict market movements accurately. Furthermore, the study finds that perceived market efficiency mediates the relationship between anchoring and adjustment bias and investment decisions, suggesting that investors who rely heavily on initial information (anchors) adjust their decisions based on their perceptions of market efficiency. The results highlight the critical role of heuristic biases in shaping investor behavior and stress the importance of market efficiency in this process. The study emphasizes the need to enhance investor awareness of these biases and implement policies to improve market transparency and efficiency. Such measures are vital for mitigating risks and fostering a more robust and resilient financial market in developing economies like Nepal.

Connectedness at extremes between real estate tokens and real estate stocks

We test the interconnectedness between returns on real estate (RE) crypto tokens and on RE stocks using a quantile vector autoregression (QVAR) methodology. Our main findings show that there is a strong dependence between RE tokens and RE stocks in the distributions of returns in the top and bottom quantiles, and a rather weaker correlation in the median quantile. From an investment perspective, the findings demonstrate that, contrary to the usual focus on the center of the general distribution of returns, RE tokens and RE stocks correlate most strongly at the tails of the distributions. For this reason, diversification benefits may be limited, and careful risk management and investment strategies should be employed to navigate this strong tail connection. Considering the limited diversification benefits at the extremes, investors should exercise caution and allocate resources to mitigate potential losses when RE crypto tokens and RE stocks experience extreme market movements.

Estimating and Forecasting Jakarta Composite Index in Pandemic Era Using ARIMA-GARCH Model

Many industries have suffered financial losses as a result of the COVID-19 epidemic. The stock market's movement has been impacted by this circumstance. Due to the influence of some people, a large number of individuals with limited trading knowledge are attempting to participate in the stock market. Market volatility should be understandable in order to gain profit instead of having losses. Therefore, it's essential to comprehend the market of the future through analysis of the data. The purpose of this study is to use ARIMA-GARCH to predict the Indonesian stock market price during. The period covered by the dataset is January 2020–December 2022. The training data indicates that ARIMA (2,1,2) is the best model for ARIMA. The results showed that data residual fitted by ARIMA (2,1,2)-GARCH (1,2) exhibits heteroscedasticity, according to the residual analysis. The MAPE score is 2%, which is relatively small. It means that ARIMA (2,1,2)-GARCH (1,2) is good enough for forecasting the Jakarta Composite Index.

Integrating Multimodal Deep Learning for Enhanced News Sentiment Analysis and Market Movement Forecasting

This paper presents a novel multimodal deep learning framework for analyzing news sentiments and forecasting market movements by leveraging natural language processing, deep learning, and auxiliary data sources. Traditional methods often rely solely on textual news data, limiting their predictive power due to the complexity and ambiguity of language. Our approach incorporates additional modalities such as stock prices, social media sentiment, and economic indicators to capture a more comprehensive view of market dynamics. We employ a hybrid deep learning architecture that combines convolutional neural networks (CNNs) for text feature extraction, long short-term memory (LSTM) networks for capturing sequential dependencies, and attention mechanisms to selectively focus on the most relevant features. To address data scarcity, we introduce advanced data augmentation techniques, generating synthetic news headlines based on historical stock price movements and sentiment patterns. The proposed system is evaluated on a comprehensive dataset spanning multiple years, including news headlines, stock prices, social media data, and economic indicators. Our method achieves an accuracy of 77.51%, significantly outperforming traditional methods and demonstrating improved robustness and predictive power. This study highlights the potential of integrating diverse data sources and sophisticated deep learning techniques to enhance news sentiment analysis and market movement forecasting.

Candlestick Pattern Recognition in Cryptocurrency Price Time-Series Data Using Rule-Based Data Analysis Methods

In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.

Insights into Herding Behavior in Indonesian Islamic Banks

Objective—This study investigates the phenomenon of herding behavior among investors in Indonesia's Shariah banks. In this situation, market participants tend to follow prevailing market trends without engaging in comprehensive fundamental analysis. Methodology/Technique – Specifically, the research examines the influence of investors' religious understanding as a mediating variable on their decision-making processes within the Shariah financial sector. Notably, the study addresses whether investors are more inclined to mimic market movements or rely on independent analysis when investing in Sharia-compliant banks. It is commonly observed that rumors can precipitate rash trading decisions among investors, leading to the buying or selling of shares even in the absence of substantial changes in asset fundamentals (Bommel, 2003). Findings – Employing a quantitative approach, this study utilizes path analysis to explore the impact of religious understanding on herding behavior. Although prior research has extensively investigated herding behavior, the introduction of religious understanding as a mediating variable offers a new dimension to assess the correlation between religiosity and market behavior. This aspect has not been widely examined in previous segmentations. The sample comprises data from 15 Islamic Banks in Indonesia, analyzed using associative data analysis techniques with AMOS software. Findings indicate significant positive relationships between stock returns and herding behavior, trading volume and herding behavior, and the level of Islamic understanding and herding behavior in these banks. Novelty – However, these relationships do not hold when Islamic understanding is applied as a mediating variable. This suggests that the level of religious understanding acts as a direct influencer rather than a mediator in herding behavior among investors in Indonesian Islamic banks. Type of Paper: Empirical JEL Classification: G41, D81, C58. Keywords: Herding behavior, Stock returns, Trading volume, Level of Understanding of Islam Reference to this paper should be made as follows: Kamaludin; Usman, B; Susanti, W. (2024). Insights into Herding Behavior in Indonesian Islamic Banks, J. Fin. Bank. Review, 9(1), 27 – 37. https://doi.org/10.35609/jfbr.2024.9.1(1)

Optimizing Stock Market Forecasts: The Role of AI and Hybrid Models in Predictive Analytics

Forecasting stock market movements is a challenging and significant task for both researchers and investors. Stock market movements are affected by local and global economic factors, as well as political developments. This field of research requires substantial knowledge of finance, statistics, and Artificial Intelligence to achieve reliable results. To understand stock market movements, we must interpret a significant amount of information from non-linear, volatile, and non-parametric raw data. To reduce the complexity of stock market forecasting, we need to extract key features from this raw data. To simplify the task of stock market forecasting for researchers and traders, we conducted a study on the Indian stock market and present a comprehensive summary report. This report includes an analysis of 50 research articles related to the Indian stock market, along with some highly cited articles pertaining to other international markets.

Green horizons: Enabling the energy transition through climate change policies

In response to the goals set by the COP21 Summit, the challenges brought about by the COVID-19 pandemic, and the ongoing conflict between Russia and Ukraine, this study aims to develop an effective policy framework to support the transition to sustainable energy. This framework considers key elements such as managing energy risks, leveraging green finance, navigating carbon markets, and evaluating three climate change policy indicators: the impact of climate change summits (CCS), government climate programs (CCGP), and climate change taxation (CCT). Utilizing the Quantile Vector Autoregressive (QVAR) approach and analyzing monthly data from the United States from August 1, 2014, to August 30, 2022, our research examines the influential role of these climate policy indicators. The results show that climate summits, governmental initiatives, and climate taxes are significant influencers, affecting both renewable and conventional energy sectors, carbon market movements, and green finance. The study underscores the importance of government initiatives and climate policies in driving the transition to renewable energy and fostering environmentally sustainable financial activities. Focused on advancing Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action), this research contributes to shaping effective policy frameworks and enriching policy debates on sustainable energy transition.

Topological Approach for Detection of Structural Breakpoints in Philippine Stock Price Data Surrounding the COVID-19 Pandemic

Topological data analysis (TDA) is a novel but rigorous framework used to identify the underlying shape and structure of a huge data set. In this study, we demonstrated how TDA can be useful in analyzing and predicting stock market movement. The study collected time-series data from the Philippine Stock Exchange of companies that were heavily affected by lockdown measures implemented during the COVID-19 pandemic and those that thrived. The study found that during market crashes, persistence diagrams revealed a presence of persistence weakening phenomenon. Moreover, by analyzing the stock price movement given an interval of time, the persistence landscape graphs produced high points or bumps during a market drop, which corresponds to structural breakpoints in the time series. Indeed, persistent homology can be used to predict early warning signs of probable market crashes.

Gambling preferences and fund company ownership: Evidence from China

This paper empirically tests the preference of China's public mutual funds for lottery-type equities, analyzing data from equity funds in the country's public mutual fund market from 2005 to 2022. The study finds that this gambling preference positively correlates with short-term fund performance, but negatively impacts long-term outcomes. Further investigation shows that the funds' propensity for gambling increases stock market volatility. However, fund company ownership can mitigate this risk amplification by implementing a risk-sharing structure. Additionally, a significant positive correlation exists between the mutual fund industry's gambling inclination and fund managers' optimism about future market movements. Finally, this article employs text analysis to explore the influence of gambling preferences from a non-quantitative information perspective, which helps control the investing behavior of institutional investors and enhances the real economy's access to the capital market.

Understanding the variance of earnings growth: The case of shipping

This study examines the relationship between the unexpected changes in earnings and the shipping market movements. The econometric method of variance decomposition proposed by Campbell (1991) is employed to empirically analyze the Panamax and Capesize markets. We find that a large proportion of unexpected earnings growth is related to news about returns that indicate subsequent price changes. The results provide important insights to practice for sustaining shipping businesses, which helps shipping companies make better investment and risk-management decisions. The contribution of this research is to deepen the understanding of the interaction between shocks to earnings growth, returns, and price–charter ratios in the present-value context.