Future Stock Research Articles

Stock Market Prediction Analysis using Deep Learning

The abstract provides an overview of a proposed approach for stock market value prediction using deep learning techniques, specifically focusing on artificial neural networks (ANN) and long-short-term memory (LSTM) algorithms. The stock market is a dynamic environment influenced by various factors, such as economic conditions and market sentiment, making accurate prediction challenging yet crucial for investors.In this study, the objective is to leverage deep learning methodologies to enhance the accuracy and robustness of stock market predictions compared to traditional methods. By harnessingthe Python programming language, the research aims to develop a model capable of analyzing historical stock data and generating forecasts of future stock prices

Audit committee personnel training and stock price crash risk

This study examines whether audit committee personnel training decreases future stock price crash risk. Using a hand-collected Korean sample comprising 2,378 firm-year observations from 2018 to 2022, we find that audit committee personnel training reduces future stock price crash risk by lowering real earnings management and curbing overinvestment. Additionally, our findings are more pronounced for firms with high information reporting opacity, high information asymmetry, audit committee chairs with accounting and financial expertise, and firms audited by high-quality auditors. The findings shed light on the value of training to audit committee personnel.

Ambiguous investor sentiment

This study examines the interaction between investor sentiment, ambiguity, and asset pricing. While the existing literature highlights the importance of sentiment in times of higher uncertainty, our study further shows the nuanced distinction between ambiguity and standard uncertainty. We examine their effects on the relationship between sentiment and future stock market returns at the daily and monthly frequencies. Our analysis demonstrates that ambiguity weakens the predictive ability of sentiment on stock returns, whereas standard uncertainty increases it. We also present additional evidence suggesting that lower market participation during periods of high ambiguity is the likely driver of this effect.

Discriminating populations of Atlantic herring mixing in the Norwegian Sea feeding ground using single nucleotide polymorphisms (SNPs)

Atlantic herring Clupea harengus feeding in the Norwegian Sea are assumed to consist of Norwegian spring spawners (NSSH), Icelandic summer spawners (ISSH) and North Sea autumn spawners (NSAH). Putative Norwegian autumn spawners (NASH), Faroese autumn (FASH) and spring (FSSH) spawners also feed in the area. However, until there is a method to discriminate between populations in mixed samples, fishery and survey data from the Norwegian Sea will be solely attributed to the predominating NSSH, ultimately causing biased stock assessments. Hence, we evaluated if a panel of 120 single nucleotide polymorphisms (SNPs) associated with spawning characteristics and salinity preferences would be an effective discrimination tool. The overall observed levels of genetic differentiation were high (FST = 0.57, p <0.001, 95% CI: 0.51-0.62). Spawners from stocks under current management (NSSH, NSAH and ISSH) were well separated, but the putative populations were not. Discriminant analysis of principal component as well as Structure runs confirmed the differentiation observed with FST. When the SNP panels were tested on commercial fishery samples of NSSH east of Iceland, up to 16% were assigned to ISSH. This implies that catch data are seriously biased and demonstrates the potential of SNP panels as a tool to solve the problem. However, work is needed to develop improved SNP panels that effectively separate the putative populations from the managed stocks. We recommend that such a tool should be established in regular sampling of fishery and surveys in the Norwegian Sea and accounted for in future stock assessments, advice and management.

Uncovering interfirm links through textual topic similarity: A comomentum analysis in financial markets

Using an unsupervised topic modelling methodology, we construct a cross-firm similarity measure based on the various topics extracted from Management Discussion and Analysis texts. Our findings indicate that the returns of firms with similar textual topics predict the focal firms’ future stock returns. A long-short portfolio constructed on this basis yields an annualised alpha of 17.03%. Further analyses show that the return predictability is stronger for stocks subject to limited investor attention and limits to arbitrage. Additionally, our textual linkage measure can also predict future earnings surprises. Overall, mispricing due to sluggish information incorporation acts as a potential explanation for return predictability.

From macro to micro: Sparse macroeconomic risks and the cross-section of stock returns

Our study presents novel evidence on the pricing effectiveness of macroeconomic risks at the firm level. We employ a sparse PCA approach to aggregate macroeconomic variables from the FRED-MD database and obtain the first eight components, namely inflation, housing, spreads, production, employment, personal income, yields, and credit. We then construct the corresponding firm-level macroeconomic risk exposures as the sensitivity of the individual stock returns to the sparse economic components. Our research yields three main findings: (i) macro betas cannot be fully captured by common firm fundamentals, indicating the unique information in macro betas; (ii) the betas of inflation, production, personal income, yields, and credit are strong predictors of future stock returns beyond other micro risks; and (iii) behavioral mispricing theory, especially arbitrage frictions and investor sentiment, can help explain the macro beta premium.

Federal judge ideology, securities class action litigation, and stock price crash risk

AbstractThis study investigates whether and how federal judge ideology affects firm‐specific stock price crash risk. Using a comprehensive sample of US firms, we find a decline in the likelihood of future stock price crashes for firms headquartered in more liberal circuits. In identifying potential mechanisms, we show that liberal judge ideology reduces information opacity, risk‐taking behaviours and overinvestment, and thus curbs stock price crash risk. Furthermore, the curbing effect is more salient for firms with poor monitoring quality and those in low social capital areas. Overall, this study elucidates how federal judge ideology affects capital markets.

Application of the K-Nearest Neighbor Method to Predict Demand for Goods from Customers at PT Sinergi Prima Enjineering

PT Sinergi Prima Enjineering, which is engaged in services, has been trusted as a contractor in several companies facing challenges in handling the large number of requests for goods and stock inventory management. This research aims to improve the prediction of demand for goods and inventory management using the calculation of the K-Nearest Neighbor (KNN) method and RapidMiner tools. With the comparison of calculations between KNN and RapidMiner using ten test data, the results are appropriate where the categorical grouping is often ordered totaling five data, moderately ordered totaling two data and rarely ordered totaling three data. The test results show that K = 3 produces a prediction accuracy of 91.98%. These results show that K-Nearest Neighbor can accurately anticipate future stock inventory and items that will be ordered by customers and it is hoped that the company can improve customer satisfaction and overall operational performance.

Impact of accounting conservatism and corporate governance on stock price breakdown in firms listed on the Palestine Stock Exchange

PurposeThis paper uses test panel data for the biggest companies listed on the boards of directors of the Palestine Stock Exchange from 2016 to 2022 and will focus on the relationship between the corporate governance index, accounting conservatism, and the comprehensive index of corporate governance.Design/methodology/approachThe relationship between corporate governance and accounting conservatism is experimentally investigated for its impact on the likelihood of stock price breakdown and decline among companies listed on the Palestine Stock Exchange between 2016 and 2022, using a mixed utilities approach.FindingsThe findings demonstrated the adverse correlation between corporate governance, accounting conservatism, and stock prices. Higher levels of corporate governance can effectively reduce the likelihood of future stock price increases, while conservative accounting policies can effectively prevent stock price collapses in these listed companies. Higher levels of corporate governance can greatly lessen the detrimental effect of accounting conservatism on the likelihood of future stock price breakdowns and declines. Both accounting conservatism and corporate governance have substitution effects in decreasing the danger of stock price collapse.Research limitations/implicationsThe limitations of the current research are that higher levels of corporate governance can significantly reduce the harmful effect of accounting conservatism on the probability of stock price breakdown and decline in the future on the study sample used, and these results cannot be generalized to all company stocks that were excluded in this study. The last research limitation is that the sample size of this study is somewhat small, and therefore the effects of the results cannot be used on all unlisted companies, and they cannot be generalized to all of these companies except only to companies listed on the Palestine Stock Exchange.Practical implicationsOur findings have interesting managerial and policy implications. Listed firms should first strengthen external audit oversight, improve the method of disclosing accounting information, and improve the system architecture to raise the level of accounting conservatism. Moreover, it is imperative to enhance and improve the ownership structure of publicly traded firms, construct a robust mechanism for replacing shareholders, fortify the duties of the board of directors, proficiently fulfil the role of independent directors, and develop and refine the internal and external framework for corporate governance.Originality/valueThis study provides insights about reducing the probability of a stock market breakdown and collapse from two sides: enhancing corporate governance, improving accounting conservatism, enhancing the reliability and integrity of disclosure, and growing the number of sustainable disclosures. These suggestions can also be used as a template for Palestine's capital market's gradual and sustainable expansion.

Anomalies, option volume, and disagreement

AbstractWe document robust amplification of stock market anomaly returns associated with elevated option trading volume driven by disagreement trades. Consistent with the correction of mispricing associated with biased beliefs, anomaly returns are higher when disagreement option volume is high prior to earnings announcements. Additionally, we demonstrate that disagreement‐based option volume is negatively related to future stock returns among stocks that are overpriced based on anomaly characteristics. Our findings also concentrate in stocks that are also difficult to short, emphasizing the combined impact of investor bias and shorting costs. Leveraging the staggered adoption of eXtensible Business Reporting Language, we establish a plausibly identified link between investor disagreement and short‐horizon mispricing in stocks.

Product Market Competition and Stock Price Crash Risk: Evidence from China

Synopsis The research problem This study analyzes the relationship between product market competition (PMC) and stock price crash risk in China, especially for firms with weak corporate governance and a lower-quality information environment. Motivation Emerging capital markets like China have less-developed financial systems and institutions compared to developed capital markets like Europe and the United States, making them more susceptible to stock price crash risk. The impact of product market competition (PMC) on corporate decision-making has attracted considerable attention from scholars, especially given the increasingly intense and complex strategic competition in global markets and uncertainty in the world today. However, the effect of PMC on a firm’s stock price crash risk has not been thoroughly explored. Therefore, this study investigated how PMC affects managers’ tendency to hoard bad news and how this behavior contributes to the risk of stock price crashes. The test hypotheses We tested two competing hypotheses: PMC is negatively correlated with crash risk, and PMC is positively correlated with crash risk. Target population This study should be of interest to stakeholders, including firm managers, practitioners, regulatory authorities, policymakers, and investors. Adopted methodology This study employed ordinary least squares (OLS), difference-in-differences analysis (DID), and path analysis. Analysis Using a sample of Chinese A-share listed manufacturing firms from 2000 to 2021, this study found that competitive pressure from the product market encouraged managers to hide bad news, thereby increasing future stock price crash risk. Moreover, we performed additional tests to examine how PMC affected stock price crash risk. Findings We found robust evidence that PMC significantly increases stock price crash risk. Further tests showed that the effect of PMC on crash risk is more pronounced for firms with weak corporate governance and a lower-quality information environment. We also provide evidence that firms in highly competitive industries tend to disclose fewer negative words in the management discussion and analysis section of the annual report and fewer risk factors in the internal control report. Moreover, we show that operational risk is the underlying driver through which PMC affects crash risk.

The Korean Spotlight: The Effect of Culture on Stock Returns Through Investor Attention*

AbstractThis study empirically examines the relationship between “investor attention” and aggregate stock returns, seeking to determine whether interest in South Korea can predict the returns of the Korean stock market. The results confirm a positive relationship between interest in Korea and future stock market returns. Furthermore, this relationship is observed to have emerged with the widespread dissemination of Korean culture starting in 2017. This impact is found to be more significant on the KOSDAQ market compared to the KOSPI market. Within the KOSPI market, the influence of interest on stock returns is particularly pronounced in the case of small‐cap stocks. It is also observed that since 2017, as interest in South Korea has increased, information related to the improvement of Korea's corruption index has begun to significantly affect stock returns.

The impact of corporate financialization on stock price crash risk

In recent years, the continuous occurrence of stock price crashes and the significant financialization trend of non-financial firms have aroused widespread attention from regulators, investors and academia. Based on the panel data of China’s A-share non-financial listed companies from 2010 to 2023, this paper designed a rigorous empirical research program and utilized descriptive statistical analysis, correlation analysis, multiple regression analysis and other analytical methods to explore the impact of enterprise financialization on stock price crash risk. The empirical results show that enterprise financialization is conducive to attenuating future stock price crash risk.

AN EVALUATION OF ML TECHNIQUES ON NASDAQ DATASET FOR STOCK MARKET FORECASTING

The uncertainty of stock pricing has popularized stock market prediction as a common practice. Forecasting prices in the market are viewed as problematic, as the hypothesis of efficient markets (EMH) explains. According to the EMH, all accessible information is represented in market prices, and price variations are only the consequence of newly available information. The approach to prediction forecasts the market as either positive or negative based on a variety of input parameters. A combination of derived, fundamental, and pure technical data is utilized in stock forecasts to project future stock prices. Algorithms for machine learning (ML) are made to find patterns in data and utilize those patterns to forecast future events. K Nearest Neighbour (KNN) can process relationships between the numerical data, it is particularly effective in numerical prediction problems for predicting changes in stock value the following day. KNN categorizes freshly input data according to how similar it is to previously taught data, and it does this by clustering the data into coherence subsets or clusters. Using the KNN approach in conjunction with technical analysis, the closest neighbour search strategy yielded the desired outcome. KEYWORDS: Hypothesis, K Nearest Neighbour, Prediction, Stock prices,

ARIMA-GARCH MODEL IN OVERCOMING HETEROSCHEDSDATICITY IN STOCK PRICE PREDICTION (CASE STUDY: PT INDOFOOD, TBK (INDF))

Ensuring access to future stock price information holds significant weight for investors in formulating investment strategies. The Indonesian capital market serves pivotal economic and financial roles within the economy, offering various instruments, such as stocks. Among these, blue-chip stocks are recognized for their minimal risk exposure. The impact of the COVID-19 pandemic is anticipated to influence stock price dynamics, including those of blue-chip stocks. Statistical methodologies, such as Autoregressive Integrated Moving Average (ARIMA), are commonly utilized for stock price prediction. However, the efficacy of ARIMA is contingent upon the fulfillment of homoscedasticity assumptions. Failure to meet this assumption due to fluctuating stock price developments poses a challenge. Consequently, an ARIMA-GARCH hybrid model has been developed to address heteroskedasticity concerns in stock price forecasting. This study focuses on INDF stock data, exemplifying a blue-chip stock with positive performance. Results indicate that combining ARIMA-GARCH models, particularly the ARIMA(0,1,3)-GARCH(1,3) model, yields optimal predictions for subsequent stock prices. The MAPE value of the ARIMA-GARCH model stands at 1.41%, indicating superior performance compared to standalone ARIMA modeling. These findings are expected to serve as a valuable resource for investors navigating investment decisions.

Mandatory audit partner rotation and stock price crash risk: causal evidence from a natural experiment

PurposeThis study aims to examine whether mandatory audit partner rotation is associated with future stock price crash risk.Design/methodology/approachThis study makes use of a regulatory change from the Ministry of Finance of China and the China Securities Regulation Commission, which requires mandatory rotation of audit partners since 2004, as a natural experiment to establish causality and applies a difference-in-difference research design.FindingsAudit partner rotation leads to a significant decrease in future stock price crash risk in the departing partner’s final year of tenure preceding mandatory rotation, consistent with peer monitoring argument of mandatory rotation. Inconsistent with other arguments, including client-specific knowledge, fresh perspective and auditor independence, no significant effect takes a place in the incoming partner’s first year of tenure following mandatory rotation. Mechanism analysis documents that mandatory audit partner rotation reduces stock price crash risk by improving audit quality and constraining managerial empire building.Originality/valueThe results shed new light on the capital market consequence of mandatory audit partner rotation and the cause of stock price crash risk.

Policy uncertainty, bad news disclosure, and stock price crash risk

This paper documents that economic policy uncertainty reduces future stock price crash risk by increasing firms’ disclosure of bad news. Our tests show that firms release more bad news during periods of high policy uncertainty – they use more conservatism accounting, exhibit stronger future earnings response coefficients, use more negative tones in their financial reports, and have managers that express more negative sentiment in earnings conference calls than during periods of low policy uncertainty. Additional analyses show that the negative relation between EPU and future stock price crash risk is more pronounced among firms with more short-sale constraints, with no actively traded credit default swap contracts, with lower options-implied negative skewness, or with higher firm-level political risks. The results from regressions adopting the instrumental variable approach and from a quasi-natural experiment suggest that the negative relation observed between policy uncertainty and stock price crash risk is unlikely to be driven by potential endogeneity.

Stock Prediction using LSTM Technique

Abstract: We attempt to use a machine learning approach to anticipate stock prices in this project. Whenit comes to stock price predictions, machine learning works well. The goal is to forecast future stock prices. make more accurate and better investment decisions We propose incorporating mathematical functions into stock prices. To arrive at an acceptable timescale, examine the prediction system, machine learning, and other external factors. delivers accurate stock predictions and lucrative trades There are some There are two types of stocks. Day trading is another name forintraday trading. The phrase "day trading"was thrown around. Interday traders invest in a diverse range ofassets. at least one day after another, and frequently forseveral days or weeks, LSTMs are quite effective in solving sequence prediction problems.because they maystore information from the past.

Do CEOs Identified as Value Investors Outperform Those Who Are Not?

The aim of this study is to examine whether good asset allocation by a CEO leads to superior stock returns and, if so, how one might be able to identify CEOs that are good asset allocators. Employing US data from May 2001 to April 2019, we find that CEOs that invest the company’s cash flows according to a value-investing style seem to outperform companies that do not. We find that high goodwill to assets and high operating margin (good asset allocator) companies outperform companies with high or low goodwill to assets and low operating margin (poor asset allocator) companies. The findings are corroborated with out-of-sample (May 2019–April 2023) robustness tests. When buying other businesses, value investor CEOs ensure that their consolidated operating margins remain high, as opposed to other firms managed by poor asset allocator CEOs who buy businesses that bring down operating margins, either because they overpay or due to an inability to materialize expected synergies. Using both summary statistics and regression analysis, the findings of this study help us identify companies that allocate assets like value investors and enable us to anticipate future stock performance. For example, if a company, on average, has a goodwill/assets ratio of 41.03%, and an operating margin of 21.38%, it is likely this firm would be at the top quartile in terms of stock return performance over at least the next three years. At the same time, if a firm has a low average goodwill/assets ratio (i.e., 1.95%), its operating margins, on average, should be 24.46%, if it wants to achieve a similar performance as that of firms with high goodwill/assets. Moreover, the future stock return predictability of high (low) goodwill/assets and high (low) operating margin firms, found in this study, can help an investor develop trading strategies that can lead to superior stock price performance by effectively taking long positions in (shorting) firms that are (not) managed by value investor CEOs. Finally, the paper’s findings can also help investors in another way. For example, investors tend to be skeptical about companies with high goodwill/assets. The rule of thumb is to beware of companies carrying goodwill on their balance sheets that is more than 25% of assets. Based on our findings, this should not be a problem as long as the company’s operating margin has remained high and is rising.

Long Short Term Memory Method and Social Media Sentiment Analysis for Stock Price Prediction

The stock market is a complex arena of interest yet uncertainty. Trading stocks, binaries, gold, and bitcoin is growing in popularity, but is prone to price fluctuations influenced by economic and political factors. Social media, particularly Twitter, is where views on companies are shared. Social media sentiment analysis can provide additional insights to evaluate potential future stock price movements, preventing unwanted speculation. The purpose of this research is to develop a Tesla stock price prediction model by integrating the Long Short-Term Memory (LSTM) method and social media sentiment analysis from Twitter to improve prediction accuracy. Stock price data is obtained from Kaggle and Twitter sentiment data is processed through pre-processing. Evaluation values such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are lower in the model with sentiment indicating the ability of the model to more accurately model the dynamics of stock price movements. Lower MSE and RMSE indicate that the model's predictions are closer to the true values, and therefore, the model can be considered more reliable in projecting future stock price changes. These results provide support for the use of Twitter sentiment analysis as a useful source of additional information in improving the prediction accuracy of LSTM regression models in the context of stock market analysis