This paper examines if investors exhibited evidence of the availability heuristic in their investment decisions when significant price changes occurred in the British stock market during the 2010–2018 period. We raise the hypothesis that if a significant stock price move takes place on a day when the stock market index also undergoes a significant change (either positive or negative), then the magnitude of that shock may be increased by the availability of positive investment or negative outcomes. We applied three different proxies for large stock price changes which yielded a robust sample of events for this study. We found no significant evidence of the availability heuristic. In addition, we also found no significant evidence of price overreaction for both price decreases and increases. Inversely, we found robust results that suggest randomness in the behavior of stock prices in this period, thus supporting the efficiency of financial markets and opposing the results from similar studies carried out in the United States.
Citation: Diogo Matos, Luís Pacheco, Júlio Lobão. Availability heuristic and reversals following large stock price changes: evidence from the FTSE 100[J]. Quantitative Finance and Economics, 2022, 6(1): 54-82. doi: 10.3934/QFE.2022003
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This paper examines if investors exhibited evidence of the availability heuristic in their investment decisions when significant price changes occurred in the British stock market during the 2010–2018 period. We raise the hypothesis that if a significant stock price move takes place on a day when the stock market index also undergoes a significant change (either positive or negative), then the magnitude of that shock may be increased by the availability of positive investment or negative outcomes. We applied three different proxies for large stock price changes which yielded a robust sample of events for this study. We found no significant evidence of the availability heuristic. In addition, we also found no significant evidence of price overreaction for both price decreases and increases. Inversely, we found robust results that suggest randomness in the behavior of stock prices in this period, thus supporting the efficiency of financial markets and opposing the results from similar studies carried out in the United States.
Throughout this work, Cp×n denotes the set involving p×n matrices with complex entries, and, for A∈Cp×n, rank(A) is its rank, A∗ is its conjugate-transpose matrix, N(A) is its null space, and R(A) is its range. The index ind(A) of A∈Cp×p is the smallest nonnegative integer k for which the equality rank(Ak)=rank(Ak+1) is satisfied. The symbol I denotes the identity matrix of adequate size. Standard notations PS and PS,T denote, respectively, the orthogonal projector onto a subspace S and a projector onto S along T when Cp is equal to the direct sum of the subspaces S and T.
Several definitions and properties of generalized inverses which are upgraded in this research are given. The Moore-Penrose inverse of A∈Cp×n is uniquely determined A†=X∈Cn×p as the solution to well-known Penrose equations [1]:
A=AXA,X=XAX,AX=(AX)∗,XA=(XA)∗. |
If X satisfies only equation XAX=X, it is an outer inverse of A. The outer inverse of A which is uniquely determined by the null space S and the range T is labeled with A(2)T,S=X∈Cn×p and satisfies
XAX=X,N(X)=S,R(X)=T, |
where s≤r=rank(A) is the dimension of the subspace T⊆Cn, and p−s is the dimension of the subspace S⊆Cp.
The following notation will be used:
Cp,n;k:={(A,W):A∈Cp×n, W∈Cn×p∖{0} and k=max{ind(AW),ind(WA)}}. |
The notion of the Drazin inverse was extended to rectangular matrices in [2]. For selected (A,W)∈Cp,n;k, the W-weighted Drazin inverse AD,W=X∈Cp×n of A is uniquely determined by the matrix equations
XWAWX=X,AWX=XWA,(AW)k+1XW=(AW)k. |
Especially, if p=n and W=I, AD,I:=AD reduces to the Drazin inverse of A. Further, for ind(A)=1, AD:=A# becomes the group inverse of A. Recall that [2]
AD,W=A[(WA)D]2=[(AW)D]2A. |
The notion of the core-EP inverse, proposed in [3] for a square matrix, was generalized to a rectangular matrix in [4]. If (A,W)∈Cp,n;k, the W-weighted core-EP inverse of A is the unique solution =X∈Cp×n to
WAWX=PR(WA)k,R((AW)k)=R(X). |
In a special case p=n and W=I, becomes the core-EP inverse
of A. According to original definitions in [5] and [6,7,8], it is important to note
![]() |
and
![]() |
Some useful characterizations and representations of the core-EP inverse are presented in [3,9,10,11,12,13,14]. In the case ind(A)=1, reduces to the core inverse
=A#AA† of A [15].
The weak group inverse (WGI) was presented for a square matrix in [16] as an extension of the group inverse. The WGI is extended in [17] to a rectangular matrix and in [18] to Hilbert space operators. For (A,W)∈Cp,n;k, the W-weighted WGI (W-WGI) of A is the unique solution Aⓦ,W=X∈Cp×n of the system [17,18]
![]() |
and it is expressed by [17,18]
![]() |
When p=n and W=I, Aⓦ,I:=Aⓦ reduces to the WGI of A
![]() |
Remark that, for 1=ind(A), Aⓦ=A#. Useful results about WGI were given in [17,18,19,20,21,22,23].
The concept of the m-weak group inverse (m-WGI) was introduced in [24] as an extension of the WGI. Exactly, if m∈N, the m-WGI of A∈Cn×n is the unique matrix Aⓦm=X∈Cn×n such that [25]
![]() |
(1.1) |
Recall that
![]() |
Clearly, Aⓦ1=Aⓦ, and particularly Aⓦ2=()3A2 becomes the generalized group (GG) inverse of A, established in [26]. It is interesting that, if ind(A)≤m, Aⓦm=AD. Various properties of m-WGI were presented in [24,25,27,28].
Recent research about m-WGI as well as the fact that m-WGI is an important extension of the WGI, GG, Drazin inverse, and group inverse motivated us to further investigate this topic. The current popular trend in the research of generalized inverses consists in defining new generalized inverses that are based on suitable combinations of existing generalized inverses as well as in their application in solving appropriate systems of linear equations. Considering the system (1.1) for defining m-WGI, our first aim is to solve a system of matrix equations which is an extension of the system (1.1) from the square matrix case to an arbitrary case. Since the m-WGI is restricted to square matrices, our main goal is to extend this notion to W-m-WGI inverses on rectangular matrices. To solve a certain system of matrix equations on rectangular complex matrices, we extend the notions of m-WGI, W-WGI, and the W-weighted Drazin inverse by introducing a wider class of generalized inverses, termed as the W-weighted m-WGI (W-m-WGI) for a rectangular matrix. Particularly, an extension of the GG inverse on rectangular matrices is obtained. It is important to mention that we recover significant results for the W-weighted Drazin inverse in a particular case. A class of systems of linear equations is found that can be efficiently solved applying W-m-WGI. This results is an extension of known results about the W-weighted Drazin solution and the Drazin solution of exact linear systems.
The global structure of the work is based on sections with the following content. Several characterizations for the W-m-WGI are proved in Section 2 without and with projectors. We develop important expressions for the W-m-WGI based on core–EP, Drazin, and Moore-Penorse inverses of proper matrices. As a consequence, we introduce the weighted version of GG inverse and give its properties. Limit and integral formulae for computing the W-m-WGI are part of Section 3. Section 4 investigates applications of the W-m-WGI in solving specific matrix equations. Numerical experiments are presented in Section 5.
We introduce the W-weighted m-WGI on rectangular matrices as a class of generalized inverses that includes notions of the m-WGI and the W-weighted WGI.
Theorem 2.1. If (A,W)∈Cp,n;k and m∈N, then X=(W)m+1(AW)m−1A is the unique solution to the matrix system
![]() |
(2.1) |
Proof. Using the identity AWW
=
, the subsequent transformations are obtained for X:=(
W)m+1(AW)m−1A:
![]() |
which further leads to
![]() |
Hence, X=(W)m+1(AW)m−1A is a solution to (2.1).
An arbitrary solution X to the system (2.1) satisfies
![]() |
which leads to the conclusion that X=(W)m+1(AW)m−1A is the unique solution to (2.1).
Definition 2.1. Under such conditions (A,W)∈Cp,n;k and m∈N, the W-weighted m-WGI (shortly W-m-WGI) inverse of A is defined by the expression
![]() |
Several special appearance forms of the W-m-WGI show its importance and are listed as follows:
− when p=n and W=I, the I-m-WGI coincides with the m-WGI Aⓦm=()m+1Am;
− if m=1, then (W)2A=Aⓦ,W, that is, the W-1-WGI reduces to the W-WGI;
− for m=2, the W-2-WGI is introduced as Aⓦ2,W=(W)3AWA and presents an extension of the GG inverse;
− in the case k≤m, it follows Aⓦm,W=AD,W (see Lemma 2.1).
Some computationally useful representations of the W-m-WGI are developed in subsequent statements.
Lemma 2.1. If (A,W)∈Cp,n;k, m∈N and l≥k, then
![]() |
Furthermore, for m≥k, it follows that Aⓦm,W=AD,W.
Proof. First, by induction on m, notice that gives
![]() |
Further, based on
![]() |
and
(WA)k[(WA)k]†=PR((WA)k)=PR((WA)l)=(WA)l[(WA)l]†, |
we obtain
![]() |
In the case m≥k, it follows that
Aⓦm,W=A[(WA)D]m+2(WA)k[(WA)k]†(WA)m=A[(WA)D]m+2(WA)m=A[(WA)D]2=AD,W. |
Remark 2.1. Note that Aⓦm,W=(WA)ⓦm implies the interesting identity WAⓦm,W=PR((WA)l)(WA)ⓦm for l≥k. This last identity is an extension of the classical property of the W-weighted Drazin inverse WAD,W=(WA)D. About the dual property AD,WW=(AW)D, if the equality
A is satisfied (which is not true in general; for details see [5]), we can verify that Aⓦm,W=
(AW)ⓦmA and so Aⓦm,WW=
(AW)ⓦmAW.
Representations for the W-2-WGI and W-weighted Drazin inverse are obtained as consequences of Lemma 2.1 when m=2 or m=l≥k, respectively.
Corollary 2.1. If (A,W)∈Cp,n;k and l≥k, then
![]() |
and
![]() |
Notice that Corollary 2.1 recovers the known expressions for the W-weighted Drazin inverse [29,30].
In Lemma 2.2, we show that the W-m-WGI Aⓦm,W is an outer inverse of WAW and find its range and null spaces.
Lemma 2.2. If (A,W)∈Cp,n;k and m∈N, the following representations are valid:
(i) Aⓦm,W=(WAW)(2)R((AW)k),N([(WA)k]∗(WA)m);
(ii) WAWAⓦm,W=PR((WA)k),N([(WA)k]∗(WA)m);
(iii) Aⓦm,WWAW=PR((AW)k),N([(WA)k]∗(WA)m+1W).
Proof. (ⅰ) Based on Lemma 2.1 it follows that Aⓦm,W=A(WA)k[(WA)k+m+2]†(WA)m, which yields R(Aⓦm,W)⊆R((AW)k) and
Aⓦm,WWAWAⓦm,W=A(WA)k[(WA)k+m+2]†(WA)k+m+2[(WA)k+m+2]†(WA)m=A(WA)k[(WA)k+m+2]†(WA)m=Aⓦm,W. |
Another application of Lemma 2.1 yields
(AW)k=[(AW)D]m+2(AW)k+m+2=A[(WA)D]m+2(WA)k+m+1W=A[(WA)D]m+2(WA)k[(WA)k]†(WA)k+m+1W=Aⓦm,W(WA)k+1W | (2.2) |
and so R((AW)k)⊆R(Aⓦm,W). Thus, R(Aⓦm,W)=R((AW)k). Also,
N([(WA)k]∗(WA)m)=N([(WA)k+m+2]∗(WA)m)=N([(WA)k+m+2]†(WA)m)=N(A(WA)k[(WA)k+m+2]†(WA)m)=N(Aⓦm,W). |
(ⅱ) By the part (ⅰ), WAWAⓦm,W is a projector, and
N(WAWAⓦm,W)=N(Aⓦm,W)=N([(WA)k]∗(WA)m). |
The equalities WAWAⓦm,W=(WA)k+2[(WA)k+m+2]†(WA)m and
(WA)k=(WA)k+2[(WA)D]2=(WA)k+2[(WA)k+2]†(WA)k+2[(WA)D]2=(WA)k+2PR([(WA)k+2]∗)[(WA)D]2=(WA)k+2PR([(WA)m+k+2]∗)[(WA)D]2=(WA)k+2[(WA)m+k+2]†(WA)m+k+2[(WA)D]2=WAWAⓦm,W(WA)k+2[(WA)D]2=WAWAⓦm,W(WA)k |
imply R(WAWAⓦm,W)=R((WA)k).
(ⅲ) It is clear, by (ⅰ), that R(Aⓦm,WWAW)=R(Aⓦm,W)=R((AW)k). The identity N(Aⓦm,WWAW)=N([(WA)k]∗(WA)m+1W) is verified in a similar manner as in (ⅰ).
Remark 2.2. For A∈Cm×n, B∈Cq×t, C∈Cs×p, M∈Cp×m, and N∈Cn×q, by [31,32], the (M,N)-weighted (B,C)-inverse of A is represented by A(2,M,N)(B,C)=(MAN)(2)R(B),N(C). By Lemma 2.2, Aⓦm,W=(WAW)(2)R((AW)k),N([(WA)k]∗(WA)m), and thus Aⓦm,W is the (W,W)-weighted ((AW)k),[(WA)k]∗(WA)m)-inverse of A. Since the (B,C)-inverse of A is given as A(2)(B,C)=A(2)R(B),N(C) [33], it follows that Aⓦm,W=(WAW)(2)R((AW)k),N([(WA)k]∗(WA)m) is the ((AW)k,[(WA)k]∗(WA)m)-inverse of WAW.
Lemma 2.2 and the Urquhart formula [1] give the next representations for Aⓦm,W.
Corollary 2.2. If (A,W)∈Cp,n;k and m∈N, the W-m-WGI of A is represented as
Aⓦm,W=(AW)k([(WA)k]∗(WA)m+k+1W)†[(WA)k]∗(WA)m. |
If m=2 or m=k in Lemma 2.2 and Corollary 2.2, we obtain the next properties related to the W-2-WGI and W-weighted Drazin inverse.
Corollary 2.3. If (A,W)∈Cp,n;k, the following statements hold:
(i) Aⓦ2,W=(WAW)(2)R((AW)k),N([(WA)k]∗(WA)2)=(AW)k([(WA)k]∗(WA)k+3W)†[(WA)k]∗(WA)2;
(ii) WAWAⓦ2,W=PR((WA)k),N([(WA)k]∗(WA)2);
(iii) Aⓦ2,WWAW=PR((AW)k),N([(WA)k]∗(WA)3W);
(iv) AD,W=(WAW)(2)R((AW)k),N((WA)k)=(AW)k([(WA)k]∗(WA)2k+1W)†[(WA)k]∗(WA)k;
(v) WAWAD,W=PR((WA)k),N((WA)k);
(vi) AD,WWAW=PR((AW)k),N((AW)k).
Some necessary and sufficient conditions for a rectangular matrix to be the W-m-WGI are considered.
Theorem 2.2. If (A,W)∈Cp,n;k, X∈Cp×n, and m∈N, the subsequent statements are equivalent:
(i) X=Aⓦm,W;
(ii) AWX=(W)m(AW)m−1A and R(X)=R((AW)k);
(iii) AWX=(W)m(AW)m−1A and R(X)⊆R((AW)k);
(iv) AWXWX=X, X(WA)k+1W=(AW)k and [(WA)k]∗(WA)m+1WX=[(WA)k]∗(WA)m;
(v) XWAWX=X, R(X)=R((AW)k) and [(WA)k]∗(WA)m+1WX=[(WA)k]∗(WA)m;
(vi) XWAWX=X, AWX=(W)m(AW)m−1A and XWA=(
W)m+1(AW)mA;
(vii) XWAWX=X, WAWX=W(W)m(AW)m−1A and XWAW=(
W)m+1(AW)m+1;
(viii) XWAWX=X, AWXWA=(W)m(AW)mA, AWX=(
W)m(AW)m−1A and XWA=(
W)m+1(AW)mA;
(ix) XWAWX=X, WAWXWAW=W(W)m(AW)m+1, WAWX=W(
W)m(AW)m−1A and XWAW=(
W)m+1(AW)m+1;
(x) X=WAWX and AWX=(
W)m(AW)m−1A (or WAWX=W(
W)m(AW)m−1A);
(xi) X=AWWX and
WX=(
W)m+1(AW)m−1A;
(xii) X=AD,WWAWX and AWX=(W)m(AW)m−1A (or WAWX=W(
W)m(AW)m−1A);
(xiii) X=(W)m+1(AW)m+1X and AWX=(
W)m(AW)m−1A (or WAWX=W(
W)m(AW)m−1A);
(xiv) X=XW(W)m(AW)m−1A and XWA=(
W)m+1(AW)mA (or XWAW=(
W)m+1(AW)m+1).
Proof. (ⅰ) ⇒ (ⅱ): It follows from Theorem 2.1 and Lemma 2.2.
(ⅱ) ⇒ (ⅲ): This implication is obvious.
(ⅲ) ⇒ (ⅰ): Because R(X)⊆R((AW)k), we have
X=(AW)kU=(AW)k[(AW)k]†(AW)kU=(AW)k[(AW)k]†X, |
for some U∈Cp×n. Notice that, by AWX=(W)m(AW)m−1A,
![]() |
An application of Theorem 2.1 leads to the conclusion X=Aⓦm,W.
(ⅰ) ⇒ (ⅳ): For X=Aⓦm,W, Theorem 2.1 implies AWXWX=X. The equality (2.2) gives X(WA)k+1W=(AW)k. Using Lemma 2.1, we get X=A(WA)k[(WA)k+m+2]†(WA)m, which implies
[(WA)k]∗(WA)m+1WX=[(WA)k]∗(WA)m+1WA(WA)k[(WA)k+m+2]†(WA)m=[(WA)k]∗(WA)k+m+2[(WA)k+m+2]†(WA)m=[(WA)k]∗(WA)k[(WA)k]†(WA)m=[(WA)k]∗(WA)m. |
(ⅳ) ⇒ (ⅴ): Notice, by X(WA)k+1W=(AW)k and
X=AWXWX=(AW)2(XW)2X=⋯=(AW)r(XW)rX, | (2.3) |
for arbitrary r∈N, that R(X)=R((AW)k). Hence, X=(AW)kU, for some U∈Cp×n, and
XWAWX=XWAW(AW)kU=(X(WA)k+1W)U=(AW)kU=X. |
(ⅴ) ⇒ (ⅰ): The assumptions R(X)=R((AW)k) and XWAWX=X give (AW)k=XV=XWAW(XV)=XW(AW)k+1, for some V∈Cn×p. Since X=(AW)kU, for some U∈Cp×n, we get
AWXWX=AWXW(AW)kU=AW(XW(AW)k+1)(AW)DU=AW(AW)k(AW)DU=(AW)kU=X. |
The assumption [(WA)k]∗(WA)m+1WX=[(WA)k]∗(WA)m yields
(WA)k[(WA)k]†(WA)m+1WX=([(WA)k]†)∗[(WA)k]∗(WA)m+1WX=([(WA)k]†)∗[(WA)k]∗(WA)m=(WA)k[(WA)k]†(WA)m. |
Because (2.3) holds, we obtain
![]() |
Theorem 2.1 gives X=Aⓦm,W.
(ⅰ) ⇒ (ⅵ) ⇒ (ⅶ): These implications are clear.
(ⅶ) ⇒ (ⅰ): Using XWAWX=X, WAWX=W(W)m(AW)m−1A and XWAW=(
W)m+1(AW)m+1, we get
![]() |
(ⅵ) ⇔ (ⅷ) and (ⅶ) ⇔ (ⅸ): These equivalences are evident.
(ⅰ) ⇒ (ⅹ): From X=(W)m+1(AW)m−1A, we observe
![]() |
(ⅹ) ⇒ (ⅰ): Applying X=WAWX and AWX=(
W)m(AW)m−1A, we obtain X=
W(AWX)=
W(
W)m(AW)m−1A=(
W)m+1(AW)m−1A.
(v) ⇔ (xiv): This equivalence follows as (ⅰ) ⇔ (ⅶ).
We also characterize the W-m-WGI in the following two ways.
Theorem 2.3. If (A,W)∈Cp,n;k and m∈N, then
(i) Aⓦm,W is the unique solution to
WAWX=PR((WA)k),N([(WA)k]∗(WA)m)andR(X)⊆R((AW)k); | (2.4) |
(ii) Aⓦm,W is the unique solution to
XWAW=PR((AW)k),N([(WA)k]∗(WA)m+1W)andR(X∗)⊆R([(WA)m]∗(WA)k). | (2.5) |
Proof. (ⅰ) By Lemma 2.2, X=Aⓦm,W is a solution to (2.4). If the system (2.4) has two solutions X and X1, notice
WAW(X−X1)=PR((WA)k),N([(WA)k]∗(WA)m)−PR((WA)k),N([(WA)k]∗(WA)m)=0 |
and R(X−X1)⊆R((AW)k). Therefore,
R(X−X1)⊆N(WAW)∩R((AW)k)⊆N((AW)k)∩R((AW)k)={0}, |
i.e., X=X1 is the unique solution of the system of Eqs (2.4).
(ⅱ) Lemmas 2.1 and 2.2 imply validity of (2.5) for X=Aⓦm,W=A(WA)k[(WA)k+m+2]†(WA)m.
The assumption that two solutions X and X1 satisfy (2.5) leads to the conclusion
R(X∗−X∗1)⊆R([(WA)m]∗(WA)k)∩N((WAW)∗)⊆R((WAWAⓦm,W)∗)∩N((WAWAⓦm,W)∗)={0}, |
that is, X=X1.
Corresponding characterizations of the W-2-WGI and W-weighted Drazin inverse are derived as particular cases m=2 and m=k of Theorem 2.3, respectively.
Corollary 2.4. The following statements are valid for (A,W)∈Cp,n;k:
(i) Aⓦ2,W is the unique solution to
WAWX=PR((WA)k),N([(WA)k]∗(WA)2)andR(X)⊆R((AW)k); |
(ii) Aⓦ2,W is the unique solution to
XWAW=PR((AW)k),N([(WA)k]∗(WA)3W)andR(X∗)⊆R([(WA)2]∗(WA)k); |
(iii) AD,W is the unique solution to
WAWX=PR((WA)k),N((WA)k)andR(X)⊆R((AW)k); |
(iv) AD,W is the unique solution to
XWAW=PR((AW)k),N((AW)k)andR(X∗)⊆R([(WA)k]∗). |
Some formulae for the W-m-WGI are given in this section.
We present a relation between a nonsingular bordered matrix and the W-m-WGI. Precisely, by Theorem 3.1, when the inverse of a proper bordered matrix is known, then the corresponding position of that inverse gives the W-m-WGI.
Theorem 3.1. Let (A,W)∈Cp,n;k and m∈N. Assume that full-column rank matrices G and H∗ fulfill
N([(WA)k]∗(WA)m)=R(G)andR((AW)k)=N(H). |
Then,
N=[WAWGH0] |
is nonsingular, and
N−1=[Aⓦm,W(I−Aⓦm,WWAW)H†G†(I−WAWAⓦm,W)−G†(WAW−WAWAⓦm,WWAW)H†]. | (3.1) |
Proof. Lemma 2.2 gives Aⓦm,W=(WAW)(2)R((AW)k),N([(WA)k]∗(WA)m). Since
R(I−WAWAⓦm,W)=N(WAWAⓦm,W)=N(Aⓦm,W)=N([(WA)k]∗(WA)m)=R(G)=R(GG†)=N(I−GG†), |
we have (I−GG†)(I−WAWAⓦm,W)=0, that is, GG†(I−WAWAⓦm,W)=(I−WAWAⓦm,W). From R(Aⓦm,W)=R((AW)k)=N(H), we get HAⓦm,W=0. Set Y for the right hand side of (3.1). Then,
NY=[WAWAⓦm,W+GG†(I−WAWAⓦm,W)WAW(I−Aⓦm,WWAW)H†−GG†(I−WAWAⓦm,W)WAWH†HAⓦmH(I−AⓦmWAW)H†]=[WAWAⓦm,W+I−WAWAⓦm,W(I−WAWAⓦm,W)WAWH†−(I−WAWAⓦm,W)WAWH†0HH†]=[I00I]=I. |
and similarly YN=I. So, N is nonsingular with N−1=Y.
We investigate limit and integral expressions for W-m-WGI motivated by limit and integral formulae of known generalized inverses [34,35,36,37].
Theorem 3.2. If (A,W)∈Cp,n;k, m∈N, and l≥k, then
Aⓦm,W=limλ→0A(WA)l[(WA)l+m+2]∗((WA)l+m+2[(WA)l+m+2]∗+λI)−1(WA)m=limλ→0A(WA)l([(WA)l+m+2]∗(WA)l+m+2+λI)−1[(WA)l+m+2]∗(WA)m. |
Proof. Lemma 2.1 gives Aⓦm,W=A(WA)l[(WA)l+m+2]†(WA)m. According to the limit representation for the Moore-Penrose inverse given in [36], we derive
[(WA)l+m+2]†=limλ→0[(WA)l+m+2]∗((WA)l+m+2[(WA)l+m+2]∗+λI)−1=limλ→0([(WA)l+m+2]∗(WA)l+m+2+λI)−1[(WA)l+m+2]∗, |
which implies the rest.
Since W-m-WGI belongs to outer inverses, the limit representation of the outer inverse proposed in [35] implies the limit representation of the W-m-WGI.
Theorem 3.3. Let (A,W)∈Cp,n;k and m∈N. Suppose that H1∈Cp×ss, R(H1)=R((AW)k), H2∈Cs×ns, and n−s is the dimension of the subspace N(H2)=N([(WA)k]∗(WA)m) in Cn. Then,
Aⓦm=limv→0H1(vI+H2WAWH1)−1H2=limu→0(uI+H1H2WAW)−1H1H2=limv→0H1H2(vI+WAWH1H2)−1. |
Proof. Since Aⓦm,W=(WAW)(2)R((AW)k),N([(WA)k]∗(WA)m), by [35, Theorem 7], it follows that
Aⓦm,W=limu→0H1(uI+H2WAWH1)−1H2. |
Some integral formulae are established for the W-m-WGI.
Theorem 3.4. If (A,W)∈Cp,n;k, m∈N, and l≥k, then
Aⓦm,W=∫∞0A(WA)l[(WA)l+m+2]∗exp(−(WA)l+m+2[(WA)l+m+2]∗v)(WA)mdv. |
Proof. According to [34],
[(WA)l+m+2]†=∫∞0[(WA)l+m+2]∗exp(−(WA)l+m+2[(WA)l+m+2]∗v)dv. |
The proof is completed utilizing Aⓦm,W=A(WA)l[(WA)l+m+2]†(WA)m.
Theorem 3.5. Let (A,W)∈Cp,n;k and m∈N. If H∈Cp×n, R(H)=R((AW)k), and N(H)=N([(WA)k]∗(WA)m), then
Aⓦm=∞∫0exp[−H(HWAWH)∗HWAWv]H(HWAWH)∗Hdv. |
Proof. Applying [37, Theorem 2.2], it follows that
Aⓦm,W=(WAW)(2)R((AW)k),N([(WA)k]∗(WA)m)=∞∫0exp[−H(HWAWH)∗HWAWv]H(HWAWH)∗Hdv, |
which completes the proof.
The W-m-WGI is applicable in studying solvability of some matrix and vector equations.
In the case that A∈Cm×n, x∈Cn, and b∈Cm, to find approximation solution to inconsistent system of linear equations Ax=b, a classical approach is to ask for, so called, generalized solutions, defined as solutions to GAx=Gb with respect to an appropriate matrix G∈Cn×m [38]. It is important to mention that the system GAx=Gb is consistent in the case rank(GA)=rank(G). Such approach has been exploited extensively. One particular choice is G=A∗, which leads to widely used least-squares solutions obtained as solutions to the normal equation A∗Ax=A∗b. Another important choice is m=n, G=Ak, and k=ind(A), which leads to the so called Drazin normal equation Ak+1x=Akb and usage of the Drazin inverse solution ADb.
Starting from the known equation WAWx=b, we use G=[(WA)k]∗(WA)m to obtain the following equation (4.1).
Theorem 4.1. If m∈N and (A,W)∈Cp,n;k, the general solution to
[(WA)k]∗(WA)m+1Wx=[(WA)k]∗(WA)mb,b∈Cn, | (4.1) |
is of the form
x=Aⓦm,Wb+(I−Aⓦm,WWAW)u, | (4.2) |
for arbitrary vector u∈Cn.
Proof. Let x be represented as in (4.2). Theorem 2.2 gives
[(WA)k]∗(WA)m+1WAⓦm,W=[(WA)k]∗(WA)m. |
So, x is a solution to (4.1) by
[(WA)k]∗(WA)m+1Wx=[(WA)k]∗(WA)m+1WAⓦm,Wb+[(WA)k]∗(WA)m+1W(I−Aⓦm,WWAW)u=[(WA)k]∗(WA)mb. |
If Eq (4.1) has a solution x, based on
Aⓦm,W=A[(WA)D]m+2(WA)k[(WA)k]†(WA)m, |
one concludes
Aⓦm,Wb=A[(WA)D]m+2(WA)k[(WA)k]†(WA)mb=A[(WA)D]m+2([(WA)k]†)∗[(WA)k]∗(WA)mb=A[(WA)D]m+2([(WA)k]†)∗[(WA)k]∗(WA)m+1Wx=A[(WA)D]m+2(WA)k[(WA)k]†(WA)m+1Wx=Aⓦm,WWAWx, |
which yields
x=Aⓦm,Wb+x−Aⓦm,WWAWx=Aⓦm,Wb+(I−Aⓦm,WWAW)x. |
Hence, x possesses the pattern (4.2).
Choosing m=2 or m≥k in Theorem 4.1, we obtain the next result.
Corollary 4.1. Let b∈Cn and (A,W)∈Cp,n;k.
(i) The general solution to
[(WA)k]∗(WA)3Wx=[(WA)k]∗(WA)2b | (4.3) |
possesses the form
x=Aⓦ2,Wb+(I−Aⓦ2,WWAW)u, |
for arbitrary u∈Cn.
(ii) If m≥k, the general solution to
(WA)m+1Wx=(WA)mb | (4.4) |
(or equivalently [(WA)k]∗(WA)m+1Wx=[(WA)k]∗(WA)mb)
possesses the form
x=AD,Wb+(I−AD,WWAW)u, |
for arbitrary u∈Cn.
We study assumptions which ensure the uniqueness of the solution to Eq (4.1).
Theorem 4.2. If m∈N and (A,W)∈Cp,n;k, x=Aⓦm,Wb is the unique solution to (4.1) in the space R((AW)k).
Proof. Theorem 4.1 implies that (4.1) has a solution x=Aⓦm,Wb∈R(Aⓦm,W)=R((AW)k).
For two solutions x,x1∈R((AW)k) to (4.1), by Lemma 2.2, we obtain
x−x1∈R((AW)k)∩N([(WA)k]∗(WA)m+1W)=R(Aⓦm,WWAW)∩N(Aⓦm,WWAW)={0}. |
Hence, the Eq (4.1) has uniquely determined solution x=Aⓦm,Wb in R((AW)k).
Theorem 4.2 gives the next particular results.
Corollary 4.2. Let b∈Cn and (A,W)∈Cp,n;k.
(i) x=Aⓦ2,Wb is the unique solution in R((AW)k) to (4.3).
(ii) x=AD,Wb is the unique solution in R((AW)k) to (4.4).
Recall that, by [39], for W∈Cn×p∖{0}, A∈Cp×n, ind(AW)=k1, ind(WA)=k2, and b∈R((WA)k2), x=AD,Wb is the uniquely determined solution to
WAWx=b,x∈R((AW)k1). |
Specifically, if A∈Cn×n, W=I, ind(A)=k, and b∈R(Ak), x=ADb is the unique solution to [40]
Ax=b,x∈R(Ak). |
For 1=ind(A) and b∈R(A), x=A#b is the uniquely determined solution to Ax=b. Notice that Theorem 4.2 and Corollary 4.2 recover the above mentioned results from [39] and [40].
The identity (resp., zero) ℓ×ℓ matrix will be denoted by Iℓ (resp., 0ℓ). Denote by Dpℓ, p≥1, the ℓ×ℓ matrix with its pth leading diagonal parallel filled by the entries of the vector 1={1,…,1}∈Cℓ−p and 0 in all other positions.
We perform numerical tests on the class of test matrices of index ℓ, given by
{(CIℓC1Iℓ0ℓC2Dpℓ), ℓ>0}, C,C1,C2∈C. | (5.1) |
Example 5.1. The test matrix A in this example is derived using ℓ=4 and C=2,C1=3,C2=1 from the test set (5.1), and W is derived using ℓ=4 and C=1,C1=3/2,C2=4 from the test set (5.1). Our intention is to perform numerical experiments on integer matrices using exact computation. Appropriate matrices are
A=(2I43I404D14)=(2000300002000300002000300002000300000100000000100000000100000000),W=(I43/2I4044D14)=(10003200001000320000100032000010003200000400000000400000000400000000). |
The matrices WA and AW fulfill k=ind(WA)=ind(AW)=2.
(a) In the first part of this example, we calculate the Drazin inverse, the core-EP inverse, and W-m-WGI class of inverses based on their definitions. The Drazin inverse of WA is computed using
(WA)D=(WA)2(WA5)†(WA)2=(1/2I43/4I4+3/8D14+3/2D24+3/4D340404)=(120003438323401200034383200120003438000120003400000000000000000000000000000000), |
and the core-EP inverse of WA is equal to
![]() |
The W-weighted Drazin inverse of A is equal to
AD,W=A[(WA)D]2=(WA)D |
and the W-weighted core-EP inverse of A is equal to
![]() |
The W-WGI (or W-1-WGI) inverse of A is given by
![]() |
the W-2-WGI inverse of A is equal to
![]() |
and for each m≥k the W-m-WGI inverse of A satisfies
![]() |
Additionally, Aⓦm=AD is checked for each m≥ind(A).
(b) Representations involved in Lemma 2.1 are verified using
![]() |
(c) In this part of the example, our goal is to verify results of Theorem 2.2.
(c1) The statements involved in Theorem 2.2(ⅳ) are verified as follows.
- In the case m=1 verification is confirmed by
AWAⓦ,WWAⓦ,W=Aⓦ,W;Aⓦ,W(WA)3W=(4I46I4+24D14+12D24+48D340404)=(4000624124804000624120040006240004000600000000000000000000000000000000)=(AW)2;[(WA)2]∗(WA)2WAⓦ,W=(8I412I4+6D140404)=(800012600080001260008000126000800012120001890061200945290246120362745291224612184527452)=[(WA)2]∗WA. |
- In the case m=2 results are confirmed by
AWAⓦ2,WWAⓦ2,W=Aⓦ2,W;Aⓦ2,W(WA)3W=(AW)2;[(WA)2]∗(WA)3WAⓦ2,W=2[(WA)2]∗WA=[(WA)2]∗(WA)2. |
- Representations in the case m≥3 are confirmed by
AWAⓦm,WWAⓦm,W=Aⓦm,W;Aⓦm,W(WA)3W=(AW)2;[(WA)2]∗(WA)m+1WAⓦm,W=m[(WA)2]∗WA=[(WA)2]∗(WA)m. |
(c2) The statements involved in Theorem 2.2(ⅵ) are verified using verification of part (ⅳ) and the following computation.
- In the case m=1
![]() |
- In the case m≥2 results are confirmed by
![]() |
Example 5.2. Consider A and W from Example 5.1 and the vector b=(22011201)T with intention to verify Theorem 4.1.
In the case m=1 of (4.1), the general solution to [(WA)2]∗(WA)2Wx=[(WA)2]∗WAb is equal to
x1=Aⓦ,Wb+(I−Aⓦ,WWAW)u, |
where u=(u1u2u3u4u5u6u7u8)T is a vector of unknown variables. Symbolic calculation gives
x1=(−3u52−6u6−3u7−12u8+52−3u62−6u7−3u8+52−3u72−6u8+3814(5−6u8)u5u6u7u8). |
Obtained vector x1 is verified using [(WA)2]∗(WA)2Wx1=[(WA)2]∗WAb=(404062060901594292).
In the case m≥2 of (4.1), the general solution to [(WA)2]∗(WA)m+1Wx=[(WA)2]∗(WA)mb is equal to
xm=Aⓦm,Wb+(I−Aⓦm,WWAW)u. |
Symbolic calculus produces
xm=(−3u52−6u6−3u7−12u8+134−3u62−6u7−3u8+4−3u72−6u8+3814(5−6u8)u5u6u7u8). |
Correctness of the vector xm is verified using
[(WA)2]∗(WA)m+1Wx2=[(WA)2]∗(WA)mb=(m−1)(1041281240156270426609). |
In this research, we present an extension of the m-weak group inverse (or m-WGI) on the set of rectangular matrices, called the W-weighted m-WGI (or W-m-WGI). The W-m-WGI class presents a new, wider class of generalized inverses since this class involves the m-WGI, W-weighted weak group, and W-weighted Drazin inverse as special cases. Various characterizations and representations of W-m-WGI are developed. Usability of the W-m-WGI class in solving some constrained and unconstrained matrix equations and linear systems is considered. Some new properties of the weighted generalized group inverse and some known properties of the W-weighted Drazin inverse are obtained as corollaries. The given numerical examples confirm the obtained results.
There is increasing interest in the investigation of the WGI and its generalizations, and so for further research in the near future, it may be interesting to consider its generalizations to Hilbert space operators or tensors, iterative methods for approximation of W-m-WGI, or recurrent neural network (RNN) models for computing W-m-WGI.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
Dijana Mosić and Predrag Stanimirović are supported by the Ministry of Education, Science and Technological Development, Republic of Serbia, grant no. 451-03-65/2024-03/200124.
Predrag Stanimirović acknowledges support from the Science Fund of the Republic of Serbia, (No. 7750185, Quantitative Automata Models: Fundamental Problems and Applications - QUAM).
This work is supported by the Ministry of Science and Higher Education of the Russian Federation (Grant No. 075-15-2022-1121).
P. S. Stanimirović is an editorial board member for Electronic Research Archive and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.
[1] |
Ajayi RA, Mehdian S (1994) Rational investors' reaction to uncertainty: Evidence from the world's major markets. J Bus Financ Account 21: 533–545. https://doi.org/10.1111/j.1468-5957.1994.tb00334.x doi: 10.1111/j.1468-5957.1994.tb00334.x
![]() |
[2] |
Amini S, Hudson R, Keasey K (2010) Stock return predictability despite low autocorrelation. Econ Lett 108: 101–103. https://doi.org/10.1016/j.econlet.2010.04.031 doi: 10.1016/j.econlet.2010.04.031
![]() |
[3] |
Atkins AB, Dyl EA (1990) Price reversals, bid-ask spreads, and market efficiency. J Financ Quant Anal 25: 535–547. https://doi.org/10.2307/2331015 doi: 10.2307/2331015
![]() |
[4] |
Baker M, Wurgler J (2006) Investor sentiment and the cross-section of stock returns. J Financ 61: 1645–1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x doi: 10.1111/j.1540-6261.2006.00885.x
![]() |
[5] |
Balvers R, Wu Y, Gilliland E (2000) Mean reversion across national stock markets and parametric contrarian investment strategies. J Financ 55: 745–772. https://doi.org/10.1111/0022-1082.00225 doi: 10.1111/0022-1082.00225
![]() |
[6] |
Barber BM, Odean T (2008) All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Rev Financ Stud 21: 785–818. https://doi.org/10.1093/rfs/hhm079 doi: 10.1093/rfs/hhm079
![]() |
[7] |
Barberis N, Shleifer A, Vishny RW (1998) A model of investor sentiment. J Financ Econ 49: 307–343. https://doi.org/10.1016/S0304-405X(98)00027-0 doi: 10.1016/S0304-405X(98)00027-0
![]() |
[8] |
Baytas A, Cakici N (1999) Do markets overreact: International evidence. J Bank Financ 23: 1121–1144. https://doi.org/10.1016/S0378-4266(98)00133-2 doi: 10.1016/S0378-4266(98)00133-2
![]() |
[9] |
Bernard V, Thomas JK (1990) Evidence that stock prices do not fully reflect the implications of current earnings for future earnings. J Account Econ 13: 305–340. https://doi.org/10.1016/0165-4101(90)90008-R doi: 10.1016/0165-4101(90)90008-R
![]() |
[10] |
Bernard V, Thomas JK (1989) Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium? J Account Res 27: 1–36. https://doi.org/10.2307/2491062 doi: 10.2307/2491062
![]() |
[11] |
Bremer M, Hiraki T, Sweeney RJ, et al. (1997) Predictable patterns after large stock price changes on the Tokyo stock exchange. J Financ Quant Anal 32: 345–365. https://doi.org/10.2307/2331204 doi: 10.2307/2331204
![]() |
[12] |
Brown KC, Harlow WV, Tinic SM (1988) Risk aversion, uncertain information, and market efficiency. J Financ Econ 22: 355–385. https://doi.org/10.1016/0304-405X(88)90075-X doi: 10.1016/0304-405X(88)90075-X
![]() |
[13] |
Campbell K, Limmack RJ (1997) Long-term over-reaction in the UK stock market and size adjustments. Appl Financ Econ 7: 537–548. https://doi.org/10.1080/096031097333402 doi: 10.1080/096031097333402
![]() |
[14] |
Chan KC, Gup BE, Pan MS (1997) International stock market efficiency and integration: A study of eighteen nations. J Bus Financ Account 24: 803–813. https://doi.org/10.1111/1468-5957.00134 doi: 10.1111/1468-5957.00134
![]() |
[15] |
Chan WC (2003) Stock price reaction to news and no-news: Drift and reversal after headlines. J Financ Econ 70: 223–260. https://doi.org/10.1016/S0304-405X(03)00146-6 doi: 10.1016/S0304-405X(03)00146-6
![]() |
[16] |
Chen C, Cheng J, Lin F, et al. (2017) The role of house money effect and availability heuristic in investor behavior. Manage Decis 55: 1598–1612. https://doi.org/10.1108/MD-10-2016-0725 doi: 10.1108/MD-10-2016-0725
![]() |
[17] |
Chuang W, Lee B (2006) An empirical evaluation of the overconfidence hypothesis. J Bank Financ 30: 2489–2515. https://doi.org/10.1016/j.jbankfin.2005.08.007 doi: 10.1016/j.jbankfin.2005.08.007
![]() |
[18] |
Conrad JS, Hameed A, Niden C (1994) Volume and autocovariances in short-horizon individual security returns. J Financ 49: 1305–1329. https://doi.org/10.1111/j.1540-6261.1994.tb02455.x doi: 10.1111/j.1540-6261.1994.tb02455.x
![]() |
[19] |
Cooper M (1999) Filter rules based on price and volume in individual security overreaction. Rev Financ Stud 12: 901–935. https://doi.org/10.1093/rfs/12.4.901 doi: 10.1093/rfs/12.4.901
![]() |
[20] |
Corrado CJ, Jordan BD (1997) Risk aversion, uncertain information, and market efficiency reexamining the evidence. Rev Quant Financ Account 8: 51–68. https://doi.org/10.1023/A:1008292620583 doi: 10.1023/A:1008292620583
![]() |
[21] |
Cox DR, Peterson DR (1994) Stock returns following large one-day declines: Evidence on short-term reversals and longer-term performance. J Financ 49: 255–267. https://doi.org/10.1111/j.1540-6261.1994.tb04428.x doi: 10.1111/j.1540-6261.1994.tb04428.x
![]() |
[22] |
Daniel KD, Hirshleifer DA, Subrahmanyam A (1998) Investor psychology and security market under- and overreactions. J Financ 53: 1839–1885. https://doi.org/10.1111/0022-1082.00077 doi: 10.1111/0022-1082.00077
![]() |
[23] |
DeBondt WFM, Thaler R (1985) Does the stock market overreact? J Financ 40: 793–805. https://doi.org/10.5694/j.1326-5377.1972.tb47639.x doi: 10.5694/j.1326-5377.1972.tb47639.x
![]() |
[24] |
DeBondt WFM, Thaler R (1987) Evidence on investor overreaction and stock market seasonality. J Financ 42: 557–581. https://doi.org/10.1111/j.1540-6261.1987.tb04569.x doi: 10.1111/j.1540-6261.1987.tb04569.x
![]() |
[25] |
Dimson E, Mussavian M (1998) A brief history of market efficiency. Eur Financ Manage 4: 91–103. https://doi.org/10.1111/1468-036X.00056 doi: 10.1111/1468-036X.00056
![]() |
[26] |
Dyl EA, Yuksel HZ, Zaynutdinova GR (2019) Price reversals and price continuations following large price movements. J Bus Res 95: 1–12. https://doi.org/10.1016/j.jbusres.2018.08.036 doi: 10.1016/j.jbusres.2018.08.036
![]() |
[27] | Fachrudin RK, Lumbanraja P, Sadalia I, et al. (2017) Analysis of heuristic behavior on investment decision. Int J Econ Res 14: 297–309. |
[28] |
Fama EF (1965) The behavior of stock-market prices. J Bus 38: 34–105. https://doi.org/10.1017/CBO9781107415324.004 doi: 10.1017/CBO9781107415324.004
![]() |
[29] |
Fama EF (1970) Efficient capital markets: A review of theory and empirical work. J Financ 25: 383–417. https://doi.org/10.2307/2325486 doi: 10.2307/2325486
![]() |
[30] |
Fama EF (1998) Market efficiency, long-term returns, and behavioral finance. J Financ Econ 49: 283–306. https://doi.org/10.1016/s0304-405x(98)00026-9 doi: 10.1016/s0304-405x(98)00026-9
![]() |
[31] |
Fama EF, French KR (1996) Multifactor explanations of asset pricing anomalies. J Financ 51: 55–84. https://doi.org/10.1111/j.1540-6261.1996.tb05202.x doi: 10.1111/j.1540-6261.1996.tb05202.x
![]() |
[32] |
Fama EF, French KR (2010) Luck versus skill in the cross-section of mutual fund returns. J Financ 65: 1915–1947. https://doi.org/10.1111/j.1540-6261.2010.01598.x doi: 10.1111/j.1540-6261.2010.01598.x
![]() |
[33] |
Ganzach Y (2000) Judging risk and return of financial assets. Organ Behav Human Decis Proc 83: 353–370. https://doi.org/10.1006/obhd.2000.2914 doi: 10.1006/obhd.2000.2914
![]() |
[34] |
Gavrilakis N, Floros C (2021) The impact of heuristic and herding biases on portfolio construction and performance: the case of Greece. Rev Behav Financ. https://doi.org/10.1108/RBF-11-2020-0295 doi: 10.1108/RBF-11-2020-0295
![]() |
[35] |
George TJ, Hwang C (2004) The 52-week high and momentum investing. J Financ 59: 2145–2176. https://doi.org/10.1111/j.1540-6261.2004.00695.x doi: 10.1111/j.1540-6261.2004.00695.x
![]() |
[36] | Grossman BSJ, Stiglitz JE (1980) On the impossibility of informationally efficient markets. Am Econ Rev 70: 393–408. https://www.jstor.org/stable/1805228 |
[37] |
Hamelink F (2003) Systematic patterns before and after large price changes: Evidence from high frequency data from the Paris Bourse. J Forecasting 22: 533–549. https://doi.org/10.1002/for.874 doi: 10.1002/for.874
![]() |
[38] |
Hendricks D, Patel J, Zeckhauser R (1993) Hot hands in mutual funds: Short-run persistence of relative performance, 1974–1988. J Financ 48: 93–130. https://doi.org/10.1111/j.1540-6261.1993.tb04703.x doi: 10.1111/j.1540-6261.1993.tb04703.x
![]() |
[39] |
Hirshleifer D (2001) Investor psychology and asset pricing. J Financ 56: 1533–1597. https://doi.org/10.1111/0022-1082.00379 doi: 10.1111/0022-1082.00379
![]() |
[40] |
Hong H, Stein JC (1999) A unified theory of underreaction, momentum trading, and overreaction in asset markets. J Financ 54: 2143–2184. https://doi.org/10.1111/0022-1082.00184 doi: 10.1111/0022-1082.00184
![]() |
[41] |
Ikenberry DL, Ramnath S (2002) Underreaction to self-selected news events: The case of stock splits. Rev Financ Stud 15: 489–526. https://doi.org/10.1093/rfs/15.2.489 doi: 10.1093/rfs/15.2.489
![]() |
[42] |
Jegadeesh N (1990) Evidence of predictable behavior of security returns. J Financ 45: 881–898. https://doi.org/10.1111/j.1540-6261.1990.tb05110.x doi: 10.1111/j.1540-6261.1990.tb05110.x
![]() |
[43] |
Jegadeesh N, Titman S (1993) Returns to buying winners and selling losers: Implications for stock market efficiency. J Financ 48: 65–91. https://doi.org/10.1111/j.1540-6261.1993.tb04702.x doi: 10.1111/j.1540-6261.1993.tb04702.x
![]() |
[44] |
Jensen MC (1968) Problems in the selection of security portfolios—The performance of mutual funds in the period 1945–1964. J Financ 23: 389–416. https://doi.org/10.2307/2325404 doi: 10.2307/2325404
![]() |
[45] |
Joy OM, Litzenberger RH, McEnally RW (1977) The adjustment of stock prices to announcements of unanticipated changes in quarterly earnings. J Account Res 15: 207–225. https://doi.org/10.2307/2490349 doi: 10.2307/2490349
![]() |
[46] |
Kendall MG, Hill AB (1953) The analysis of economic time-series—Part I: Prices. J Royal Stat Soc 116: 11–34. https://doi.org/10.2307/2980947 doi: 10.2307/2980947
![]() |
[47] |
Kliger D, Kudryavtsev A (2010) The availability heuristic and investors' reaction to company-specific events. J Behav Financ 11: 50–65. https://doi.org/10.1080/15427561003591116 doi: 10.1080/15427561003591116
![]() |
[48] | Kudryavtsev A (2017) "I'll think about it tomorrow": Price drifts following large pre-holiday stock price moves. Rev Financ Bank 09: 043–062. |
[49] |
Kudryavtsev A (2018) The availability heuristic and reversals following large stock price changes. J Behav Financ 19: 159–176. https://doi.org/10.1080/15427560.2017.1374276 doi: 10.1080/15427560.2017.1374276
![]() |
[50] | Kudryavtsev A (2019) Psychological aspects of stock returns accompanied by high trading volumes. J Risk Control 6: 1–17. |
[51] |
Larson SJ, Madura J (2003) What drives stock price behavior following extreme one-day returns. J Financ Res 26: 113–127. https://doi.org/10.1111/1475-6803.00048 doi: 10.1111/1475-6803.00048
![]() |
[52] |
Lasfer MA, Melnik A, Thomas D (2003) Short-term reaction of stock markets in stressful circumstances. J Bank Financ 27: 1959–1977. https://doi.org/10.1016/S0378-4266(02)00313-8 doi: 10.1016/S0378-4266(02)00313-8
![]() |
[53] |
Lee B, O'Brien J, Sivaramakrishnan K (2008) An analysis of financial analysts' optimism in long-term growth forecasts. J Behav Financ 9: 171–184. https://doi.org/10.1080/15427560802341889 doi: 10.1080/15427560802341889
![]() |
[54] |
Lee CM (1992) Earnings news and small traders. J Account Econ 15: 265–302. https://doi.org/10.1016/0165-4101(92)90021-s doi: 10.1016/0165-4101(92)90021-s
![]() |
[55] |
Lee CM, Swaminathan B (2000) Price momentum and trading volume. J Financ 55: 2017–2069. https://doi.org/10.1111/0022-1082.00280 doi: 10.1111/0022-1082.00280
![]() |
[56] |
Lee M, Tsai C, Chen J, et al. (2019) Can information and communication technology improve stock market efficiency? A cross-country study. Bull Econ Res 71: 113–135. https://doi.org/10.1111/boer.12151 doi: 10.1111/boer.12151
![]() |
[57] |
Lehmann BN (1990) Fads, martingales, and market efficiency. Q J Econ 105: 1–28. https://doi.org/10.2307/2937816 doi: 10.2307/2937816
![]() |
[58] |
Lo AW, MacKinlay AC (1990) When are contrarian profits due to stock market overreaction? Rev Financ Stud 3: 175–205. https://doi.org/10.1093/rfs/3.2.175 doi: 10.1093/rfs/3.2.175
![]() |
[59] |
Lo AW, MacKinlay AC (1988) Stock market prices do not follow random walks: Evidence from a simple specification test. Rev Financ Stud 1: 17–46. https://doi.org/10.1515/9781400829095.17 doi: 10.1515/9781400829095.17
![]() |
[60] |
MacDonald R, Power DM (1993) Persistence in UK share returns: Some evidence from disaggregated data. Appl Financ Econ 3: 27–38. https://doi.org/10.1080/758527814 doi: 10.1080/758527814
![]() |
[61] |
Mazouz K, Alrabadi DWH, Yin S (2012) Systematic liquidity risk and stock price reaction to shocks. Account Financ 52: 467–493. https://doi.org/10.1111/j.1467-629X.2011.00403.x doi: 10.1111/j.1467-629X.2011.00403.x
![]() |
[62] |
Mazouz K, Joseph NL, Joulmer J (2009) Stock price reaction following large one-day price changes: UK evidence. J Bank Financ 33: 1481–1493. https://doi.org/10.1016/j.jbankfin.2009.02.010 doi: 10.1016/j.jbankfin.2009.02.010
![]() |
[63] |
Menkhoff L, Schmidt U, Brozynski T (2006) The impact of experience on risk taking, overconfidence, and herding of fund managers: Complementary survey evidence. Eur Econ Rev 50: 1753–1766. https://doi.org/10.1016/j.euroecorev.2005.08.001 doi: 10.1016/j.euroecorev.2005.08.001
![]() |
[64] |
Michaely R, Womack KL (1999) Conflict of interest and the credibility of underwriter analyst recommendations. Rev Financ Stud 12: 653–686. https://doi.org/10.1093/rfs/12.4.653 doi: 10.1093/rfs/12.4.653
![]() |
[65] |
Oehler A, Horn M, Wendt S (2017) Brexit: Short-term stock price effects and the impact of firm-level internationalization. Financ Res Lett 22: 175–181. https://doi.org/10.1016/j.frl.2016.12.024 doi: 10.1016/j.frl.2016.12.024
![]() |
[66] |
Park J (1995) A market microstructure explanation for predictable variations in stock returns following large price changes. J Financ Quant Anal 30: 241–256. https://doi.org/10.2307/2331119 doi: 10.2307/2331119
![]() |
[67] |
Piccoli P, Chaudhury M, Souza A, et al. (2017) Stock overreaction to extreme market events. North Am J Econ Financ 41: 97–111. https://doi.org/10.1016/j.najef.2017.04.002 doi: 10.1016/j.najef.2017.04.002
![]() |
[68] |
Pritamani M, Singal V (2001) Return predictability following large price changes and information releases. J Bank Financ 25: 631–656. https://doi.org/10.1016/S0378-4266(00)00091-1 doi: 10.1016/S0378-4266(00)00091-1
![]() |
[69] | Raddant M (2016) The response of European stock markets to the Brexit. Kiel Policy Brief, N. 100, August. |
[70] |
Ramiah V, Pham HNA, Moosa I (2017) The sectoral effects of Brexit on the British economy: early evidence from the reaction of the stock market. Appl Econ 49: 2508–2514. https://doi.org/10.1080/00036846.2016.1240352 doi: 10.1080/00036846.2016.1240352
![]() |
[71] |
Rendleman RJ, Jones CP, Latané HA (1982) Empirical anomalies based on unexpected earnings and the importance of risk adjustments. J Financ Econ 10: 269–287. https://doi.org/10.1016/0304-405X(82)90003-4 doi: 10.1016/0304-405X(82)90003-4
![]() |
[72] |
Savor PG (2012) Stock returns after major price shocks: The impact of information. J Financ Econ 106: 635–659. https://doi.org/10.1016/j.jfineco.2012.06.011 doi: 10.1016/j.jfineco.2012.06.011
![]() |
[73] |
Scheinkman JA, Xiong W (2003) Overconfidence, short-sale constraints, and bubbles. J Political Econ 111: 1183–1219. https://doi.org/10.1086/378531 doi: 10.1086/378531
![]() |
[74] | Shiller RJ (1998) Human behavior and the efficiency of the financial system. NBER Working Paper Series No. 6375. |
[75] | Shleifer A (2000) Inefficient Markets—An Introduction to Behavioural Finance, New York: Oxford University Press, 11–12. |
[76] |
Tetlock P (2010) Does public financial news resolve asymmetric information? Rev Financ Stud 23: 3520–3557. https://doi.org/10.1093/rfs/hhq052 doi: 10.1093/rfs/hhq052
![]() |
[77] | Tobin J (1984) On the efficiency of the financial system. Lloyds Bank Rev 3: 1–15. |
[78] |
Tversky A, Kahneman D (1973) Availability: A heuristic for judging frequency and probability. Cognit Psychol 5: 207–232. https://doi.org/10.1016/0010-0285(73)90033-9 doi: 10.1016/0010-0285(73)90033-9
![]() |
[79] |
Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185: 1124–1131. https://doi.org/10.4324/9781912282562 doi: 10.4324/9781912282562
![]() |
[80] |
Watts RL (1978) Systematic "abnormal" returns after quarterly earnings announcements. J Financ Econ 6: 127–150. https://doi.org/10.1016/0304-405X(78)90027-2 doi: 10.1016/0304-405X(78)90027-2
![]() |
[81] |
Worthington AC, Higgs H (2004) Random walks and market efficiency in European equity markets. Global J Financ Econ 1: 59–78. https://doi.org/10.19139/soic.v7i1.499 doi: 10.19139/soic.v7i1.499
![]() |
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