Decision-makers usually have an aspiration level,a target,or a benchmark they aim to achieve.This behavior can be rationalized within the expected utility framework,which incorporates the probability of success(achiev...Decision-makers usually have an aspiration level,a target,or a benchmark they aim to achieve.This behavior can be rationalized within the expected utility framework,which incorporates the probability of success(achieving the aspiration level)as an important aspect of decision-making.Motivated by these theories,this study defines the probability of success as the number of days a firm’s return outperformed its benchmark in the portfolio formation month.This study uses portfolio-level and firm-level analyses,revealing an economically substantial and statistically significant relationship between the probability of success and expected stock returns,even after controlling for common risk factors and various characteristics.Additional analyses support the behavioral theory of the firm,which posits that firms act to achieve short-term aspiration levels.展开更多
This study investigates the significance of e-commerce consumer opinions regarding value in China’s A-share market.By analyzing a large dataset comprising over 18 million online consumer reviews on JD.com,we demonstr...This study investigates the significance of e-commerce consumer opinions regarding value in China’s A-share market.By analyzing a large dataset comprising over 18 million online consumer reviews on JD.com,we demonstrate that sentiments expressed in e-commerce reviews can influence stock returns.This indicates that consumer opinions on the e-commerce platform contain valuable information that can impact the stock market.Our findings show that Consumer Negative Sentiment Tendency(CNST)and One-Star Tendency(OST)have a negative effect on expected stock returns,even after controlling for firm characteristics such as market risk,illiquidity,idiosyncratic volatility,and asset growth.Further analysis indicates that CNST demonstrates stronger predictive power within the home appliance industry,under high sentiment conditions,in growth companies,and among firms with lower accounting transparency.We also find that CNST negatively predicts revenue surprises,earnings surprises,and cash flow shocks.These results suggest that consumer opinions and sentiments derived from e-commerce reviews highlight firms’intrinsic worth and prospects.Future research could explore how firms,including suppliers and logistics companies,can leverage the information conveyed by consumer opinions on e-commerce platforms.展开更多
This paper proposes a new predictor by calculating the difference between the Japanese candlestick’s upper and lower shadows(ULD)constructed from CBOE volatility index(VIX)data.ULD is a powerful predictor for future ...This paper proposes a new predictor by calculating the difference between the Japanese candlestick’s upper and lower shadows(ULD)constructed from CBOE volatility index(VIX)data.ULD is a powerful predictor for future stock returns,and higher ULD leads to the subsequent decline of stock returns.Our results show that our new predictor generates R^2 values of up to 2.531%and 3.988%in-sample and out-of-sample,respectively;these values are much larger than the previous fundamental predictors.Moreover,the predictive information contained in ULD can help mean–variance investors achieve certainty equivalent return gains of as high as 327.1 basis points.Finally,the extension analysis and robustness tests indicate that recession is the primary cause of return predictability;our results are robust under different settings.展开更多
A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employ...A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.展开更多
This study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020.The endogenou...This study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020.The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in the oil and stock markets over the period under study.Moreover,the time-varying estimates indicate that the oil–gas sectors of these countries are riskier than the overall stock market.The results further suggest that,except for Indonesia,oil prices have a positive impact on the sectoral returns of all markets,whereas the impact of the exchange rates on the oil–gas sector returns varies across time and countries.展开更多
Based on the valid patent data and stock price data of China A-shares,the patent effects of four patent species including the invention publication,the invention grant,the utility model grant,and the design grant,on t...Based on the valid patent data and stock price data of China A-shares,the patent effects of four patent species including the invention publication,the invention grant,the utility model grant,and the design grant,on the stock price and the stock return rate were analyzed via analysis of variance(ANOVA).It was proved that the A-shares having new patents of any patent species shown the higher stock price mean and the higher stock return rate mean than those A-shares having no new patents did.The A-shares having new design grants were found to show the highest stock price mean among the A-shares having new patents of any patent species.The A-shares in the group of top 25%patent count of either the invention publication or the invention grant shown the highest stock return rates mean than those A-shares in other groups of less patent count did.The invention grant,following the general concept,showed its excellent patent effect.The design grant,beyond the expectation,also showed patent effects on the higher stock price and the higher stock return rate.The finding would improve the state of the art in the patent valuation and the listing company evaluation.展开更多
In the field of empirical asset pricing,the challenges of high dimensionality,non-linear relationships,and interaction effects have led to the increasing popularity of machine learning(ML)methods.This study investigat...In the field of empirical asset pricing,the challenges of high dimensionality,non-linear relationships,and interaction effects have led to the increasing popularity of machine learning(ML)methods.This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context.Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables.However,the inclusion of macroeconomic factors from the financial market,real economic activities,and investor sentiment leads to substantial improvements in the model performance.Notably,the degree of improvement varies with the specific measures of stock returns under consideration.Furthermore,our analysis indicates that,after the inclusion of macroeconomic factors,there is a dissimilarity in model performance,variable importance,and interaction effects among macroeconomic and firm-specific variables,particularly concerning abnormal returns derived from the Fama–French three-and five-factor models compared with excess returns.This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables.These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models,stock returns,and macroeconomic conditions in the domain of empirical asset pricing.展开更多
An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into...An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.展开更多
This study discusses the trading behavior of foreign investors with respect to economic uncertainty in the South Korean stock market from a time-varying perspective.We employ a news-based measure of economic uncertain...This study discusses the trading behavior of foreign investors with respect to economic uncertainty in the South Korean stock market from a time-varying perspective.We employ a news-based measure of economic uncertainty along with the model of time-varying parameter vector autoregression with stochastic volatility.The empirical analysis reveals several new findings about foreign investors’trading behaviors.First,we find evidence that positive feedback trading often appears during periods of high economic uncertainty,whereas negative feedback trading is exclusively observable during periods of low economic uncertainty.Second,the foreign investors’feedback trading appears mostly to be well-timed and often leads the time-varying economic uncertainty except in periods of global crises.Third,lagged negative(positive)response of net flows to economic uncertainty is found to be coupled with lagged positive(negative)feedback trading.Fourth,the study documents an asymmetric response of foreign investors with regard to negative and positive shocks of economic uncertainty.Specifically,we find that they instantly turn to positive feedback trading after a negative contemporaneous response of net flows to shocks of economic uncertainty.In contrast,they move slowly toward negative feedback trading after a positive response of net flows to uncertainty shocks.展开更多
In this paper,we examine if COVID-19 has impacted the relationship between oil prices and stock returns predictions using daily Japanese stock market data from 01/04/2020 to 03/17/2021.We make a novel contribution to ...In this paper,we examine if COVID-19 has impacted the relationship between oil prices and stock returns predictions using daily Japanese stock market data from 01/04/2020 to 03/17/2021.We make a novel contribution to the literature by testing whether the COVID-19 pandemic has changed this predictability relationship.Employing an empirical model that controls for seasonal effects,return-related control variables,heteroskedasticity,persistency,and endogeneity,we demonstrate that the influence of oil prices on stock returns declined by around 89.5%due to COVID-19.This implies that when COVID-19 reduced economic activity and destabilized financial markets,the influence of oil prices on stock returns declined.This finding could have implications for trading strategies that rely on oil prices.展开更多
This paper compares the stock return distribution models of mixture normal distribution, mixed diffusion-jump and GARCH models based on the data of Chinese stock market. The Schwarz criterion is also used. We find all...This paper compares the stock return distribution models of mixture normal distribution, mixed diffusion-jump and GARCH models based on the data of Chinese stock market. The Schwarz criterion is also used. We find all these models can capture the features of stock returns partly. EGARCH model is the best fitting to daily return and stable during different period. When the weekly and monthly returns are tested, the differences of the models＇ fitness become unobvious and unstable.展开更多
The fundamental relationship between accounting variables and stock returns is a recurring theme in financial research. One of the major purposes of accounting is to help investors provide reliable, comparable and acc...The fundamental relationship between accounting variables and stock returns is a recurring theme in financial research. One of the major purposes of accounting is to help investors provide reliable, comparable and accurate information. If accounting data are informative about fundamental values and changes in values, they should be correlated with stock price changes. This study provides theory and evidence showing how accounting variables explain stock returns and examines the relationship between the stock returns and accounting variables of listed non financial companies in ISE-100 Indice for 2006-2008 period by using panel data methodology. Empirical analysis consists of 192 observations of 64 companies in years 2006-2008 to examine the effects of inventory, accounts receivable, gross margin, operating expense, return on assets, cash flow, leverage, liquidity, price/earnings, return on equity on stock returns. The results of the study confirm that the predicted roles of fundamental factors and stock returns are significantly related to gross margin, cash flow, leverage and equity variables. The model explains about 13.35 % of the variation of annual stock returns with the leverage variable with most of the significant power.展开更多
This paper expounds the nitty-gritty of stock returns transitory, periodical behavior </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><...This paper expounds the nitty-gritty of stock returns transitory, periodical behavior </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">its markets’ demands and cyclical-like tenure-changing of number of the stocks sold. Mingling of autoregressive random processes via Poisson and Extreme-Value-Distributions (Fréchet, Gumbel, and Weibull) error terms were designed, generalized and imitated to capture stylized traits of </span><span style="font-family:Verdana;">k-serial tenures (ability to handle cycles), Markov transitional mixing weights</span><span style="font-family:Verdana;">, switching of mingling autoregressive processes and full range shape changing </span><span style="font-family:Verdana;">predictive distributions (multimodalities) that are usually caused by large fluctuation</span><span style="font-family:Verdana;">s (outliers) and long-memory in stock returns. The Poisson and Extreme-Value-Distributions Mingled Autoregressive (PMA and EVDs) models were applied to a monthly number of stocks sold in Nigeria from 1960 to 2020. It was deduced that fitted Gumbel-MAR (2:1, 1) outstripped other linear models as well as best</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">fitted among the Poisson and Extreme-Value-</span><span style="font-family:Verdana;">Distributions Mingled autoregressive models subjected to the discrete monthly</span><span style="font-family:Verdana;"> stocks sold series.展开更多
Capital structure is regarded as the combination of debt and equity firms used to finance operations and investments.The choice of capital structure significantly impacts a company’s cost of capital,profitability,and...Capital structure is regarded as the combination of debt and equity firms used to finance operations and investments.The choice of capital structure significantly impacts a company’s cost of capital,profitability,and risk profile.Among a series of factors that affect capital structure,this paper focuses on stock returns and market timing.In this review,an array of papers is analyzed to summarize what current research claims regarding the influence of stock returns and market timing on capital structure.This paper centers on the stock return and market timing theories and also discusses other theories like the trade-off theory,the pecking order theory,and the signaling theory.展开更多
Due to the ongoing global debate regarding the relationship between fintech and banks,including developing countries,this study aims to investigate this relationship in the case of Vietnam,an emerging nation.The study...Due to the ongoing global debate regarding the relationship between fintech and banks,including developing countries,this study aims to investigate this relationship in the case of Vietnam,an emerging nation.The study analyzes the relationship between fintech search and bank stock returns,which are measures of fintech and banks,respectively.The time series data for fintech and bank stock returns were obtained from Google Trends and Vietstock,respectively.Exploratory factor analysis was utilized to derive the fintech variables,while the bank stock return variable was calculated using a basket of eight listed banks from 2017w46 to 2021w46.The results were estimated using the vector autoregression and Granger causality method and validated with the copula method.A key finding of this study is the presence of a simultaneous negative change and bidirectional causality between bank stock returns and fintech lending.Furthermore,several other interesting findings were discovered:(1)the causal relationship from fintech to bank stock returns is weaker compared with the opposite direction;(2)unidirectional causality exists between different types of fintech,such as influence from FinFintech to FinLending,from FinPayment to FinLending and FinWallet,from FinMoney to FinFintech,from FinWallet to FinLending,and from FinProduct to FinFintech;and(3)there is an equal occurrence of simultaneous increase or decrease between bank stock returns and certain types of fintech,specifically between BankReturn and FinPayment,BankReturn and FinLending,as well as BankReturn and FinWallet.These findings shed light on the complex relationship between fintech and banks,offering insights that contribute to our understanding of this dynamic interplay in the context of Vietnam’s emerging fintech landscape.展开更多
The availability of many variables with predictive power makes their selection in a regression context difficult.This study considers robust and understandable low-dimensional estimators as building blocks to improve ...The availability of many variables with predictive power makes their selection in a regression context difficult.This study considers robust and understandable low-dimensional estimators as building blocks to improve overall predictive power by optimally combining these building blocks.Our new algorithm is based on generalized cross-validation and builds a predictive model step-by-step from a simple mean to more complex predictive combinations.Empirical applications to annual fnancial returns and actuarial telematics data show its usefulness in the financial and insurance industries.展开更多
Using unbalanced panel data on 3326 Chinese listed companies from 2014 to 2021,this study investigates the impact of corporate environmental performance on China’s excess stock returns.The results show that stocks of...Using unbalanced panel data on 3326 Chinese listed companies from 2014 to 2021,this study investigates the impact of corporate environmental performance on China’s excess stock returns.The results show that stocks of companies with better environmental performance earn significantly higher excess returns,indicating the existence of green returns in the Chinese stock market.We further reveal that heightened climate-change concerns can boost the stock market’s green returns,while tightened climate policies decrease green returns by increasing long-term carbon risk.Our findings are robust to endogeneity problems and hold great implications for both investors and policymakers.展开更多
This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, an...This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, and the second one is to analyze the dependence behavior of oil prices, expectations of investors and stock returns from January 02, 1990, to June06, 2017. Lyapunov exponents and Kolmogorov entropy determined that the oil price and the stock return series exhibited chaotic behavior. TAR-TR-GARCH and TAR-TR-TGARCH copula methods were applied to study the co-movement among the selected variables. The results showed significant evidence of nonlinear tail dependence between the volatility of the oil prices, the expectations of investors and the stock returns. Further, upper and lower tail dependence and comovement between the analyzed series could not be rejected. Moreover, the TAR-TR-GARCH and TAR-TR-TGARCH copula methods revealed that the volatility of oil price had crucial effects on the stock returns and on the expectations of investors in the long run.展开更多
This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇...This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇s t distribution to analyze the proposed model. The empirical results show that the two stock markets are mutually affected each other, and the dynamic conditional correlation （DCC） and the bivariate asymmetric-GARCH （1, 2） model is appropriate in evaluating the relation between them. The empirical result also indicates that Italy and Germany＇s stock markets show a positive relationship. The average value of correlation coefficient equals to 0.8424, which implies that the two stock markets return volatility have a synchronized influence on each other. In addition, the empirical result also shows that there is an asymmetrical effect between Italy and the Germany＇s stock markets, and demonstrates that the good news and bad news of the stock returns＇ volatility will produce the different variation risks for Italy and the Germany＇s stock price markets.展开更多
Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on f...Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.展开更多
文摘Decision-makers usually have an aspiration level,a target,or a benchmark they aim to achieve.This behavior can be rationalized within the expected utility framework,which incorporates the probability of success(achieving the aspiration level)as an important aspect of decision-making.Motivated by these theories,this study defines the probability of success as the number of days a firm’s return outperformed its benchmark in the portfolio formation month.This study uses portfolio-level and firm-level analyses,revealing an economically substantial and statistically significant relationship between the probability of success and expected stock returns,even after controlling for common risk factors and various characteristics.Additional analyses support the behavioral theory of the firm,which posits that firms act to achieve short-term aspiration levels.
基金supported by the National Natural Science Foundation of China under projects No.62302319R&D Program of Beijing Municipal Education Commission(Grant No.KM202210038002).
文摘This study investigates the significance of e-commerce consumer opinions regarding value in China’s A-share market.By analyzing a large dataset comprising over 18 million online consumer reviews on JD.com,we demonstrate that sentiments expressed in e-commerce reviews can influence stock returns.This indicates that consumer opinions on the e-commerce platform contain valuable information that can impact the stock market.Our findings show that Consumer Negative Sentiment Tendency(CNST)and One-Star Tendency(OST)have a negative effect on expected stock returns,even after controlling for firm characteristics such as market risk,illiquidity,idiosyncratic volatility,and asset growth.Further analysis indicates that CNST demonstrates stronger predictive power within the home appliance industry,under high sentiment conditions,in growth companies,and among firms with lower accounting transparency.We also find that CNST negatively predicts revenue surprises,earnings surprises,and cash flow shocks.These results suggest that consumer opinions and sentiments derived from e-commerce reviews highlight firms’intrinsic worth and prospects.Future research could explore how firms,including suppliers and logistics companies,can leverage the information conveyed by consumer opinions on e-commerce platforms.
基金supported by the National Natural Science Foundation of China granted:72131011Ministry of Education Humanities and Social Sciences Project granted:22YJA790011.
文摘This paper proposes a new predictor by calculating the difference between the Japanese candlestick’s upper and lower shadows(ULD)constructed from CBOE volatility index(VIX)data.ULD is a powerful predictor for future stock returns,and higher ULD leads to the subsequent decline of stock returns.Our results show that our new predictor generates R^2 values of up to 2.531%and 3.988%in-sample and out-of-sample,respectively;these values are much larger than the previous fundamental predictors.Moreover,the predictive information contained in ULD can help mean–variance investors achieve certainty equivalent return gains of as high as 327.1 basis points.Finally,the extension analysis and robustness tests indicate that recession is the primary cause of return predictability;our results are robust under different settings.
基金This work is supported by the National Natural Science Foundation of China(71320107003 and 71532009).
文摘A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.
文摘This study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020.The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in the oil and stock markets over the period under study.Moreover,the time-varying estimates indicate that the oil–gas sectors of these countries are riskier than the overall stock market.The results further suggest that,except for Indonesia,oil prices have a positive impact on the sectoral returns of all markets,whereas the impact of the exchange rates on the oil–gas sector returns varies across time and countries.
文摘Based on the valid patent data and stock price data of China A-shares,the patent effects of four patent species including the invention publication,the invention grant,the utility model grant,and the design grant,on the stock price and the stock return rate were analyzed via analysis of variance(ANOVA).It was proved that the A-shares having new patents of any patent species shown the higher stock price mean and the higher stock return rate mean than those A-shares having no new patents did.The A-shares having new design grants were found to show the highest stock price mean among the A-shares having new patents of any patent species.The A-shares in the group of top 25%patent count of either the invention publication or the invention grant shown the highest stock return rates mean than those A-shares in other groups of less patent count did.The invention grant,following the general concept,showed its excellent patent effect.The design grant,beyond the expectation,also showed patent effects on the higher stock price and the higher stock return rate.The finding would improve the state of the art in the patent valuation and the listing company evaluation.
文摘In the field of empirical asset pricing,the challenges of high dimensionality,non-linear relationships,and interaction effects have led to the increasing popularity of machine learning(ML)methods.This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context.Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables.However,the inclusion of macroeconomic factors from the financial market,real economic activities,and investor sentiment leads to substantial improvements in the model performance.Notably,the degree of improvement varies with the specific measures of stock returns under consideration.Furthermore,our analysis indicates that,after the inclusion of macroeconomic factors,there is a dissimilarity in model performance,variable importance,and interaction effects among macroeconomic and firm-specific variables,particularly concerning abnormal returns derived from the Fama–French three-and five-factor models compared with excess returns.This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables.These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models,stock returns,and macroeconomic conditions in the domain of empirical asset pricing.
基金the Hunan Natural Science Foundation(No. 09JJ3129)the Hunan Key Social Science Foundation (No. 09ZDB04)the Hunan Social Science Foundation (No. 08JD28)
文摘An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.
文摘This study discusses the trading behavior of foreign investors with respect to economic uncertainty in the South Korean stock market from a time-varying perspective.We employ a news-based measure of economic uncertainty along with the model of time-varying parameter vector autoregression with stochastic volatility.The empirical analysis reveals several new findings about foreign investors’trading behaviors.First,we find evidence that positive feedback trading often appears during periods of high economic uncertainty,whereas negative feedback trading is exclusively observable during periods of low economic uncertainty.Second,the foreign investors’feedback trading appears mostly to be well-timed and often leads the time-varying economic uncertainty except in periods of global crises.Third,lagged negative(positive)response of net flows to economic uncertainty is found to be coupled with lagged positive(negative)feedback trading.Fourth,the study documents an asymmetric response of foreign investors with regard to negative and positive shocks of economic uncertainty.Specifically,we find that they instantly turn to positive feedback trading after a negative contemporaneous response of net flows to shocks of economic uncertainty.In contrast,they move slowly toward negative feedback trading after a positive response of net flows to uncertainty shocks.
基金support from the General Projects of the National Social Science Fund,China(No.19BJY225).
文摘In this paper,we examine if COVID-19 has impacted the relationship between oil prices and stock returns predictions using daily Japanese stock market data from 01/04/2020 to 03/17/2021.We make a novel contribution to the literature by testing whether the COVID-19 pandemic has changed this predictability relationship.Employing an empirical model that controls for seasonal effects,return-related control variables,heteroskedasticity,persistency,and endogeneity,we demonstrate that the influence of oil prices on stock returns declined by around 89.5%due to COVID-19.This implies that when COVID-19 reduced economic activity and destabilized financial markets,the influence of oil prices on stock returns declined.This finding could have implications for trading strategies that rely on oil prices.
文摘This paper compares the stock return distribution models of mixture normal distribution, mixed diffusion-jump and GARCH models based on the data of Chinese stock market. The Schwarz criterion is also used. We find all these models can capture the features of stock returns partly. EGARCH model is the best fitting to daily return and stable during different period. When the weekly and monthly returns are tested, the differences of the models＇ fitness become unobvious and unstable.
文摘The fundamental relationship between accounting variables and stock returns is a recurring theme in financial research. One of the major purposes of accounting is to help investors provide reliable, comparable and accurate information. If accounting data are informative about fundamental values and changes in values, they should be correlated with stock price changes. This study provides theory and evidence showing how accounting variables explain stock returns and examines the relationship between the stock returns and accounting variables of listed non financial companies in ISE-100 Indice for 2006-2008 period by using panel data methodology. Empirical analysis consists of 192 observations of 64 companies in years 2006-2008 to examine the effects of inventory, accounts receivable, gross margin, operating expense, return on assets, cash flow, leverage, liquidity, price/earnings, return on equity on stock returns. The results of the study confirm that the predicted roles of fundamental factors and stock returns are significantly related to gross margin, cash flow, leverage and equity variables. The model explains about 13.35 % of the variation of annual stock returns with the leverage variable with most of the significant power.
文摘This paper expounds the nitty-gritty of stock returns transitory, periodical behavior </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">its markets’ demands and cyclical-like tenure-changing of number of the stocks sold. Mingling of autoregressive random processes via Poisson and Extreme-Value-Distributions (Fréchet, Gumbel, and Weibull) error terms were designed, generalized and imitated to capture stylized traits of </span><span style="font-family:Verdana;">k-serial tenures (ability to handle cycles), Markov transitional mixing weights</span><span style="font-family:Verdana;">, switching of mingling autoregressive processes and full range shape changing </span><span style="font-family:Verdana;">predictive distributions (multimodalities) that are usually caused by large fluctuation</span><span style="font-family:Verdana;">s (outliers) and long-memory in stock returns. The Poisson and Extreme-Value-Distributions Mingled Autoregressive (PMA and EVDs) models were applied to a monthly number of stocks sold in Nigeria from 1960 to 2020. It was deduced that fitted Gumbel-MAR (2:1, 1) outstripped other linear models as well as best</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">fitted among the Poisson and Extreme-Value-</span><span style="font-family:Verdana;">Distributions Mingled autoregressive models subjected to the discrete monthly</span><span style="font-family:Verdana;"> stocks sold series.
文摘Capital structure is regarded as the combination of debt and equity firms used to finance operations and investments.The choice of capital structure significantly impacts a company’s cost of capital,profitability,and risk profile.Among a series of factors that affect capital structure,this paper focuses on stock returns and market timing.In this review,an array of papers is analyzed to summarize what current research claims regarding the influence of stock returns and market timing on capital structure.This paper centers on the stock return and market timing theories and also discusses other theories like the trade-off theory,the pecking order theory,and the signaling theory.
文摘Due to the ongoing global debate regarding the relationship between fintech and banks,including developing countries,this study aims to investigate this relationship in the case of Vietnam,an emerging nation.The study analyzes the relationship between fintech search and bank stock returns,which are measures of fintech and banks,respectively.The time series data for fintech and bank stock returns were obtained from Google Trends and Vietstock,respectively.Exploratory factor analysis was utilized to derive the fintech variables,while the bank stock return variable was calculated using a basket of eight listed banks from 2017w46 to 2021w46.The results were estimated using the vector autoregression and Granger causality method and validated with the copula method.A key finding of this study is the presence of a simultaneous negative change and bidirectional causality between bank stock returns and fintech lending.Furthermore,several other interesting findings were discovered:(1)the causal relationship from fintech to bank stock returns is weaker compared with the opposite direction;(2)unidirectional causality exists between different types of fintech,such as influence from FinFintech to FinLending,from FinPayment to FinLending and FinWallet,from FinMoney to FinFintech,from FinWallet to FinLending,and from FinProduct to FinFintech;and(3)there is an equal occurrence of simultaneous increase or decrease between bank stock returns and certain types of fintech,specifically between BankReturn and FinPayment,BankReturn and FinLending,as well as BankReturn and FinWallet.These findings shed light on the complex relationship between fintech and banks,offering insights that contribute to our understanding of this dynamic interplay in the context of Vietnam’s emerging fintech landscape.
基金financial support from Ministerio de Ciencia,Innovacion y Universidades(PID2020-116587GB-I00)financial support from Austrian National Bank(Jubilaumsfondsprojekt 18901)。
文摘The availability of many variables with predictive power makes their selection in a regression context difficult.This study considers robust and understandable low-dimensional estimators as building blocks to improve overall predictive power by optimally combining these building blocks.Our new algorithm is based on generalized cross-validation and builds a predictive model step-by-step from a simple mean to more complex predictive combinations.Empirical applications to annual fnancial returns and actuarial telematics data show its usefulness in the financial and insurance industries.
基金Supports from the National Natural Science Foundation of China under Grant Nos.72348003,72022020,72203016,71974181 and 71974159 are acknowledged.
文摘Using unbalanced panel data on 3326 Chinese listed companies from 2014 to 2021,this study investigates the impact of corporate environmental performance on China’s excess stock returns.The results show that stocks of companies with better environmental performance earn significantly higher excess returns,indicating the existence of green returns in the Chinese stock market.We further reveal that heightened climate-change concerns can boost the stock market’s green returns,while tightened climate policies decrease green returns by increasing long-term carbon risk.Our findings are robust to endogeneity problems and hold great implications for both investors and policymakers.
文摘This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, and the second one is to analyze the dependence behavior of oil prices, expectations of investors and stock returns from January 02, 1990, to June06, 2017. Lyapunov exponents and Kolmogorov entropy determined that the oil price and the stock return series exhibited chaotic behavior. TAR-TR-GARCH and TAR-TR-TGARCH copula methods were applied to study the co-movement among the selected variables. The results showed significant evidence of nonlinear tail dependence between the volatility of the oil prices, the expectations of investors and the stock returns. Further, upper and lower tail dependence and comovement between the analyzed series could not be rejected. Moreover, the TAR-TR-GARCH and TAR-TR-TGARCH copula methods revealed that the volatility of oil price had crucial effects on the stock returns and on the expectations of investors in the long run.
文摘This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇s t distribution to analyze the proposed model. The empirical results show that the two stock markets are mutually affected each other, and the dynamic conditional correlation （DCC） and the bivariate asymmetric-GARCH （1, 2） model is appropriate in evaluating the relation between them. The empirical result also indicates that Italy and Germany＇s stock markets show a positive relationship. The average value of correlation coefficient equals to 0.8424, which implies that the two stock markets return volatility have a synchronized influence on each other. In addition, the empirical result also shows that there is an asymmetrical effect between Italy and the Germany＇s stock markets, and demonstrates that the good news and bad news of the stock returns＇ volatility will produce the different variation risks for Italy and the Germany＇s stock price markets.
文摘Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.
