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
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
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
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
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
Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the au...Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.展开更多
Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate.The random walk hypothesis and efficient market hypothesis essentially state that it is not poss...Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate.The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically,reliably predict future stock prices or forecast changes in the stock market overall.Nonetheless,machine learning(ML)techniques that use historical data have been applied to make such predictions.Previous studies focused on a small number of stocks and claimed success with limited statistical confidence.In this study,we construct feature vectors composed of multiple previous relative returns and apply the random forest(RF),support vector machine(SVM),and long short-term memory(LSTM)ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days.We apply this approach to all S&P 500 companies for the period 2017-2022.We assess performance using accuracy,precision,and recall and compare our results with a random choice strategy.We observe that the LSTM classifier outperforms RF and SVM,and the data-driven ML methods outperform the random choice classifier(p=8.46e^(-17) for accuracy of LSTM).Thus,we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.展开更多
The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of...The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.展开更多
Although the benefits of auditing are uncontroversial in developed markets,there is scant evidence about its effect in emerging economies.Auditing derives its value by increasing the credibility of financial statement...Although the benefits of auditing are uncontroversial in developed markets,there is scant evidence about its effect in emerging economies.Auditing derives its value by increasing the credibility of financial statements,which in turn increases investors’reliance on them in developed markets.Financial statement information is common to all investors and therefore increased reliance on it should reduce divergence in investors’assessment of firm value.We examine the effect of interim auditing on inter-investor divergence with a large sample of listed Chinese firms and find that it decreases more for firms whose reports are audited compared to non-audited firms.This finding suggests that investors rely more on audited financial information.Results of this study are robust to variations in event window length and specification of empirical measures.展开更多
Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for inv...Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making.Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices.However,there have been very few studies of groups of stock markets or indices.The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets.In this context,this study aimed to examine the predictive performance of linear,nonlinear,artificial intelligence,frequency domain,and hybrid models to find an appropriate model to forecast the stock returns of developed,emerging,and frontier markets.We considered the daily stock market returns of selected indices from developed,emerging,and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models.The results showed that no single model out of the five models could be applied uniformly to all markets.However,traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.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
We examine whether management earnings forecasts(MEFs)help reduce the stock return seasonality associated with earnings seasonality around earnings announcements(EAs)in Chinese A-share markets.We find that firms in hi...We examine whether management earnings forecasts(MEFs)help reduce the stock return seasonality associated with earnings seasonality around earnings announcements(EAs)in Chinese A-share markets.We find that firms in historically low earnings seasons outperform firms in high earnings seasons by 2.1%around MEFs.Firms in low earnings seasons also have higher trading volume and return volatility than their counterparts around EAs and MEFs.MEFs significantly reduce the ability of historical seasonal earnings rankings to negatively predict announcement returns,volume and volatility around EAs.The reduction effects are stronger when MEFs are voluntary or made closer to EAs.The evidence suggests that MEFs facilitate the correction of investors’tendency to extrapolate earnings seasonality and its resulted stock mispricing.展开更多
Empirical studies have shown that a large number of financial asset returns exhibit fat tails （leptokurtosis） and are often characterized by volatility clustering and asymmetry. This paper considers the ability of t...Empirical studies have shown that a large number of financial asset returns exhibit fat tails （leptokurtosis） and are often characterized by volatility clustering and asymmetry. This paper considers the ability of the GARCH-Type （Generalized Autoregressive Conditional Heteroskedasticity） models to capture the stylized features of volatility in national stock market returns for three countries （Portugal, Spain and Greece）. The results of this paper suggest that in the presence of asymmetric responses to innovations in the market, the ARMA （1,1）-GJRGARCH（1,1） skewed Student-t model which accommodates both the skewness and the kurtosis of financial time series is preferred.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
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 aim of the present work is to examine whether the price volatility of nonferrous metal futures can be used to predict the aggregate stock market returns in China. During a sample period from January of 2004 to Dec...The aim of the present work is to examine whether the price volatility of nonferrous metal futures can be used to predict the aggregate stock market returns in China. During a sample period from January of 2004 to December of 2011, empirical results show that the price volatility of basic nonferrous metals is a good predictor of value-weighted stock portfolio at various horizons in both in-sample and out-of-sample regressions. The predictive power of metal copper volatility is greater than that of aluminum. The results are robust to alternative measurements of variables and econometric approaches. After controlling several well-known macro pricing variables, the predictive power of copper volatility declines but remains statistically significant. Since the predictability exists only during our sample period, we conjecture that the stock market predictability by metal price volatility is partly driven by commodity financialization.展开更多
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.
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.
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.
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.
基金supported by the Natural Science Foundation of CQ CSTC under Grant No.cstc.2018jcyj A2073Chongqing Social Science Plan Project under Grant No.2019WT59+3 种基金Science and Technology Research Program of Chongqing Education Commission under Grant No.KJZD-M202100801Mathematic and Statistics Team from Chongqing Technology and Business University under Grant No.ZDPTTD201906Open Project from Chongqing Key Laboratory of Social Economy and Applied Statistics under Grant No.KFJJ2022056Chongqing Graduate Research Innovation Project under Grant No.CYS23568。
文摘Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.
基金funded by The University of Groningen and Prospect Burma organization.
文摘Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate.The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically,reliably predict future stock prices or forecast changes in the stock market overall.Nonetheless,machine learning(ML)techniques that use historical data have been applied to make such predictions.Previous studies focused on a small number of stocks and claimed success with limited statistical confidence.In this study,we construct feature vectors composed of multiple previous relative returns and apply the random forest(RF),support vector machine(SVM),and long short-term memory(LSTM)ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days.We apply this approach to all S&P 500 companies for the period 2017-2022.We assess performance using accuracy,precision,and recall and compare our results with a random choice strategy.We observe that the LSTM classifier outperforms RF and SVM,and the data-driven ML methods outperform the random choice classifier(p=8.46e^(-17) for accuracy of LSTM).Thus,we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.
文摘The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.
文摘Although the benefits of auditing are uncontroversial in developed markets,there is scant evidence about its effect in emerging economies.Auditing derives its value by increasing the credibility of financial statements,which in turn increases investors’reliance on them in developed markets.Financial statement information is common to all investors and therefore increased reliance on it should reduce divergence in investors’assessment of firm value.We examine the effect of interim auditing on inter-investor divergence with a large sample of listed Chinese firms and find that it decreases more for firms whose reports are audited compared to non-audited firms.This finding suggests that investors rely more on audited financial information.Results of this study are robust to variations in event window length and specification of empirical measures.
文摘Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making.Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices.However,there have been very few studies of groups of stock markets or indices.The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets.In this context,this study aimed to examine the predictive performance of linear,nonlinear,artificial intelligence,frequency domain,and hybrid models to find an appropriate model to forecast the stock returns of developed,emerging,and frontier markets.We considered the daily stock market returns of selected indices from developed,emerging,and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models.The results showed that no single model out of the five models could be applied uniformly to all markets.However,traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.
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the financial support of the National Natural Science Foundation of China(NSFC)(Grant#91746109,#71773100 and#72073109)
文摘We examine whether management earnings forecasts(MEFs)help reduce the stock return seasonality associated with earnings seasonality around earnings announcements(EAs)in Chinese A-share markets.We find that firms in historically low earnings seasons outperform firms in high earnings seasons by 2.1%around MEFs.Firms in low earnings seasons also have higher trading volume and return volatility than their counterparts around EAs and MEFs.MEFs significantly reduce the ability of historical seasonal earnings rankings to negatively predict announcement returns,volume and volatility around EAs.The reduction effects are stronger when MEFs are voluntary or made closer to EAs.The evidence suggests that MEFs facilitate the correction of investors’tendency to extrapolate earnings seasonality and its resulted stock mispricing.
文摘Empirical studies have shown that a large number of financial asset returns exhibit fat tails （leptokurtosis） and are often characterized by volatility clustering and asymmetry. This paper considers the ability of the GARCH-Type （Generalized Autoregressive Conditional Heteroskedasticity） models to capture the stylized features of volatility in national stock market returns for three countries （Portugal, Spain and Greece）. The results of this paper suggest that in the presence of asymmetric responses to innovations in the market, the ARMA （1,1）-GJRGARCH（1,1） skewed Student-t model which accommodates both the skewness and the kurtosis of financial time series is preferred.
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.
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Project(71071166)supported by the National Natural Science Foundation of China
文摘The aim of the present work is to examine whether the price volatility of nonferrous metal futures can be used to predict the aggregate stock market returns in China. During a sample period from January of 2004 to December of 2011, empirical results show that the price volatility of basic nonferrous metals is a good predictor of value-weighted stock portfolio at various horizons in both in-sample and out-of-sample regressions. The predictive power of metal copper volatility is greater than that of aluminum. The results are robust to alternative measurements of variables and econometric approaches. After controlling several well-known macro pricing variables, the predictive power of copper volatility declines but remains statistically significant. Since the predictability exists only during our sample period, we conjecture that the stock market predictability by metal price volatility is partly driven by commodity financialization.
