Search Results (91)

Sentiment analysis is widely applied in the financial domain. However, financial documents, particularly those concerning the stock market, often contain complex and often ambiguous information, and their conclusions frequently deviate from actual market fluctuations. Thus, in comparison to sentiment polarity, financial analysts are primarily concerned with understanding the underlying rationale behind an article’s judgment. Therefore, providing an explainable foundation in a document classification model has become a critical focus in the financial sentiment analysis field. In this study, we propose a novel approach integrating financial domain knowledge within a hierarchical BERT-GRU model via a Query-Guided Dual Attention (QGDA) mechanism. Driven by domain-specific queries derived from securities knowledge, QGDA directs attention to text segments relevant to financial concepts, offering interpretable concept-level explanations for sentiment predictions and revealing the ’why’ behind a judgment. Crucially, this explainability is validated by designing diverse query categories. Utilizing attention weights to identify dominant query categories for each document, a case study demonstrates that predictions guided by these dominant categories exhibit statistically significant higher consistency with actual stock market fluctuations (p-value = 0.0368). This approach not only confirms the utility of the provided explanations but also identifies which conceptual drivers are more indicative of market movements. While prioritizing interpretability, the proposed model also achieves a 2.3% F1 score improvement over baselines, uniquely offering both competitive performance and structured, domain-specific explainability. This provides a valuable tool for analysts seeking deeper and more transparent insights into market-related texts. Full article

Predicting stock prices is a popular area of study within the realms of data mining and machine learning. Precise forecasting can assist investors in mitigating the risks associated with their investments. Given the unpredictable nature of the stock market, influenced by policy changes, stock data often display high levels of fluctuation and randomness, aligning closely with the prevailing market sentiment. Moreover, diverse datasets related to stocks are rich in historical data that can be leveraged to forecast future trends. However, traditional forecasting models struggle to harness this information effectively, which restricts their predictive capabilities and accuracy. To improve the existing issues, this research introduces a novel stock prediction model based on a deep-learning neural network, named after EL-MTSA, which leverages the multifaceted characteristics of stock data along with ensemble learning optimization. In addition, a new evaluation index via market-wide sentiment analysis is designed to enhance the forecasting performance of the stock prediction model by adeptly identifying the latent relationship between the target stock index and dynamic market sentiment factors. Subsequently, many demonstration experiments were conducted on three practical stock datasets, the CSI 300, SSE 50, and CSI A50 indices, respectively. Experiential results show that the proposed EL-MTSA model has achieved a superior predictive performance, surpassing various comparison models. In addition, the EL-MTSA can analyze the impact of market sentiment and media reports on the stock market, which is more consistent with the real trading situation in the stock market, and indicates good predictive robustness and credibility. Full article

In numerous domains of finance and economics, modelling and predicting stock market volatility is essential. Predicting stock market volatility is widely used in the management of portfolios, analysis of risk, and determination of option prices. This study is about volatility modelling of the daily Johannesburg Stock Exchange All Share Index (JSE ALSI) stock price data between 1 January 2014 and 29 December 2023. The modelling process incorporated daily log returns derived from the JSE ALSI. The following volatility models were presented for the period: sGARCH(1, 1) and fGARCH(1, 1). The models for volatility were fitted using five unique error distribution assumptions, including Student’s t, its skewed version, the generalized error and skewed generalized error distributions, and the generalized hyperbolic distribution. Based on information criteria such as Akaike, Bayesian, and Hannan–Quinn, the ARMA(0, 0)-fGARCH(1, 1) model with a skewed generalized error distribution emerged as the best fit. The chosen model revealed that the JSE ALSI prices are highly persistent with the leverage effect. JSE ALSI price volatility was notably influenced during the COVID-19 pandemic. The forecast over the next 10 days shows a rise in volatility. A comparative study was then carried out with the JSE Top 40 and the S&P500 indices. Comparison of the FTSE/JSE Top 40, S&P 500, and JSE ALLSI return indices over the COVID-19 pandemic indicated higher initial volatility in the FTSE/JSE Top 40 and S&P 500, with the JSE ALLSI following a similar trend later. The S&P 500 showed long-term reliability and high rolling returns in spite of short-run volatility, the FTSE/JSE Top 40 showed more pre-pandemic risk and volatility but reduced levels of rolling volatility after the pandemic, similar in magnitude for each index with low correlations among them. These results provide important insights for risk managers and investors navigating the South African equity market. Full article

This study investigates the multifractal properties of daily returns of the Standard and Poor’s 500 Index (SPX), the Dow Jones Industrial Average (DJI), and the Nasdaq Composite Index (IXIC), the three main indices representing the U.S. stock market, from 1 January 2005 to 1 November 2024. The multifractal detrended fluctuation analysis (MF-DFA) method is applied in this study. The origins of the multifractal properties of these returns are both long-range correlation and fat-tail distribution properties. Our findings show that the SPX exhibits the highest multifractal degree, and the DJI exhibits the lowest for the whole sample. This study also examines the multifractal behaviors of cross-correlations among the three major indices through the multifractal detrended cross-correlation analysis (MF-DCCA) method. It is concluded that the indices are cross-correlated and the cross-correlations also exhibit multifractal properties. Meanwhile, these returns exhibit different multifractal properties in different stages of the market, which shows some asymmetrical dynamics of the multifractal properties. These empirical results may have some important managerial and academic implications for investors, policy makers, and other market participants. Full article

Accurate forecasts of stock indexes can not only provide reference information for investors to formulate relevant strategies but also provide effective channels for the government to regulate the market. However, due to its volatility and complexity, predicting the stock price index has always been a challenging task. This paper proposes a hybrid forecasting system based on comprehensive feature selection and intelligent optimization for stock price index forecasting. First, a recursive feature elimination with a cross-validation (RFECV) algorithm is designed to filter variables that have a significant impact on the target data from multiple datasets. Then, the stack autoencoder (SAE) algorithm is constructed to compress the feature variables. At last, an enhanced least squares support vector machine (LSSVM) algorithm is established to obtain high-precision point prediction results, and the Gaussian process regression (GPR) algorithm is used to obtain reasonable interval prediction results. Taking the Shanghai Stock Exchange (SSE) as an example, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the model were 6.989 and 0.158%, respectively. In addition, the prediction interval coverage probability (PICP) is 99.792%. Through experimental comparison, the model shows high prediction accuracy and generalization ability. Full article

This paper investigates the impact of the Luckin Coffee accounting scandal on stock liquidity and spillover effects in the financial market, focusing on Chinese companies listed on U.S. exchanges. Utilizing event studies, we analyze eight pivotal events related to the scandal to examine stock liquidity and market quality changes. The results show a significant decline in Luckin’s stock liquidity during the scandal, while spillover effects on other Chinese stocks are limited. Comparisons with the Satyam accounting scandal suggest that individual company scandals may not substantially affect the liquidity of other stocks from the same country. The findings highlight the importance of robust regulatory frameworks and investor due diligence in safeguarding market integrity and restoring investor confidence. Full article

In this paper, we propose a modified synthetic control causal analysis for time series data with volatility in terms of absolute value of return outcomes taken into account in constructing the prediction of potential outcomes for time series causal analysis. The consistency property of the synthetic weight parameter estimators is developed theoretically under a time series data-generating process framework. The application to evaluate the UK’s mini-budget policy, announced by the then Chancellor on 23 September 2022, which had significant implications for the stock market, is examined and analysed. Comparisons with traditional synthetic control and synthetic difference in difference (DID) methods for evaluation of the effect of the mini-budget policy on the UK’s stock market are also discussed. Full article

In the knowledge era, intellectual capital has been considered a key factor in creating value within organisations. This study examines the relationships and interactions between the components of intellectual capital and the profitability of Panamanian banking and financial institutions listed on the Latin American Stock Exchange (LATINEX) from 2014 to 2020. A theoretical framework based on agency theories, signalling theory, and stakeholder theory was employed to support the results. The Valued-Added Intellectual Coefficient (VAIC)™ model, which evaluates the intellectual capital of organisations based on information from financial statements, was also utilised. In this study, stepwise regression was applied to select the optimal number of predictors to be included in each multiple regression model to examine the relationship between the return on equity (ROE) and the components of the VAIC™ in addition to control variables such as size and indebtedness. The findings confirm this study’s hypothesis, demonstrating that the structural capital efficiency (SCE) and company size (SIZE) variables explain 57% of the variance in the ROE for the analysed institutions. The results suggest that the intellectual capital (IC) of financial sector institutions listed on LATINEX is significantly influenced by the SCE coefficient, which shows a negative relationship, suggesting that investment in structural capital does not enhance profitability. On the other hand, larger institutions exhibited higher profitability during the study period. This study was limited to the analysis of two sectors: banking and finance in companies listed on LATINEX. However, its rigorous theoretical and empirical foundation opens the way for future research in which other sectors can be considered, and cross-country comparisons can be made, strengthening the research in this field for Latin America. At the same time, this study offers market regulators a scientific methodology to oversee the activities of issuing companies. Full article

No form of economic activity is unaffected by climate change, which has emerged as a new risk factor impacting financial market stability and sustainable development. This study examines the impact of extreme weather on the stock returns of A-share listed companies in China. Utilizing a decade-long dataset, we construct monthly proportions of extreme high-temperature days and extreme humid days using a percentile comparison approach. The findings reveal a significant negative impact of extreme weather on stock returns. Specifically, each standard deviation increase in the monthly proportion of extreme high-temperature days and extreme humid days corresponds to a decrease in annualized returns by 0.09% and 0.15%, respectively. The mediation analysis suggests that extreme weather primarily affects stock returns through its influence on investor sentiment, impacting economic decision making, with minimal direct effects on corporate performance. Additionally, the sensitivity of stock returns to extreme weather varies notably among different types of companies. Larger, more profitable, and less risky firms show lower sensitivity to extreme weather. The impact is observed not only in heat-sensitive industries but also in non-heat-sensitive industries and remains significant even after excluding company announcement days. This study offers new insights and relevant recommendations for businesses and policymakers on sustainable development and financial stability. Full article

This paper examines the correlation between three prospective investing options: the Bitcoin cryptocurrency price, gold, and the Dow Jones stock index. The main research question is whether there is a causal effect of gold and the DWJ on Bitcoin and how this effect varies on time. The study begins with a background analysis that explains the definitions and operation of cryptocurrencies, followed by a brief overview of gold and its derivatives. In addition, a historical review of stock markets is provided, with a focus on the Dow Jones index. Then, a literature review follows. Daily data from three separate periods are used, each spanning four years. The first period, running from October 2014 to September 2018, provides an overview of the introduction of official cryptocurrency price data. The second period, running from Oct 2018 to Sept 2022, captures more recent trends preceding COVID-19. The third period, from January 2020 to December 2023, is the whole COVID-19 period with the initiation, embedded, and terminal phases. Classical inductive statistical methods (descriptive, correlations, multiple linear regression) as well as time series analysis methods (autocorrelation, cross-correlation, Granger causality tests, and ARIMA modeling) are used to analyze the data. Rigorous testing for autocorrelation, multicollinearity, and homoskedasticity is performed on the estimated models. The results show a correlation of Bitcoin with gold and the DWJ. This correlation varies over time, as in the first period the correlation mainly concerns the DWJ and in the second it mainly concerns gold. By using ARIMA models, it was possible to make a forecast in a time horizon of a few days. In addition, the structure of the forecasting mechanism of gold and DWJ on Bitcoin seems to have changed during the COVID-19 crisis. The findings suggest that future research should encompass a broader dataset, facilitating comprehensive comparisons and enhancing the reliability of the conclusions drawn. Full article

This research aims at applying the Artificial Organic Network (AON), a nature-inspired, supervised, metaheuristic machine learning framework, to develop a new algorithm based on this machine learning class. The focus of the new algorithm is to model and predict stock markets based on the Index Tracking Problem (ITP). In this work, we present a new algorithm, based on the AON framework, that we call Artificial Halocarbon Compounds, or the AHC algorithm for short. In this study, we compare the AHC algorithm against genetic algorithms (GAs), by forecasting eight stock market indices. Additionally, we performed a cross-reference comparison against results regarding the forecast of other stock market indices based on state-of-the-art machine learning methods. The efficacy of the AHC model is evaluated by modeling each index, producing highly promising results. For instance, in the case of the IPC Mexico index, the R-square is 0.9806, with a mean relative error of $7\times {10}^{-4}$. Several new features characterize our new model, mainly adaptability, dynamism and topology reconfiguration. This model can be applied to systems requiring simulation analysis using time series data, providing a versatile solution to complex problems like financial forecasting. Full article

Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPBiGRU that integrates candlestick patterns and a sparrow search algorithm (SSA). The incorporation of candlestick patterns endows the input data with structural characteristics and time series relationships. Moreover, the hyperparameters of the CPBiGRU model are optimized using an SSA. Subsequently, the optimized hyperparameters are employed within the network model to conduct predictions. We selected six stocks from different industries in the Chinese stock market for experimentation. The experimental results demonstrate that the model proposed in this paper can effectively enhance the prediction accuracy and has universal applicability. In comparison to the LSTM model, the proposed model produces an average of 31.13%, 24.92%, and 30.42% less test loss in terms of MAPE, RMSE and MAE, respectively. Moreover, it achieves an average improvement of 2.05% in $R^2$. Full article

This study investigates governance mechanisms and their relation to firm value, i.e., executive compensation restrictions during the regulatory period and their effects on the performance of firms that received Troubled Asset Relief Program (TARP) funds. We employ an event study to investigate the market reactions for TARP recipients, followed by OLS regression to examine the stock return effects of 10 announcements. For comparison, we also employ a multivariate regression model (MVRM) based on a system of equations with seemingly unrelated regressions (SURs). Our evidence shows that changes in firm value have a negative and significant relationship with changes in total compensation for TARP companies that have paid back their debts to the government. However, the relationship is weaker than that for TARP companies that have not paid back the bailout money. Full article

On the capital market, price movements of stock corporations can be observed independent of overall market developments as a result of company-specific news, which suggests the occurrence of a sudden risk event. In recent years, numerous concepts from statistical physics have been transferred to econometrics to model these effects and other issues, e.g., in socioeconomics. Like other studies, we extend the approaches based on the “buy” and “sell” positions of agents (investors’ stance) with a third “hold” position. We develop the corresponding theory within the framework of the microcanonical and canonical ensembles for an ideal agent system and apply it to a capital market example. We thereby design a procedure to estimate the required model parameters from time series on the capital market. The aim is the appropriate modeling and the one-step-ahead assessment of the effect of a sudden risk event. From a one-step-ahead performance comparison with selected benchmark approaches, we infer that the model is well-specified and the model parameters are well determined. Full article

Stock prediction has garnered considerable attention among investors, with a recent focus on the application of machine learning techniques to enhance predictive accuracy. Prior research has established the effectiveness of machine learning in forecasting stock market trends, irrespective of the analytical approach employed, be it technical, fundamental, or sentiment analysis. In the context of fiscal year-end selection, the decision may initially seem straightforward, with December 31 being the apparent choice, as discussed by B. Kamp in 2002. The primary argument for a uniform fiscal year-end centers around comparability. When assessing the financial performance of two firms with differing fiscal year-ends, substantial shifts in the business environment during non-overlapping periods can impede meaningful comparisons. Moreover, when two firms merge, the need to synchronize their annual reporting often results in shorter or longer fiscal years, complicating time series analysis. In the US S&P stock market, misaligned fiscal years lead to variations in report publication dates across different industries and market segments. Since the financial reporting dates of US S&P companies are determined independently by each listed entity, relying solely on these dates for investment decisions may prove less than entirely reliable and impact the accuracy of return prediction models. Hence, our interest lies in the synchronized fiscal year of the TW stock market, leveraging machine learning models for fundamental analysis to forecast returns. We employed four machine learning models: Random Forest (RF), Feedforward Neural Network (FNN), Gated Recurrent Unit (GRU), and Financial Graph Attention Network (FinGAT). We crafted portfolios by selecting stocks with higher predicted returns using these machine learning models. These portfolios outperformed the TW50 index benchmarks in the Taiwan stock market, demonstrating superior returns and portfolio scores. Our study’s findings underscore the advantages of using aligned financial ratios for predicting the top 20 high-return stocks in a mid-to-long-term investment context, delivering over 50% excess returns across the four models while maintaining lower risk profiles. Using the top 10 high-return stocks produced over 100% relative returns with an acceptable level of risk, highlighting the effectiveness of employing machine learning techniques based on financial ratios for stock prediction. Full article