Which equations and models are used most in finance, accounting, economics, management, operations research and AI research?

Counting papers that mention them by name, the most frequent in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Difference-in-differences (121), Instrumental variables (two-stage least squares) (101), Logistic regression (logit) (97), Ordinary least squares (OLS) (93) and Graph neural network (graph convolution) (75).

Which equations and models are used most in finance research?

Counting papers that mention them by name, the most frequent in the tracked finance journals over the last 30 days were Difference-in-differences (43), Instrumental variables (two-stage least squares) (35), Ordinary least squares (OLS) (35), Mediation model (indirect effect) (26) and Two-way fixed-effects panel regression (23).

Which equations and models are used most in accounting research?

Counting papers that mention them by name, the most frequent in the tracked accounting journals over the last 30 days were Instrumental variables (two-stage least squares) (6), Difference-in-differences (5), Ordinary least squares (OLS) (4), Structural equation modeling (SEM) (3) and Mediation model (indirect effect) (2).

Which equations and models are used most in economics research?

Counting papers that mention them by name, the most frequent in the tracked economics journals over the last 30 days were Difference-in-differences (60), Instrumental variables (two-stage least squares) (45), Logistic regression (logit) (35), Ordinary least squares (OLS) (34) and Two-way fixed-effects panel regression (21).

Which equations and models are used most in management research?

Counting papers that mention them by name, the most frequent in the tracked management journals over the last 30 days were Structural equation modeling (SEM) (34), Mediation model (indirect effect) (20), Ordinary least squares (OLS) (17), Instrumental variables (two-stage least squares) (13) and Logistic regression (logit) (13).

Which equations and models are used most in operations research?

Counting papers that mention them by name, the most frequent in the tracked operations research journals over the last 30 days were Linear and integer programming (29), Logistic regression (logit) (8), Data envelopment analysis (DEA) (4), Gradient descent (4) and Proximal policy optimization (PPO) (4).

Which equations and models are used most in AI research?

Counting papers that mention them by name, the most frequent in the tracked AI journals over the last 30 days were Graph neural network (graph convolution) (73), Scaled dot-product attention (Transformer) (55), Contrastive loss (InfoNCE) (39), Convolutional neural network (CNN) (30) and Logistic regression (logit) (25).

Most-used equations and models

A paper counts once for each named equation or model it mentions, in its title, abstract, keywords or full text; a paper that mentions several counts for each of them. Each row gives the formula and the name. Window: 2026-09-27 – 2026-10-26 (30 days).

Key findings

The most-mentioned equations and models in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Difference-in-differences (121), Instrumental variables (two-stage least squares) (101), Logistic regression (logit) (97), Ordinary least squares (OLS) (93) and Graph neural network (graph convolution) (75).
Most-mentioned in each journal category: Finance: Difference-in-differences (43); Accounting: Instrumental variables (two-stage least squares) (6); Economics: Difference-in-differences (60); Management: Structural equation modeling (SEM) (34); Operations Research: Linear and integer programming (29); AI: Graph neural network (graph convolution) (73).
894 of the 2,971 papers (30%) mention at least one of the 112 watched equations and models.

Most-used equations and models · 2026-09-27 – 2026-10-26 (30 days)
#	Equation or model	Formula	Area	Papers	By journal category	Last 7 days
1	Difference-in-differences	$Y_{it} = \alpha_i + \lambda_t + \delta\,(\mathit{Treat}_i \times \mathit{Post}_t) + \varepsilon_{it}$	Causal inference	121	Economics 60 · Finance 43 · Management 12 · Accounting 5 · AI 1	5
2	Instrumental variables (two-stage least squares)	$\hat\beta_{\mathrm{2SLS}} = (X^\top P_Z X)^{-1} X^\top P_Z\, y,\quad P_Z = Z (Z^\top Z)^{-1} Z^\top$	Causal inference	101	Economics 45 · Finance 35 · Management 13 · Accounting 6 · Operations Research 1 · AI 1	5
3	Logistic regression (logit)	$\Pr(y = 1 \mid x) = \frac{1}{1 + e^{-x^\top\beta}}$	Regression models	97	Economics 35 · AI 25 · Finance 15 · Management 13 · Operations Research 8 · Accounting 1	8
4	Ordinary least squares (OLS)	$\hat\beta = (X^\top X)^{-1} X^\top y$	Regression models	93	Finance 35 · Economics 34 · Management 17 · Accounting 4 · AI 2 · Operations Research 1	7
5	Graph neural network (graph convolution)	$H^{(l+1)} = \sigma\big(\tilde D^{-1/2} \tilde A\, \tilde D^{-1/2} H^{(l)} W^{(l)}\big)$	Machine learning	75	AI 73 · Finance 1 · Management 1	6
6	Mediation model (indirect effect)	$M = \alpha_0 + a X + e_1,\quad Y = \beta_0 + c' X + b M + e_2,\quad \text{indirect effect} = a\,b$	Causal inference	60	Finance 26 · Management 20 · Economics 11 · Accounting 2 · Operations Research 1	5
7	Scaled dot-product attention (Transformer)	$\mathrm{Attention}(Q, K, V) = \mathrm{softmax}\Big(\frac{Q K^\top}{\sqrt{d_k}}\Big) V$	Machine learning	56	AI 55 · Operations Research 1	6
8	Two-way fixed-effects panel regression	$y_{it} = \alpha_i + \lambda_t + x_{it}^\top\beta + \varepsilon_{it}$	Regression models	55	Finance 23 · Economics 21 · Management 9 · Operations Research 1 · AI 1	4
9	Structural equation modeling (SEM)	$\eta = B\eta + \Gamma\xi + \zeta,\quad y = \Lambda_y\,\eta + \varepsilon$	Regression models	50	Management 34 · Finance 8 · Accounting 3 · AI 3 · Economics 1 · Operations Research 1	4
10	Vector autoregression (VAR)	$y_t = c + \sum_{i=1}^{p} A_i\, y_{t-i} + \varepsilon_t$	Time series	49	Finance 23 · Economics 20 · Management 3 · Operations Research 2 · AI 1	0
11	Propensity score matching	$e(x) = \Pr(D = 1 \mid X = x)$	Causal inference	48	Finance 20 · Economics 18 · Management 7 · Accounting 2 · AI 1	1
12	Quantile regression	$\hat\beta_\tau = \operatorname*{arg\,min}_{\beta} \sum_{i} \rho_\tau(y_i - x_i^\top\beta),\quad \rho_\tau(u) = u\,(\tau - \mathbf{1}\{u < 0\})$	Regression models	43	Economics 18 · Finance 15 · Management 4 · AI 3 · Accounting 2 · Operations Research 1	3
13	Contrastive loss (InfoNCE)	$\mathcal{L} = -\log \frac{\exp(\mathrm{sim}(z_i, z_j)/\tau)}{\sum_{k \ne i} \exp(\mathrm{sim}(z_i, z_k)/\tau)}$	Machine learning	39	AI 39	0
14	Generalized method of moments (GMM)	$\hat\theta = \operatorname*{arg\,min}_{\theta}\ \bar g(\theta)^\top W\, \bar g(\theta),\quad \bar g(\theta) = \frac{1}{n}\sum_{i=1}^{n} g(w_i, \theta)$	Regression models	35	Economics 14 · Finance 13 · Management 6 · Accounting 1 · Operations Research 1	0
15	GARCH	$\sigma_t^2 = \omega + \alpha\,\varepsilon_{t-1}^2 + \beta\,\sigma_{t-1}^2$	Time series	33	Finance 21 · Economics 10 · Management 1 · AI 1	2
15	Probit model	$\Pr(y = 1 \mid x) = \Phi(x^\top\beta)$	Regression models	33	Economics 14 · Finance 11 · Management 6 · Accounting 2	2
17	Linear and integer programming	$\min_{x}\ c^\top x\ \ \text{s.t.}\ \ A x \le b,\ x \ge 0,\ x_j \in \mathbb{Z}\ (j \in I)$	Efficiency and operations	32	Operations Research 29 · AI 3	2
18	Convolutional neural network (CNN)	$(I * K)(i, j) = \sum_{m}\sum_{n} I(i + m,\, j + n)\, K(m, n)$	Machine learning	31	AI 30 · Finance 1	4
19	Cointegration and error correction (VECM)	$\Delta y_t = \Pi\, y_{t-1} + \sum_{i=1}^{p-1}\Gamma_i\,\Delta y_{t-i} + \varepsilon_t$	Time series	24	Economics 14 · Finance 7 · Management 3	3
19	LASSO	$\hat\beta = \operatorname*{arg\,min}_{\beta}\ \lVert y - X\beta \rVert_2^2 + \lambda \lVert \beta \rVert_1$	Regression models	24	Economics 8 · Finance 7 · AI 6 · Operations Research 2 · Management 1	4
19	Long short-term memory (LSTM)	$c_t = f_t \odot c_{t-1} + i_t \odot \tilde c_t,\quad h_t = o_t \odot \tanh(c_t)$	Machine learning	24	AI 15 · Economics 4 · Management 3 · Finance 1 · Operations Research 1	4
22	Gradient boosting (XGBoost)	$\mathcal{L} = \sum_i l(y_i, \hat y_i) + \sum_k \Omega(f_k),\quad \Omega(f) = \gamma T + \tfrac{1}{2}\lambda \lVert w \rVert^2$	Machine learning	22	AI 10 · Finance 4 · Economics 4 · Management 4	4
22	Shapley value (SHAP)	$\phi_i(v) = \sum_{S \subseteq N \setminus \{i\}} \frac{\lvert S \rvert!\,(n - \lvert S \rvert - 1)!}{n!}\big[v(S \cup \{i\}) - v(S)\big]$	Game theory and decision analysis	22	AI 10 · Economics 6 · Finance 2 · Management 2 · Operations Research 2	1
24	Total factor productivity (Solow residual)	$\ln A_t = \ln Y_t - \alpha \ln K_t - (1 - \alpha) \ln L_t$	Growth, trade and environment	21	Economics 9 · Finance 8 · Management 4	1
25	Random forest	$\hat f(x) = \frac{1}{B}\sum_{b=1}^{B} T_b(x)$	Machine learning	20	AI 9 · Finance 4 · Economics 3 · Management 2 · Operations Research 2	4
26	ARIMA	$\phi(L)\,(1 - L)^{d}\, y_t = \theta(L)\,\varepsilon_t$	Time series	19	AI 7 · Economics 6 · Finance 4 · Accounting 1 · Management 1	2
26	Autoregressive distributed lag (ARDL)	$y_t = \alpha + \sum_{i=1}^{p}\phi_i\, y_{t-i} + \sum_{j=0}^{q}\beta_j^\top x_{t-j} + \varepsilon_t$	Time series	19	Economics 10 · Finance 6 · Management 3	1
28	Granger causality	$y_t = \sum_{i=1}^{p} a_i\, y_{t-i} + \sum_{j=1}^{p} b_j\, x_{t-j} + \varepsilon_t,\quad H_0: b_1 = \dots = b_p = 0$	Time series	16	Finance 8 · Economics 4 · Management 2 · AI 2	1
28	Regression discontinuity design	$\tau = \lim_{x \downarrow c} E[Y \mid X = x] - \lim_{x \uparrow c} E[Y \mid X = x]$	Causal inference	16	Economics 14 · Accounting 1 · AI 1	0
30	Gini coefficient	$G = \frac{\sum_{i=1}^{n}\sum_{j=1}^{n} \lvert x_i - x_j \rvert}{2 n^2 \bar x}$	Labour, inequality and competition	15	Economics 7 · Finance 3 · Management 2 · Operations Research 2 · AI 1	1
31	Markov-switching model	$y_t = \mu_{s_t} + \varepsilon_t,\quad \Pr(s_t = j \mid s_{t-1} = i) = p_{ij}$	Time series	13	Finance 6 · Economics 4 · Management 1 · Operations Research 1 · AI 1	1
31	Spatial autoregressive models (spatial Durbin)	$y = \rho\, W y + X\beta + W X\theta + \varepsilon$	Regression models	13	Economics 8 · Finance 5	1
33	Support vector machine (SVM)	$\min_{w, b, \xi}\ \tfrac{1}{2}\lVert w \rVert^2 + C \sum_i \xi_i\ \ \text{s.t.}\ \ y_i (w^\top x_i + b) \ge 1 - \xi_i,\ \xi_i \ge 0$	Machine learning	12	AI 9 · Economics 1 · Management 1 · Operations Research 1	1
34	Event study (abnormal returns)	$AR_{it} = R_{it} - (\hat\alpha_i + \hat\beta_i R_{mt}),\quad CAR_i = \sum_{t=t_1}^{t_2} AR_{it}$	Causal inference	11	Finance 8 · Accounting 1 · Management 1 · Operations Research 1	1
34	Herfindahl-Hirschman index	$\mathit{HHI} = \sum_{i=1}^{N} s_i^2$	Labour, inequality and competition	11	Finance 8 · Management 2 · Operations Research 1	0
36	Value at risk (VaR)	$\mathrm{VaR}_\alpha(L) = \inf\{\ell \in \mathbb{R} : \Pr(L > \ell) \le 1 - \alpha\}$	Asset pricing and risk	10	Finance 4 · Economics 3 · AI 2 · Operations Research 1	1
37	Expected shortfall (CVaR)	$\mathrm{ES}_\alpha(L) = \frac{1}{1 - \alpha}\int_\alpha^{1} \mathrm{VaR}_u(L)\,du$	Asset pricing and risk	9	Finance 3 · Economics 2 · Operations Research 2 · AI 2	0
37	Nash equilibrium	$u_i(s_i^{}, s_{-i}^{}) \ge u_i(s_i, s_{-i}^{*})\quad \forall i,\ \forall s_i$	Game theory and decision analysis	9	Economics 4 · Operations Research 3 · Finance 1 · Management 1	0
37	Synthetic control method	$\hat Y^{N}_{1t} = \sum_{j=2}^{J+1} w_j^{}\, Y_{jt},\quad w_j^{} \ge 0,\ \sum_j w_j^{*} = 1$	Causal inference	9	Economics 5 · Finance 2 · Accounting 1 · Management 1	0
40	Data envelopment analysis (DEA)	$\max_{u, v}\ \frac{\sum_r u_r\, y_{r0}}{\sum_i v_i\, x_{i0}}\ \ \text{s.t.}\ \ \frac{\sum_r u_r\, y_{rj}}{\sum_i v_i\, x_{ij}} \le 1\ \ \forall j$	Efficiency and operations	8	Operations Research 4 · AI 2 · Finance 1 · Economics 1	0
40	Gradient descent	$\theta_{t+1} = \theta_t - \eta\, \nabla_\theta \mathcal{L}(\theta_t)$	Machine learning	8	Operations Research 4 · AI 4	2
40	Heckman selection model	$E[y_i \mid x_i, s_i = 1] = x_i^\top\beta + \rho\,\sigma_\varepsilon\,\lambda(z_i^\top\gamma),\quad \lambda(\cdot) = \frac{\phi(\cdot)}{\Phi(\cdot)}$	Causal inference	8	Economics 3 · Management 3 · Finance 2	1
40	Principal component analysis (PCA)	$\max_{\lVert w \rVert = 1}\ w^\top \Sigma\, w$	Machine learning	8	Finance 4 · Management 2 · AI 2	1
40	Q-learning	$Q(s, a) \leftarrow Q(s, a) + \alpha\big[r + \gamma \max_{a'} Q(s', a') - Q(s, a)\big]$	Machine learning	8	AI 7 · Economics 1	1
40	Sharpe ratio	$S_p = \frac{E[R_p] - R_f}{\sigma_p}$	Asset pricing and risk	8	Finance 4 · Economics 2 · AI 2	2
46	Kalman filter	$\hat x_{t \mid t} = \hat x_{t \mid t-1} + K_t\,(y_t - H\,\hat x_{t \mid t-1})$	Time series	7	AI 4 · Finance 2 · Economics 1	0
47	Denoising diffusion model	$\mathcal{L} = E_{t, x_0, \epsilon}\Big[\left\lVert \epsilon - \epsilon_\theta\big(\sqrt{\bar\alpha_t}\, x_0 + \sqrt{1 - \bar\alpha_t}\, \epsilon,\ t\big) \right\rVert^2\Big]$	Machine learning	6	AI 6	0
47	Low-rank adaptation (LoRA)	$W = W_0 + B A,\quad B \in \mathbb{R}^{d \times r},\ A \in \mathbb{R}^{r \times k},\ r \ll \min(d, k)$	Machine learning	6	AI 6	0
47	Proximal policy optimization (PPO)	$L(\theta) = E_t\big[\min\big(r_t(\theta)\hat A_t,\ \mathrm{clip}(r_t(\theta), 1 - \epsilon, 1 + \epsilon)\,\hat A_t\big)\big]$	Machine learning	6	Operations Research 4 · AI 2	0
47	Variational autoencoder (ELBO)	$\log p_\theta(x) \ge E_{q_\phi(z \mid x)}\big[\log p_\theta(x \mid z)\big] - D_{\mathrm{KL}}\big(q_\phi(z \mid x) \parallel p(z)\big)$	Machine learning	6	AI 5 · Finance 1	1
51	Gated recurrent unit (GRU)	$h_t = (1 - z_t) \odot h_{t-1} + z_t \odot \tilde h_t$	Machine learning	5	AI 5	0
51	Generative adversarial network (GAN)	$\min_G \max_D\ E_x\big[\log D(x)\big] + E_z\big[\log\big(1 - D(G(z))\big)\big]$	Machine learning	5	AI 4 · Finance 1	0
51	k-means clustering	$\min_{C_1, \dots, C_k} \sum_{j=1}^{k} \sum_{x \in C_j} \lVert x - \mu_j \rVert^2$	Machine learning	5	AI 4 · Economics 1	1
54	Kullback-Leibler divergence	$D_{\mathrm{KL}}(P \parallel Q) = \sum_x P(x) \log \frac{P(x)}{Q(x)}$	Machine learning	4	AI 4	0
55	Analytic hierarchy process (AHP)	$A\, w = \lambda_{\max}\, w,\quad CR = \frac{(\lambda_{\max} - n)/(n - 1)}{RI}$	Game theory and decision analysis	3	AI 3	0
55	Cox proportional hazards model	$h(t \mid x) = h_0(t)\,\exp(x^\top\beta)$	Regression models	3	Finance 1 · Accounting 1 · Management 1	0
55	Gravity model of trade	$X_{ij} = G\,\frac{Y_i^{\beta_1}\, Y_j^{\beta_2}}{D_{ij}^{\beta_3}}$	Growth, trade and environment	3	Economics 3	0
55	Mean-variance portfolio (Markowitz)	$\min_{w}\ w^\top \Sigma\, w\ \ \text{s.t.}\ \ w^\top \mu = \mu_p,\ \ w^\top \mathbf{1} = 1$	Asset pricing and risk	3	Finance 1 · Operations Research 1 · AI 1	0
55	New Keynesian Phillips curve	$\pi_t = \beta\,E_t[\pi_{t+1}] + \kappa\, x_t$	Macroeconomics	3	Economics 2 · Finance 1	0
55	TOPSIS	$C_i = \frac{d_i^{-}}{d_i^{+} + d_i^{-}}$	Game theory and decision analysis	3	AI 2 · Finance 1	1
61	Amihud illiquidity ratio	$\mathit{ILLIQ}_i = \frac{1}{D_i}\sum_{d=1}^{D_i}\frac{\lvert R_{id} \rvert}{\mathit{DVOL}_{id}}$	Asset pricing and risk	2	Finance 2	0
61	Bellman equation	$V(s) = \max_{a}\left\{ r(s, a) + \beta \sum_{s'} P(s' \mid s, a)\, V(s') \right\}$	Game theory and decision analysis	2	Finance 1 · Economics 1	0
61	Capital asset pricing model (CAPM)	$E[R_i] = R_f + \beta_i\,(E[R_m] - R_f)$	Asset pricing and risk	2	Finance 2	0
61	Carhart four-factor model	$R_{it} - R_{ft} = \alpha_i + b_i\,\mathit{MKT}_t + s_i\,\mathit{SMB}_t + h_i\,\mathit{HML}_t + m_i\,\mathit{UMD}_t + \varepsilon_{it}$	Asset pricing and risk	2	Finance 1 · Economics 1	0
61	Dechow-Dichev accruals quality	$\Delta WC_{it} = \beta_0 + \beta_1\, CFO_{i,t-1} + \beta_2\, CFO_{it} + \beta_3\, CFO_{i,t+1} + \varepsilon_{it}$	Accounting	2	Finance 1 · Accounting 1	1
61	Fama-French five-factor model	$R_{it} - R_{ft} = \alpha_i + b_i\,\mathit{MKT}_t + s_i\,\mathit{SMB}_t + h_i\,\mathit{HML}_t + r_i\,\mathit{RMW}_t + c_i\,\mathit{CMA}_t + \varepsilon_{it}$	Asset pricing and risk	2	Finance 2	0
61	Hodrick-Prescott filter	$\min_{\{\tau_t\}} \sum_{t=1}^{T}(y_t - \tau_t)^2 + \lambda \sum_{t=2}^{T-1}\big[(\tau_{t+1} - \tau_t) - (\tau_t - \tau_{t-1})\big]^2$	Time series	2	Finance 1 · Economics 1	0
61	Newsvendor model	$Q^{*} = F^{-1}\Big(\frac{c_u}{c_u + c_o}\Big)$	Efficiency and operations	2	Economics 1 · Operations Research 1	0
61	Softmax function	$\mathrm{softmax}(z)_i = \frac{e^{z_i}}{\sum_{j=1}^{K} e^{z_j}}$	Machine learning	2	AI 2	0
61	Solow growth model	$\dot k = s\, f(k) - (n + \delta)\, k$	Growth, trade and environment	2	Economics 2	1
61	Stackelberg game	$\max_{x_L}\ \pi_L\big(x_L, x_F^{}(x_L)\big),\quad x_F^{}(x_L) = \operatorname*{arg\,max}_{x_F}\ \pi_F(x_L, x_F)$	Game theory and decision analysis	2	Management 1 · Operations Research 1	0
61	Stochastic frontier analysis	$\ln y_i = f(x_i; \beta) + v_i - u_i,\quad u_i \ge 0$	Efficiency and operations	2	Economics 2	0
61	Taylor rule	$i_t = r^{} + \pi_t + 0.5\,(\pi_t - \pi^{}) + 0.5\,(y_t - \bar y_t)$	Macroeconomics	2	Economics 2	0
61	Tobin's q	$q = \frac{\text{market value of assets}}{\text{replacement cost of assets}}$	Asset pricing and risk	2	Finance 2	0
75	Adam optimizer	$\theta_t = \theta_{t-1} - \alpha\,\frac{\hat m_t}{\sqrt{\hat v_t} + \epsilon}$	Machine learning	1	Operations Research 1	0
75	Bayes' theorem	$P(A \mid B) = \frac{P(B \mid A)\, P(A)}{P(B)}$	Game theory and decision analysis	1	Finance 1	0
75	Best-worst method	$\min\ \xi\ \ \text{s.t.}\ \ \lvert w_B - a_{Bj}\, w_j \rvert \le \xi,\ \ \lvert w_j - a_{jW}\, w_W \rvert \le \xi,\ \ \sum_j w_j = 1$	Game theory and decision analysis	1	AI 1	0
75	Coupling coordination degree	$C = \frac{2\sqrt{U_1 U_2}}{U_1 + U_2},\quad T = \alpha U_1 + \beta U_2,\quad D = \sqrt{C\, T}$	Game theory and decision analysis	1	Finance 1	0
75	CoVaR (systemic risk)	$\Pr\big(X^{\mathrm{sys}} \le \mathrm{CoVaR}_q^{\mathrm{sys} \mid i} \mid X^{i} = \mathrm{VaR}_q^{i}\big) = q$	Asset pricing and risk	1	Finance 1	0
75	Cross-entropy loss	$\mathcal{L} = -\sum_{c=1}^{C} y_c \log \hat y_c$	Machine learning	1	AI 1	0
75	DEMATEL	$T = X\,(I - X)^{-1}$	Game theory and decision analysis	1	AI 1	0
75	Economic order quantity (EOQ)	$Q^{*} = \sqrt{\frac{2 D S}{H}}$	Efficiency and operations	1	Operations Research 1	0
75	Entropy weight method	$e_j = -\frac{1}{\ln n}\sum_{i=1}^{n} p_{ij}\ln p_{ij},\quad w_j = \frac{1 - e_j}{\sum_k (1 - e_k)}$	Game theory and decision analysis	1	AI 1	1
75	Environmental Kuznets curve	$\ln E_{it} = \beta_0 + \beta_1 \ln Y_{it} + \beta_2 (\ln Y_{it})^2 + \varepsilon_{it}$	Growth, trade and environment	1	Economics 1	1
75	Lerner index	$L = \frac{P - MC}{P}$	Labour, inequality and competition	1	Finance 1	0
75	Merton model (distance to default)	$DD = \frac{\ln(V/D) + (\mu - \sigma_V^2/2)\,T}{\sigma_V\sqrt{T}}$	Asset pricing and risk	1	Economics 1	0
75	Residual income valuation (Ohlson model)	$P_t = B_t + \sum_{\tau=1}^{\infty}\frac{E_t[NI_{t+\tau} - r\,B_{t+\tau-1}]}{(1 + r)^{\tau}}$	Accounting	1	Operations Research 1	0
75	Shannon entropy	$H(X) = -\sum_x p(x) \log p(x)$	Machine learning	1	AI 1	0

Also watched, with no paper in this window: Altman Z-score, Bass diffusion model, Basu asymmetric timeliness, Beneish M-score, Black-Scholes formula, CES production function, Cobb-Douglas production function, Consumption Euler equation, Covered interest parity, Dividend discount (Gordon growth) model, Fama-French three-factor model, Fisher equation, Heston stochastic volatility model, Jensen's alpha, Kaya identity, Little's law, Mincer earnings equation, Modified Jones model (discretionary accruals), Nash bargaining solution, Nelson-Siegel yield curve, Okun's law, ReLU activation, STIRPAT model, Uncovered interest parity.

How the equations are found

The site watches a fixed list of 112 named equations and models in 11 areas, each with the names papers use for it (full names, abbreviations and common spellings). A paper counts when one of them appears in its title, abstract or OpenAlex keywords, or, for papers whose full text OpenAlex has indexed, anywhere in the full text (found with OpenAlex full-text search).

In this window OpenAlex has the abstract of 773 of the 2,971 papers (26%) and searchable full text for 545 (18%); titles and keywords are always there.

The full-text lookups go a group of names at a time, the stalest first, as far as the day's OpenAlex request budget allows: 30 of the 69 groups have had a lookup so far (between 2026-10-23 and 2026-10-26) and the rest are queued, so for now their counts rest on titles, abstracts and keywords.

The counts are therefore lower bounds that lean toward papers with open full text, and a mention is not proof that a paper uses the equation or dataset. Abstracts are only read to spot the names; they are neither stored nor republished.

Questions this page answers

Which equations and models are used most in finance, accounting, economics, management, operations research and AI research?: Counting papers that mention them by name, the most frequent in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Difference-in-differences (121), Instrumental variables (two-stage least squares) (101), Logistic regression (logit) (97), Ordinary least squares (OLS) (93) and Graph neural network (graph convolution) (75).
Which equations and models are used most in finance research?: Counting papers that mention them by name, the most frequent in the tracked finance journals over the last 30 days were Difference-in-differences (43), Instrumental variables (two-stage least squares) (35), Ordinary least squares (OLS) (35), Mediation model (indirect effect) (26) and Two-way fixed-effects panel regression (23).
Which equations and models are used most in accounting research?: Counting papers that mention them by name, the most frequent in the tracked accounting journals over the last 30 days were Instrumental variables (two-stage least squares) (6), Difference-in-differences (5), Ordinary least squares (OLS) (4), Structural equation modeling (SEM) (3) and Mediation model (indirect effect) (2).
Which equations and models are used most in economics research?: Counting papers that mention them by name, the most frequent in the tracked economics journals over the last 30 days were Difference-in-differences (60), Instrumental variables (two-stage least squares) (45), Logistic regression (logit) (35), Ordinary least squares (OLS) (34) and Two-way fixed-effects panel regression (21).
Which equations and models are used most in management research?: Counting papers that mention them by name, the most frequent in the tracked management journals over the last 30 days were Structural equation modeling (SEM) (34), Mediation model (indirect effect) (20), Ordinary least squares (OLS) (17), Instrumental variables (two-stage least squares) (13) and Logistic regression (logit) (13).
Which equations and models are used most in operations research?: Counting papers that mention them by name, the most frequent in the tracked operations research journals over the last 30 days were Linear and integer programming (29), Logistic regression (logit) (8), Data envelopment analysis (DEA) (4), Gradient descent (4) and Proximal policy optimization (PPO) (4).
Which equations and models are used most in AI research?: Counting papers that mention them by name, the most frequent in the tracked AI journals over the last 30 days were Graph neural network (graph convolution) (73), Scaled dot-product attention (Transformer) (55), Contrastive loss (InfoNCE) (39), Convolutional neural network (CNN) (30) and Logistic regression (logit) (25).