Papers, wiki, Option Blackboard, encyclopedia, and cards.
Within the context of risk integration, we introduce in risk measurement stochastic holding period (SHP) models. This is done in order to obtain a `liquidity-adjusted risk measure' characterized by the absence of a fixed time horizon. The underlying assumption is that - due to changes on market liquidity conditions - one operates along an `operational time' to which the P&L process of liquidating a market portfolio i…
In this work we propose a framework to construct Market-Implied Sustainability (MIS) scores for individual firms by exploiting fund-level sustainability classifications and granular portfolio holdings. The central idea is that the relative over/under-representation of a stock in sustainability-oriented funds reveals a market-based assessment of its sustainability profile. We implement the methodology in the European …
In this paper, we review modern approaches to building interpretable models of property markets using machine learning on the base of mass valuation of property in the Primorye region, Russia. There are numerous potential difficulties one could encounter in the effort to build a good model. Their main source is the huge difference between noisy real market data and ideal data usually used in tutorials on machine lear…
Recent deep reinforcement learning (DRL) methods in finance show promising outcomes. However, there is limited research examining the behavior of these DRL algorithms. This paper aims to investigate their tendencies towards holding or trading financial assets as well as purchase diversity. By analyzing their trading behaviors, we provide insights into the decision-making processes of DRL models in finance application…
In response to growing demand for resilient and transparent financial instruments, we introduce a novel framework for replicating private equity (PE) performance using liquid, AI-enhanced strategies. Despite historically delivering robust returns, private equity's inherent illiquidity and lack of transparency raise significant concerns regarding investor trust and systemic stability, particularly in periods of height…
Deep learning offers new tools for portfolio optimization. We present an end-to-end framework that directly learns portfolio weights by combining Long Short-Term Memory (LSTM) networks to model temporal patterns, Graph Attention Networks (GAT) to capture evolving inter-stock relationships, and sentiment analysis of financial news to reflect market psychology. Unlike prior approaches, our model unifies these elements …
The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility. To address the challenge of managing dynamic portfolios in such an environment, this paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations. Using data on daily frequencies of the ten most capitalized crypt…
In this paper, we consider a generic interest rate market in the presence of roll-over risk, which generates spreads in spot/forward term rates. We do not require classical absence of arbitrage and rely instead on a minimal market viability assumption, which enables us to work in the context of the benchmark approach. In a Markovian setting, we extend the control theoretic approach of Gombani & Runggaldier (2013) and…
The success of a cross-sectional systematic strategy depends critically on accurately ranking assets prior to portfolio construction. Contemporary techniques perform this ranking step either with simple heuristics or by sorting outputs from standard regression or classification models, which have been demonstrated to be sub-optimal for ranking in other domains (e.g. information retrieval). To address this deficiency,…
Financial markets change their behaviours abruptly. The mean, variance and correlation patterns of stocks can vary dramatically, triggered by fundamental changes in macroeconomic variables, policies or regulations. A trader needs to adapt her trading style to make the best out of the different phases in the stock markets. Similarly, an investor might want to invest in different asset classes in different market regim…
Building event-conditioned market models requires separating macro-event labels from persistent microstructure state. We study this distinction in Binance BTCUSDT and ETHUSDT futures from 2023-2026, combining top-20 L2 order book data, trade-flow records, and macro-event windows. We define a supervised discrete L2 liquidity-state transition task, distinct from latent-regime detection and price-direction prediction, a…
Leveraged ETFs (L-ETFs) are exchange-traded funds that achieve price movements several times greater than an index by holding index-linked futures such as Nikkei Stock Average Index futures. It is known that when the price of an L-ETF falls, the L-ETF uses the liquidity of futures to limit the decline through arbitrage trading. Conversely, when the price of a futures contract falls, the futures contract uses the liqu…
Transaction costs and regime shifts are major reasons why paper portfolios fail in live trading. We introduce FR-LUX (Friction-aware, Regime-conditioned Learning under eXecution costs), a reinforcement learning framework that learns after-cost trading policies and remains robust across volatility-liquidity regimes. FR-LUX integrates three ingredients: (i) a microstructure-consistent execution model combining proporti…
This study examines active liquidity management by Indian open-ended equity mutual funds. We find that fund managers respond to inflows by increasing cash holdings, which are later used to purchase less-liquid stocks at favourable valuations. Funds with less liquid portfolios tend to maintain larger cash reserves to manage flows. Funds that make active liquidity choices yield statistically and economically significan…
The tokenization of real-world assets (RWAs) promises to transform financial markets by enabling fractional ownership, global accessibility, and programmable settlement of traditionally illiquid assets such as real estate, private credit, and government bonds. While technical progress has been rapid, with over \$25 billion in tokenized RWAs brought on-chain as of 2025, liquidity remains a critical bottleneck. This pa…
As decentralized finance (DeFi) evolves, distinguishing between user behaviors - liquidity provision versus active trading - has become vital for risk modeling and on-chain reputation. We propose a behavioral scoring framework for Uniswap that assigns two complementary scores: a Liquidity Provision Score that assesses strategic liquidity contributions, and a Swap Behavior Score that reflects trading intent, volatilit…
Meme tokens represent a distinctive asset class within the cryptocurrency ecosystem, characterized by high community engagement, significant market volatility, and heightened vulnerability to market manipulation. This paper introduces an innovative approach to assessing liquidity risk in meme token markets using entity-linked address identification techniques. We propose a multi-dimensional method integrating fund fl…
Mid-cap companies, generally valued between \$2 billion and \$10 billion, provide investors with a well-rounded opportunity between the fluctuation of small-cap stocks and the stability of large-cap stocks. This research builds upon the long-short equity approach (e.g., Michaud, 2018; Dimitriu, Alexander, 2002) customized for mid-cap equities, providing steady risk-adjusted returns yielding a significant Sharpe ratio…
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.
Fractional-reserve banking is taking deposits and holding only a fraction in reserves or liquid assets — credit creation with a run risk.
Gold price reflects demand for a non-yielding reserve asset and is often used as a signal for real yields, macro uncertainty, and confidence in fiat systems.
Gold Real Yields Correlation — Gold as non-yielding asset inversely sensitive to real rates and USD.
Theta is the sensitivity of option value to the passing of time — the daily rent of holding convexity.
Window dressing is quarter- or year-end portfolio cosmetics: dump the losers, buy the winners or the cash, so the snapshot holdings look like the brochure.
September 2022’s UK gilt crash was a liability-driven-investment margin spiral: leveraged duration in pension LDI funds met a fiscal shock and forced gilt sales until the BoE bought the market.
Carry and Roll Down — Expected return from holding higher-yielding tenor as it rolls down a positively sloped curve.
Cash Market Liquidity Premium — Extra yield demanded for holding less liquid cash instruments.
The ECB balance sheet reflects the scale of European Central Bank asset holdings and helps track euro-area liquidity, policy transmission, and duration absorption.
The Fed balance sheet reflects the scale of Federal Reserve asset holdings and is a major driver of reserves, liquidity conditions, and policy transmission.
Primary dealer holdings track how much inventory dealers are carrying, offering insight into balance-sheet absorption, market-making capacity, and Treasury market strain.
Liability Driven Investing — Pension hedging of liabilities with long duration bonds/swaps.
Term premium is the extra compensation investors demand for holding longer-term bonds instead of rolling short-term debt, reflecting duration risk, uncertainty, and market structure.
Copy (with a lag) the disclosed long holdings of selected 13F filers — a delayed clone of someone else’s book.
Short high asset-growth firms and long low/negative growth — the investment/empire-building anomaly.
Intersect 12-1 momentum with low asset growth — keep winners that are not empire-building.
Copy (with a lag) the disclosed long holdings of selected 13F filers — a delayed clone of someone else’s book.
Short high asset-growth firms and long low/negative growth — the investment/empire-building anomaly.
Carry and Roll Down — Expected return from holding higher-yielding tenor as it rolls down a positively sloped curve.
Cash Market Liquidity Premium — Extra yield demanded for holding less liquid cash instruments.
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 ECB balance sheet reflects the scale of European Central Bank asset holdings and helps track euro-area liquidity, policy transmission, and duration absorption.
The Fed balance sheet reflects the scale of Federal Reserve asset holdings and is a major driver of reserves, liquidity conditions, and policy transmission.
Fractional-reserve banking is taking deposits and holding only a fraction in reserves or liquid assets — credit creation with a run risk.
Gold price reflects demand for a non-yielding reserve asset and is often used as a signal for real yields, macro uncertainty, and confidence in fiat systems.
Gold Real Yields Correlation — Gold as non-yielding asset inversely sensitive to real rates and USD.
Intersect 12-1 momentum with low asset growth — keep winners that are not empire-building.
Primary dealer holdings track how much inventory dealers are carrying, offering insight into balance-sheet absorption, market-making capacity, and Treasury market strain.
Term premium is the extra compensation investors demand for holding longer-term bonds instead of rolling short-term debt, reflecting duration risk, uncertainty, and market structure.
Theta is the sensitivity of option value to the passing of time — the daily rent of holding convexity.
September 2022’s UK gilt crash was a liability-driven-investment margin spiral: leveraged duration in pension LDI funds met a fiscal shock and forced gilt sales until the BoE bought the market.
Window dressing is quarter- or year-end portfolio cosmetics: dump the losers, buy the winners or the cash, so the snapshot holdings look like the brochure.