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Exploring the impact of investor sentiment on stock returns of petroleum companies
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Energy Procedia 158 Energy Procedia 00(2019) (2017)4079–4085 000–000 th
www.elsevier.com/locate/procedia 10 International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China 10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China
Exploring the impact of investor sentiment on stock returns of petroleum companies Exploring the impact of investor sentiment on stock of petroleum The 15th International Symposium on District Heatingreturns and Cooling a,b a,b Yue-Jun Zhang , Jia-Min Pei companies
Assessing theBusiness feasibility using the410082, heat demand-outdoor a,b Changsha a,b China School, Hunan of University, PR Yue-Jun Zhang , Jia-Min Pei Center for Resource and Environmental Management, Hunan University, Changsha 410082, PR China temperature function for a long-term district heat demand forecast Business School, Hunan University, Changsha 410082, PR China a
b
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Center for Resource and Environmental Management, Hunan University, Changsha 410082, PR China
I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
& Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, Based on the stock indexbVeolia of 20 Recherche internationally well-known petroleum companies from France 27 February, 2001 to 3 July, Abstract
Systèmes Énergétiques et Environnement - IMT 4 rue Alfred Kastler, 44300 Nantes, France index to 2018, we useDépartement the binomial probability distribution model to Atlantique, build a daily investor sentiment endurance exploreonthetheimpact investor on stock returns ofpetroleum petroleumcompanies companies. The27results show2001 that,tofirst, the Based stock of index of 20sentiment internationally well-known from February, 3 July, investor endurance index candistribution effectivelymodel predict returns of petroleum and the 2018, wesentiment use the binomial probability to the buildstock a daily investor sentiment companies, endurance index to predictive power will be weakened over time. Second, after considering the impact of macroeconomic environment, explore the impact of investor sentiment on stock returns of petroleum companies. The results show that, first, the Abstract the sentiment effectendurance remains significant all the time the horizons. Meanwhile, the sentiment effect and appears investor sentiment index can across effectively predict stock returns of petroleum companies, the stronger in the period of economic expansion and in stock market downturns of oil and gas company. predictive power will be weakened over time. Second, after considering the impact of macroeconomic environment, District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the the sentiment remains significant acrossThese all the timerequire horizons. Meanwhile,which the sentiment greenhouse gaseffect emissions from the building sector. systems high investments are returnedeffect throughappears the heat stronger in the period of economic expansion and and building in stock renovation market downturns oildemand and gasincompany. sales. Due to the changed climate conditions policies, of heat the future could decrease, c
Copyright © the 2018 Elsevier Ltd. All rights reserved. investment return ©prolonging 2019 The Authors. Published byperiod. Elsevier Ltd. of the scientific committee of the 10th International Conference on Applied Selection and peer-review under Theismain scopeaccess of thisarticle paperunder is toresponsibility assess feasibilitylicense of using the heat demand – outdoor temperature function for heat demand This an open the CCthe BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/) Energy (ICAE2018). Copyright © 2018 Elsevier Ltd. All rights reserved. forecast. The district of Alvalade, located in committee Lisbon (Portugal), was –used a International case study. The district on is Applied consistedEnergy. of 665 Peer-review under responsibility of the scientific of ICAE2018 The as 10th Conference International Applied Selection responsibility scientificThree committee the 10th (low, buildingsand thatpeer-review vary in bothunder construction period of andthetypology. weatherofscenarios medium,Conference high) and on three district Keywords: petroleum sentiment effect, investor sentiment deep). endurance predictive regression. Energy (ICAE2018). renovation scenarioscompanies, were developed (shallow, intermediate, To index, estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: companies, sentiment effect, investor endurance index,of predictive regression. The results petroleum showed that when only weather change is sentiment considered, the margin error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.scenarios, Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and 1.decrease Introduction In recent scenarios years, with the development of behavioural finance, many researchers have explained market anomalies renovation considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the psychologically and stressed the roles of many irrational factors in forecasting financial market movements. coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and In recent with the development of behavioural finance, many researchers have explained market anomalies improve the years, accuracy of heat demand estimations.
psychologically and stressed the roles of many irrational factors in forecasting financial market movements. © 2017 The Authors. Published by Elsevier Ltd. under responsibility of the Scientific (Yue-Jun Committee of The 15th International Symposium on District Heating and Corresponding author. E-mail: [email protected] Zhang). Cooling.
Peer-review
1876-6102 Copyright © [email protected] Elsevier Ltd. All(Yue-Jun rights reserved. Corresponding author. E-mail: Zhang). Keywords: and Heat peer-review demand; Forecast; Climate change Selection under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018).
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.828
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Yue-Jun Zhang et al. / Energy Procedia 158 (2019) 4079–4085 Author name / Energy Procedia 00 (2018) 000–000
According to De Long et al. [1], investor sentiment can be broadly defined as a belief about future cash flows and investment risks that is not justified by the facts at hand. Therefore, the underlying issue of investor sentiment is about how investors interpret and react to news and information to form their beliefs. In the stock market, there is a high probability that noise trades exist in that of oil and gas companies, for the reason that the fluctuation in oil prices, which is hardly to be predicted, greatly influence the stock returns of oil and gas companies, which means that it is necessary to explore the impact of investor sentiment on the stock returns of oil and gas companies. In this paper, therefore, we single out 20 internationally well-known oil and gas companies to investigate the role that investor sentiment, as an irrational factor, plays in forecasting the stock returns of oil and gas companies. In recent years, as an essential part of the study of irrational factors in behavioural finance, investor sentiment has been a hot topic in many studies and most of them have revealed significant sentiment-return relationships. On the one hand, no matter whether at a country-level or at industry-level, it has been proved that investor sentiment affects the financial markets. On the other hand, as research concerning the sentiment-returns relationship developed, the multifarious ways of constructing investor sentiment indices emerged in an endless stream. Among them, some are market-based measures of sentiment, such as BW index [2, 3] and the investor sentiment aligned [4]. Some are survey-based measures of sentiment, such as managerial sentiment formed from the data of five industry surveys [5] and consumer confidence [6]. There are also some indices constructed from textual analysis of media [7]. There are many proxies for investor sentiment; however, most are static and short-lived. For example, consumer confidence can only reflect consumer reactions on a certain point, but with the appearance of new information and changes in the external environment, the reaction and cognition of consumers will change, which means that this kind of investor sentiment cannot reflect the dynamics of investor sentiment [8, 9]. There are many studies suggest that investor sentiment plays a significant role in explaining the financial anomalies and predicting stock returns; however, most of them focus on the aggregate level, but there are few constructing daily investor sentiment from the individual firm-level. Besides previous investor sentiment is often static and short-lived, which is not reflective of the persistence and endurance of investor sentiment. This paper, based on the stock index of 20 internationally well-known oil and gas companies from 27 February, 2001 to 3 July, 2018, uses the binomial probability distribution model to build a daily investor sentiment endurance index to explore its forecasting ability on the daily stock returns of oil and gas companies. The main contributions of this paper are as follows: first, we build a daily investor sentiment endurance index to reflect investor sentiment of oil and gas companies. Second, we reveal the role of investor sentiment in predicting the stock returns of oil and gas companies at firm-level, which helps us to understand the role of investor sentiment in the stock market from a more micro-perspective and then get a clear understanding of trading behaviours in the stock market of oil and gas companies.
2. Methods and data definitions 2.1 Methods (1) The investor sentiment endurance index and stock returns According to He [8], the binomial probability distribution model is developed to capture the investor sentiment caused by up-to-day t information and news as Eq. (1): H t * Pt + Lt * (1- Pt ) = Ct (1) where Pt represents the probability of the high price ( H t ) being the closing price ( Ct ) and takes a value between zero to unity, while ( 1-Pt ) is the probability of the low price ( Lt ) being the closing price. When Pt 0.5 , the overall investor sentiment is neutral; when Pt 0.5 , the overall sentiment is optimistic; and when Pt 0.5 , the overall sentiment is pessimistic. Therefore, the investor sentiment endurance index ( Se ) at time t is measured by Se Pt 0.5 . He [8] points out that a positive Se indicates a positive sentiment towards the closing price; while a t negative Se represents a higher probability of the low price being the closing price. This index can measure investor reactions toward different information and news, which thus reflects the dynamics of investor sentiment.
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Meanwhile, stock returns are the logarithmic difference of the original price series multiplied by 100, i.e., Rt 100 * Ln （St / St -1）, where St represents the closing price of oil and gas company stocks. (2) Predictive regression with different forecasting-horizons To investigate the impact of the investor sentiment endurance index on future stock returns, we estimate randomeffect predictive regression as Eq. (2): 1 K (2) Ri,t k 0( K ) 1( K ) Sei ,t i( K ) i(,Kt )1t k K k 1 where i represents an oil and gas company, K denotes different horizons, 0 and are the intercept, and random disturbance terms, respectively; the investor sentiment endurance index Se is an independent variable, while the dependent variable is the average k-period stock returns of these oil and gas companies; 1 is the influence coefficient of investor sentiment endurance index on the stock returns of oil and gas companies. This equation allows for random intercept i , which represents the heterogeneous impact of the investor sentiment on different petroleum companies’ stock returns. As usual, we employ known up-to-day t information to not only forecast mean stock returns beginning in day t + 1 but also define the forecasting horizon K as 1, 5, 10, and 22 days. Besides, to test the robustness of our results, we specify the control variables as shown in Eq. (3): 1 K (3) Ri ,t k 0( K ) 1( K ) Sei ,t 2( K )（t K） i( K ) i(,Kt )1t k K k 1 where t represent the additional macroeconomic variables matrix, and 2 is the coefficient matrix of the impact of control variables on the stock returns of oil and gas companies. 2.2 Data definitions Considering the length of our research sample and the availability of data, we use the daily stock index data of 20 internationally well-known oil and gas companies from 27 February, 2001 to 3 July, 2018 as quoted on: https://finance.yahoo.com/ (86707 observations are included).
3. Empirical results and discussion Before discussing how investor sentiment impacts stock returns of oil and gas companies, we employ a series of primary tests to the panel data. And then we present the main results and its robustness checks. 3.1 The results of primary tests First of all, we examine the correlation coefficients among the investor sentiment endurance index. The result shows that we do not handle with the same series because the correlation coefficients of the investor sentiment endurance index are positive, ranging from 0.23 to 0.71. Then, we use the LLC test, IPS test, and Fisher test and all the results reject the null hypothesis of unit root at the 1% significance level. Therefore, we can say that the series in this paper are unit-root stationary. Above all, the Granger causality test result, shown in Table 1, suggests that there is two-way Granger causality between the investor sentiment endurance index and the stock returns of oil and gas companies. On the one hand, just as Scheming [10] suggests that investors tend to be overly optimistic or pessimistic about the good or bad news, returns and macroeconomics, thus the return drives the investor sentiment seems reasonable. On the other hand, it confirms the significance of our paper that the investor sentiment endurance index impacts the stock returns of oil and gas companies. Table 1. Granger causality test results Null hypothesis Chi-square statistics P-value 8.42 0.00*** Se does not Granger cause R R does not Granger cause Se 90.27 0.00*** Note: The lag length is 1. Chi-square statistics is for the Granger causality test. *** indicates the significance at the 1% level.
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3.2 The impact of investor sentiment endurance index on stock returns of oil and gas companies Considering the characteristics of our panel data, we use random-effect panel model and the Panel Corrected Standard Errors method (PCSE) [11], to estimate the parameters in Eq. (2) to improve the accuracy of estimation. We start with the results for random-effect panel regression as shown in Table 2. It can be seen that the investor sentiment endurance index has a significant negative impact on future stock returns regardless of which forecasting horizon are examined, although the influence coefficient is limited to 0.03% on the longest horizon. Overall, when the investor sentiment endurance index rises by 1%, the stock returns of oil and gas companies will decline by 0.22% in the next day. The result agrees with theoretical considerations of the impact of noise trades and some earlier empirical findings. As for oil and gas company, which is susceptible to the fluctuation of oil and gas prices, there are noise trades and irrational behaviour. Although the influence coefficient thereof is small, taking the stock returns of all oil and gas companies into consideration, the impact of the investor sentiment endurance index cannot be ignored, e.g., the market capitalisation of XOM currently is 325.92 billion dollars. Furthermore, it is interesting to note that the impact of investor sentiment on average future stock returns declines over time. The predictive power of sentiment for next-day stock returns is -0.22%. As for the longer horizon of 22 days (corresponding to the trading days in a month), it declines to -0.03%, especially in one week with a decline of nearly 70%; however, the result remains statistically significant. Economically, the phenomenon that the impact of investor sentiment on average future stock returns declines over time can be explained in two ways: on the one hand, the impact of investor sentiment on stock returns is persistent, which means that the demand shock caused by uninformed investor noise trading causes the strong and persistent mispricing of assets and that may be the reason why the impact of investor sentiment on the next day stock returns is strong and significant. On the other hand, for those investors who are informed, they will not enter the market immediately to eliminate the impact of noise traders due to transaction costs and short selling constraints; however, investors will adjust their strategies over a limited time to reduce irrational investment behaviour. Over time, all such mispricing will be corrected and the market recovers its equilibrium, i.e., the impact of investor sentiment endurance index on the stock returns of oil and gas companies will tend to zero. Table 2. Panel regression results
Forecasting horizons (days) 1 5 10 -0.0337 -0.0106*** -0.0314*** C -0.2180*** -0.0784*** -0.0464*** Se T-statistic -8.83 -7.42 -6.40 F-statistic 102.14*** 71.15*** 52.59*** Note: C denotes the constant term. T-statistic is for the independent variable Se , and F-statistic is for the estimation of the significance at the 1% level.
22 -0.0317*** -0.0273*** -5.75 42.33*** Eq. (2). *** indicates
3.3 Robustness checks To examine the robustness of our results, we examine the impact of different situations and then include the macroeconomic variables. The results are as follows. (1) The impact of different economic situations Considering that economic cycle may have an impact on the sentiment effect of oil and gas companies, we relate economic cycle to our research and explore the impact of invest sentiment on stock returns of oil and gas companies under different economic situations. According to the definition of the National Bureau of Economic Research of the United States on economic cycle, our sample includes complete periods of economic expansion and recession and we divide it into two sub-periods: Panel A: an expansion from 1 December, 2001 to 30 November, 2007; and Panel B: a recession from 1 December, 2008 to 30 June, 2009. First, the descriptive statistics for different sub-periods (not reported here for the sake of brevity) show that the average stock returns of all oil and gas companies are negative in a recession (except that of XOM is positive) and that the investor sentiment endurance index is higher in an expansion. Then, we employ a random-effect panel data model and the PCSE method [11] to estimate the parameters in Eq. (2) to explore the sentiment effect in different economic situations. The results are shown in Table
Yue-Jun Zhang et al. / Energy Procedia 158 (2019) 4079–4085 Author name / Energy Procedia 00 (2018) 000–000
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3.
As is shown in Table 3, on the one hand, regardless as to whether it is in the economic expansion or recession periods, the impact of investor sentiment on stock returns of petroleum companies is always negative and the impact on the average stock returns declines over time, just as the results for the whole sample period. Table 3. Panel regression results in different economic situations Forecasting horizons (days) 1 5 10 22 Panel A：Economic expansion 0.1341*** 0.0946*** -0.0659*** 0.00817*** C -0.8633*** -0.1329* -0.2576*** -0.1517*** Se T-statistic -5.12 -1.86 -4.80 -5.95 F-statistic 15.82*** 2.16 18.65*** 17.65*** Panel B：Economic recession 0.0948*** -0.0923*** 0.0920*** 0.0919 C -0.3048*** -0.1279*** -0.0706*** -0.0403** Se T-statistic -7.55 -7.54 -6.17 -5.47 F-statistic 78.46 78.03*** 52.26*** 41.13*** Note: Pane A represents the expansion period from December 1, 2001 to November 30, 2007, while Panel B represents the recession period from December 1, 2008 to June 30, 2009. C denotes the constant term. T-statistic is for the independent variable Se , and F-statistic is for the estimation of Eq. (2). *, ** and *** indicate the significance at the 10%, 5% and 1% levels, respectively.
On the other hand, compared with the period of economic recession, the stock returns of petroleum companies are more sensitive to the investor sentiment in the expansion period, regardless of which forecasting horizons are examined. Specifically, when the investor sentiment endurance index rises by 1%, the stock returns of petroleum companies will decline by 0.86% the next day in expansion, but will decline by 0.30% in recession. Yu and Yuan [12] suggest that the sentiment effect is stronger when sentiment is high because individual investors, who are typical noise traders, are usually unwilling to sell their assets for more profits when sentiment is high. So this is also the reason why, in the present work, the sentiment effect is stronger in expansion. (2) The impact of macroeconomic variables Considering the impact of macroeconomic variables on the sentiment effect, we choose three macroeconomic variables: term premium ( Term ), default spread ( Def ), and short-term risk-free interest rate ( Rate ) for the control variable matrix given in Eq. (3). Term premium ( Term ) is defined as the spread between yields on 10-year and 3month Treasury notes, default spread ( Def ) is defined as the spread between Moody’s BAA and AAA rated corporate bond yields, and the short-term risk-free interest rate ( Rate ) is defined of 3-month Treasury bills in the U.S. All the macroeconomic data are obtained from St Louis Federal Reserve Bank. The sample period ranges from 27 February, 2001 to 3 July, 2018. Then, we employ the random-effect panel data model and the PCSE method [11] to estimate the parameters of Eq. (3) to explore the sentiment effect in oil and gas companies after the typical macroeconomic variables are included. The results are shown in Table 4. Table 4. The results incorporating macroeconomic variables Forecasting horizons (days) 1 5 10 22 -0.0372 -0.0289*** -0.0290*** 0.0307*** C -0.2097*** -0.0798*** -0.0464*** -0.0275*** Se -0.0152 -0.0196* -0.0192** -0.0158*** Term -0.3351*** 0.0471 -0.0152 -0.0038 Rate Def -0.0135 0.0017 -0.0019 0.0010 F-statistic 25.42*** 19.31*** 14.78*** 13.55*** Note: This table displays the estimated results of Eq. (3), in which the control variable matrix t includes Term , Def and Rate . C denotes the constant term. F-statistic is for the estimation of Eq. (3). *, ** and *** indicate the significance at the 10%, 5% and 1% levels, respectively.
We can find that Table 4 shows that the central results still hold after including the macroeconomic variables. On the one hand, the impact of investor sentiment endurance index on stock returns of oil and gas companies is negative. On the other hand, the impact decreases over time but remains statistically significant regardless of which
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forecasting horizon are used. 3.4 The impact of stock market situations Considering that the state of stock market might have an impact on the sentiment effect in oil and gas companies [13], we explore whether, or not, there is a difference in up and down markets. We include the dummy variable for stock market situation ( D ) in Eq. (4) and then employ a random-effect panel data model and the PCSE method to regress the equation:
Ri ,t 0 1 D * Sei ,t -1 （ * Sei ,t -1 i i ,t 2 1- D）
(4)
where D represents the dummy variable of stock market situation. When the stock returns of oil and gas companies exceed the risk-free rate, we call it an up market, i.e., D = 1 ; otherwise, it is a down market, and D = 0 . Following Huang and Hueng [13], we use the 3-month Treasury bills as the risk-free rate. The sample period is from 27 February, 2001 to 3 July, 2018. The estimated results are summarised in Table 5. Table 5. The results in different stock market situations F-statistic C D * Se （1- D）* Se Coefficient 0.0322 -0.1828*** -0.2400*** 47.48*** T-statistic 1.34 -5.57 -6.85 Note: C denotes the constant term. D denotes the dummy variable of stock market situation; specifically, when it is an up market, D = 1 ; otherwise, D = 0 . T-statistic is for regression variables, while F statistic is for the estimation of Eq. (4). *** indicates the significance at the 1% level.
It can be seen that, first, regardless of market changing trends, the impact of investor sentiment endurance index on stock returns is negative, which is in line with the above results. Second, when the investor sentiment endurance index rises by 1%, the stock returns will decline by 0.18% in an up market and 0.24% in a down market, which means that it is asymmetric and there is a big difference in different stock market situations. We can therefore conclude that the stock returns are more sensitive to investor sentiment in a down market, which is in line with the theory whereby “the decline is fast and the rise is slow” [14]. When the stock market of oil and gas companies is down, investors are more prone to panic and then sell their assets in bulk following a herding effect, which leads the stock prices to decline rapidly. Besides, the result suggests that the sentiment effect is more significant in a down market from the value of T-statistic.
4. Conclusions and suggestions We build a daily investor sentiment endurance index and adopt predictive regression with different forecasting horizons to explore the impact of investor sentiment on stock returns of petroleum companies. Several main conclusions are drawn as follows. For one thing, in the sample period, the investor sentiment endurance index can predict the stock returns of petroleum companies; in particular, when the investor sentiment endurance index rises by 1%, the stock returns of petroleum companies will decline by 0.22% in the next day, and the predictive power will be weakened over time. For another, robustness checks show that, after considering the economic cycle and macroeconomic environment, the central results still hold. Besides, the sentiment effect is stronger in the period of economic expansion and in the stock market downturns of oil and gas company. In the future, there are still many research directions: for instance, how to select effective proxy variables based on industry and market characteristics will still be the focus of future research. Acknowledgements We are grateful to the financial support from the National Natural Science Foundation of China (nos. 71322103,
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71774051), National Program for Support of Top-notch Young Professionals (no. W02070325), Changjiang Scholars Program of the Ministry of Education of China (no. Q2016154), and Hunan Youth Talent Program. References [1] DeLong, J. B.D., Shleifer, A., Summers, L.H. and Waldmann, R.J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703-738. [2] Baker, M. and Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645-1680. [3] Baker, M., Wugler, J. and Yuan, Y. (2012). Global, local and contagious investor sentiment. Journal of Financial Economics, 104(2), 272-287. [4] Huang, D.S., Jiang, F.W., Tu, J. and Zhou, G. F. (2014). Investor sentiment aligned: a powerful predictor of stock returns. Oxford University Press. Oxford [5] Salhin, A., Sherif, M. and Jones, E. (2016). Managerial sentiment, consumer confidence and sector returns. International Review of Financial Analysis, 47, 24-38. [6] Lemmon, M. and Portniaguina, E. (2006). Consumer confidence and asset prices: some empirical evidence. Review of Finance Studies, 19(4), 1499-1529. [7] Da, Z., Engelberg, J. and Gao, P.J. (2015). Editor’s choice the sum of All FEARS investor sentiment and asset prices. Review of Financial Studies, 28(1), 1-32. [8] He, L.T. (2012). The investor sentiment endurance index and its forecasting ability. International Journal of Financial Markets and Derivatives, 3(1), 61-70. [9] He, L.T. and Casey, K.M. (2015). Forecasting ability of the investor sentiment endurance index: the case of oil service stock returns and crude oil prices. Energy Economics, 47, 121-128. [10] Schmeling, M. (2009). Investor sentiment and stock returns: some international evidence. Journal of Empirical Finance, 16(3), 394-408. [11] Beck, N. and Katz, J.N. (1995). What to do (and not do) with time-series cross-section data. American Political Science Review, 89(3), 634-647. [12] Yu, J.F. and Yuan, Y. (2011). Investor sentiment and the mean-variance relation. Journal of Financial Economics, 100(2), 367-381. [13] Huang, P. and Hueng, C.J. (2008). Conditional risk-return relationship in a time-varying beta model. Quantitative Finance, 8(4), 381-390. [14] Tan, L., Chaing, T.C., Mason, J.R. and Nelling, E. (2008). Herding behaviour in Chinese stock market: an examination of A and B shares. Pacific-Basin Finance Journal, 16, 61-17.
Biography Dr. Yue-Jun Zhang is a Professor at Business School, Hunan University, China, and the Director of Center for Resource and Environmental Management, Hunan University. He got his PhD degree in Management Sciences from Chinese Academy of Sciences (CAS) in 2009, and his research focuses on energy economics and policy modeling. Up to now, he has published more than 90 peerreviewed journal articles in this field.
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ScienceDirect ScienceDirect
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Energy Procedia 158 Energy Procedia 00(2019) (2017)4079–4085 000–000 th
www.elsevier.com/locate/procedia 10 International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China 10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China
Exploring the impact of investor sentiment on stock returns of petroleum companies Exploring the impact of investor sentiment on stock of petroleum The 15th International Symposium on District Heatingreturns and Cooling a,b a,b Yue-Jun Zhang , Jia-Min Pei companies
Assessing theBusiness feasibility using the410082, heat demand-outdoor a,b Changsha a,b China School, Hunan of University, PR Yue-Jun Zhang , Jia-Min Pei Center for Resource and Environmental Management, Hunan University, Changsha 410082, PR China temperature function for a long-term district heat demand forecast Business School, Hunan University, Changsha 410082, PR China a
b
a
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Abstract a
Center for Resource and Environmental Management, Hunan University, Changsha 410082, PR China
I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
& Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, Based on the stock indexbVeolia of 20 Recherche internationally well-known petroleum companies from France 27 February, 2001 to 3 July, Abstract
Systèmes Énergétiques et Environnement - IMT 4 rue Alfred Kastler, 44300 Nantes, France index to 2018, we useDépartement the binomial probability distribution model to Atlantique, build a daily investor sentiment endurance exploreonthetheimpact investor on stock returns ofpetroleum petroleumcompanies companies. The27results show2001 that,tofirst, the Based stock of index of 20sentiment internationally well-known from February, 3 July, investor endurance index candistribution effectivelymodel predict returns of petroleum and the 2018, wesentiment use the binomial probability to the buildstock a daily investor sentiment companies, endurance index to predictive power will be weakened over time. Second, after considering the impact of macroeconomic environment, explore the impact of investor sentiment on stock returns of petroleum companies. The results show that, first, the Abstract the sentiment effectendurance remains significant all the time the horizons. Meanwhile, the sentiment effect and appears investor sentiment index can across effectively predict stock returns of petroleum companies, the stronger in the period of economic expansion and in stock market downturns of oil and gas company. predictive power will be weakened over time. Second, after considering the impact of macroeconomic environment, District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the the sentiment remains significant acrossThese all the timerequire horizons. Meanwhile,which the sentiment greenhouse gaseffect emissions from the building sector. systems high investments are returnedeffect throughappears the heat stronger in the period of economic expansion and and building in stock renovation market downturns oildemand and gasincompany. sales. Due to the changed climate conditions policies, of heat the future could decrease, c
Copyright © the 2018 Elsevier Ltd. All rights reserved. investment return ©prolonging 2019 The Authors. Published byperiod. Elsevier Ltd. of the scientific committee of the 10th International Conference on Applied Selection and peer-review under Theismain scopeaccess of thisarticle paperunder is toresponsibility assess feasibilitylicense of using the heat demand – outdoor temperature function for heat demand This an open the CCthe BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/) Energy (ICAE2018). Copyright © 2018 Elsevier Ltd. All rights reserved. forecast. The district of Alvalade, located in committee Lisbon (Portugal), was –used a International case study. The district on is Applied consistedEnergy. of 665 Peer-review under responsibility of the scientific of ICAE2018 The as 10th Conference International Applied Selection responsibility scientificThree committee the 10th (low, buildingsand thatpeer-review vary in bothunder construction period of andthetypology. weatherofscenarios medium,Conference high) and on three district Keywords: petroleum sentiment effect, investor sentiment deep). endurance predictive regression. Energy (ICAE2018). renovation scenarioscompanies, were developed (shallow, intermediate, To index, estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: companies, sentiment effect, investor endurance index,of predictive regression. The results petroleum showed that when only weather change is sentiment considered, the margin error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.scenarios, Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and 1.decrease Introduction In recent scenarios years, with the development of behavioural finance, many researchers have explained market anomalies renovation considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the psychologically and stressed the roles of many irrational factors in forecasting financial market movements. coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and In recent with the development of behavioural finance, many researchers have explained market anomalies improve the years, accuracy of heat demand estimations.
psychologically and stressed the roles of many irrational factors in forecasting financial market movements. © 2017 The Authors. Published by Elsevier Ltd. under responsibility of the Scientific (Yue-Jun Committee of The 15th International Symposium on District Heating and Corresponding author. E-mail: [email protected] Zhang). Cooling.
Peer-review
1876-6102 Copyright © [email protected] Elsevier Ltd. All(Yue-Jun rights reserved. Corresponding author. E-mail: Zhang). Keywords: and Heat peer-review demand; Forecast; Climate change Selection under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018).
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.828
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According to De Long et al. [1], investor sentiment can be broadly defined as a belief about future cash flows and investment risks that is not justified by the facts at hand. Therefore, the underlying issue of investor sentiment is about how investors interpret and react to news and information to form their beliefs. In the stock market, there is a high probability that noise trades exist in that of oil and gas companies, for the reason that the fluctuation in oil prices, which is hardly to be predicted, greatly influence the stock returns of oil and gas companies, which means that it is necessary to explore the impact of investor sentiment on the stock returns of oil and gas companies. In this paper, therefore, we single out 20 internationally well-known oil and gas companies to investigate the role that investor sentiment, as an irrational factor, plays in forecasting the stock returns of oil and gas companies. In recent years, as an essential part of the study of irrational factors in behavioural finance, investor sentiment has been a hot topic in many studies and most of them have revealed significant sentiment-return relationships. On the one hand, no matter whether at a country-level or at industry-level, it has been proved that investor sentiment affects the financial markets. On the other hand, as research concerning the sentiment-returns relationship developed, the multifarious ways of constructing investor sentiment indices emerged in an endless stream. Among them, some are market-based measures of sentiment, such as BW index [2, 3] and the investor sentiment aligned [4]. Some are survey-based measures of sentiment, such as managerial sentiment formed from the data of five industry surveys [5] and consumer confidence [6]. There are also some indices constructed from textual analysis of media [7]. There are many proxies for investor sentiment; however, most are static and short-lived. For example, consumer confidence can only reflect consumer reactions on a certain point, but with the appearance of new information and changes in the external environment, the reaction and cognition of consumers will change, which means that this kind of investor sentiment cannot reflect the dynamics of investor sentiment [8, 9]. There are many studies suggest that investor sentiment plays a significant role in explaining the financial anomalies and predicting stock returns; however, most of them focus on the aggregate level, but there are few constructing daily investor sentiment from the individual firm-level. Besides previous investor sentiment is often static and short-lived, which is not reflective of the persistence and endurance of investor sentiment. This paper, based on the stock index of 20 internationally well-known oil and gas companies from 27 February, 2001 to 3 July, 2018, uses the binomial probability distribution model to build a daily investor sentiment endurance index to explore its forecasting ability on the daily stock returns of oil and gas companies. The main contributions of this paper are as follows: first, we build a daily investor sentiment endurance index to reflect investor sentiment of oil and gas companies. Second, we reveal the role of investor sentiment in predicting the stock returns of oil and gas companies at firm-level, which helps us to understand the role of investor sentiment in the stock market from a more micro-perspective and then get a clear understanding of trading behaviours in the stock market of oil and gas companies.
2. Methods and data definitions 2.1 Methods (1) The investor sentiment endurance index and stock returns According to He [8], the binomial probability distribution model is developed to capture the investor sentiment caused by up-to-day t information and news as Eq. (1): H t * Pt + Lt * (1- Pt ) = Ct (1) where Pt represents the probability of the high price ( H t ) being the closing price ( Ct ) and takes a value between zero to unity, while ( 1-Pt ) is the probability of the low price ( Lt ) being the closing price. When Pt 0.5 , the overall investor sentiment is neutral; when Pt 0.5 , the overall sentiment is optimistic; and when Pt 0.5 , the overall sentiment is pessimistic. Therefore, the investor sentiment endurance index ( Se ) at time t is measured by Se Pt 0.5 . He [8] points out that a positive Se indicates a positive sentiment towards the closing price; while a t negative Se represents a higher probability of the low price being the closing price. This index can measure investor reactions toward different information and news, which thus reflects the dynamics of investor sentiment.
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Meanwhile, stock returns are the logarithmic difference of the original price series multiplied by 100, i.e., Rt 100 * Ln （St / St -1）, where St represents the closing price of oil and gas company stocks. (2) Predictive regression with different forecasting-horizons To investigate the impact of the investor sentiment endurance index on future stock returns, we estimate randomeffect predictive regression as Eq. (2): 1 K (2) Ri,t k 0( K ) 1( K ) Sei ,t i( K ) i(,Kt )1t k K k 1 where i represents an oil and gas company, K denotes different horizons, 0 and are the intercept, and random disturbance terms, respectively; the investor sentiment endurance index Se is an independent variable, while the dependent variable is the average k-period stock returns of these oil and gas companies; 1 is the influence coefficient of investor sentiment endurance index on the stock returns of oil and gas companies. This equation allows for random intercept i , which represents the heterogeneous impact of the investor sentiment on different petroleum companies’ stock returns. As usual, we employ known up-to-day t information to not only forecast mean stock returns beginning in day t + 1 but also define the forecasting horizon K as 1, 5, 10, and 22 days. Besides, to test the robustness of our results, we specify the control variables as shown in Eq. (3): 1 K (3) Ri ,t k 0( K ) 1( K ) Sei ,t 2( K )（t K） i( K ) i(,Kt )1t k K k 1 where t represent the additional macroeconomic variables matrix, and 2 is the coefficient matrix of the impact of control variables on the stock returns of oil and gas companies. 2.2 Data definitions Considering the length of our research sample and the availability of data, we use the daily stock index data of 20 internationally well-known oil and gas companies from 27 February, 2001 to 3 July, 2018 as quoted on: https://finance.yahoo.com/ (86707 observations are included).
3. Empirical results and discussion Before discussing how investor sentiment impacts stock returns of oil and gas companies, we employ a series of primary tests to the panel data. And then we present the main results and its robustness checks. 3.1 The results of primary tests First of all, we examine the correlation coefficients among the investor sentiment endurance index. The result shows that we do not handle with the same series because the correlation coefficients of the investor sentiment endurance index are positive, ranging from 0.23 to 0.71. Then, we use the LLC test, IPS test, and Fisher test and all the results reject the null hypothesis of unit root at the 1% significance level. Therefore, we can say that the series in this paper are unit-root stationary. Above all, the Granger causality test result, shown in Table 1, suggests that there is two-way Granger causality between the investor sentiment endurance index and the stock returns of oil and gas companies. On the one hand, just as Scheming [10] suggests that investors tend to be overly optimistic or pessimistic about the good or bad news, returns and macroeconomics, thus the return drives the investor sentiment seems reasonable. On the other hand, it confirms the significance of our paper that the investor sentiment endurance index impacts the stock returns of oil and gas companies. Table 1. Granger causality test results Null hypothesis Chi-square statistics P-value 8.42 0.00*** Se does not Granger cause R R does not Granger cause Se 90.27 0.00*** Note: The lag length is 1. Chi-square statistics is for the Granger causality test. *** indicates the significance at the 1% level.
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3.2 The impact of investor sentiment endurance index on stock returns of oil and gas companies Considering the characteristics of our panel data, we use random-effect panel model and the Panel Corrected Standard Errors method (PCSE) [11], to estimate the parameters in Eq. (2) to improve the accuracy of estimation. We start with the results for random-effect panel regression as shown in Table 2. It can be seen that the investor sentiment endurance index has a significant negative impact on future stock returns regardless of which forecasting horizon are examined, although the influence coefficient is limited to 0.03% on the longest horizon. Overall, when the investor sentiment endurance index rises by 1%, the stock returns of oil and gas companies will decline by 0.22% in the next day. The result agrees with theoretical considerations of the impact of noise trades and some earlier empirical findings. As for oil and gas company, which is susceptible to the fluctuation of oil and gas prices, there are noise trades and irrational behaviour. Although the influence coefficient thereof is small, taking the stock returns of all oil and gas companies into consideration, the impact of the investor sentiment endurance index cannot be ignored, e.g., the market capitalisation of XOM currently is 325.92 billion dollars. Furthermore, it is interesting to note that the impact of investor sentiment on average future stock returns declines over time. The predictive power of sentiment for next-day stock returns is -0.22%. As for the longer horizon of 22 days (corresponding to the trading days in a month), it declines to -0.03%, especially in one week with a decline of nearly 70%; however, the result remains statistically significant. Economically, the phenomenon that the impact of investor sentiment on average future stock returns declines over time can be explained in two ways: on the one hand, the impact of investor sentiment on stock returns is persistent, which means that the demand shock caused by uninformed investor noise trading causes the strong and persistent mispricing of assets and that may be the reason why the impact of investor sentiment on the next day stock returns is strong and significant. On the other hand, for those investors who are informed, they will not enter the market immediately to eliminate the impact of noise traders due to transaction costs and short selling constraints; however, investors will adjust their strategies over a limited time to reduce irrational investment behaviour. Over time, all such mispricing will be corrected and the market recovers its equilibrium, i.e., the impact of investor sentiment endurance index on the stock returns of oil and gas companies will tend to zero. Table 2. Panel regression results
Forecasting horizons (days) 1 5 10 -0.0337 -0.0106*** -0.0314*** C -0.2180*** -0.0784*** -0.0464*** Se T-statistic -8.83 -7.42 -6.40 F-statistic 102.14*** 71.15*** 52.59*** Note: C denotes the constant term. T-statistic is for the independent variable Se , and F-statistic is for the estimation of the significance at the 1% level.
22 -0.0317*** -0.0273*** -5.75 42.33*** Eq. (2). *** indicates
3.3 Robustness checks To examine the robustness of our results, we examine the impact of different situations and then include the macroeconomic variables. The results are as follows. (1) The impact of different economic situations Considering that economic cycle may have an impact on the sentiment effect of oil and gas companies, we relate economic cycle to our research and explore the impact of invest sentiment on stock returns of oil and gas companies under different economic situations. According to the definition of the National Bureau of Economic Research of the United States on economic cycle, our sample includes complete periods of economic expansion and recession and we divide it into two sub-periods: Panel A: an expansion from 1 December, 2001 to 30 November, 2007; and Panel B: a recession from 1 December, 2008 to 30 June, 2009. First, the descriptive statistics for different sub-periods (not reported here for the sake of brevity) show that the average stock returns of all oil and gas companies are negative in a recession (except that of XOM is positive) and that the investor sentiment endurance index is higher in an expansion. Then, we employ a random-effect panel data model and the PCSE method [11] to estimate the parameters in Eq. (2) to explore the sentiment effect in different economic situations. The results are shown in Table
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3.
As is shown in Table 3, on the one hand, regardless as to whether it is in the economic expansion or recession periods, the impact of investor sentiment on stock returns of petroleum companies is always negative and the impact on the average stock returns declines over time, just as the results for the whole sample period. Table 3. Panel regression results in different economic situations Forecasting horizons (days) 1 5 10 22 Panel A：Economic expansion 0.1341*** 0.0946*** -0.0659*** 0.00817*** C -0.8633*** -0.1329* -0.2576*** -0.1517*** Se T-statistic -5.12 -1.86 -4.80 -5.95 F-statistic 15.82*** 2.16 18.65*** 17.65*** Panel B：Economic recession 0.0948*** -0.0923*** 0.0920*** 0.0919 C -0.3048*** -0.1279*** -0.0706*** -0.0403** Se T-statistic -7.55 -7.54 -6.17 -5.47 F-statistic 78.46 78.03*** 52.26*** 41.13*** Note: Pane A represents the expansion period from December 1, 2001 to November 30, 2007, while Panel B represents the recession period from December 1, 2008 to June 30, 2009. C denotes the constant term. T-statistic is for the independent variable Se , and F-statistic is for the estimation of Eq. (2). *, ** and *** indicate the significance at the 10%, 5% and 1% levels, respectively.
On the other hand, compared with the period of economic recession, the stock returns of petroleum companies are more sensitive to the investor sentiment in the expansion period, regardless of which forecasting horizons are examined. Specifically, when the investor sentiment endurance index rises by 1%, the stock returns of petroleum companies will decline by 0.86% the next day in expansion, but will decline by 0.30% in recession. Yu and Yuan [12] suggest that the sentiment effect is stronger when sentiment is high because individual investors, who are typical noise traders, are usually unwilling to sell their assets for more profits when sentiment is high. So this is also the reason why, in the present work, the sentiment effect is stronger in expansion. (2) The impact of macroeconomic variables Considering the impact of macroeconomic variables on the sentiment effect, we choose three macroeconomic variables: term premium ( Term ), default spread ( Def ), and short-term risk-free interest rate ( Rate ) for the control variable matrix given in Eq. (3). Term premium ( Term ) is defined as the spread between yields on 10-year and 3month Treasury notes, default spread ( Def ) is defined as the spread between Moody’s BAA and AAA rated corporate bond yields, and the short-term risk-free interest rate ( Rate ) is defined of 3-month Treasury bills in the U.S. All the macroeconomic data are obtained from St Louis Federal Reserve Bank. The sample period ranges from 27 February, 2001 to 3 July, 2018. Then, we employ the random-effect panel data model and the PCSE method [11] to estimate the parameters of Eq. (3) to explore the sentiment effect in oil and gas companies after the typical macroeconomic variables are included. The results are shown in Table 4. Table 4. The results incorporating macroeconomic variables Forecasting horizons (days) 1 5 10 22 -0.0372 -0.0289*** -0.0290*** 0.0307*** C -0.2097*** -0.0798*** -0.0464*** -0.0275*** Se -0.0152 -0.0196* -0.0192** -0.0158*** Term -0.3351*** 0.0471 -0.0152 -0.0038 Rate Def -0.0135 0.0017 -0.0019 0.0010 F-statistic 25.42*** 19.31*** 14.78*** 13.55*** Note: This table displays the estimated results of Eq. (3), in which the control variable matrix t includes Term , Def and Rate . C denotes the constant term. F-statistic is for the estimation of Eq. (3). *, ** and *** indicate the significance at the 10%, 5% and 1% levels, respectively.
We can find that Table 4 shows that the central results still hold after including the macroeconomic variables. On the one hand, the impact of investor sentiment endurance index on stock returns of oil and gas companies is negative. On the other hand, the impact decreases over time but remains statistically significant regardless of which
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forecasting horizon are used. 3.4 The impact of stock market situations Considering that the state of stock market might have an impact on the sentiment effect in oil and gas companies [13], we explore whether, or not, there is a difference in up and down markets. We include the dummy variable for stock market situation ( D ) in Eq. (4) and then employ a random-effect panel data model and the PCSE method to regress the equation:
Ri ,t 0 1 D * Sei ,t -1 （ * Sei ,t -1 i i ,t 2 1- D）
(4)
where D represents the dummy variable of stock market situation. When the stock returns of oil and gas companies exceed the risk-free rate, we call it an up market, i.e., D = 1 ; otherwise, it is a down market, and D = 0 . Following Huang and Hueng [13], we use the 3-month Treasury bills as the risk-free rate. The sample period is from 27 February, 2001 to 3 July, 2018. The estimated results are summarised in Table 5. Table 5. The results in different stock market situations F-statistic C D * Se （1- D）* Se Coefficient 0.0322 -0.1828*** -0.2400*** 47.48*** T-statistic 1.34 -5.57 -6.85 Note: C denotes the constant term. D denotes the dummy variable of stock market situation; specifically, when it is an up market, D = 1 ; otherwise, D = 0 . T-statistic is for regression variables, while F statistic is for the estimation of Eq. (4). *** indicates the significance at the 1% level.
It can be seen that, first, regardless of market changing trends, the impact of investor sentiment endurance index on stock returns is negative, which is in line with the above results. Second, when the investor sentiment endurance index rises by 1%, the stock returns will decline by 0.18% in an up market and 0.24% in a down market, which means that it is asymmetric and there is a big difference in different stock market situations. We can therefore conclude that the stock returns are more sensitive to investor sentiment in a down market, which is in line with the theory whereby “the decline is fast and the rise is slow” [14]. When the stock market of oil and gas companies is down, investors are more prone to panic and then sell their assets in bulk following a herding effect, which leads the stock prices to decline rapidly. Besides, the result suggests that the sentiment effect is more significant in a down market from the value of T-statistic.
4. Conclusions and suggestions We build a daily investor sentiment endurance index and adopt predictive regression with different forecasting horizons to explore the impact of investor sentiment on stock returns of petroleum companies. Several main conclusions are drawn as follows. For one thing, in the sample period, the investor sentiment endurance index can predict the stock returns of petroleum companies; in particular, when the investor sentiment endurance index rises by 1%, the stock returns of petroleum companies will decline by 0.22% in the next day, and the predictive power will be weakened over time. For another, robustness checks show that, after considering the economic cycle and macroeconomic environment, the central results still hold. Besides, the sentiment effect is stronger in the period of economic expansion and in the stock market downturns of oil and gas company. In the future, there are still many research directions: for instance, how to select effective proxy variables based on industry and market characteristics will still be the focus of future research. Acknowledgements We are grateful to the financial support from the National Natural Science Foundation of China (nos. 71322103,
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71774051), National Program for Support of Top-notch Young Professionals (no. W02070325), Changjiang Scholars Program of the Ministry of Education of China (no. Q2016154), and Hunan Youth Talent Program. References [1] DeLong, J. B.D., Shleifer, A., Summers, L.H. and Waldmann, R.J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703-738. [2] Baker, M. and Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645-1680. [3] Baker, M., Wugler, J. and Yuan, Y. (2012). Global, local and contagious investor sentiment. Journal of Financial Economics, 104(2), 272-287. [4] Huang, D.S., Jiang, F.W., Tu, J. and Zhou, G. F. (2014). Investor sentiment aligned: a powerful predictor of stock returns. Oxford University Press. Oxford [5] Salhin, A., Sherif, M. and Jones, E. (2016). Managerial sentiment, consumer confidence and sector returns. International Review of Financial Analysis, 47, 24-38. [6] Lemmon, M. and Portniaguina, E. (2006). Consumer confidence and asset prices: some empirical evidence. Review of Finance Studies, 19(4), 1499-1529. [7] Da, Z., Engelberg, J. and Gao, P.J. (2015). Editor’s choice the sum of All FEARS investor sentiment and asset prices. Review of Financial Studies, 28(1), 1-32. [8] He, L.T. (2012). The investor sentiment endurance index and its forecasting ability. International Journal of Financial Markets and Derivatives, 3(1), 61-70. [9] He, L.T. and Casey, K.M. (2015). Forecasting ability of the investor sentiment endurance index: the case of oil service stock returns and crude oil prices. Energy Economics, 47, 121-128. [10] Schmeling, M. (2009). Investor sentiment and stock returns: some international evidence. Journal of Empirical Finance, 16(3), 394-408. [11] Beck, N. and Katz, J.N. (1995). What to do (and not do) with time-series cross-section data. American Political Science Review, 89(3), 634-647. [12] Yu, J.F. and Yuan, Y. (2011). Investor sentiment and the mean-variance relation. Journal of Financial Economics, 100(2), 367-381. [13] Huang, P. and Hueng, C.J. (2008). Conditional risk-return relationship in a time-varying beta model. Quantitative Finance, 8(4), 381-390. [14] Tan, L., Chaing, T.C., Mason, J.R. and Nelling, E. (2008). Herding behaviour in Chinese stock market: an examination of A and B shares. Pacific-Basin Finance Journal, 16, 61-17.
Biography Dr. Yue-Jun Zhang is a Professor at Business School, Hunan University, China, and the Director of Center for Resource and Environmental Management, Hunan University. He got his PhD degree in Management Sciences from Chinese Academy of Sciences (CAS) in 2009, and his research focuses on energy economics and policy modeling. Up to now, he has published more than 90 peerreviewed journal articles in this field.