Research article Special Issues

Impact of online public opinion regarding the Japanese nuclear wastewater incident on stock market based on the SOR model


  • Received: 11 January 2023 Revised: 02 March 2023 Accepted: 07 March 2023 Published: 16 March 2023
  • The exposure of the Japanese nuclear wastewater incident has shaped online public opinion and has also caused a certain impact on stocks in aquaculture and feed industries. In order to explore the impact of network public opinion caused by public emergencies on relevant stocks, this paper uses the stimulus organism response(SOR) model to construct a framework model of the impact path of network public opinion on the financial stock market, and it uses emotional analysis, LDA and grounded theory methods to conduct empirical analysis. The study draws a new conclusion about the impact of online public opinion on the performance of relevant stocks in the context of the nuclear waste water incident in Japan. The positive change of media sentiment will lead to the decline of stock returns and the increase of volatility. The positive change of public sentiment will lead to the decline of stock returns in the current period and the increase of stock returns in the lag period. At the same time, we have proved that media attention, public opinion theme and prospect theory value have certain influences on stock performance in the context of the Japanese nuclear wastewater incident. The conclusion shows that after the public emergency, the government and investors need to pay attention to the changes of network public opinion caused by the event, so as to avoid the possible stock market risks.

    Citation: Wei Hong, Yiting Gu, Linhai Wu, Xujin Pu. Impact of online public opinion regarding the Japanese nuclear wastewater incident on stock market based on the SOR model[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 9305-9326. doi: 10.3934/mbe.2023408

    Related Papers:

    [1] Chiara De Santi, Sucharitha Gadi, Agnieszka Swiatecka-Urban, Catherine M. Greene . Identification of a novel functional miR-143-5p recognition element in the Cystic Fibrosis Transmembrane Conductance Regulator 3’UTR. AIMS Genetics, 2018, 5(1): 53-62. doi: 10.3934/genet.2018.1.53
    [2] Mohammad Hashemi, Fatemeh Bizhani, Hiva Danesh, Behzad Narouie, Mehdi Sotoudeh, Mohammad Hadi Radfar, Mehdi Honarkar Ramezani, Gholamreza Bahari, Mohsen Taheri, Saeid Ghavami . MiR-608 rs4919510 C>G polymorphism increased the risk of bladder cancer in an Iranian population. AIMS Genetics, 2016, 3(4): 212-218. doi: 10.3934/genet.2016.4.212
    [3] Tahereh Karamzadeh, Hamzeh Alipour, Marziae Shahriari-Namadi, Abbasali Raz, Kourosh Azizi, Masoumeh Bagheri, Mohammad D. Moemenbellah-Fard . Molecular characterization of the netrin-1 UNC-5 receptor in Lucilia sericata larvae. AIMS Genetics, 2019, 6(3): 46-54. doi: 10.3934/genet.2019.3.46
    [4] Huong Thi Thu Phung, Hoa Luong Hieu Nguyen, Dung Hoang Nguyen . The possible function of Flp1 in homologous recombination repair in Saccharomyces cerevisiae. AIMS Genetics, 2018, 5(2): 161-176. doi: 10.3934/genet.2018.2.161
    [5] Michael T. Fasullo, Mingzeng Sun . Both RAD5-dependent and independent pathways are involved in DNA damage-associated sister chromatid exchange in budding yeast. AIMS Genetics, 2017, 4(2): 84-102. doi: 10.3934/genet.2017.2.84
    [6] Jeffrey M. Marcus . Our love-hate relationship with DNA barcodes, the Y2K problem, and the search for next generation barcodes. AIMS Genetics, 2018, 5(1): 1-23. doi: 10.3934/genet.2018.1.1
    [7] Noel Pabalan, Neetu Singh, Eloisa Singian, Caio Parente Barbosa, Bianca Bianco, Hamdi Jarjanazi . Associations of CYP1A1 gene polymorphisms and risk of breast cancer in Indian women: a meta-analysis. AIMS Genetics, 2015, 2(4): 250-262. doi: 10.3934/genet.2015.4.250
    [8] Xiaojuan Wang, Jianghong Wu, Zhongren Yang, Fenglan Zhang, Hailian Sun, Xiao Qiu, Fengyan Yi, Ding Yang, Fengling Shi . Physiological responses and transcriptome analysis of the Kochia prostrata (L.) Schrad. to seedling drought stress. AIMS Genetics, 2019, 6(2): 17-35. doi: 10.3934/genet.2019.2.17
    [9] Jue Er Amanda Lee, Linda May Parsons, Leonie M. Quinn . MYC function and regulation in flies: how Drosophila has enlightened MYC cancer biology. AIMS Genetics, 2014, 1(1): 81-98. doi: 10.3934/genet.2014.1.81
    [10] John E. La Marca, Wayne Gregory Somers . The Drosophila gonads: models for stem cell proliferation, self-renewal, and differentiation. AIMS Genetics, 2014, 1(1): 55-80. doi: 10.3934/genet.2014.1.55
  • The exposure of the Japanese nuclear wastewater incident has shaped online public opinion and has also caused a certain impact on stocks in aquaculture and feed industries. In order to explore the impact of network public opinion caused by public emergencies on relevant stocks, this paper uses the stimulus organism response(SOR) model to construct a framework model of the impact path of network public opinion on the financial stock market, and it uses emotional analysis, LDA and grounded theory methods to conduct empirical analysis. The study draws a new conclusion about the impact of online public opinion on the performance of relevant stocks in the context of the nuclear waste water incident in Japan. The positive change of media sentiment will lead to the decline of stock returns and the increase of volatility. The positive change of public sentiment will lead to the decline of stock returns in the current period and the increase of stock returns in the lag period. At the same time, we have proved that media attention, public opinion theme and prospect theory value have certain influences on stock performance in the context of the Japanese nuclear wastewater incident. The conclusion shows that after the public emergency, the government and investors need to pay attention to the changes of network public opinion caused by the event, so as to avoid the possible stock market risks.



    The head groups of membrane lipids have either single charge (e.g. tetraether lipids [1], phosphatidic acid (PA), phosphatidylserine (PS), phosphatidylethanolamine (PE), and phosphatidylinositol (PI)) or electric dipole (e.g. phospholipids, such as dimyristoyl-, dipalmitoyl- and distearoylphosphatidyl choline (DMPC, DPPC and DSPC, respectively)).

    Between lipids containing head groups with electric dipole there is short range interaction, i.e. where the two-body potential decays algebraically at large distances with a power equal or larger than the spatial dimension [2]. Theoretical models of lipid membranes usually focus on systems where there is short range lateral interactions between nearest neighbor lipids [3],[4] because it is enough to consider only the interactions between nearest-neighbor lipid molecules. It is much more difficult to model a lipid membrane containing single charged head groups [5]. Between lipids with single charged head groups there is long range interaction, i.e. where the two-body potential decays algebraically at large distances with a power smaller than the spatial dimension [2] and thus modeling this system one has to consider the entire system rather than the interactions between the nearest-neighbor lipids. In order to get closer to the solution of this problem recently we developed a generalized version of Newton's Shell Theorem [6],[7] to calculate the electric potential, V around a surface-charged sphere (of radius R1) surrounded by electrolyte at a distance Z from the center of the sphere (see also Eqs 9,10 in ref.7):

    V(Z)=ke·Q1·λDϵr·Z·R1·eZλD·sinh(R1λD)   atZ>R1

    V(Z)=ke·Q1·λDϵr·Z·R1·eR1λD·sinh(ZλD)   atZ<R1

    where ke=(4πϵ0)1 is the Coulomb's constant, λD is the Debye length, Q1 is the total charge of the homogeneously charged surface of the sphere of radius R1, ϵr is the relative static permittivity of the electrolyte. Deriving Eqs 1,2 the general solution of the Screened Poisson Equation was utilized (see Eq 4 in ref.7 or A5 in Appendix 1), an equation that is valid if the electrolyte is electrically neutral [8]. It is important to note that the Screened Poisson Equation (Eq A4) is different from the Poisson-Boltzmann equation (see Eqs A1,A3). The Poisson-Boltzmann equation can be used to calculate the potential energy of an arbitrary, electroneutral, ion solution (i.e. electrolyte). However, for the solution (see Eq A2) one has to know the charge density of the ions in the electrolyte (i.e. the Boltzmann distribution; see Eq A3), which depends on the potential, V, itself. Thus only approximative solution is available (the Debye- Hückel approximation [9]), that is valid when |zieV/(kBT)|1 (where e: charge of an electron, zi: charge number of the i-th type of ion, kB: Boltzmann constant, T: absolute temperature).

    Using the Screened Poisson Equation (Eq A4) one can calculate the potential energy of an electrolyte that contains also external charges. The external charges are embedded into the electrolyte (like the charges of the surface-charged sphere) but not part of the electrolyte itself. For the solution one has to know the charge density of the external charges (see Eq 4 in ref.7 or Eq A5 in Appendix 1), i.e. distribution of the charges on the surface-charged sphere and not the distribution of the ions in the electrolyte. In our case it is assumed that the charges on the surface of the sphere are homogeneously distributed and in this case Eqs 1,2 is the exact solution of the Screened Poisson Equation.

    Note that recently by using Eqs 1,2 electric energies have been calculated [10], such as the electric potential energy needed to build up a surface-charged sphere, and the field and polarization energy of the electrolyte inside and around the surface-charged sphere.

    In this paper the density of electric field energy is calculated around two surface-charged spheres where the smaller sphere is located inside the larger one and the entire system is embedded in neutral electrolyte. This system is close to a charged vesicle [1] or to a cell [11] where charged lipids are located both on the outer and inner leaflet of the membrane, i.e. two concentric surface-charged spheres. It also models an eukaryote [12] where neutral phospholipids such as sphingomyelin and zwitterionic phosphatidylcholine are located primarily in the outer leaflet of the plasma membrane, and most anionic phospholipids, such as phosphatidic acid (PA), phosphatidylserine (PS), phosphatidylethanolamine (PE), and phosphatidylinositol (PI) are located in the inner leaflet of the plasma membrane (represented by the large surface-charged sphere of our model). Eukaryotes also have a single nucleus enveloped by double layer of lipid membranes which may contain charged lipids too (representing the smaller surface-charged sphere of our model). Note that these two charged spheres of an eukaryote are not necessarily concentric. Finally, our model is generalized for the case when the large surface-charged sphere contains several smaller surface-charged spheres. This system may also model osteoclast cells [12] containing many nuclei.

    In this work the density of the electric field energy inside and outside of two surface-charged spheres are calculated at different locations. The density of the electric field energy at a point can be calculated by the following equation [13]:

    uF=ϵrϵ02E¯·E¯

    where E is the vector of the electric field strength at the considered point, ϵ0 is the absolute vacuum permittivity and ϵr is the relative permittivity of the electrolyte.

    Here by using the recently generalized Shell Theorem [7] we calculate the density of electric field energy, uF produced by two surface-charged spheres (see Figure 1) surrounded outside and inside by electrolyte where the smaller sphere is located inside the larger sphere.

    Figure 1.  Two surface-charged spheres surrounded outside and inside by electrolyte where the smaller sphere is located inside the larger one.

    Z: the distance between the centers of the spheres (dashed blue line); R1 and RL is the radius of the smaller and larger sphere, respectively; D1 and DL is the distance between point P1 and the center of the smaller and larger sphere, respectively; E1 and EL is the field strength created in point P1 by the smaller and larger surface-charged sphere, respectively.

    The origin of the coordinate system (x, y) is attached to the center of the larger sphere and the coordinates of point P1 are xp and yp. The coordinates of the center of the larger and smaller sphere are (xL,yL)=(0,0) and (x1,y1)=(Z,0), respectively.

    In order to calculate the density of electric field energy one has to determine the electric field strength (see Eq 3), i.e. the gradient of the electric potential. The potential produced by the smaller sphere, V1 at a distance D1 from its center can be calculated by Eqs 1,2 (or Eqs 9,10 in ref.[7]). The electric field strength created by the smaller sphere at point P1 (see Figure 1) is:

    E¯1=grad(V1)=(dV1dD1dD1dxP,dV1dD1dD1dyP)=dV1dD1(d(xPx1)2+(yPy1)2dxP,d(xPx1)2+(yPy1)2dyP)=dV1dD1(xPx1D1,yPy1D1)=(dV1dD1xP+ZD1,dV1dD1yPD1)=(E1x,E1y)

    where

    dV1dD1={keQ1λDϵrR1sinh(R1/λD)[eD1λDD21eD1λDD1λD]  ifD1>R1keQ1λDϵrR1eR1λD[sinh(D1λD)D21+cosh(D1λD)D1λD]  ifD1<R1

    where λD is the Debye length and ZRLR1.

    Similarly, the electric field strength created by the large sphere at point P1 (i.e. at a distance DL from its center; see Figure 1) is:

    E¯L=grad(VL)=(dVLdDLdDLdxP,dVLdDLdDLdyP)=dVLdDL(d(xPxL)2+(yPyL)2dxP,d(xPxL)2+(yPyL)2dyP)=dVLdDL(xPxLDL,yPyLDL)=(dVLdDLxPDL,dVLdDLyPDL)=(ELx,ELy)

    where one can construct dVLdDL from Eq 5 by changing D1 to DL, R1 to RL and Q1 to QL.

    uF(xP,yP)=ϵrϵ02E¯·E¯=ϵrϵ02(E¯1+E¯L)·(E¯1+E¯L)=ϵrϵ02([E1x+ELx],[E1y+ELy])·([E1x+ELx],[E1y+ELy])=ϵrϵ02([E1x+ELx]2+[E1y+ELy]2)

    Here by using Eq 7 the density of the electric field energy, uF, is calculated around two surface- charged spheres (where the smaller sphere is located inside the larger sphere) surrounded in- and outside by electrolyte. The radius of the larger and smaller sphere is: RL=106m and R1=0.2RL, respectively. The surface charge density of the homogeneously charged spheres is ρs = −0.266 C/m2 (the surface charge density of the PLFE lipid vesicles [1]). The total charge of the larger and the smaller sphere is, Q2=ρs4πR22=3.3427·1012C and Q1=ρs4πR21=1.337·1013C, respectively. This system is axially symmetric, where the symmetry axis is the straight line connecting the centers of the spheres. The center of the attached coordinate system is at the center of large sphere and the x axis is defined by the symmetry axis. Because of the axial symmetry of the system it is enough to calculate uF along straight lines parallel to the symmetry axis (see Figure 2), where the same yP coordinate belongs to each straight line. The surface-charged spheres are surrounded by electrolyte containing monovalent ions. The considered electrolyte ion concentrations (of the positive ion) are: 0.00001, 0.001 and 0.1 mol/m3 and the respective Debye lengths are: 3.05 · 10−6, 3.05 · 10−7 and 3.05 · 10−8 m (see Table 1 in ref.7), and the relative permittivity of the electrolyte is ϵr = 78.

    Figure 2.  Locations of a small surface-charged sphere inside a large surface-charged sphere.

    Inside a large surface-charged sphere of radius RL(=106m) a small surface-charged sphere of radius R1(=0.2RL) is located. The electric field energy density, uF is calculated at the three different locations of the small sphere, i.e. in Figure 3, Figure 4 and Figure 5 the center of the small sphere is at Z=0.8RL (see small orange circle), Z=0.5RL (see small purple circle) and Z = 0 (see small dark red circle at the center of the large circle), respectively. In Figures 35 the electric field energy densities are calculated along the five horizontal (dashed red, green, blue, grey, black) lines.

    Figure 3.  Density of the electric field energy around two surface-charged spheres.

    Z=0.8RL.

    Dotted red line: yP=0.75R1; green line: yP=R1; blue line: yP=0.7RL; grey line: yP=RL; black line: yP=1.01RL. The concentration of the monovalent positive (or negative) ion in the electrolyte is: A) C=0.1mol/m3; B) C=0.001mol/m3; C) C=0.00001mol/m3.

    The connection point between the large sphere and the small sphere (represented by orange circle in Figure 2) is at xP=RL and yP=0. At this point there is no electrolyte and Eqs 47 are not applicable. Similar situations take place when the horizontal line crosses the circles in Figure 2. The xp coordinates of these cross sections, xcp can be calculated by:

    xcp=±R2Ly2p(crossing the large circle, i.e.yP<RL)

    and

    xcp=Z±R21y2p(crossing the small circle located at RL<x1<0 and yP<R1)

    xcp=Z±R21y2p(crossing the small circle located at 0<x1<RL and yP<R1)

    where x1 is the x coordinate of the center of the small sphere.

    Figure 4.  Density of the electric field energy around two surface-charged spheres.

    Z=0.5RL

    Dotted red line: yP=0.75R1; green line: yP=R1; blue line: yP=0.7RL; grey line: yP=RL; black line: yP=1.01RL. The concentration of the monovalent positive (or negative) ion in the electrolyte is: A) C=0.1mol/m3; B) C=0.001mol/m3; C) C=0.00001mol/m3.

    Figure 5.  Density of the electric field energy around two surface-charged spheres.

    Z=0

    Dotted red line: yP=0.75R1; green line: yP=R1; blue line: yP=0.7RL; grey line: yP=RL; black line: yP=1.01RL. The concentration of the monovalent positive (or negative) ion in the electrolyte is: A) C=0.1mol/m3; B) C=0.001mol/m3; C) C=0.00001mol/m3.

    Note in Figures 35 the sharp maxima of the density of the electric field energy appear where the horizontal line at the respective yP crosses the charged sphere(s). These crossing points, xcp's, can be calculated by Eqs 810. In the case of yP=0.75R1 the values of the crossing points are listed at the first column of Table 1.

    It is also important to note that |E¯1|~Q1 and |E¯L|~QL (see Eqs 46) and thus in the case of total surface charges a·Q1 and a·QL (where 0<a<1 is a constant) the electric field energy density will be a2 times of the above calculated uF(xP,yP) values (see Eq 7).

    In this work the solution of the screened Poisson equation ([7] and Eq A5 in Appendix 1) is used to calculate the field energy density around two surface-charged spheres where the small sphere is located inside the large sphere. This solution is not restricted to small potentials (<< 25 mV) like in the case of the Debye-Hückel approximation of the Poisson-Boltzmann equation [9] where the superposition principle is not applicable either. This is an important advantage because the measured absolute value of the Zeta potentials of the cells are usually higher than 25 mV (e.g. –57.89 ± 22.63 mV on ARO cells, –40.41 ± 5.10 mV on C32TG cells, −46.99 ± 18.71 mV on RT4 cells, –40.13 ± 9.28 mV on TK cells, and −43.03 ± 5.52 mV on UM-UC-14 cells [14].

    The considered two spheres (with homogeneously charged surfaces) electrically interact. If the lateral movement of the charges on the spheres would not be restricted the interaction of the smaller sphere (located inside the larger sphere) with the larger sphere would result in inhomogeneous distribution of the surface charges on both spheres. However, the free lateral diffusion of proteins and lipids are usually restricted in biological membranes not only by direct collisions with structures where immobile proteins are crowded, but also by electrostatic deflection, hydrophobic mismatches, and other mechanisms [15].

    The density of the electric field energy depends on the electric field strength (Eq 3), i.e. the gradient of the electric potential (Eqs 4,6). In the case of a single surface-charged sphere surrounded by electrolyte with low ion concentration the potential inside the sphere is close to constant (see red curve in Figure 3A in ref. [7]) and thus the absolute value of the electric field strength is close to zero. On the other hand, outside the sphere the absolute value of the potential and also the electric field strength decrease with increasing distance from the surface of the sphere (see red curve in Figure 3A in ref. [7]). At higher electrolyte ion concentration, because of the increased screening effect, the absolute value of the potential and also the electric field strength decrease faster with increasing distance from the surface of the sphere. In this case inside the sphere toward its center the absolute value of the potential and the electric field strength also decrease (see curves in Figure 3A,B in ref. [7]).

    In this work two surface-charged spheres (with the same surface charge density) are considered where the smaller sphere is located inside the larger sphere. The above mentioned electric properties of a single surface-charged sphere remain the same for the smaller sphere (located inside a larger sphere) if the surfaces of the spheres are far enough from each other (farther than 4 λD), i.e. the absolute value of the potential decreases close to zero between the surfaces of the two spheres. However, when part of the surfaces of the two spheres are close enough to each other one sphere contributes to the potential and electric field strength around the other sphere. The electric field energy density is particularly high at the place where the surfaces of the two spheres touch each other. This maximal electric field energy density is very close to the outer surface of the larger sphere. Thus one can detect at the outer surface of the erythrocyte when the nucleus is getting close.

    The electric field energy density has maximum when the horizontal line crosses the circles in Figure 2. The xp coordinates of these cross sections, xcp can be calculated by Eqs 810. When the x axis of the coordinate system is the horizontal line (i.e. yP=0 ) the electric field energy density is particularly high at the place where the surfaces of the two spheres touch each other (see orange circles in Figure 2) at close to zero electrolyte ion concentration (i.e. C=0.00001mol/m3):

    uF(xP=RL+,yP=0;x1=Z=0.8RL=RLR1)=ϵrϵ02(·[E1x+ELx]2+[E1y+ELy]2)=ϵrϵ02([(dV1dD1)D1=R1+xpx1R1++(dVLdDL)DL=RL+xpRL+]2+[0+0]2)ϵrϵ02([keQ1ϵrR21+keQLϵrR2L+]2)=2ρ2sϵrϵ0=1.4798·108Jm3

    This maximal electric field energy density is very close to the outer surface of the larger sphere (|xP|=RL+RL) on the x axis of the coordinate system. The x axis also crosses the small and large spheres at xp=3R1 and xp=RL, respectively. Very close to these coss sections, at the outer side of the spheres, the field energy density is only a quarter of the above maximal value.

    In general the first maximum of uF (see the left maximum in Figures 35) is getting smaller when the center of the small sphere approaches the center of the large sphere. This is the case because the interaction between the spheres is reducing when the average distance between the surfaces of the two spheres is increasing.

    In the case of horizontal lines where yP>0E1y+ELy contributes also to uF. This contribution is particularly high by E1y when xpZ or by ELy when xp0 relative to the contribution by E1x and ELx, respectively.

    When the location of the center of the small and large sphere is identical (i.e. Z=0) then because of the additional symmetry uF(xP,yP)=uF(xp,yP) at any value of yp (see Figure 5).

    When yp>R1the horizontal line crosses only the surface of the large sphere at two points and these are symmetric crossing points (where the y axis is the symmetry axis). The distance of the left crossing point from the y axis is similar to the distance of the right crossing point from the y axis (see Eq 8). Because of this symmetry if the small sphere only slightly affect the field strength along the horizontal line then uF(xP,yP)uF(xp,yP) at any location of the small sphere along the x axis (see blue, black and grey lines in Figures 35).

    In the case of 0<yp<R1 the horizontal line crosses twice the large and twice the small sphere. In the case of the dotted red lines in Figures 35 yp=0.75R1 and each curve has four maxima. The hight of each maxima depends on the square of the field strength at the respective crossing point (see Eq 3), which is related to the x and y components of the field strengths created by the small sphere (E1x, E1y) and by the large sphere (ELx, ELy) (see Eq 7). In order to find out the reason of the hight of each maximum of the curves shown in Figures 3B5B (i.e. at C=0.001mol/m3) in Table 1 these x and y components of the field strengths are listed.

    Table 1.  Values of the x and y components of the electric field strength at the cross sections between a horizontal line (at yp=0.75·R1) and two spheres of radii R1 and RL.
    Cross # xP [m] uF [J] E1x [V/m] ELx [V/m] E1y [V/m] ELy [V/m]
    Z = 0.8 RL
    1 −9.9·10−7 7.4·107 1.8·108 2.47·108 −1.42·108 −3.74·107
    2 −9.4·10−7 2.2·107 2.29·108 −1.16·108 −2.45·108 1.86·107
    3 −6.6·10−7 4.65·107 −2.29·108 −5.45·107 −2.45·108 1.24·107
    4 9.9·10−7 2.15·107 −9.7·104 −2.47·108 −8.2·103 −3.74·107
    Z = 0.5 RL
    1 −9.9·10−7 2.73·107 3·107 2.47·108 −9.22·106 −3.74·107
    2 −6.4·10−7 2.97·107 2.29·108 −5.16·107 −2.45·108 1.2·107
    3 −3.6·10−7 4.1·107 −2.29·108 −2.2·107 −2.45·108 9.16·106
    4 9.9·10−7 2.15·107 −3.2·105 −2.47·108 −3.22·104 −3.74·107
    Z = 0.0 RL
    1 −9.9·10−7 2.2·107 2.6·106 2.47·108 −3.97·105 −3.74·107
    2 −1.4·10−7 3.63·107 2.29·108 −7.6·106 −2.45·108 8.17·106
    3 1.4·10−7 3.63·107 −2.29·108 7.6·106 −2.45·108 8.17·106
    4 9.9·10−7 2.2·107 −2.6·106 −2.47·108 −3.97·105 −3.74·107

     | Show Table
    DownLoad: CSV

    For example in the case of Z=0.5RL (thus x1=Z=5·107m) the reason that the mimum at cross section 3 is higher than at cross section 2 is that at cross section 3 both E1x(3) and ELx(3) are negative while at cross section 2 E1x(2) is positive and ELx(2) is negative. Because of this at cross section 3 [E1x+ELx]2 much larger than at cross section 2. Actually because of the symmetry |E1x(3)|=|E1x(2)| but signE1x(3)signE1x(2) because sign(xp(3)x1D1)sign(xp(2)x1D1) (see Eq 4).

    As an other example in the case of Z=0.0RL the maximum at cross section 3 is higher than at cross section 4. The reason is that |ELy(4)|·|ELx(4)| while |E1y(3)||E1x(3)||ELx(4)|. Note that |ELy(4)|·|ELx(4)|because the direction of E¯L(4) is close to the direction of the x axis.

    Finally, the analytical equation, Eq 7, for the calculation of the electric field energy density of two surface-charged spheres (the smaller sphere located inside the larger sphere), can be generalized for the case when N small surface-charged spheres are located inside the large sphere (see Appendix 2). Also when the radius of the smaller sphere approaches zero the total surface charge of the smaller sphere, Q1 approaches zero too and consequently the electric field strength of the smaller sphere, E1 approaches zero. Thus, based on Eq 7 one can calculate the field energy density around a single charged sphere by:

    uF(xP,yP)=ϵrϵ02E¯·E¯=ϵrϵ02(E¯L)·(E¯L)=ϵrϵ02(ELx,ELy)·(ELx,ELy)=ϵrϵ02([ELx]2+[ELy]2)

    Based on the generalized version of Newton's Shell Theorem [7] the electric field energy density, uF around two surface-charged spheres surrounded by electrolyte where the smaller sphere is inside the larger one is analytically calculated. According to the calculations when the surfaces of the spheres are farther from each other than four times of the Debye length the field energy density around and inside the smaller sphere is basically independent from the presence of the larger sphere. The electric field energy density is maximal when the smaller sphere touches the inner surface of the larger sphere and the maximum of uF is located at the touching point on the outer surface of the larger sphere.



    [1] B. Wang, S. Zhang, J. Dong, Y. Li, Y. Jin, H. Xiao, et al., Ambient temperature structures the gut microbiota of zebrafish to impact the response to radioactive pollution, Environ. Pollut., 293 (2022), 118539. https://doi.org/10.1016/j.envpol.2021.118539 doi: 10.1016/j.envpol.2021.118539
    [2] K. Liu, J. Zhou, D. Dong, Improving stock price prediction using the long short-term memory model combined with online social networks, J. Behav. Exp. Finance, 30 (2021). https://doi.org/10.1016/j.jbef.2021.100507 doi: 10.1016/j.jbef.2021.100507
    [3] Y. Lv, J. Piao, B. Li, M. Yang, Does online investor sentiment impact stock returns? Evidence from the Chinese stock market, Appl. Econ. Lett., 29 (2022), 1434–1438. https://doi.org/10.1080/13504851.2021.1937490 doi: 10.1080/13504851.2021.1937490
    [4] G. Huberman, T. Regev, Contagious speculation and a cure for cancer: A nonevent that made stock prices soar, J. Finance, 56 (2001), 387–396. https://doi.org/10.1111/0022-1082.00330 doi: 10.1111/0022-1082.00330
    [5] U. Bhattacharya, N. Galpin, R. Ray, X. Yu, The role of the media in the internet IPO bubble, J. Finance Quant. Anal., 44 (2009), 657–682. https://doi.org/10.1017/S0022109009990056 doi: 10.1017/S0022109009990056
    [6] H. J. V. Heerde, E. Gijsbrechts, K. Pauwels, Fanning the flames? how media coverage of a price war affects retailers, consumers, and investors, J. Mark. Res., 52 (2015), 674–693. https://doi.org/10.1509/jmr.13.0260 doi: 10.1509/jmr.13.0260
    [7] L. Fang, J. Peress, Media coverage and the cross-section of stock returns, J. Finance, 64 (2009), 2023–2052. https://doi.org/10.1111/j.1540-6261.2009.01493.x doi: 10.1111/j.1540-6261.2009.01493.x
    [8] P. C. Tetlock, Giving content to investor sentiment: The role of media in the stock market, J. Finance, 62 (2007), 1139–1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x doi: 10.1111/j.1540-6261.2007.01232.x
    [9] J. Engelberg, Costly information processing: evidence from earnings announcements, AFA 2009 San Francisco Meetings Paper, 2008. http://dx.doi.org/10.2139/ssrn.1107998.
    [10] H. Du, J. Hao, F. He, W. Xi, Media sentiment and cross-sectional stock returns in the Chinese stock market, Res. Int. Bus. Finance, 60 (2022), 101590, https://doi.org/10.1016/j.ribaf.2021.101590 doi: 10.1016/j.ribaf.2021.101590
    [11] M. W. Uhl, The Long-run impact of sentiment on stock returns, Working Paper, 2011.
    [12] M. T. Suleman, Stock market reaction to good and bad political news, Asian J. Finance Account., 4 (2012), 299–312. https://doi.org/10.5296/ajfa.v4i1.1705 doi: 10.5296/ajfa.v4i1.1705
    [13] G. W. Brown, M. T. Cliff., Investor sentiment and the near-term stock market, J. Empir. Finance, 11 (2004), 1–27. https://doi.org/10.1016/j.jempfin.2002.12.001 doi: 10.1016/j.jempfin.2002.12.001
    [14] J. Wurgler, M. Baker, Investor sentiment and the cross-section of stock returns, J. Finance, 61 (2006), 1645–1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x doi: 10.1111/j.1540-6261.2006.00885.x
    [15] F. M. Statman, Investor sentiment and stock returns, Finance Anal. J., 56 (2000), 16–23. https://doi.org/10.2469/faj.v56.n2.2340 doi: 10.2469/faj.v56.n2.2340
    [16] Z. Li, S. Wang, M. Hu, International investor sentiment and stock returns: Evidence from China, Invest. Anal. J., 50 (2021), 60–76. https://doi.org/10.1080/10293523.2021.1876968 doi: 10.1080/10293523.2021.1876968
    [17] Y. Kim, K. Y. Lee, Impact of investor sentiment on stock returns, Asia-Pac. J. Finance Stud., 51 (2022), 132–162. https://doi.org/10.1111/ajfs.12362 doi: 10.1111/ajfs.12362
    [18] T. Renault, Intraday online investor sentiment and return patterns in the US stock market, J. Bank Finance, 84 (2017), 25–40. https://doi.org/10.1016/j.jbankfin.2017.07.002 doi: 10.1016/j.jbankfin.2017.07.002
    [19] E. Bartov, L. Faurel, P. S. Mohanram, Can twitter help predict firm-level earnings and stock returns?, Account. Rev., 93 (2018), 25–57. https://doi.org/10.2308/accr-51865 doi: 10.2308/accr-51865
    [20] Y. Shynkevich, T. M. Mcginnity, S. Coleman, A. Belatreche, Stock price prediction based on stock-specific and sub-industry-specific news articles, in 2015 International Joint Conference on Neural Networks (IJCNN), (2015), 1–8. https://doi.org/10.1109/IJCNN.2015.7280517
    [21] H. Yun, G. Sim, J. Seok, Stock prices prediction using the title of newspaper articles with Korean natural language processing, in 2019 International Conference on Artificial Intelligence in Information and Communication (ICAⅡC), (2019). https://doi.org/10.1109/ICAⅡC.2019.8668996
    [22] M. Zhang, J. Yang, M. Wan, X. Zhang, J. Zhou., Predicting long-term stock movements with fused textual features of Chinese research reports, Expert Syst. Appl., 210 (2022), 118312. https://doi.org/10.1016/j.eswa.2022.118312 doi: 10.1016/j.eswa.2022.118312
    [23] N. Barberis, M. Huang, Stock as lotteries: The implications of probability weighting for security prices, Am. Econ. Rev., 98 (2008), 2066–2100. https://doi.org/10.1257/aer.98.5.2066 doi: 10.1257/aer.98.5.2066
    [24] B. Boyer, T. Mitton, K. Vorkink, Expected idiosyncratic skewness, Rev. Finance Stud., 23 (2010), 169–202. https://doi.org/10.1093/rfs/hhp041 doi: 10.1093/rfs/hhp041
    [25] T. G. Bali, N. Cakici, R. F. Whitelaw, Maxing out: Stock as lotteries and the cross-section of expected returns, J. Finance Econ., 99 (2011), 427–446. https://doi.org/10.1016/j.jfineco.2010.08.014 doi: 10.1016/j.jfineco.2010.08.014
    [26] J. Conrad, R. F. Dittmar, E. Ghysels, Ex ante skewness and expected stock returns, J. Finance, 68 (2013), 85–124. https://doi.org/10.1111/j.1540-6261.2012.01795.x doi: 10.1111/j.1540-6261.2012.01795.x
    [27] N. Barberis, A. Mukherjee, B. Wang, Prospect theory and stock returns: An empirical test, Rev. Finance Stud., 29 (2016), 3068–3107. https://doi.org/10.1093/rfs/hhw049 doi: 10.1093/rfs/hhw049
    [28] J. Wang, C. Wu, X. Zhong, Prospect theory and stock returns: Evidence from foreign share markets, Pac.-Basin Finance J., 69 (2021), 101644. https://doi.org/10.1016/j.pacfin.2021.101644 doi: 10.1016/j.pacfin.2021.101644
    [29] A. J. N. Junior, M. C. Klotzle, L. E. T. Brandão, A. C. F. Pinto, Prospect theory and narrow framing bias: Evidence from emerging markets, Q. Rev. Econ. Finance, 80 (2021), 90–101. https://doi.org/10.1016/j.qref.2021.01.016 doi: 10.1016/j.qref.2021.01.016
    [30] X. Yang, D. Gu, J. Wu, C. Liang, Y. Ma, J. Li, Factors influencing health anxiety: The stimulus–organism-response model perspective, Internet Res., 31 (2021), 2033–2054. https://doi.org/10.1108/INTR-10-2020-0604 doi: 10.1108/INTR-10-2020-0604
    [31] Z. Tang, M. Warkentin, L. Wu, Understanding employees' energy saving behavior from the perspective of stimulus-organism-responses, Resour. Conserv. Recycl., 140 (2019), 216–223. https://doi.org/10.1016/j.resconrec.2018.09.030 doi: 10.1016/j.resconrec.2018.09.030
    [32] B. J. Bushee, J. E. Core, W. Guay, S. Hamm, The role of the business press as an information intermediary, J. Account. Res., 48 (2010), 1–19. https://doi.org/10.1111/j.1475-679X.2009.00357.x doi: 10.1111/j.1475-679X.2009.00357.x
    [33] Z. Da, J. Engelberg, P. J. Gao, In search of attention, J. Finance, 66 (2011), 1461–1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x doi: 10.1111/j.1540-6261.2011.01679.x
    [34] F. Comiran, T. Fedyk, J. Ha, Accounting quality and media attention around seasoned equity offerings, Int. J. Account. Inf. Manage., 26 (2017), 443–462. https://doi.org/10.1108/IJAIM-02-2017-0029 doi: 10.1108/IJAIM-02-2017-0029
    [35] W. S. Chan., Stock price reaction to news and no-news: drift and reversal after headlines, J. Finance Econ., 70 (2003), 223–260. https://doi.org/10.1016/S0304-405X(03)00146-6 doi: 10.1016/S0304-405X(03)00146-6
    [36] P. C. Tetlock, M. Saar-Tsechansky, S. Macskassy, More than words: Quantifying language to measure firms' fundamentals, J. Finance, 63 (2008), 1437–1467. https://doi.org/10.1111/j.1540-6261.2008.01362.x doi: 10.1111/j.1540-6261.2008.01362.x
    [37] P. Jiao, A. Veiga, A. Walther, Social media, news media and the stock market, J. Econ. Behav. Organ., 176 (2020), 63–90. https://doi.org/10.1016/j.jebo.2020.03.002 doi: 10.1016/j.jebo.2020.03.002
    [38] T. Huang, X. Zhang, Industry-level media tone and the cross-section of stock returns, Int. Rev. Econ. Finance, 77 (2021), 59–77. https://doi.org/10.1016/j.iref.2021.09.002 doi: 10.1016/j.iref.2021.09.002
    [39] Y. He, L. Qu, R. Wei, X. Zhao, Media-based investor sentiment and stock returns: A textual analysis based on newspapers, Appl. Econ., 54 (2022), 774–792. https://doi.org/10.1080/00036846.2021.1966369 doi: 10.1080/00036846.2021.1966369
    [40] W. Wang, C. Su, D. Duxbury, The conditional impact of investor sentiment in global stock markets: A two-channel examination, J. Bank Finance, 138 (2022), 106458. https://doi.org/10.1016/j.jbankfin.2022.106458 doi: 10.1016/j.jbankfin.2022.106458
    [41] R. B. Cohen, C. Polk, T. Vuolteenaho, The price is (almost) right, J. Finance, 64 (2009), 2739–2782. https://doi.org/10.1111/j.1540-6261.2009.01516.x doi: 10.1111/j.1540-6261.2009.01516.x
    [42] Z. Da, J. Engelberg, P. Gao, The sum of all FEARS investor sentiment and asset prices, Rev. Finance Stud., 28 (2015), 1–32. https://doi.org/10.1093/rfs/hhu072 doi: 10.1093/rfs/hhu072
    [43] H. Yang, D. Ryu, D. Ryu, Investor sentiment, asset returns and firm characteristics: Evidence from the Korean stock market, Invest. Anal. J., 46 (2017), 1–16. https://doi.org/10.1080/10293523.2016.1277850 doi: 10.1080/10293523.2016.1277850
    [44] J. Li, Y. Zhang, L. Wang, Information transmission between large shareholders and stock volatility, N. Am. Econ. Finance, 58 (2021), 101551. https://doi.org/10.1016/j.najef.2021.101551 doi: 10.1016/j.najef.2021.101551
    [45] M. Ammann, R. Frey, M. Verhofen, Do newspaper articles predict aggregate stock returns?, J. Behav. Finance, 15 (2014), 195–213. https://doi.org/10.1080/15427560.2014.941061 doi: 10.1080/15427560.2014.941061
    [46] F. Wong, Z. Liu, M. Chiang, Stock market prediction from WSJ: text mining via sparse matrix factorization, in Proceedings of the 2014 IEEE International Conference on Data, (2014), 430–439. https://doi.org/10.48550/arXiv.1406.7330
    [47] N. C. Barberis, Thirty years of prospect theory in economics: A review and assessment, J. Econ. Perspect., 27 (2013), 173–195. https://doi.org/10.1257/jep.27.1.173 doi: 10.1257/jep.27.1.173
    [48] D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk, Econometrica, 47 (1979), 263–291. http://www.jstor.org/stable/1914185
    [49] H. Chen, P. De, Y. Hu, B. H. Hwang, Wisdom of crowds: The value of stock opinions transmitted through social media, Rev. Finance Stud., 27 (2013), 1367–1403. https://doi.org/10.1093/rfs/hhu001 doi: 10.1093/rfs/hhu001
  • This article has been cited by:

    1. Abdelali Hannoufa, Craig Matthews, Biruk A. Feyissa, Margaret Y. Gruber, Muhammad Arshad, 2018, Chapter 25, 978-3-030-36326-0, 41, 10.1007/124_2018_25
    2. Sagar Prasad Nayak, Priti Prasad, Vinayak Singh, Abhinandan Mani Tripathi, Sumit Kumar Bag, Chandra Sekhar Mohanty, Role of miRNAs in the regulation of proanthocyanidin biosynthesis in the legume Psophocarpus tetragonolobus (L.) DC., 2023, 0167-6903, 10.1007/s10725-023-00971-9
    3. Habibullah Khan Achakzai, Muhammad Younas Khan Barozai, Muhammad Din, Iftekhar Ahmed Baloch, Abdul Kabir Khan Achakzai, Allah Bakhsh, Identification and annotation of newly conserved microRNAs and their targets in wheat (Triticum aestivum L.), 2018, 13, 1932-6203, e0200033, 10.1371/journal.pone.0200033
    4. Sevgi Marakli, Identification and functional analyses of new sesame miRNAs (Sesamum indicum L.) and their targets, 2018, 45, 0301-4851, 2145, 10.1007/s11033-018-4373-7
    5. Mohandas Snigdha, Duraisamy Prasath, Transcriptomic analysis to reveal the differentially expressed miRNA targets and their miRNAs in response to Ralstonia solanacearum in ginger species, 2021, 21, 1471-2229, 10.1186/s12870-021-03108-0
    6. Lan Li, Guangling Chen, Mingzhu Yuan, Shirong Guo, Yu Wang, Jin Sun, CsbZIP2-miR9748-CsNPF4.4 Module Mediates High Temperature Tolerance of Cucumber Through Jasmonic Acid Pathway, 2022, 13, 1664-462X, 10.3389/fpls.2022.883876
    7. Thiago F. Martins, Pedro F. N. Souza, Murilo S. Alves, Fredy Davi A. Silva, Mariana R. Arantes, Ilka M. Vasconcelos, Jose T. A. Oliveira, Identification, characterization, and expression analysis of cowpea (Vigna unguiculata [L.] Walp.) miRNAs in response to cowpea severe mosaic virus (CPSMV) challenge, 2020, 39, 0721-7714, 1061, 10.1007/s00299-020-02548-6
    8. Muhammad Younas Khan Barozai, Zhujia Ye, Sasikiran Reddy Sangireddy, Suping Zhou, Bioinformatics profiling and expressional studies of microRNAs in root, stem and leaf of the bioenergy plant switchgrass (Panicum virgatum L.) under drought stress, 2018, 8, 23522151, 1, 10.1016/j.aggene.2018.02.001
    9. Yusuf Ceylan, Yasemin Celik Altunoglu, Erdoğan Horuz, HSF and Hsp Gene Families in sunflower: a comprehensive genome-wide determination survey and expression patterns under abiotic stress conditions, 2023, 0033-183X, 10.1007/s00709-023-01862-6
    10. Abdul Baqi, Wajid Rehman, Iram Bibi, Farid Menaa, Yousaf Khan, Doha A. Albalawi, Abdul Sattar, Identification and Validation of Functional miRNAs and Their Main Targets in Sorghum bicolor, 2023, 1073-6085, 10.1007/s12033-023-00988-5
    11. Caoli Zhu, Yicheng Yan, Yaning Feng, Jiawei Sun, Mingdao Mu, Zhiyuan Yang, Genome-Wide Analysis Reveals Key Genes and MicroRNAs Related to Pathogenic Mechanism in Wuchereria bancrofti, 2024, 13, 2076-0817, 1088, 10.3390/pathogens13121088
    12. Kishan Saha, Onyinye C. Ihearahu, Vanessa E. J. Agbor, Teon Evans, Labode Hospice Stevenson Naitchede, Supriyo Ray, George Ude, In Silico Genome-Wide Profiling of Conserved miRNAs in AAA, AAB, and ABB Groups of Musa spp.: Unveiling MicroRNA-Mediated Drought Response, 2025, 26, 1422-0067, 6385, 10.3390/ijms26136385
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2749) PDF downloads(255) Cited by(5)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog