Computing the matrix exponential $\tilde R (t) = \exp(\tilde B \cdot t) \in \mathbb{R}^{3 \times 3}$, introduced in (<ref>), for all $t \in [0, \infty)$,  is very similar to the computations in Section <ref>. Again, we decompose the matrix $\tilde B = \tilde{U}\tilde{D}\tilde{U}^{-1}$ in (<ref>) into a diagonal matrix $\tilde{D} \in \mathbb{R}^{3 \times 3}$ and an invertible matrix $\tilde{U}\in \mathbb{R}^{3 \times 3}$. The eigenvalues $\tilde{\nu}_1,\tilde{\nu}_2,\tilde{\nu}_3$ are the roots of the equation

\begin{equation} \label{eq:cubicroot2}
x^3 + \frac{2\lambda\rho+\gamma\kappa}{2\lambda} x^2 - \frac{\phi}{\lambda} x - \frac{\rho\phi}{\lambda} = 0.
\end{equation}

Introducing the constants

\begin{equation*}
\tilde a = \frac{2\lambda\rho+\gamma\kappa}{2\lambda}, \quad \tilde b = - \frac{\phi}{\lambda}, \quad \tilde c = -\frac{\phi\rho}{\lambda}, \quad \tilde p = \tilde b -\frac{\tilde{a}^2}{3}, \quad \tilde q = \frac{2\tilde{a}^3}{27}-\frac{\tilde{a}\tilde{b}}{3}+\tilde{c},
\end{equation*}

and substituting $x = z-\tilde{a}/3$ in (<ref>) yields the equivalent equation $z^3+\tilde p z + \tilde q =0$ with discriminant $(\tilde{q}/2)^2 + (\tilde{p}/3)^3 < 0$. As above this implies that the latter has three real-valued distinct roots allowing for the analytical representations

\begin{equation}
\begin{aligned}
\tilde\nu_1 = & \, - \sqrt{-\frac{4}{3} \tilde p} \cdot \cos\left(\frac{1}{3} \arccos \left(-\frac{\tilde q}{2} \cdot \sqrt{-\frac{27}{\tilde{p}^3}}\right) +\frac{\pi}{3}\right) - \frac{\tilde a}{3}, \\
\tilde\nu_2 = & \, \sqrt{-\frac{4}{3} \tilde p} \cdot \cos\left(\frac{1}{3} \arccos \left(-\frac{\tilde q}{2} \cdot \sqrt{-\frac{27}{\tilde{p}^3}}\right) \right) - \frac{\tilde a}{3}, \\
\tilde\nu_3 = & \, - \sqrt{-\frac{4}{3} \tilde p} \cdot \cos\left(\frac{1}{3} \arccos \left(-\frac{\tilde q}{2} \cdot \sqrt{-\frac{27}{\tilde{p}^3}}\right) -\frac{\pi}{3}\right) - \frac{\tilde a}{3}.
\end{aligned}
\end{equation}

The corresponding eigenvectors are given by

\begin{equation*} \label{def:eigenvectortildeB}
\tilde{v}_i \triangleq
\begin{pmatrix}
-\frac{1}{\tilde{\nu}_i} \\
\frac{\gamma}{\tilde{\nu}_i + \rho} \\
\end{pmatrix} \qquad (i=1,2,3).
\end{equation*}

That is, we obtain

\begin{equation*}
\tilde D = \begin{pmatrix}
\tilde{\nu}_1 & 0 & 0 \\
0 & \tilde{\nu}_2 & 0 \\
0 & 0 & \tilde{\nu}_3
\end{pmatrix}, \quad
\tilde U =
\begin{pmatrix}
-\frac{1}{\tilde{\nu}_1} & -\frac{1}{\tilde{\nu}_2} & -\frac{1}{\tilde{\nu}_3}\\
\frac{\gamma}{\tilde{\nu}_1 + \rho} & \frac{\gamma}{\tilde{\nu}_2 + \rho} & \frac{\gamma}{\tilde{\nu}_3 + \rho} \\
1 & 1 & 1
\end{pmatrix},
\end{equation*}

and, introducing the differences

\begin{equation*}
\tilde\nu_{i,j} = \tilde\nu_{i} - \tilde\nu_{j} \qquad (i,j \in \{1,2,3\}),
\end{equation*}

we have that

\begin{equation*}
\tilde U^{-1} = \frac{1}{\gamma\rho} \begin{pmatrix}
-\frac{\gamma\rho\phi(\tilde\nu_1+\rho)}{\lambda\tilde\nu_{1,2}\tilde\nu_{1,3}} & -\frac{\tilde\nu_1(\tilde\nu_1+\rho)(\tilde\nu_2+\rho)(\tilde\nu_3+\rho)}{\tilde\nu_{1,2}\tilde\nu_{1,3}} & \frac{\gamma\rho\tilde\nu_1(\tilde\nu_1+\rho)}{\tilde\nu_{1,2}\tilde\nu_{1,3}} \\
\frac{\gamma\rho\phi(\tilde\nu_2+\rho)}{\lambda\tilde\nu_{1,2}\tilde\nu_{2,3}} & \frac{\tilde\nu_2(\tilde\nu_1+\rho)(\tilde\nu_2+\rho)(\tilde\nu_3+\rho)}{\tilde\nu_{1,2}\tilde\nu_{2,3}} & -\frac{\gamma\rho\tilde\nu_2(\tilde\nu_2+\rho)}{\tilde\nu_{1,2}\tilde\nu_{2,3}}\\
-\frac{\gamma\rho\phi(\tilde\nu_3+\rho)}{\lambda\tilde\nu_{1,3}\tilde\nu_{2,3}} & -\frac{\tilde\nu_3(\tilde\nu_1+\rho)(\tilde\nu_2+\rho)(\tilde\nu_3+\rho)}{\tilde\nu_{1,3}\tilde\nu_{2,3}} & \frac{\gamma\rho\tilde\nu_3(\tilde\nu_3+\rho)}{\tilde\nu_{1,3}\tilde\nu_{2,3}}
\end{pmatrix}.
\end{equation*}

Therefore, the matrix exponential $\tilde R(t) = (\tilde R_{ij}(t))_{1 \leq i,j \leq 3}$ can be computed as

\begin{equation}
\tilde R(t) = \tilde U \begin{pmatrix}
e^{\tilde\nu_1 t} & 0 & 0 \\
0 & e^{\tilde\nu_2 t} & 0 \\
0 & 0 & e^{\tilde\nu_3 t} \\
\end{pmatrix} \tilde U^{-1} \qquad (t \geq 0),
\end{equation}

and we obtain that $\tilde K$ defined in (<ref>) is given by

\begin{align}
\tilde K_1(T-t) = & \, \frac{\phi}{2\lambda^2} \left( \frac{2\varrho(\tilde{\nu}_1 + \rho)+\kappa\gamma\tilde{\nu}_1+2\lambda\tilde{\nu}_1(\tilde{\nu}_1+\rho)}{\tilde\nu_1\tilde{\nu}_{1,2}\tilde{\nu}_{1,3}} e^{\tilde\nu_1(T-t)} \right. \nonumber \\
& \, \hspace{30pt} \left. - \frac{2\varrho(\tilde{\nu}_2+\rho)+\kappa\gamma\tilde{\nu}_2 + 2\lambda\tilde{\nu}_2 (\tilde{\nu}_2+\rho) }{\tilde\nu_2\tilde{\nu}_{1,2}\tilde{\nu}_{2,3}} e^{\tilde\nu_2(T-t)} \right. \nonumber \\
& \, \left. \hspace{30pt} + \frac{2\varrho(\tilde{\nu}_3+\rho)+\kappa\gamma\tilde{\nu}_3+2\lambda\tilde{\nu}_3(\tilde{\nu}_3+\rho) }{\tilde\nu_3\tilde{\nu}_{1,3}\tilde{\nu}_{2,3}} e^{\tilde\nu_3(T-t)} \right), \label{def:Ktilde1}
\end{align}

\begin{align}
\tilde K_2(T-t) = & \, \frac{1}{2\gamma\lambda\rho} \label{def:Ktilde2} \\
& \left(\frac{(2\varrho(\tilde{\nu}_1 + \rho)+\kappa\gamma\tilde\nu_1+2\lambda\tilde{\nu}_1(\tilde{\nu}_1 + \rho))(\tilde{\nu}_2 + \rho)(\tilde{\nu}_3 + \rho)}{\tilde{\nu}_{1,2}\tilde{\nu}_{1,3}} e^{\tilde\nu_1(T-t)} \right. \nonumber \\
& \hspace{10pt} - \frac{(2\varrho(\tilde{\nu}_2 + \rho)+\kappa\gamma\tilde\nu_2+2\lambda\tilde{\nu}_2(\tilde{\nu}_2 + \rho))(\tilde{\nu}_1 + \rho)(\tilde{\nu}_3 + \rho)}{\tilde{\nu}_{1,2}\tilde{\nu}_{2,3}} e^{\tilde\nu_2(T-t)} \nonumber \\
& \left. \hspace{10pt} + \frac{(2\varrho(\tilde{\nu}_3 + \rho)+\kappa\gamma\tilde\nu_3+2\lambda\tilde{\nu}_3(\tilde{\nu}_3 + \rho))(\tilde{\nu}_1 + \rho)(\tilde{\nu}_2 + \rho)}{\tilde{\nu}_{1,3}\tilde{\nu}_{2,3}} e^{\tilde\nu_3(T-t)} \right), \nonumber
\end{align}

\begin{align}
\tilde K_3(T-t) = & \, \frac{1}{2\lambda} \left( -\frac{2\varrho(\tilde{\nu}_1 + \rho)+\kappa\gamma\tilde\nu_1+2\lambda\tilde{\nu}_1(\tilde{\nu}_1 + \rho)}{\tilde{\nu}_{1,2}\tilde{\nu}_{1,3}} e^{\tilde\nu_1(T-t)} \right. \nonumber \\
& \, \hspace{24pt} + \frac{2\varrho(\tilde{\nu}_2 + \rho)+\kappa\gamma\tilde\nu_2 + 2\lambda\tilde{\nu}_2 (\tilde{\nu}_2 + \rho)}{\tilde{\nu}_{1,2}\tilde{\nu}_{2,3}} e^{\tilde\nu_2(T-t)} \nonumber \\
& \, \left. \hspace{24pt} - \frac{2\varrho(\tilde{\nu}_3 + \rho)+\kappa\gamma\tilde\nu_3 + 2\lambda\tilde{\nu}_3 (\tilde{\nu}_3 + \rho)}{\tilde{\nu}_{1,3}\tilde{\nu}_{2,3}} e^{\tilde\nu_3(T-t)} \right), \label{def:Ktilde3}
\end{align}

for all $t \in [0,T]$.

§ ACKNOWLEDGMENTS

We are very grateful to the Associate Editor and to the anonymous referees for careful reading of the manuscript and for a number of useful comments and suggestions that significantly improved this paper.

§ DATA AVAILABILITY STATEMENT

No data was used in this article.

[Barroso et al., 2017]
P. Barroso, R. M. Edelen, and P. Karehnke.
Institutional crowding and the moments of momentum.
SSRN.3045019, 2017.

[Belak et al., 2019]
C. Belak, J. Muhle-Karbe, and K. Ou.
Liquidation in target zone models.
Market Microstructure and Liquidity, 2019.
URL <https://doi.org/10.1142/S2382626619500102>.

[Bucci et al., 2020]
F. Bucci, I. Mastromatteo, Z. Eisler, F. Lillo, J. P. Bouchaud, and C. A.
Co-impact: crowding effects in institutional trading activity.
Quantitative Finance, 200 (2):0 193–205,
URL <https://doi.org/10.1080/14697688.2019.1660398>.

[Caccioli et al., 2014]
F. Caccioli, M. Shrestha, C. Moore, and J. D. Farmer.
Stability analysis of financial contagion due to overlapping
Journal of Banking & Finance, 46:0 233–245, 2014.

[Caccioli et al., 2015]
F. Caccioli, J. D. Farmer, N. Foti, and D. Rockmore.
Overlapping portfolios, contagion, and financial stability.
Journal of Economic Dynamics and Control, 51:0 50–63,

[Cardaliaguet and Lehalle, 2018]
P. Cardaliaguet and C.-A. Lehalle.
Mean field game of controls and an application to trade crowding.
Mathematics and Financial Economics, 120 (3):0
335–363, 2018.
URL <https://doi.org/10.1007/s11579-017-0206-z>.

[Carmona and Delarue, 2018]
R. Carmona and F. Delarue.
Probabilistic Theory of Mean Field Games with Applications I:
Mean Field FBSDEs, Control, and Games.
Probability Theory and Stochastic Modelling. Springer International
Publishing, 2018.
ISBN 9783319589206.
URL <https://books.google.com/books?id=fZFODwAAQBAJ>.

[Carmona et al., 2016]
R. Carmona, F. Delarue, and D. Lacker.
Mean field games with common noise.
The Annals of Probability, 440 (6):0 3740 –
3803, 2016.
URL <https://doi.org/10.1214/15-AOP1060>.

[Casgrain and Jaimungal, 2018]
P. Casgrain and S. Jaimungal.
Mean field games with partial information for algorithmic trading.
arXiv:1803.04094v2, 2018.

[Casgrain and Jaimungal, 2020]
P. Casgrain and S. Jaimungal.
Mean-field games with differing beliefs for algorithmic trading.
Mathematical Finance, 300 (3):0 995–1034,
URL <https://onlinelibrary.wiley.com/doi/abs/10.1111/mafi.12237>.

[Cont and Bouchaud, 2000]
R. Cont and J. P. Bouchaud.
Herd behavior and aggregate fluctuations in financial markets.
Macroeconomic Dynamics, 40 (2):0 170–196,
DOI: 10.1017/S1365100500015029.

[Cont and Wagalath, 2013]
R. Cont and L. Wagalath.
Running for the exit: distressed selling and endogenous correlation
in financial markets.
Mathematical Finance, 230 (4):0 718–741,

[Cont and Wagalath, 2016]
R. Cont and L. Wagalath.
Fire sale forensics: measuring endogenous risk.
Mathematical Finance, 260 (4):0 835–866,

[Djete, 2021]
M. F. Djete.
Large population games with interactions through controls and common
noise: convergence results and equivalence between open–loop and
closed–loop controls.
arXiv:2108.02992, 2021.

[Drapeau et al., 2021]
S. Drapeau, P. Luo, A. Schied, and D. Xiong.
An FBSDE approach to market impact games with stochastic
Probability, Uncertainty and Quantitative Risk, 60
(3):0 237–260, 2021.

[Evangelista and Thamsten, 2020]
D. Evangelista and Y. Thamsten.
On finite population games of optimal trading.
arXiv:2004.00790v1, 2020.

[Féron et al., 2020]
O. Féron, Peter P. Tankov, and L. Tinsi.
Price formation and optimal trading in intraday electricity markets
with a major player.
Risks, 80 (4), 2020.
ISSN 2227-9091.
URL <https://www.mdpi.com/2227-9091/8/4/133>.

[Fu et al., 2020]
G. Fu, H. Horst, and X. Xia.
Portfolio liquidation games with self-exciting order flow.
arXiv:2011.05589, 2020.

[Fu et al., 2021]
G. Fu, P. Graewe, U. Horst, and A. Popier.
A mean field game of optimal portfolio liquidation.
To appear in Mathematics of Operations Research, 2021.

[Huang et al., 2019]
X. Huang, S. Jaimungal, and M. Nourian.
Mean-field game strategies for optimal execution.
Applied Mathematical Finance, 260 (2):0
153–185, 2019.
URL <https://doi.org/10.1080/1350486X.2019.1603183>.

[Khandani and Lo, 2011]
A. E. Khandani and A. W. Lo.
What happened to the quants in august 2007? evidence from factors and
transactions data.
Journal of Financial Markets, 140 (1):0 1–46,
ISSN 1386-4181.

[Lacker and Flem, 2021]
D. Lacker and L. Le Flem.
Closed-loop convergence for mean field games with common noise.
arXiv:2107.03273, 2021.

[Lasry and Lions, 2007]
J.-M. Lasry and P.-L. Lions.
Mean field games.
Jpn. J. Math., 20 (1):0 229–260, 2007.
ISSN 0289-2316.
URL <http://dx.doi.org/10.1007/s11537-007-0657-8>.

[Laurière and Tangpi, 2020]
M. Laurière and L. Tangpi.
Convergence of large population games to mean field games with
interaction through the controls.
arXiv:2004.08351, 2020.

[Lehalle and Neuman, 2019]
C. A. Lehalle and E. Neuman.
Incorporating signals into optimal trading.
Finance and Stochastics, 230 (2):0 275–311,
URL <https://doi.org/10.1007/s00780-019-00382-7>.

[Luo and Schied, 2020]
X. Luo and A. Schied.
Nash equilibrium for risk-averse investors in a market impact game
with transient price impact.
To appear in Market Microstructure and Liquidity, 2020.
URL <https://doi.org/10.1142/S238262662050001X>.

[Micheli and Neuman, 2020]
A. Micheli and E. Neuman.
Evidence of crowding on russell 3000 reconstitution events.
arXiv:2006.07456, 2020.

[Neuman and Voß, 2022]
Eyal Neuman and Moritz Voß.
Optimal signal-adaptive trading with temporary and transient price
To appear in SIAM Journal on Financial Mathematics, 2022.

[Schied and Zhang, 2019]
A. Schied and T. Zhang.
A market impact game under transient price impact.
Mathematics of Operations Research, 440 (1):0
102–121, 2019.
URL <https://doi.org/10.1287/moor.2017.0916>.

[Schied et al., 2017]
A. Schied, E. Strehle, and T. Zhang.
High-frequency limit of nash equilibria in a market impact game with
transient price impact.
SIAM Journal on Financial Mathematics, 80
(1):0 589–634, 2017.
URL <https://doi.org/10.1137/16M107030X>.

[Strehle, 2017]
E. Strehle.
Optimal execution in a multiplayer model of transient price impact.
Market Microstructure and Liquidity, 030
(03n04):0 1850007, 2017.
URL <https://doi.org/10.1142/S2382626618500077>.

[Volpati et al., 2020]
V. Volpati, M. Benzaquen, Z. Eisler, I. Mastromatteo, B. Toth, and J. P.
Zooming in on equity factor crowding.
arXiv:2001.04185, 2020.

[Barroso et al., 2017]
P. Barroso, R. M. Edelen, and P. Karehnke.
Institutional crowding and the moments of momentum.
SSRN.3045019, 2017.

[Belak et al., 2019]
C. Belak, J. Muhle-Karbe, and K. Ou.
Liquidation in target zone models.
Market Microstructure and Liquidity, 2019.
URL <https://doi.org/10.1142/S2382626619500102>.

[Bucci et al., 2020]
F. Bucci, I. Mastromatteo, Z. Eisler, F. Lillo, J. P. Bouchaud, and C. A.
Co-impact: crowding effects in institutional trading activity.
Quantitative Finance, 200 (2):0 193–205,
URL <https://doi.org/10.1080/14697688.2019.1660398>.

[Caccioli et al., 2014]
F. Caccioli, M. Shrestha, C. Moore, and J. D. Farmer.
Stability analysis of financial contagion due to overlapping
Journal of Banking & Finance, 46:0 233–245, 2014.

[Caccioli et al., 2015]
F. Caccioli, J. D. Farmer, N. Foti, and D. Rockmore.
Overlapping portfolios, contagion, and financial stability.
Journal of Economic Dynamics and Control, 51:0 50–63,

[Cardaliaguet and Lehalle, 2018]
P. Cardaliaguet and C.-A. Lehalle.
Mean field game of controls and an application to trade crowding.
Mathematics and Financial Economics, 120 (3):0
335–363, 2018.
URL <https://doi.org/10.1007/s11579-017-0206-z>.

[Carmona and Delarue, 2018]
R. Carmona and F. Delarue.
Probabilistic Theory of Mean Field Games with Applications I:
Mean Field FBSDEs, Control, and Games.
Probability Theory and Stochastic Modelling. Springer International
Publishing, 2018.
ISBN 9783319589206.
URL <https://books.google.com/books?id=fZFODwAAQBAJ>.

[Carmona et al., 2016]
R. Carmona, F. Delarue, and D. Lacker.
Mean field games with common noise.
The Annals of Probability, 440 (6):0 3740 –
3803, 2016.
URL <https://doi.org/10.1214/15-AOP1060>.

[Casgrain and Jaimungal, 2018]
P. Casgrain and S. Jaimungal.
Mean field games with partial information for algorithmic trading.
arXiv:1803.04094v2, 2018.

[Casgrain and Jaimungal, 2020]
P. Casgrain and S. Jaimungal.
Mean-field games with differing beliefs for algorithmic trading.
Mathematical Finance, 300 (3):0 995–1034,
URL <https://onlinelibrary.wiley.com/doi/abs/10.1111/mafi.12237>.

[Cont and Bouchaud, 2000]
R. Cont and J. P. Bouchaud.
Herd behavior and aggregate fluctuations in financial markets.
Macroeconomic Dynamics, 40 (2):0 170–196,
DOI: 10.1017/S1365100500015029.

[Cont and Wagalath, 2013]
R. Cont and L. Wagalath.
Running for the exit: distressed selling and endogenous correlation
in financial markets.
Mathematical Finance, 230 (4):0 718–741,

[Cont and Wagalath, 2016]
R. Cont and L. Wagalath.
Fire sale forensics: measuring endogenous risk.
Mathematical Finance, 260 (4):0 835–866,

[Djete, 2021]
M. F. Djete.
Large population games with interactions through controls and common
noise: convergence results and equivalence between open–loop and
closed–loop controls.
arXiv:2108.02992, 2021.

[Drapeau et al., 2021]
S. Drapeau, P. Luo, A. Schied, and D. Xiong.
An FBSDE approach to market impact games with stochastic
Probability, Uncertainty and Quantitative Risk, 60
(3):0 237–260, 2021.

[Evangelista and Thamsten, 2020]
D. Evangelista and Y. Thamsten.
On finite population games of optimal trading.
arXiv:2004.00790v1, 2020.

[Féron et al., 2020]
O. Féron, Peter P. Tankov, and L. Tinsi.
Price formation and optimal trading in intraday electricity markets
with a major player.
Risks, 80 (4), 2020.
ISSN 2227-9091.
URL <https://www.mdpi.com/2227-9091/8/4/133>.

[Fu et al., 2020]
G. Fu, H. Horst, and X. Xia.
Portfolio liquidation games with self-exciting order flow.
arXiv:2011.05589, 2020.

[Fu et al., 2021]
G. Fu, P. Graewe, U. Horst, and A. Popier.
A mean field game of optimal portfolio liquidation.
To appear in Mathematics of Operations Research, 2021.

[Huang et al., 2019]
X. Huang, S. Jaimungal, and M. Nourian.
Mean-field game strategies for optimal execution.
Applied Mathematical Finance, 260 (2):0
153–185, 2019.
URL <https://doi.org/10.1080/1350486X.2019.1603183>.

[Khandani and Lo, 2011]
A. E. Khandani and A. W. Lo.
What happened to the quants in august 2007? evidence from factors and
transactions data.
Journal of Financial Markets, 140 (1):0 1–46,
ISSN 1386-4181.

[Lacker and Flem, 2021]
D. Lacker and L. Le Flem.
Closed-loop convergence for mean field games with common noise.
arXiv:2107.03273, 2021.

[Lasry and Lions, 2007]
J.-M. Lasry and P.-L. Lions.
Mean field games.
Jpn. J. Math., 20 (1):0 229–260, 2007.
ISSN 0289-2316.
URL <http://dx.doi.org/10.1007/s11537-007-0657-8>.

[Laurière and Tangpi, 2020]
M. Laurière and L. Tangpi.
Convergence of large population games to mean field games with
interaction through the controls.
arXiv:2004.08351, 2020.

[Lehalle and Neuman, 2019]
C. A. Lehalle and E. Neuman.
Incorporating signals into optimal trading.
Finance and Stochastics, 230 (2):0 275–311,
URL <https://doi.org/10.1007/s00780-019-00382-7>.

[Luo and Schied, 2020]
X. Luo and A. Schied.
Nash equilibrium for risk-averse investors in a market impact game
with transient price impact.
To appear in Market Microstructure and Liquidity, 2020.
URL <https://doi.org/10.1142/S238262662050001X>.

[Micheli and Neuman, 2020]
A. Micheli and E. Neuman.
Evidence of crowding on russell 3000 reconstitution events.
arXiv:2006.07456, 2020.

[Neuman and Voß, 2022]
Eyal Neuman and Moritz Voß.
Optimal signal-adaptive trading with temporary and transient price
To appear in SIAM Journal on Financial Mathematics, 2022.

[Schied and Zhang, 2019]
A. Schied and T. Zhang.
A market impact game under transient price impact.
Mathematics of Operations Research, 440 (1):0
102–121, 2019.
URL <https://doi.org/10.1287/moor.2017.0916>.

[Schied et al., 2017]
A. Schied, E. Strehle, and T. Zhang.
High-frequency limit of nash equilibria in a market impact game with
transient price impact.
SIAM Journal on Financial Mathematics, 80
(1):0 589–634, 2017.
URL <https://doi.org/10.1137/16M107030X>.

[Strehle, 2017]
E. Strehle.
Optimal execution in a multiplayer model of transient price impact.
Market Microstructure and Liquidity, 030
(03n04):0 1850007, 2017.
URL <https://doi.org/10.1142/S2382626618500077>.

[Volpati et al., 2020]
V. Volpati, M. Benzaquen, Z. Eisler, I. Mastromatteo, B. Toth, and J. P.
Zooming in on equity factor crowding.
arXiv:2001.04185, 2020.