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This paper analyses the effects of US monetary policy on stock markets.We find that, on average, a tightening of 50 basis points reduces returns by about 3%.Moreover, returns react more strongly when no change had been expected, when there is a directional change in the monetary policy stance and during periods of high market uncertainty.We show that individual stocks react in a highly heterogeneous fashion and relat…
Proper conduct of monetary policy requires understanding the monetary transmission mechanism, to monitor the economy, make decisions on the stance of policy, and explain the policy actions to the public. Hence, gathering evidence on the monetary transmission mechanism in the euro area has been a priority for the Eurosystem. This 2003 book presents the results of a multi-year collaborative project conducted by the Eur…
The ‘credit channel’ theory of monetary policy transmission holds that informational frictions in credit markets worsen during tight-money periods. The resulting increase in the external finance premium--the difference in cost between internal and external funds--enhances the effects of monetary policy on the real economy. The authors document the responses of GDP and its components to monetary policy shocks and desc…
A model is developed to assess the profitability of loans or mortgages with a specified repayment schedule. Financial institutions face two competing risks: default and prepayment, both influenced by the stochastic evolution of credit market conditions. This study focuses on the Random Net Present Value (RNPV) as a key performance metric. The analysis evaluates the mean and variance of the RNPV at both the individual…
It is well known that the out-of-sample performance of Markowitz's mean-variance portfolio criterion can be negatively affected by estimation errors in the mean and covariance. In this paper we address the problem by regularizing the mean-variance objective function with a weighted elastic net penalty. We show that the use of this penalty can be motivated by a robust reformulation of the mean-variance criterion that …
In high frequency trading, accurate prediction of Order Flow Imbalance (OFI) is crucial for understanding market dynamics and maintaining liquidity. This paper introduces a hybrid predictive model that combines Vector Auto Regression (VAR) with a simple feedforward neural network (FNN) to forecast OFI and assess trading intensity. The VAR component captures linear dependencies, while residuals are fed into the FNN to…
We study the formation of an optimal interbank network in a model where banks control both their supply of liquidity, through cash reserves, and their exposures to other banks' risky projects. The value of each bank's project may suddenly decline depending on their cash reserves and both the occurence and magnitude of liquidity shocks. In two distinct settings, we solve the system-wide optimal control problem and obt…
This paper examines the effect of the political network of Chinese municipal leaders on the pricing of municipal corporate bonds. Using municipal leaders' working experience to measure the political network, we find that this network reduces the bond issuance yield spreads by improving the credit ratings of the issuer, the local government financing vehicle. The relationship between political networks and issuance yi…
Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market. It is a type of deep neural network which is optimized by Q Learning. To make the DQN adapt to fina…
In order to scale transaction rates for deployment across the global web, many cryptocurrencies have deployed so-called "Layer-2" networks of private payment channels. An idealized payment network behaves like a Credit Network, a model for transactions across a network of bilateral trust relationships. Credit Networks capture many aspects of traditional currencies as well as new virtual currencies and payment mechani…
We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade. The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative "opinion" to all agents about whether the traded asset price will go up or down. This opinion is reflect…
We study insolvency cascades in an interbank system when banks are allowed to insure their loans with credit default swaps (CDS) sold by other banks. We show that, by properly shifting financial exposures from one institution to another, a CDS market can be designed to rewire the network of interbank exposures in a way that makes it more resilient to insolvency cascades. A regulator can use information about the topo…
We present a dialogue on Counterparty Credit Risk touching on Credit Value at Risk (Credit VaR), Potential Future Exposure (PFE), Expected Exposure (EE), Expected Positive Exposure (EPE), Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA), DVA Hedging, Closeout conventions, Netting clauses, Collateral modeling, Gap Risk, Re-hypothecation, Wrong Way Risk, Basel III, inclusion of Funding costs, First t…
Credit networks represent a way of modeling trust between entities in a network. Nodes in the network print their own currency and trust each other for a certain amount of each other's currency. This allows the network to serve as a decentralized payment infrastructure---arbitrary payments can be routed through the network by passing IOUs between trusting nodes in their respective currencies---and obviates the need f…
Financial markets such as bond, derivatives, and repo markets form networks of interdependent obligations. Existing multilateral netting methods typically trade off the extent of netting against preservation of counterparty exposure: central clearing reallocates exposure to a central counterparty, while trade compression may alter bilateral counterparty relationships. TradeMech is a mechanism for markets in which one…
We develop a model of interbank networks with random liquidity shocks. Networks of dilutable debt---e.g., long-term, unsecured---facilitate efficient liquidity transfers: Shocked banks pledge interbank claims as collateral for new senior debt, diluting existing debt. Unlike with non-dilutable debt, indebtedness and connectedness are sources of stability, not fragility. Dilution is thus a ``backdoor bail-in'' that rea…
Classical correlation and rolling PCA summarize market dependence through covariance spectra, but they do not provide a unified operator representation for entropy, purity-based mixing, and standardized structural deviations built from rolling multi-feature trajectories. We propose the Quantum Network of Assets (QNA), a quantum-inspired but non-physical density-operator framework in which normalized asset-level state…
Stablecoins have emerged as a significant component of global financial infrastructure, with aggregate market capitalization surpassing USD250 billion in 2025. Their increasing integration into payment and settlement systems has simultaneously introduced novel channels of systemic exposure, particularly liquidity risk during periods of market stress. This study develops a hybrid monetary architecture that embeds fiat…
A CNN shares a local kernel across spatial (or temporal) positions, building translation-equivariant features. It is the inductive bias that cracked modern computer vision.
DQN approximates Q(s, a) with a deep net, using experience replay and a frozen target network so the TD target does not chase itself every step.
Dropout randomly zeroes hidden units during training so the net cannot rely on any single co-adaptation, then scales weights at test time (or uses inverted dropout).
A GAN trains a generator and a discriminator against each other: the generator maps noise to fake samples, the discriminator learns real vs fake, and the equilibrium is a generator whose samples match the data distribution.
A GNN updates each node from its neighbors. Message passing lets the model use relational structure — markets, molecules, citation graphs — instead of forcing a grid.
A neural network is a layered function approximator: units compute a weighted sum, apply a nonlinearity, and pass the result forward so the whole stack can learn a mapping from inputs to outputs.
The perceptron is the original trainable linear classifier: a weighted sum plus a threshold. It is the atom of neural nets, and it cannot learn XOR without a hidden layer.
An RNN applies the same transition to a sequence, threading a hidden state through time: h_t = f(h_{t−1}, x_t). Plain RNNs struggle to learn long dependencies.
A ResNet learns a residual f(x) added back to x, so extra layers can default to identity. That skip connection made 100+ layer nets trainable.
U-Net is an encoder–decoder CNN with skip connections from downsampling to upsampling paths, designed so fine spatial detail survives compression.
Xavier/Glorot initialization scales initial weights so variance is preserved through a layer — the default that made deep tanh/sigmoid nets trainable before BatchNorm.
Net Stable Funding Ratio — Stable funding versus long-term assets — constrains maturity transformation.
A CTA whose signals come from ML (trees, nets, representations) rather than a hand-written MA — still a futures risk engine underneath.
Gross is the sum of |positions|; net is the signed residual — in a CTA both move with signal agreement, unlike a 130/30 that is always ~100 net.
Gold, silver, copper, and sometimes aluminum, zinc, nickel — a mix of monetary metals and industrial cycle metals.
Crowding out is when public borrowing or spending raises rates or absorbs real resources so private investment or net exports fall, shrinking the net fiscal impulse.
The impossible trinity (trilemma) says a country cannot simultaneously have a fixed exchange rate, free capital mobility, and an independent monetary policy — it must drop one.
Money supply is the measured stock of money — M0/MB, M1, M2 — a quantity that depends on what you count as money.
The quantity theory is MV = PY: money times velocity equals nominal income. In the strong form, a one-off money increase raises prices one-for-one if V and Y are stable.
The Taylor rule is a simple policy reaction: set the policy rate to a neutral real rate plus inflation, then add weights on the inflation gap and the output gap.
Inflation is a sustained rise in the general price level — a decline in purchasing power, not a one-off relative-price shock.
Monetary Dominance Regime — Regime where the central bank anchors inflation over fiscal needs.
Earnings per share is net income attributable to common, divided by weighted-average shares — basic or diluted.
Enterprise value is the market value of operating assets — equity plus net debt and other non-equity claims, minus non-operating cash.
Goodwill is the residual purchase-price premium over identifiable net assets in an acquisition — an accounting plug that must be tested, not amortized in US GAAP.
NAV is the fund’s assets minus liabilities, per share — the accounting price at which open-end vehicles deal.
Return on assets is net income (or EBIT) divided by total assets — how much profit the whole sheet produces before you celebrate leverage.
Return on equity is net income divided by book equity — the accounting yield on residual capital, levered.
The February–March 2020 COVID crash was a dash-for-cash that hit even Treasuries, ended by an unprecedented joint monetary-fiscal backstop — a health shock that became a market-function crisis.
John Law’s Mississippi Company and Banque Royale fused monetary expansion, colonial equity, and French public finance until 1720 — a state-run bubble that ended in a paper-money collapse.
Tulip mania was a 1636–37 Dutch futures craze in rare bulbs that collapsed in February 1637 — the template for a story-driven, lightly margined, socially contagious bubble.
Macroprudential Policy — Countercyclical tools that alter credit creation before traditional monetary policy reacts.
Monetary policy is the central bank’s control of short rates, liquidity, and sometimes the balance sheet — the price of reserves and the path of the front end.
Buy stocks trading below net current assets (Graham’s net-nets) — a deep-value liquidation screen, not a quality compounder book.
Long high net-payout (dividends plus net buybacks) names and short low/negative payout — shareholder yield as a value/quality hybrid.
A CTA whose signals come from ML (trees, nets, representations) rather than a hand-written MA — still a futures risk engine underneath.
A CNN shares a local kernel across spatial (or temporal) positions, building translation-equivariant features. It is the inductive bias that cracked modern computer vision.
The February–March 2020 COVID crash was a dash-for-cash that hit even Treasuries, ended by an unprecedented joint monetary-fiscal backstop — a health shock that became a market-function crisis.
Crowding out is when public borrowing or spending raises rates or absorbs real resources so private investment or net exports fall, shrinking the net fiscal impulse.
Gross is the sum of |positions|; net is the signed residual — in a CTA both move with signal agreement, unlike a 130/30 that is always ~100 net.
DQN approximates Q(s, a) with a deep net, using experience replay and a frozen target network so the TD target does not chase itself every step.
Dropout randomly zeroes hidden units during training so the net cannot rely on any single co-adaptation, then scales weights at test time (or uses inverted dropout).
Earnings per share is net income attributable to common, divided by weighted-average shares — basic or diluted.
Enterprise value is the market value of operating assets — equity plus net debt and other non-equity claims, minus non-operating cash.
A GAN trains a generator and a discriminator against each other: the generator maps noise to fake samples, the discriminator learns real vs fake, and the equilibrium is a generator whose samples match the data distribution.
Goodwill is the residual purchase-price premium over identifiable net assets in an acquisition — an accounting plug that must be tested, not amortized in US GAAP.
A GNN updates each node from its neighbors. Message passing lets the model use relational structure — markets, molecules, citation graphs — instead of forcing a grid.
The impossible trinity (trilemma) says a country cannot simultaneously have a fixed exchange rate, free capital mobility, and an independent monetary policy — it must drop one.
Macroprudential Policy — Countercyclical tools that alter credit creation before traditional monetary policy reacts.
Gold, silver, copper, and sometimes aluminum, zinc, nickel — a mix of monetary metals and industrial cycle metals.
John Law’s Mississippi Company and Banque Royale fused monetary expansion, colonial equity, and French public finance until 1720 — a state-run bubble that ended in a paper-money collapse.
Monetary Dominance Regime — Regime where the central bank anchors inflation over fiscal needs.
Monetary policy is the central bank’s control of short rates, liquidity, and sometimes the balance sheet — the price of reserves and the path of the front end.
NAV is the fund’s assets minus liabilities, per share — the accounting price at which open-end vehicles deal.
Buy stocks trading below net current assets (Graham’s net-nets) — a deep-value liquidation screen, not a quality compounder book.
Long high net-payout (dividends plus net buybacks) names and short low/negative payout — shareholder yield as a value/quality hybrid.
Net Stable Funding Ratio — Stable funding versus long-term assets — constrains maturity transformation.
A neural network is a layered function approximator: units compute a weighted sum, apply a nonlinearity, and pass the result forward so the whole stack can learn a mapping from inputs to outputs.
The perceptron is the original trainable linear classifier: a weighted sum plus a threshold. It is the atom of neural nets, and it cannot learn XOR without a hidden layer.