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We examine the value of due diligence recommendations on Reddit's Wallstreetbets (WSB) platform. Before the Gamestop (GME) short squeeze, recommendations are significant predictors of returns and cash-flow news. This predictability is eliminated post-GME. Post-GME, the fraction of reports emphasizing price-pressure or attention-grabbing stocks dramatically increases, and the decline in informativeness is concentrated in these reports. Similarly, retail trade informativeness is particularly strong following DD reports in the pre-GME period, but not post-GME. Our findings are consistent with the view that the Gamestop event altered the culture of WSB , leading to a deterioration in investment quality that adversely affected smaller investors. [ABSTRACT FROM AUTHOR]

We document a new empirical phenomenon in which the aggregate positions of money managers, who are sophisticated speculators in the commodity futures market, as disclosed by the Disaggregated Commitments of Traders reports, can predict the cross section of commodity producers' stock returns in the subsequent week. We employ a number of cross-sectional methods, including calendar-time regression analysis, single-sort, double-sort, and Fama–MacBeth regressions, to confirm the predictability results. The results are more pronounced in firms with higher information asymmetry. We thus add more empirical evidence to the literature on costly information processing, which leads to gradual information diffusion across asset markets. [ABSTRACT FROM AUTHOR]

Our study aims to investigate the interdependence between international stock markets and sentiments from financial news in stock forecasting. We adopt the Temporal Fusion Transformers (TFT) to incorporate intra and inter-market correlations and the interaction between the information flow, i.e. causality, of financial news sentiment and the dynamics of the stock market. The current study distinguishes itself from existing research by adopting Dynamic Transfer Entropy (DTE) to establish an accurate information flow propagation between stock and sentiments. DTE has the advantage of providing time series that mine information flow propagation paths between certain parts of the time series, highlighting marginal events such as spikes or sudden jumps, which are crucial in financial time series. The proposed methodological approach involves the following elements: a FinBERT-based textual analysis of financial news articles to extract sentiment time series, the use of the Transfer Entropy and corresponding heat maps to analyze the net information flows, the calculation of the DTE time series, which are considered as co-occurring covariates of stock Price, and TFT-based stock forecasting. The Dow Jones Industrial Average index of 13 countries, along with daily financial news data obtained through the New York Times API, are used to demonstrate the validity and superiority of the proposed DTE-based causality method along with TFT for accurate stock Price and Return forecasting compared to state-of-the-art time series forecasting methods. [ABSTRACT FROM AUTHOR]

Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate. The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes in the stock market overall. Nonetheless, machine learning (ML) techniques that use historical data have been applied to make such predictions. Previous studies focused on a small number of stocks and claimed success with limited statistical confidence. In this study, we construct feature vectors composed of multiple previous relative returns and apply the random forest (RF), support vector machine (SVM), and long short-term memory (LSTM) ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days. We apply this approach to all S&P 500 companies for the period 2017–2022. We assess performance using accuracy, precision, and recall and compare our results with a random choice strategy. We observe that the LSTM classifier outperforms RF and SVM, and the data-driven ML methods outperform the random choice classifier (p = 8.46e−17 for accuracy of LSTM). Thus, we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small. [ABSTRACT FROM AUTHOR]

A major aspiration of investors is to better forecast stock performance. Interestingly, emerging "neuroforecasting" research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study, we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional MRI, we sampled investors' neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We found that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors' predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance. [ABSTRACT FROM AUTHOR]

Applications for the stock market that can be relied upon to provide the information regular and professional investors need to make better-informed purchases are a boon to both. A well-thought-out sales approach may help buyers mitigate risk, zero in on the companies most likely to provide the highest returns, and increase their chances of making a purchase. Due to numerous interrelationships between various company values, executing stock market research utilizing batch processing methods provides an exceptionally tough task. Technology advancements, such as the licensing of global processes, usher in a new age for stock market forecasting. The present market climate has increased the significance of data applications. An interesting and new contribution made by this work is the proposal of a deep learning-based resilience time series model for forecasting future market values. By analyzing financial time series, this study aimed to build a sophisticated strategy for forecasting stock market prices. The use of artificial intelligence (AI) to forecast future market behavior is one of the most intriguing developments in this generation of technological breakthroughs. Particularly promising results have been found when applying deep learning techniques to the task of predicting the actions of market players. In this paper, we propose a method for forecasting the final prices of publicly traded firms like Tesla, Inc. and Apple, Inc. by merging a convolutional neural network with long short-term memory (LSTM). These two methods were both developed with the help of deep learning techniques. The time frame covered by statistics used in these projections is two years. To evaluate the performance of our deep learning models, we compared their market predictions using several different metrics, including mean squared error (MSE), root mean squared error (RMSE), normalized root mean squared error (NRMSE), and Pearson's R. When compared to the options, the CNN-LSTM deep learning algorithm did the best. When comparing convolution neural networks CNN-LSTM model to the normal LSTM and a simpler deep learning variant, the latter showed to be more effective at forecasting stock market values. This was the case when examining all three variants together. [ABSTRACT FROM AUTHOR]

Accurate forecasting of stock prices not only guides investor behavior but also assesses financial risk and promotes balanced economic and social development. This paper uses a dynamic factor-enhanced model averaging method to forecast the daily closing price of the Shanghai Composite Index, maximizing the use of valid information by weighting the forecast values of different models. Firstly, the common factor is extracted from the smoothed original explanatory variables; then the dynamic factor augmented model selection method and the model averaging method based on different criteria are used to predict different lag orders of the common factor and the explanatory variables, and the effectiveness of the dynamic factor augmented censored group cross-validation model averaging method is verified using multiple predictor error indicators as well as the DM test. The experimental results show that the dynamic factor augmented censored group cross-validation model averaging method has better prediction results and is more robust. [ABSTRACT FROM AUTHOR]

Stock forecasting has long drawn people's attention because the stock market is a crucial source of financing for publicly traded corporations and a sizable investment market. To fully use the evidence from dissimilar typical prices and recover the stock forecasting effect, a Blockchain-based secure stock value forecasting model TL-EMD-LSTM-MA (TELM) is projected. Other methods are selected for prediction according to the oscillation frequencies of the details, and the high-frequency components use the depth of the transfer learning method to train the stacked LSTM. Deep transferable learning-trained stacked LSTMs incorporate data from several equities and have a deeper understanding of the marketplace or commerce, which can significantly lower forecasting mistakes. Furthermore, it is possible to more accurately estimate the low-frequency components and the overall trend of the stock by employing the average movement approach. 500 stocks in the stock market are shown, as well as indices such as the Stock Exchange Index and Stock Exchange Component Index, the outcomes demonstrate that compared with other models, TELM has the least prediction error and the highest goodness of fit. Finally, simulate the stock trading process based on the stock closing price predicted by TELM, and the results show that TELM investment has low risk and high returns. [ABSTRACT FROM AUTHOR]

In recent years, uncertain fractional differential equations was proposed for the description of complex uncertain dynamic systems with historical characteristics. For wider applications of uncertain fractional differential equations, researches on parameter estimation for uncertain fractional differential equations are of great importance. In this paper, based on the thought of least squares estimation and uncertain hypothesis test, an algorithm of parameter estimation for uncertain fractional differential equations is discussed. Finally, we consider the application of uncertain fractional differential equations based model to predict the forecasting stock price of three major indexes of U.S. stocks and make a comparison between uncertain fractional differential equations, uncertain differential equations and stochastic differential equations. [ABSTRACT FROM AUTHOR]

Combining the stock prediction with portfolio optimization can improve the performance of the portfolio construction. In this article, we propose a novel portfolio construction approach by utilizing a two‐stage ensemble model to forecast stock prices and combining the forecasting results with the portfolio optimization. To be specific, there are two phases in the approach: stock prediction and portfolio optimization. The stock prediction has two stages. In the first stage, three neural networks, that is, multilayer perceptron (MLP), gated recurrent unit (GRU), and long short‐term memory (LSTM) are used to integrate the forecasting results of four individual models, that is, LSTM, GRU, deep multilayer perceptron (DMLP), and random forest (RF). In the second stage, the time‐varying weight ordinary least square model (OLS) is utilized to combine the first‐stage forecasting results to obtain the ultimate forecasting results, and then the stocks having a better potential return on investment are chosen. In the portfolio optimization, a diversified mean‐variance with forecasting model named DMVF is proposed, in which an average predictive error term is considered to obtain excess returns, and a 2‐norm cost function is introduced to diversify the portfolio. Using the historical data from the Shanghai stock exchange as the study sample, the results of the experiments indicate the DMVF model with two‐stage ensemble prediction outperforms benchmarks in terms of return and return‐risk characteristics. [ABSTRACT FROM AUTHOR]

Proponents of quantitative easing (QE) unconventional policy have rather overstated some evidence that structural time series models do not predict long‐term asset prices and yields as well as naive random walk forecasts, implying that predictions of price reversals cannot be profitable and, therefore, that QE effects are not transitory. Indeed, in this work we present evidence that naive models do not outperform structural vector autoregressive and Markov switching models in out‐of‐sample forecasting of corporate bond yields purchased by the European Central Bank, when the information set includes base money growth. It turns out that structural time series models provide additional information regarding the likelihood of price reversals, thus motivating investors to offset the effects of QE interventions if they perceive unconventional monetary policy regimes as temporary. [ABSTRACT FROM AUTHOR]

This study proposes a new efficient predictor called a three-dimensional (3D) membership function-based Type-2 Fuzzy Brain-Emotional Learning Predictor (T2FBELP) to model and predict Taiwan stock prices. This study has primarily contributed to improve the learning ability and flexibility of type-2 fuzzy system by changing the structure in the membership function space. The use of a 3D Gaussian membership function in the fuzzy network allows for fewer rules while maintaining accurate predictions, especially when dealing with nonlinear time series such as stock prices. Additionally, learning-rate parameters are optimized using the Modified Social Ski-Driver (MSSD) algorithm to further impoving the predict ability. The T2FBELP is then used to predict the stock prices for including the Taiwan Semiconductor Manufacturing Company (TSMC), the Honhai Precision Industry and the Formosa Plastics Corporation. These efforts show the effectiveness of T2FBELP in predicting the Taiwan stock market. [ABSTRACT FROM AUTHOR]

Abstract Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, and pandemic text data using natural language processing, and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices. An interval constraint-based trading strategy is constructed. Using data from several typical stocks in the Chinese stock market during the COVID-19 period, the empirical studies and trading simulations show, first, that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting. Second, the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus, can assist investors in decision-making.

We apply a machine-learning algorithm, calibrated using general human vision, to predict the visual salience of prices of stock price charts. We hypothesize that the visual salience of adjacent prices increases the decision weights on returns computed from those prices. We analyze the inferred impact of these weights in two experimental studies that use either historical price charts or simpler artificial sequences. We find that decision weights derived from visual salience are associated with experimental investments. The predictability is not subsumed by statistical features and goes beyond established models. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

The object of research is the fundamental and technical indicators of companies after the release of the earnings report. This study attempts to address the issue of understanding the impact of fundamental and technical analysis indicator dynamics on profits and loss news releases. This research provides an in-depth analysis of stock price forecasting models, focusing on the influence of earning report seasons as catalysts for stock price growth. The study explores the relationship between key financial indicators, including earnings per share (EPS), revenue, and the maximum price observed in the 52-week period of the previous year (MaxW52). A trading algorithm is developed based on the adaptive neurofuzzy inference system (ANFIS). Through a comprehensive analysis of the neural network's training sample, it is concluded that abnormally large negative indicators have a profound impact on traders' emotional reactions. This results leads to a hypothesis for further research, suggesting that report indicators may be processed by computational algorithms, potentially including artificial intelligence (AI). Consequently, the emergence of emotional trading robots managed by investment funds becomes a crucial area for investigation. Understanding the behavior of these algorithms enables proactive decision-making, allowing traders to leverage their knowledge and sell-purchased securities to these algorithms before their transactions occur. The implications of this research shed light on the evolving landscape of trading strategies and the role of emotionality in financial markets. [ABSTRACT FROM AUTHOR]

We analyze whether and how internet searching impacts stock price informativeness. Using the 2010 Google withdrawal in China as a quasi-natural experiment, we establish a causal effect between internet searching and stock price informativeness using a difference-in-difference framework. We find that firms with higher Google search volume experience a 10% decrease in stock price informativeness after the Google withdrawal. The negative effect of the Google withdrawal on stock price informativeness is pronounced in firms with more retail investors, larger state-ownership, and poor analysts' earnings forecasts. Our results suggest that retail investors can benefit from internet searching to collect and process firm-specific information more efficiently. [ABSTRACT FROM AUTHOR]

The aims of this study are to predict the stock price trend in the stock market in an emerging economy. Using the Long Short Term Memory (LSTM) algorithm, and the corresponding technical analysis indicators for each stock code include: simple moving average (SMA), convergence divergence moving average (MACD), and relative strength index (RSI); and the secondary data from VN-Index and VN-30 stocks, the research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model and the test set data is used to evaluate the model's performance. The research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model in analyzing and forecasting stock price movements on the machine learning platform. [ABSTRACT FROM AUTHOR]

Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions. This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning models. These models include two very recent methods: the eXtreme Gradient Boosting (XGBoost) and CatBoost. The empirical findings indicate the superiority of XGBoost over other advanced machine learning models. Second, it proposes Shapley additive explanations (SHAP) in order to help policy makers to interpret the predictions of complex machine learning models and to examine the importance of various features that affect gold prices. Our results illustrate that the utilization of XGBoost along with SHAP approach could provide a significant boost in increasing the gold price forecasting performance. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India. Design/methodology/approach: The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank's balance sheet features and bank's stock features which may potentially determine the factors of systemic risk emission. Findings: The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk. Practical implications: The findings will help banks and regulators with the key features that can be used to formulate the policy decisions. Originality/value: This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk. [ABSTRACT FROM AUTHOR]

The process of decision-making is increasingly supported by algorithms in a wide variety of contexts. However, the phenomenon of algorithm aversion conflicts with the development of the technological potential that algorithms bring with them. Economic agents tend to base their decisions on those of other economic agents. Therefore, this experimental approach examines the willingness to use an algorithm when making stock price forecasts when information about the prior adoption of an algorithm is provided. It is found that decision makers are more likely to use an algorithm if the majority of preceding economic agents have also used it. Willingness to use an algorithm varies with social information about prior weak or strong adoption. In addition, the affinity for technological interaction of the economic agents shows an effect on decision behavior. [ABSTRACT FROM AUTHOR]

The new energy industry is strongly supported by the state, and accurate forecasting of stock price can lead to better understanding of its development. However, factors such as cost and ease of use of new energy, as well as economic situation and policy environment, have led to continuous changes in its stock price and increased stock price volatility. By calculating the Lyapunov index and observing the Poincaré surface of the section, we find that the sample of the China Securities Index Green Power 50 Index has chaotic characteristics, and the data indicate strong volatility and uncertainty. This study proposes a new method of stock price index prediction, namely, EWT-S-ALOSVR. Empirical wavelet decomposition extracts features from multiple factors affecting stock prices to form multiple sub-columns with features, significantly reducing the complexity of the stock price series. Support vector regression is well suited for dealing with nonlinear stock price series, and the support vector machine model parameters are selected using random wandering and picking elites via Ant Lion Optimization, making stock price prediction more accurate. [ABSTRACT FROM AUTHOR]

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 . [ABSTRACT FROM AUTHOR]

Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, and pandemic text data using natural language processing, and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices. An interval constraint-based trading strategy is constructed. Using data from several typical stocks in the Chinese stock market during the COVID-19 period, the empirical studies and trading simulations show, first, that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting. Second, the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus, can assist investors in decision-making. [ABSTRACT FROM AUTHOR]

Stock prices forecasting has always been a challenging task. Although many research projects adopt machine learning and deep learning algorithms to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices across time windows, while LSTM analyses the initial time‐series data with the combination of dependencies which considered as residuals. In predicting the SSE Composite Index, our experiment reveals that when the closing price data for the previous five consecutive trading days is used as the input, the performance of the model (ResNLS‐5) is optimal compared to those with other inputs. Furthermore, ResNLS‐5 outperforms vanilla CNN, RNN, LSTM, and BiLSTM models in terms of prediction accuracy. It also demonstrates at least a 20% improvement over the current state‐of‐the‐art baselines. To verify whether ResNLS‐5 can help clients effectively avoid risks and earn profits in the stock market, we construct a quantitative trading framework for back testing. The experimental results show that the trading strategy based on predictions from ResNLS‐5 can successfully mitigate losses during declining stock prices and generate profits in the periods of rising stock prices. [ABSTRACT FROM AUTHOR]

Various researches have been conducted on forecasting stock prices. Several tools ranging from statistical techniques to quantitative methods have been used by researchers to forecast the market. But so far, very little research has been done on forecasting the stock markets of the Gulf countries such as Saudi Arabia, United Arab Emirates, Oman, Kuwait, Bahrain, and Qatar. Our approach is to predict the market indices of the Gulf countries using Long Short‐Term Memory (LSTM) techniques. Thereafter, we optimized the hyperparameters of the LSTM technique using various optimization methods such as Grid Search and Bayesian Optimization with Gaussian Process and found out the best‐suited hyperparameter for the LSTM model. We tried the LSTM method for predicting the indices using data from the last twenty years. [ABSTRACT FROM AUTHOR]

In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456. [ABSTRACT FROM AUTHOR]

Traders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a unique filtering-combination approach to increase forecast accuracy. The first step is to filter the original series of stock market prices into two new series, consisting of a nonlinear trend series in the long run and a stochastic component of a series, using the Hodrick-Prescott filter. Next, all possible filtered combination models are considered to get the forecasts of each filtered series with linear and nonlinear time series forecasting models. Then, the forecast results of each filtered series are combined to extract the final forecasts. The proposed filtering-combination technique is applied to Pakistan's daily stock market price index data from January 2, 2013 to February 17, 2023. To assess the proposed forecasting methodology's performance in terms of model consistency, efficiency and accuracy, we analyze models in different data set ratios and calculate four mean errors, correlation coefficients and directional mean accuracy. Last, the authors recommend testing the proposed filtering-combination approach for additional complicated financial time series data in the future to achieve highly accurate, efficient and consistent forecasts. [ABSTRACT FROM AUTHOR]

Stock price prediction is a challenging research problem due to various dependencies like news headlines, Twitter, microblogs, and price history. News headlines and price history are the most significant features for predicting stock prices, which help investors decide to sell, buy, or hold that maximizes the profits. The manual analysis of large amounts of news and price history information is impossible. Hence, automation is required to summarize the available data for wise trading decisions. We propose an ingenious Future Trading System Using Ensemble Deep Learning (FTSEDL) to improve the accuracy of the prediction. The objective is to develop FTSEDL that improves the Deep learning predictive model with a new context-based clustering, CbCA, and information fusion for an accurate stock price forecast. The CbCA filters the required features from the news datasets to generate quality datasets through context-based clustering. Information fusion combines the two vital features of stock price prediction: news headlines and price history. The present research experimented with six stocks, BHARATIARTL, SBIN, PNB, NXTDIGITAL, TCS, and WIPRO, covering possible up, down, and sideways trends to improve the predictive model evaluation. The experiment data considered the stock news and price history data from 8th August 2016 to 31th March 2023, i.e., 2427 trading days. CbCA achieved a homogeneity of 0.95, Completeness of 0.98, and Silhouette coefficient of 0.88. The proposed model, FTSEDL, achieved an accuracy of 91.693.09, RMSE of 13.14, and MAPE of 0.02, which outperformed the models in the recent literature. The FTSEDL has many advantages when compared with contemporary literature. In addition, there is vast scope to apply this concept in real time. [ABSTRACT FROM AUTHOR]

Accurately predicting future stock prices is crucial for investors, particularly during market stress, enabling informed decisions to mitigate losses and reduce financial exposure. While machine learning techniques have shown promise in this field, most studies have focused on local models. Global forecasting models, which are trained on a variety of time series, have demonstrated encouraging outcomes in outperforming local models. This article presents a global forecasting approach utilizing Light Gradient-Boosting Machine (LightGBM) to predict stock prices in the Moroccan market during highly volatile period. The study includes a sector-wise analysis, with the most volatile sector being evaluated, and correlated stocks from other sectors were considered to enhance the data. Our findings highlight the effectiveness of utilizing a global forecasting approach with an optimized LightGBM model for Moroccan stock price prediction, outperforming models such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). Additionally, incorporating data from multiple stocks improves the accuracy of stock price prediction. [ABSTRACT FROM AUTHOR]

This study analyzes oil price movements through the lens of an agnostic random forest model, which is based on 1000 regression trees. It shows that this highly disciplined, yet flexible computational model reduces in-sample root-mean-square errors (RMSEs) by 65% relative to a standard linear least square model that uses the same comprehensive set of 11 high-frequency explanatory factors. In 1–3 months ahead price forecasting exercises the RMSE reduction relative to OLS ranges exceeds 50%, highlighting the relevance of non-linearities in oil markets. The results underscore the importance of incorporating financial factors into oil models: US interest rates, the dollar and the VIX together account for 39% of the models' RMSE reduction in the post-2010 sample, rising to 48% in the post-2020 sample. [ABSTRACT FROM AUTHOR]

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, keeps a fully flexible form, and accounts for time variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation, and pricing errors and identifies the key factors that drive asset prices. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4695. [ABSTRACT FROM AUTHOR]

This paper explores into stock market analysis using computer models to predict stock price changes, particularly focusing on the AAPL dataset from Apple Inc. Accurate stock forecasts are vital for informed investment decisions. By comparing different models—Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—the study offers a comprehensive performance analysis of deep learning approaches. The use of different deep learning architectures allowed us to capture different aspects of the stock price data and assess their effectiveness in making predictions. The project's findings would enable us to make informed decisions about which model to choose for future stock price prediction tasks, benefiting traders, investors, and other stakeholders in the financial domain. LSTM exhibited the best predictive capability, capturing intricate patterns due to its ability to handle long-term dependencies GRU also performed well. However, the MLP model didn't do as well because it couldn't grasp the complex patterns in the data as effectively as the other two models. So, for forecasting stock prices, the LSTM model proved to be the most accurate and reliable choice. These findings offer valuable insights for financial practitioners aiming to enhance stock price prediction using deep learning techniques, aiding in smarter investment strategies. [ABSTRACT FROM AUTHOR]

Determining which variables affect price realized volatility has always been challenging. This paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day forecast. The methodological proposal is based on using the best econometric and machine learning models to forecast realized volatility. In particular, the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the influence of the Standard and Poor's 500 index, euro–US dollar exchange rate, price of gold, and price of Brent crude oil on the realized volatility of natural gas. These financial assets influenced the realized volatility of natural gas in 87.4% of the days analyzed; the euro–US dollar exchange rate was the primary financial asset and explained 40.1% of the influence. The results of the proposed daily analysis differed from those of the methodology used to study the entire period. The traditional model, which studies the entire period, cannot determine temporal effects, whereas the proposed methodology can. The proposed methodology allows us to distinguish the effects for each day, week, or month rather than averages for entire periods, with the flexibility to analyze different frequencies and periods. This methodological capability is key to analyzing influences and making decisions about realized volatility. [ABSTRACT FROM AUTHOR]

This research paper distinguishes between stocks and bonds as distinct financial instruments and addresses the relative lack of computational finance research in bond price forecasting compared to stock market prediction. To bridge this gap, we propose and evaluate three forecasting models using historical bond price data: Support Vector Regression (SVR), Facebook Prophet, and RNN-LSTM. We also look into the possibility of increasing forecast accuracy by adding sentiment analysis as a new parameter. Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Square Percentage Error (MSPE) are some of the evaluation metrics used for model comparison. Our findings show that when sentiment encoding is included as a contributing element, Facebook Prophet beats the other models and exhibits improved accuracy. This study has useful implications for improving bond price forecasting methodologies and makes significant contributions to the field of computational finance. [ABSTRACT FROM AUTHOR]

Predicting the Indian stock market with accuracy is a considerable challenge, mainly due to its nonlinear time series nature. Nevertheless, the advent of diverse Machine Learning methodologies has sparked an extensive investigation in this domain. Primarily, scholarly inquiries have concentrated on harnessing Supervised Machine Learning algorithms for predicting the prices of individual stocks and forecasting movements in stock indices. Furthermore, researchers have explored the utilization of Unsupervised Machine Learning algorithms and statistical approaches to anticipate stock prices. Another area of exploration involves constructing portfolios that maximize profits while minimizing risks. Moreover, the influence of social media, which generates an abundance of data, has become a crucial factor impacting experts' decisions to buy or sell stocks. This survey aims to identify the various approaches researchers employ to improve the accuracy of machine learning algorithms in analyzing the Indian Stock Market. It highlights the importance of taking into account all pertinent aspects of the stock market prior to making predictions. [ABSTRACT FROM AUTHOR]

Investing in stock market is considered a high-risk monetary motion where investor may not take into version the factor of stock charge fluctuations or destroy their profits without gaining professional knowledge and investment experience. In addition, when working with stock information, people increase the importance of available and self-correcting information, a habit that runs counter to objective and reasonable investment decisions. In recent years, various methods of stock market forecasting have been proposed to enhance the quality and profitability of investor decision making. Recent machine learning models have been able to reduce the risk of stock market forecasting. However, diversity remains a major challenge in developing better learning models and in identifying intellectually valuable traits to further improve diagnosis. In this paper, we propose an optimal characteristic selection with hybrid machine learning classifier for stock market price forecasting (OFML-SMF) using social media and secondary data sources. First, we introduce a multi-objective earthworm optimization (MOEO) algorithm for optimal feature selection among multiple features i.e. called feature optimization which reduces the data dimensionality problem. Second, we develop a hybrid Quantile regression learning based deep belief neural network (HQR-DBNN) to classify the stock market price movement prediction which enhances the statistical metrics. Finally, to appraise the presentation of planned OFML-SMF model through the influential international stock market indices and the performance can be compare with the existing state-of-art models in conditions of accuracy, precision, recall and F-measure. [ABSTRACT FROM AUTHOR]

The new concept of forecasting market movements is introduced to fill the gap in stock price forecasting that uses a fixed time window. The candlestick data are grouped into waves before being input into the Transformer model to capture many different chart patterns and provide longer price movement patterns for better feature extraction. This work is divided into two main parts. The first part is wave extraction, for which we propose a new algorithm for grouping candlestick data into waves. The second part deals with Transformer modeling. The result of this work has shown that the Transformer model trained using wave series can help traders reduce their losses and choose a suitable trading strategy based on the prediction results. The model trading result of the test set has earned about 19.8% (using fixed 1 contract size per order) which is better than using MACD signal which can give earning of 12.5%. [ABSTRACT FROM AUTHOR]

This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts. [ABSTRACT FROM AUTHOR]

With technological advances, humans are constantly generating data through various electronic devices and sensors, and this data is stored in digital environments. A vast amount of data has served as a valuable asset that has facilitated the rise and progression of novel fields, including data science, artificial intelligence (AI), deep learning (DL), and the internet of things (IoT). Effectively managing and analyzing data provides a competitive advantage for modern businesses. The objective of this study is to forecast the stock price of Turkish Airlines (THY), a publicly traded corporation listed on Borsa Istanbul. In order to achieve the intended objective, the utilization of machine learning approaches like SVM and XGBoost, as well as the deep learning algorithm Long Short-Term Memory (LSTM), are used. The models are trained over a time period including daily data from January 4, 2010 to September 5, 2023. The forecast performance of the models is evaluated by comparing the actual and predicted stock prices and the model with the lowest error is identified. The proposed models' performances are assessed using the RMSE, MSE, MAE, and R2 error statistics. According to the results obtained, it is determined that the LSTM model has lower error coefficients than SVM and XGBoost models and gives the best performance. [ABSTRACT FROM AUTHOR]

We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models. [ABSTRACT FROM AUTHOR]

The distribution laws of various natural and anthropogenic processes in the world around us are stochastic in nature. The development of mathematics and, in particular, of stochastic modeling allows us to study regularities in such processes. In practice, stochastic modeling finds a huge number of applications in various fields, including finance and economics. In this work, some particular applications of stochastic processes in finance are examined in the conditions of financial crisis, aiming to provide a solid approach for stock price forecasting. More specifically, autoregressive integrated moving average (ARIMA) models and modified ordinary differential equation (ODE) models, previously developed by some of the authors to predict the asset prices of four Bulgarian companies, are validated against a time period during the crisis. Estimated rates of return are calculated from the models for one period ahead. The errors are estimated and the models are compared. The return values predicted with each of the two approaches are used to derive optimal risk portfolios based on the Markowitz model, which is the second major aim of this study. The third aim is to compare the resulting portfolios in terms of distribution (i.e., weights of the stocks), risk, and rate of return. [ABSTRACT FROM AUTHOR]

Forecasting time series is crucial for financial research and decision-making in business. The non-linearity of stock market prices has a profound impact on global economic and financial sectors. This study focuses on modeling and forecasting the daily prices of key stock indices - MASI, CAC 40, DAX, FTSE 250, NASDAQ, and HKEX, representing the Moroccan, French, German, British, US, and Hong Kong markets, respectively. We compare the performance of machine learning models, including Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), and the hybrid LSTM-XGBoost, and utilize the skforecast library for backtesting. Results show that the hybrid LSTM-XGBoost model, optimized using Grid Search (GS), outperforms other models, achieving high accuracy in forecasting daily prices. This contribution offers financial analysts and investors valuable insights, facilitating informed decision-making through precise forecasts of international stock prices. [ABSTRACT FROM AUTHOR]

Given the widespread use of technical analysis and the tight relationship between derivatives and the underlying assets, we employ the copula approach to investigate whether the technical indicators based on underlying assets convey extra information about the future movements of implied volatility (IV) indexes. The empirical results, based on long samples of five well‐known IV indexes, suggest that although the technical indicators are not informative for forecasting the future prices of IV indexes, they can provide extra information about the size of forecasting errors of the IV indexes. The findings are also robust to the impact of COVID‐19. The technical indicators are then used to extend Threshold ARCH and Exponencial GARCH models for improving the estimation of Value at Risks (VaRs). The out‐of‐sample forecast results show that the proposed model outperforms the benchmark in estimating the VaRs. These findings have implications for pricing options of IV indexes and managing the risks of IV‐related portfolios. [ABSTRACT FROM AUTHOR]

This paper aims to construct a trader model tailored to offer individualized investment strategies for users engaged in the US stock market. The approach taken involves two key aspects: forecasting stock prices and discerning users' personality traits to formulate optimal trading recommendations. The framework is realized through the integration of neural networks and the Moving Averages algorithm. This combined approach allows forecasting upcoming changes in stock prices and generating appropriate trading signals. Complementing this, a user risk assessment is administered, and its outcomes are leveraged to furnish personalized trading advice, offering invaluable support for informed decision-making amid intricate real-time scenarios. By synergizing predictive modeling and user-specific insights, this comprehensive system contributes to enhancing trading precision and user empowerment. This work can be useful in decision making for a wide range of stock market participants, as well as for institutions specialized for this area of operation. [ABSTRACT FROM AUTHOR]