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A two-sided "credit-line channel"—relating to drawdowns and repayments—explains the severe drop and partial subsequent recovery in bank stock prices during the COVID-19 pandemic. Banks with greater exposure to undrawn credit lines saw larger stock price declines but performed better outside of crises periods. Despite deposit inflows, high drawdowns led to reduced bank lending, suggestive of capital encumbrance upon drawdowns. Repayments of credit lines unencumbered capital which explains the stock price recovery starting Q2 2020. Bank provision of credit lines resembles writing put options on aggregate risk, and we propose how to incorporate this feature into bank stress tests. [ABSTRACT FROM AUTHOR]

We use a high-frequency identification approach to document that individual politicians affect asset prices. We exploit the regular flow of viewpoints contained in Congress members' tweets. Supportive (critical) tweets increase (decrease) the stock prices of the targeted firm and the corresponding industry in minutes around the tweet. The bulk of the stock price effects is concentrated in the tweets revealing news about future legislative action. The effects are amplified around committee meeting days, especially when the tweet originates from committee members and influential politicians. Overall, we show that Congress members' social media accounts are an important source of political news. [ABSTRACT FROM AUTHOR]

Using monthly holdings data, we show that in contrast to findings in existing literature, mutual fund trades in aggregate do not have predictive power for future earnings information. We identify active trades by mutual funds, that is, trades not passively driven by fund flows, and show that only large active trades by mutual funds contain information on future earnings. Moreover, we show evidence that large active trades by mutual funds help incorporate future earnings information into stock prices and significantly improve stock price efficiency. Finally, we show that large active trades by mutual funds prior to earnings announcements deliver superior abnormal returns. Mutual fund managers that actively trade on future earnings information are skilled; the top quintile skilled funds on average outperform those in the bottom quintile by 127 bps in four-factor alpha over subsequent four quarters. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present a method for determining whether errors in expectations explain asset pricing puzzles without imposing assumptions about the error mechanism. Using accounting identities and survey forecasts, we find that errors in expected long-term inflation explain price variation, return predictability, and the rejection of the expectations hypothesis for aggregate stock and bond markets. Errors in short-term (long-term) nominal earnings growth expectations explain (do not explain) stock price variation and return predictability. The relevant errors are consistent with mistakes about the persistence of forecasted variables and the response to surprises. A simple framework based on fundamental extrapolation successfully replicates these findings. (JEL G40, G12, G14, E71) [ABSTRACT FROM AUTHOR]

We document the causal effects of single-name options trading on the absolute level of information content of prices (stock price informativeness) by exploiting the Penny Pilot Program as an exogenous shock to options trading volume. We find that options trading increases underlying stock price informativeness and information acquisition by both option and stock investors, consistent with the framework of Goldstein and Yang (2015). The findings are driven by firms for which options are more important sources of information and firms with more efficiently priced options. Options market introduction in a sample of 25 other economies also leads to higher price informativeness. [ABSTRACT FROM AUTHOR]

This article studies how industry peers' stock prices respond when another firm in the industry is acquired by a foreign firm. The average stock price reactions of industry peers in horizontal foreign acquisitions around deal announcements are significantly negative. Peers' returns are more negative in growing, less specialized, and competitive industries. Moreover, the negative stock price reactions of industry peers are related to future decreases in their operating performance. Overall, these results suggest that foreign acquisitions have strong competitive effects for the industry peers of U.S. target companies. [ABSTRACT FROM AUTHOR]

We introduce a new approach to estimating long-term aggregate discount rates using the cross section of earnings and book values to explain current stock prices and extract expected market returns. The proposed discount rate measure is countercyclical. Shocks to it account for nearly half of historical market return variation; in contrast, shocks to other discount rate measures account for no more than 2%. It dominates other measures in explaining time-series variation in returns on duration-sorted portfolios and delivers out-of-sample predictability that exceeds that afforded by other expected return measures and predictive variables. It also performs well in international equity markets. [ABSTRACT FROM AUTHOR]

The purpose of the article is to analyze the relevance of earnings fundamentals in predicting extreme price reversals of an emerging stock market. We collect monthly price data on six sector indices from Bombay Stock Exchange (BSE) of India for the period 2004–2019. The research decomposes industry stock returns into Potential Maximum Gains (PMG) and Potential Maximum Losses (PML) with price extremes at first and then tests price reversal behavior using Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) and vector autoregressive (VAR) models. The study finds symmetry between PMG and PML in the banking, realty, and oil sectors, while the asymmetric reversal behavior is noted in the automobiles and capital goods industries. The presence of industry fundamentals in the models estimating the reversal behavior of share prices enhances their predictive power, which suggests the significance of value strategies in making gains from extreme price variations. The price reversal behavior is sector specific and found inconsistent in emerging market. Hence, the investors cannot overlook the relevance of the industry characteristics and earnings fundamentals while predicting the stock price behavior in emerging markets. [ABSTRACT FROM AUTHOR]

Credit default swaps (CDSs) are a major financial innovation related to debt contracting. Because CDS markets facilitate bad news being incorporated into equity prices via cross-market information spillover, CDS availability may curb firms' information hoarding. We find that CDS trading on a firm's debt reduces the future stock price crash risk. This effect is stronger in active CDS markets, when the main lenders are CDS market dealers with securities trading subsidiaries, or when managers have more motivation to hoard information. Our findings suggest that debt market financial innovations curtail the negative equity market effects of firms withholding bad news. [ABSTRACT FROM AUTHOR]

We examine the effects of implementing a U.S. approach to the enforcement of mandatory disclosure in China. Using a hand-collected sample of comment letters (CLs) issued by the Shanghai Stock Exchange over the period of 2013 to 2018, we show that stock price reactions to CL receipts and replies are negative and significant. Using textual analysis to match issues raised by regulators to targeted firms' changes in disclosure, we show that these firms do address CL issues point by point, but do not experience significant improvements in their information environments. Our article highlights the importance of incentives rather than regulation/enforcement in reducing information asymmetry. [ABSTRACT FROM AUTHOR]

We test the signaling view of corporate social responsibility (CSR) engagement using two complementary quasi-natural experiments that impose exogenous negative pressure on stock prices. Firms under such adverse price pressure increase CSR activities compared to otherwise similar firms. This effect concentrates among firms with stronger signaling incentives, namely, those facing greater information asymmetry, more product market competition, higher shareholder litigation risk, and higher stock price crash risk. Firms under the exogenous negative price pressure mainly improve CSR strengths, including costly environmental investments. We also find that CSR engagement attracts socially responsible investors and lowers the cost of capital for signaling firms. [ABSTRACT FROM AUTHOR]

A short squeeze occurs if borrowed shares are recalled and the short seller is unable to find another source of shares. This forces the short seller to terminate a position early. For most stocks, the probability of a short squeeze is very low. Short squeezes, however, are not unusual for the hardest to borrow stocks. For these stocks, trading costs from squeezes are high and have a significant impact on the returns to short selling. For hard-to-borrow stocks, short sellers also miss out on significant abnormal returns because squeezes force them to close positions. [ABSTRACT FROM AUTHOR]

We analyze how individual investors respond to inflation. We introduce a unique data set containing information on local inflation and security portfolios of more than 2,000 clients of a German bank between 1920 and 1924, covering the German hyperinflation. We find that individual investors buy fewer (sell more) stocks when facing higher local inflation. This effect is more pronounced for less sophisticated investors. Moreover, we document a positive relation between local inflation and forgone returns following stock sales. Our findings are consistent with individual investors suffering from money illusion. Alternative explanations, such as consumption needs, are unlikely to drive our results. 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]

While the causal impact of limited attention to macroeconomic news is difficult to detect, this article proposes one solution: exploiting when foreign investors are "distracted" by risk factors in their home markets. I demonstrate that financial activity in the average foreign investor's home market decreases foreign attention paid to 21 emerging economies, measured using Google search volume for economy-specific financial terms that emanate from outside each economy's border. Exploiting this effect using an instrumental variables approach, I find that an exogenous increase in foreign attention preceding a scheduled monetary policy rate announcement raises preannouncement stock returns and announcement day turnover. [ABSTRACT FROM AUTHOR]

How synchronized are short sellers? We examine a unique data set on the distribution of profits across a stock's short sellers and find evidence of substantial dispersion in the initiation of their positions. Consistent with this dispersion reflecting "synchronization risk," that is, uncertainty among short sellers about when others will short sell, more dispersed short selling signals i) greater stock overpricing and ii) longer delays in overpricing correction. These effects are prevalent even among stocks facing low short-selling costs or other explicit constraints. Overall, our findings provide novel cross-sectional evidence of synchronization problems among short sellers and their pricing implications. [ABSTRACT FROM AUTHOR]

This study uncovers how household investors intensify the effect of the 52-week high (52WH): increased volume and momentum-like returns at the 52WH price. Using daily household and institutional trading data, we find that households sharply increase their selling, particularly with limit orders at the 52WH price. This behavior is indicative of anchoring, as it is robust to past returns and intensified by proximity, market uncertainty, and salience of the 52WH. This uninformed limit order selling at and prior to the 52WH leads to a doubling of unconditional 52WH anomaly returns. Post-event returns benefit institutions, which act as counterparties. [ABSTRACT FROM AUTHOR]

This article examines the effect of board governance on investment efficiency. I use the staggered enactment of board reforms in 41 countries as a shock to board structure that exogenously improves the quality of board oversight of managers. I find that investment-Q sensitivity improves by roughly half post-reform. This effect is more pronounced for firms that are more exposed to the reforms or when external governance mechanisms are less likely to discipline managers. These findings suggest that increased board oversight strengthens managers' incentives to make investment decisions that are more in line with their firms' growth opportunities. [ABSTRACT FROM AUTHOR]

We study the stock market effects of the arrival of the three rounds of "stimulus checks" to U.S. taxpayers and the single round of direct payments to Hong Kong citizens. The first two rounds of U.S. checks appear to have increased retail buying and share prices of retail-dominated portfolios. The Hong Kong payments increased overall turnover and share prices on the Hong Kong Stock Exchange. We cannot rule out that these price effects were permanent. The findings raise novel questions about the role of fiscal stimulus in the stock market. 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]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings-announcement drift (PEAD.txt) larger than the classic PEAD. The magnitude of PEAD.txt is considerable even in recent years when the classic PEAD is close to 0. We explore our text-based empirical model to show that the calls' news content is about details behind the earnings number and the fundamentals of the firm. [ABSTRACT FROM AUTHOR]

This article examines whether and how stock mispricing can affect the probability of CEO turnover. In a sample of 1,573 US public firms, I find that, after controlling for fundamental performance, a 1-standard-deviation negative uninformative stock price shock increases the likelihood of CEO turnover by 10%. The mispricing-turnover sensitivity is stronger at firms with an independent board, and a difference-in-difference analysis further supports that finding. Ancillary results suggest that independent directors' career concerns may play a role in the response of independent boards to mispricing. [ABSTRACT FROM AUTHOR]

In theory, financial markets promote innovation by selectively allocating capital to high-quality projects. In this article, I show that equity markets can also inhibit innovation. In public firms, I find that short-term equity market declines cause pharmaceutical companies to abandon early-stage drug developments, irrespective of drug quality or changes in a firm's stock price. I show that financing constraints drive this behavior, highlighting that even short-term market fluctuations can have long-term effects on pharmaceutical innovation and prevent potentially life-saving drugs from progressing to the market. [ABSTRACT FROM AUTHOR]

In a sample of non-U.S. regulatory regime shifts, we find that expanded short selling is associated with stock price declines, reductions in capital expenditure, and lower asset growth. In a reversal of results found for U.S. stocks in a study of Regulation SHO by Grullon, Michenaud, and Weston (2015), our results are stronger for large firms than for small firms. We also show that this investment effect is stronger for firms that previously relied on outside financing. Our results suggest that short-sale policies affect corporate investment and that this effect is not driven by capital constraints. [ABSTRACT FROM AUTHOR]

We document that a significant number of insiders violate the Securities and Exchange Commission (SEC) reporting requirements by filing open market transactions after the legally required deadline. Prior to the Sarbanes–Oxley Act (SOX), 29% of transactions fell outside the required reporting window. Following SOX, 8% are delinquent. Violations cluster in periods of high information asymmetry, incentivizing insiders to keep trades private and earn abnormal returns. Collectively, these findings suggest that a subgroup of insiders personally benefit from violating SEC disclosure requirements. Evidence also suggests that blockholders provide governance for violations. Guilty insiders experience a reduction in board seats and an increased likelihood of turnover. [ABSTRACT FROM AUTHOR]

Socially responsible (SR) institutions tend to focus more on the environmental, social, and governance (ESG) performance and less on quantitative signals of value. Consistent with this difference in focus, we find that SR institutions react less to quantitative mispricing signals. Our evidence suggests that the increased focus on ESG may have influenced stock return patterns. Specifically, abnormal returns associated with these mispricing signals are greater for stocks held more by SR institutions. The link between SR ownership and the efficacy of mispricing signals only emerges in recent years with the rise of ESG investing, and is significant only when there are arbitrage-related funding constraints. [ABSTRACT FROM AUTHOR]

The ability to accurately predict the stock market is a crucial financial topic. The basic assumption is that future stock returns can be somewhat predicted based on publicly available data from the past. Economic variables like interest rates and currency rates, information related to political influences, and company-specific data like statements of income and dividends are some examples of this information. Yet, attempting to forecast stock returns goes against the idea that markets are generally efficient. A prediction model has been created that leverages machine learning, big data analytics, and social media analytics to predict stock market trends regularly. The main objective of this work is to propose a novel approach for stock market prediction for handling big data. This research develops a new SP-PDA (Stock Price Prediction with Political Data Analysis) which includes preprocessing, feature extraction, and prediction. In this work, we employed three types of data (Stock data, News data and Political data). For the stock data, preprocessing was done using Z-score normalization, while tokenization and lemmatization were used for the news and political data. Next, to handle the big data, we used a Map-Reduce architecture. Here, feature extraction from preprocessed stock, news, and political data occurs in the mapper function. The reduction face yields the final extracted feature set. Commodity channel index (CCI), Chaikin Volatility (CV), and Donchian Channel (DC) are examples of technical indicator-based features that are extracted for stock data during the feature extraction phase. Mutual Information (MI), Improved Pointwise mutual information (IPMI), Term Frequency-Inverse Document Frequency (TF-IDF), and correlation features are retrieved for news and political data. Stock market prediction is based on the features extracted. To predict stock prices, we employed an ensemble classification model that incorporates classifiers such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Gated Recurrent Unit (Bi-GRU), Long-short term memory (LSTM), and Deep Maxout. An improved score level fusion is carried out to define the final prediction outcome from the obtained intermediate prediction results. The suggested Dwarf Mongoose Updated War Strategy-based Generalized Normal Distribution (DMUWS-based GND) Optimization Algorithm is used to optimizing the weights of ensemble classifiers. Finally, the performance of the proposed model is evaluated over existing models in terms of error measures like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Squared Log Error (MSLE). The SP-PDA achieved the desired MAE of 0.012, which is low when compared to the other traditional systems for accurate prediction of the stock market. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Finance Letters / Maliye Finans Yazıları Dergisi is the property of Maliye Finans Yazilari Yayimcilik Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Literature on investor sentiment has predominantly been conducted at the market level. Recent studies employing firm‐level sentiment have increasingly turned to textual analysis; however, the suitability of this type of proxy is still unverified. We utilize the Chinese stock market data from 2015 to 2023, aiming to discern whether the jump component of realized volatility possesses characteristics of investor sentiment. Our analysis reveals a pronounced short‐term persistence in jump volatility, particularly, among stocks that are hard to value and those with minimal institutional ownership. Further, we find that stocks exhibiting high monthly jump volatility consistently exhibit underperformance over extended periods, corroborating the hypothesis of sentiment‐induced temporary mispricing phenomena. Significantly, our findings advocate for the adoption of the jump component of realized volatility as a proxy for firm‐specific investor sentiment, offering a novel perspective in the sentiment analysis literature. [ABSTRACT FROM AUTHOR]

Recently it was shown that the news-based stock pricing model (NBSPM) outperforms the momentum-enhanced five-factor Fama–French model (FF5M) for a representative list of holdings of the major US equity sector ETFs both in-sample and out-of-sample. The leading term in NBSPM besides the market (CAPM) beta is the industry beta estimated using returns of the relevant equity sector ETFs or industry ETFs. In this article, the industry beta is added to FF5M. It is found that the resulting model (FF5MI) is generally more accurate in-sample than NBSPM in terms of the mean squared error but not necessarily in terms of the mean absolute error. FF5MI is, however, always inferior to NBSPM out-of-sample. This implies that the industry beta has the major impact on stock prices while the FF5M factors may yield an overfitted model. [ABSTRACT FROM AUTHOR]

This research delves into the intricate effects of mergers and acquisitions (M&A) on stock prices within the Saudi Arabian banking sector, employing the event study methodology (ESM) to examine both short-term and long-term impacts on stock valuations. The study meticulously calculates expected and abnormal returns (ARs) around merger events, shedding light on the significant influence these corporate actions exert on market perceptions and investor behavior. The methodology encompasses an analysis of daily stock price data before and after merger events, providing a detailed view of market reactions. Key findings reveal an immediate positive market response to merger announcements, driven by the anticipation of synergistic benefits, alongside diverse long-term stock price effects that underscore the complex relationship between initial expectations and actual merger outcomes. The research concludes that strategic merger planning and effective communication with stakeholders are crucial for realizing the anticipated benefits of mergers. By offering new insights into the sector-specific and regional dynamics that affect M&A outcomes, this study enhances understanding of the significant implications for investors, financial institutions, and policymakers engaged with the Middle Eastern financial markets. [ABSTRACT FROM AUTHOR]

Venture capital firms (VCs) provide certification and monitoring services to the initial public offering (IPO) companies they finance, as has been well documented in the academic literature. Yet, there are few studies that explore what role – if any – VCs play when an IPO company is subsequently acquired, particularly if that acquisition is legally contested. Using a sample of 721 merger and acquisition (M&A) offers for US VC‐backed IPO companies, announced between 1996 and 2018, we find that a takeover bid that occurs in the presence of the lead VC commands a higher target firm valuation and is less likely to be legally contested than a bid for a company from which the lead VC has already exited. In addition, companies in which the lead VC is present enjoy higher stock‐price returns in response to M&A announcements. Our results provide new evidence regarding VC certification and monitoring – including its role as a litigation deterrent – long after the IPO. [ABSTRACT FROM AUTHOR]

Recent studies suggest that greater exposure to the market for corporate control matters for managers and shareholders since it affects firms' ex‐post risk of experiencing a stock price crash. The findings though question the direction of the effect. In contrast, in this study, we are the first to examine the effects of firms' ex‐ante risk of experiencing a stock price crash, a likely antecedent of which is managers' concealment of news on aspects of the market for corporate control. We find that higher crash risk leads to greater takeover target likelihood. This relationship, which is robust to duly circumventing reverse causality, depends to a significant extent on inferior managerial quality and greater managerial discretion around financial accruals, affording richer insight into the notion that correction of managerial behaviour is a stimulus for the market for corporate control, but one that depends on the likely extent of managers' concealment of news. We also concurrently find that actual takeover targets with higher crash risk generate a lower bid premium and receive more payment with stock. Overall, our findings strongly suggest that decision‐making in the market for corporate control is at least partially explained by incentives linked to opportunistic prices and takeovers of lemons. [ABSTRACT FROM AUTHOR]

We investigate the role of monetary policy in stock price misalignments and explore whether central banks can attenuate excessive mispricing as suggested by the proponents of a "leaning against the wind" monetary policy. Decomposing stock prices into expected excess dividends, an equity risk premium, and a mispricing component, we find that prices fall more strongly in response to an increase in the policy rate than what is implied by their underlying fundamentals. This systematic overreaction suggests that tighter monetary policy may contain emerging asset price misalignments. Our findings are at odds with the predictions of a rational bubble framework, but can be explained by mispricing arising from false subjective expectations of irrational investors. [ABSTRACT FROM AUTHOR]

Global temperatures have increased at a historically unprecedented pace. The paper finds the negative impact of high temperatures on global stock prices and offers evidence on three channels that justify this impact, i.e. the effect on firms' assets, on investments, and on leverage costs. The results encourage market stakeholders to consider the role of temperature shocks in their market involvement. [ABSTRACT FROM AUTHOR]

We use cointegration-based techniques to investigate the relationship between oil prices and the euro effective exchange rate taking also into account the influence of interest rates and stock prices. We find that higher oil prices cause a depreciation in the euro exchange rate (either nominal or real) in the short run. This suggests that during episodes of higher energy prices, the euro area consumer is faced with additional price pressures, as a depreciated euro causes import prices to further rise. Our study also indicates that as stock prices rise in the short and long term, the euro appreciates, while higher oil prices will likely result in overall higher policy rates and lower stock prices. [ABSTRACT FROM AUTHOR]

We estimate conditional value-at-risk (CoVaR) for stock and currency markets under different market statuses and then apply them to study the asymmetry of risk spillovers between currency and stock markets under different market statuses. Empirical results show that risk spillovers between different statuses in stock and currency markets are significant and asymmetric. Next, for most countries, the scale and volatility of CoVaR are more prominent when return-chasing effects, rather than portfolio rebalancing effects, dominate. Finally, when stock prices in major industrial countries fall sharply or currencies depreciate significantly, investing in Japanese stocks is relatively safe for U.S. investors. [ABSTRACT FROM AUTHOR]

Forecasting stock prices, a task complicated by the inherent volatility of the stock market, poses a significant challenge. The ability to accurately forecast stock prices is crucial, as it provides investors with crucial insights, enabling them to make informed strategic decisions. In this paper, we propose a novel investment strategy that relies on predicting stock prices. Our approach utilizes a hybrid predictive model that combines light gradient-boosting machine (LightGBM) and extreme gradient boosting (XGBoost). This model is designed to generate short to medium-term forecasts for a wide range of stocks. The strategy has shown promising results, surpassing the local market indices used as benchmarks in terms of both risk and return. Our findings demonstrate the strategy's effectiveness in both upward and downward market trends, underscoring its potential as a robust tool for portfolio management in diverse market conditions. [ABSTRACT FROM AUTHOR]

I propose a novel measure of information share, termed tail information share (TIS), which focuses on modeling the tail dependence of price innovations using copulas. I discuss its detailed technical properties, including unique identifiability, estimation procedures, and statistical properties. The proposed TIS improves over two commonly used measures by providing meaningful economic rationale and unique identifiability. My simulation studies indicate that TIS can yield more accurate estimates of market-specific contributions to price discovery when tail dependence is present. Additionally, I demonstrate the asymptotic consistency and efficiency of TIS estimators. An empirical illustration is provided using a new dataset of high-frequency crude oil futures. [ABSTRACT FROM AUTHOR]

Predicting stock price movements remains a major challenge in time series analysis. Despite extensive research on various machine learning techniques, few models have consistently achieved success in automated stock trading. One of the main challenges in stock price forecasting is that the optimal model changes over time due to market dynamics. This paper aims to predict stock prices using automated reinforcement learning algorithms and to analyse their efficiency compared with conventional methods. We automate DQN models and their variants, known for their adaptability, by continuously retraining them using recent data to capture market dynamics. We demonstrate that our dynamic models improve the accuracy of predicting the directions of various DAX stocks from 50.00% to approximately 60.00%, compared with conventional methods. Additionally, we conclude that dynamic models should be updated in response to shifts rather than at fixed intervals. [ABSTRACT FROM AUTHOR]

Given the numerous factors that can influence stock prices such as a company's financial health, economic conditions, and the political climate, predicting stock prices can be quite diffcult. However, the advent of the newer learning algorithm such as extreme learning machine (ELM) offers the potential to integrate ARIMA and ANN methods within a hybrid framework. This study aims to examine how hybrid time series models and an artificial neural network (ANN)-based ELM performed when analyzing daily Johannesburg Stock Exchange/Financial Times Stock Exchange (JSE/FTSE) closing stock prices over 5 years, from 15 June 2018 to 15 June 2023, encompassing 1,251 data points. The methods used in the study are autoregressive integrated moving average (ARIMA), ANN-based ELM, and a hybrid of ARIMA-ANN-based ELM. The ARIMA method was used to model linearity, while nonlinearity was modeled using an ANN- based ELM. The study further modeled both linearity and non-linearity using the hybrid ARIMA-ANN-based ELM model. The model was then compared to identify the best model for closing stock prices using error matrices. The error metrics revealed that the hybrid ARIMA-ANN-based ELM model performed better than the ARIMA [1, 6, 6] and ANN-based ELM models. It is evident from the literature that better forecasting leads to better policies in the future. Therefore, this study recommends policymakers and practitioners to use the hybrid model, as it yields better results. Furthermore, researchers may also delve into assessing the effectiveness of models by utilizing additional conventional linear models and hybrid variants such as ARIMA-generalized autoregressive conditional heteroskedasticity (GARCH) and ARIMA-EGARCH. Future studies could also integrate these with non-linear models to better capture both linear and non-linear patterns in the data. [ABSTRACT FROM AUTHOR]

This paper uses Long Short-Term Memory (LSTM) networks to predict the stock prices of the Yuanta Taiwan Top 50 ETF (ETF50). To improve the accuracy of the model's predictions, a calibration procedure called "Short-Term Bias Compensation" (STBC) is proposed to adjust the predicted stock prices. In STBC, the daily prediction error is calculated to estimate the short-term bias (STB) in prediction. Then, the predicted price of its next day will be corrected if this STB has exceeded a certain threshold. In this paper, we apply Genetic Algorithms (GAs) to optimize the parameters used in STBC for providing more confidence in its estimation. Based on these predicted stock prices, we propose a Genetic Fuzzy System (GFS) to determine the trading strategy, with trading points for buying and selling stocks. In GFS, various technical indicators are used to establish the fuzzy rules of the trading strategy, and GAs are used to evolve the best parameters for these fuzzy rules. Our experiments cover over 17 years of data (from 2003 to 2020) for ETF50 to consider black swan events such as the 2020 COVID-19 pandemic, the 2018 US–China trade war, and the 2011 US debt crisis. The first 90% of the data is used as training data, and the last 10% is used as testing data. We use 12 technical indicators of these data as the input of LSTM. The predicted values of LSTM are corrected using STBC and compared to the uncorrected prices. We use Mean Square Error (MSE) to evaluate the prediction accuracy. The results show that STBC can nearly reduce 90% of the prediction error (where MSE drops from 11.5758 to 1.2687). By using GFS with STBC to determine trading points, we achieve a return rate of 32.0%. [ABSTRACT FROM AUTHOR]

Stock market investments have become an essential part of our lives as they offer a means of growing wealth and securing financial stability for individuals and businesses alike. However, predicting and investing in a stock is a complex method and demands significant levels of knowledge, proficiency, and skill. Stock market prediction is the act of analyzing historical information and trends in the market in order to make informed forecasts about the potential future worth of a given stock or instrument that is traded on a financial instrument exchange. When making stock predictions, most stockbrokers use both fundamental and technical analysis and time series analysis. The study focuses on the implementation of Multi-Linear Regression, LSTM, CNN, and LSTM + GRU based Machine learning techniques using technical analysis to predict stock's closing values from the NYSE, and NASDAQ markets for multiple days. The dataset has been taken from Yahoo, of 10-year span. The factors taken into consideration for predicting stock prices are open, close, low, and high. The model's effectiveness is measured using common strategic metrics like RMSE, MSE, MAE, and R2. A lower value for these variables suggests that the models are good at forecasting stock closing prices. After conducting a comprehensive evaluation, we found that LSTM + GRU model performs the best among the tested models for predicting multiple days, followed by CNN and LSTM. The tested models demonstrate a remarkable level of accuracy in predicting stock market prices. This research work provides a valuable contribution to the fields of financial and technical analysis in the stock market research community. [ABSTRACT FROM AUTHOR]

Forecasting stock prices is always considered as complicated process due to the dynamic and noisy characteristics of stock data influenced by external factors. For predicting the stock market, several approaches have been put forward. Many academics have successfully forecasted stock prices using soft computing models. Recently, there has been growing interest in applying deep learning techniques in combination with technical indicators to forecast stock prices, attracting attention from both investors and researchers. This paper focuses on developing a reliable model for anticipating future stock prices in one day advance using Long Short-Term Memory (LSTM). Three steps make up the suggested model. The approach begins with ten technical indicators computed from previous data as feature vectors. The second phase involves data normalization to scale the feature vectors. Finally, in the third phase, the LSTM model analyzes the closing price for the next day using the normalized characteristics as input. Two stock markets, NASDAQ and NYSE are chosen to evaluate the efficacy of the developed model. To demonstrate how effective the new model is in making predictions, its performance is compared to earlier models. Comparing the suggested model to other models, the findings revealed that it had a high level of prediction accuracy. [ABSTRACT FROM AUTHOR]

This study investigated the impact of exchange rate volatility on sectoral stock volatility by employing the intraday volatility measure directly calculated from the original data, using daily data from 27 Borsa Istanbul sectors between April 29, 2003, and April 25, 2023. In the literature, GARCH models are commonly used to study the volatility spillovers between exchange rates and stock prices, typically using aggregate data. However, the GARCH family models provide inefficient and biased estimates if they are misspecified. Moreover, using aggregate-level data may lead to biased and misleading conclusions. The research used intraday volatility measures to overcome the shortcomings of GARCH models. The ordinary least squares (OLS), GARCH (1,1) methods, and Garman and Klass (1980) volatility estimator are used. The empirical results showed that the estimates from each method vary significantly, and these disparities in the results might be due to misspecification in GARCH (1,1) models. The intraday volatility model estimation results showed that although stock price volatilities in all sectors are positively and significantly affected by exchange rate volatility, their magnitudes vary significantly. Taken together, this implies the presence of vast heterogeneities in the responses of sectoral stock price volatilities to exchange rate volatility. The results encourage policymakers to pay special attention to these heterogeneities to prevent capital flights and underinvestment. Additionally, the findings assist investors in making more effective decisions by helping them adapt their investment strategies to factor in exchange rate fluctuations and mitigate the impact of unexpected events in the exchange rate market. [ABSTRACT FROM AUTHOR]