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This paper examines the possibility of using derivative-implied risk premia to explain stock returns. The rapid development of derivative markets has led to the possibility of trading various kinds of risks, such as credit and interest rate risk, separately from each other. This paper uses credit default swaps and equity options to determine risk premia which are then used to form portfolios that are regressed agains…
This paper considers liquidity as an explanation for the positive association between expected idiosyncratic volatility (IV) and expected stock returns. Liquidity costs may affect the stock returns, through bid-ask bounce and other microstructure-induced noise, which will affect the estimation of IV. We use a novel method (developed by Weaver, 1991) to eliminate microstructure influences from stock closing price-base…
Geometric mean market makers (G3Ms), such as Uniswap and Balancer, comprise a popular class of automated market makers (AMMs) defined by the following rule: the reserves of the AMM before and after each trade must have the same (weighted) geometric mean. This paper extends several results known for constant-weight G3Ms to the general case of G3Ms with time-varying and potentially stochastic weights. These results inc…
We analyse the effect of a proportional wealth tax on asset returns, portfolio choice, and asset pricing. The tax is levied annually on the market value of all holdings at a uniform rate. We show that such a tax is economically equivalent to the government acquiring a proportional stake in the investor's portfolio each period -- a form of risk sharing in which expected wealth and risk are reduced by the same factor, …
Forming quantitative portfolios using statistical risk models presents a significant challenge for hedge funds and portfolio managers. This research investigates three distinct statistical risk models to construct quantitative portfolios of 1,000 floating stocks in the US market. Utilizing five different investment strategies, these models are tested across four periods, encompassing the last three major financial cr…
The paper examines the Chinese market reaction to the ADR issue by comparing returns and their stochastic variances of the Chinese firms cross-listed in the U.S. stock market. First, It was implemented capital asset pricing model (CAPM) to determine expected returns A and N shares. The CAPM provided with a methodology to quantify risk and translate that risk into estimates of expected return on equity. Overall findin…
We propose a 4-factor model for overnight returns and give explicit definitions of our 4 factors. Long horizon fundamental factors such as value and growth lack predictive power for overnight (or similar short horizon) returns and are not included. All 4 factors are constructed based on intraday price and volume data and are analogous to size (price), volatility, momentum and liquidity (volume). Historical regression…
This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates the need for inequality constraints on parameters, and (3) allows for a straightforward interpretation of the "persistence" of shocks to volatility. In the above respects, it is an improvement over the widely-used GARCH …
Commodity futures risk premiums vary across commodities and over time depending on the level of physical inventories, as predicted by the Theory of Storage.Using a comprehensive dataset on 31 commodity futures and physical inventories between 1969 and 2006, we show that the convenience yield is a decreasing, non-linear relationship of inventories.Price measures, such as the futures basis, prior futures returns, and s…
We propose a multivariate generalisation of the Lo-MacKinlay (1988) variance ratio that decomposes long-horizon equity-return dynamics into separate return-channel and volatility-channel memory components across the cross-section of asset returns. The framework identifies a parsimonious five-factor model - capturing persistent, antipersistent, and multi-scale memory in returns and volatility - that fits four U.S. por…
In a deep forecasting pipeline for fat-tailed financial returns at short horizons, which matters more - the backbone architecture or the output head? We compare four modern backbones (TimesNet, DLinear, N-BEATS, iTransformer) under three output heads: a point head, a single-Gaussian density head, and a Gaussian mixture density head with K=4 components. On S and P 500 monthly log-returns (1871-2023) under anchored wal…
Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically from market data. However, these models fail to account for fat-tailed returns, which characterize actual market behavior with more frequent extreme events. Furthermore, historical data is treated homogeneously, without accounting for temporal importance, leading models to…
This paper proposes a Structural Matrix Autoregressive (SMAR) model for the joint analysis of asset returns, realized volatility, and trading volume in a large-dimensional setting. This framework simultaneously captures dynamic spillovers across financial variables and cross-sectional dependence across assets while preserving a parsimonious parameterization relative to conventional vector autoregressive models. The m…
Classical portfolio optimization treats expected returns, covariances, and allocations as deterministic. Modern practice replaces at least one by a distribution: a posterior over parameters, a law of future returns, a stochastic allocation policy, or a distributional-robustness set. We call distributional portfolio optimization (DPO) the unified framework in which weights, returns, and parameters are all modeled as p…
We consider a generalization of the variance-gamma (generalized asymmetric Laplace) distribution, defined as a normal mean - variance mixture with a gamma mixing distribution. While this model is typically studied in the univariate setting, we assume that the gamma mixing variable is observed alongside the primary variable, resulting in a bivariate framework. In this setting, maximum likelihood estimation becomes sig…
We utilize FinBERT, a domain-specific transformer model, to parse 6.5 million sentences from 16,428 S&P 500 quarterly earnings call transcripts (2015-2025) and demonstrate that post-earnings stock returns are not equally affected by all speakers in a conference call. Our section-weighted sentiment, with empirically derived speaker weights (Analyst 49%, CFO 30%, Executive 16%, Other 5%), achieves an out-of-sample Spea…
This paper examines whether SEC Form 4 insider purchase filings predict abnormal returns in U.S. microcap stocks. The analysis covers 17,237 open-market purchases across 1,343 issuers from 2018 through 2024, restricted to market capitalizations between \$30M and \$500M. A gradient boosting classifier trained on insider identity, transaction history, and market conditions at disclosure achieves AUC of 0.70 on out-of-s…
Can fully agentic AI nowcast stock returns? We deploy a state-of-the-art Large Language Model to evaluate the attractiveness of each Russell 1000 stock daily, starting from April 2025 when AI web interfaces enabled real-time search. Our data contribution is unique along three dimensions. First, the nowcasting framework is completely out-of-sample and free of look-ahead bias by construction: predictions are collected …
A debt covenant is a contractual limit on the borrower — maintain a ratio, not do a thing, or report a thing — that turns a miss into a default or a fee.
A CTA that tries to pick turns — anticipatory shorts of highs and buys of lows — the opposite personality of a breakout shop.
A listed product that tries to match SG Trend-like returns with a small liquid futures set — cheap access, incomplete universe, visible crowding.
Distressed Debt Ratio — Share of debt trading at deep discounts — early warning for credit cycle turns.
Use same-calendar-month returns in prior years as a cross-sectional signal — annual seasonality in the stock sort.
Time equity beta with oil’s recent move or level — a macro overlay that treats crude as a growth/inflation signal.
Overweight names (or the market) into scheduled earnings because average returns cluster around announcement windows.
Use how similar this year’s 10-K/10-Q language is to last year’s as a signal — boilerplate vs change as alternative data.
Long commodity futures with the strongest trailing returns and short the weakest — cross-sectional commodity momentum.
Rotate country equity indexes toward those with the strongest trailing returns — momentum at the index, not the stock, layer.
Allocate to the mutual funds (or share classes) with the strongest trailing returns — momentum on the manager wrapper.
Long high R&D (scaled by assets or market) names and short low-R&D — a capitalized-intangibles / innovation sort.
Rank on residual (idiosyncratic) past returns after taking out market/factor beta — momentum with less factor crash.
Hold the equity sectors with the strongest trailing returns and drop the laggards — industry momentum as a monthly rotation.
Be long the index around month-end / month-start and lighter mid-month — a calendar clustering of returns.
Use same-calendar-month returns in prior years as a cross-sectional signal — annual seasonality in the stock sort.
A CTA that tries to pick turns — anticipatory shorts of highs and buys of lows — the opposite personality of a breakout shop.
Time equity beta with oil’s recent move or level — a macro overlay that treats crude as a growth/inflation signal.
A listed product that tries to match SG Trend-like returns with a small liquid futures set — cheap access, incomplete universe, visible crowding.
A debt covenant is a contractual limit on the borrower — maintain a ratio, not do a thing, or report a thing — that turns a miss into a default or a fee.
Distressed Debt Ratio — Share of debt trading at deep discounts — early warning for credit cycle turns.
Overweight names (or the market) into scheduled earnings because average returns cluster around announcement windows.
Use how similar this year’s 10-K/10-Q language is to last year’s as a signal — boilerplate vs change as alternative data.
Long commodity futures with the strongest trailing returns and short the weakest — cross-sectional commodity momentum.
Rotate country equity indexes toward those with the strongest trailing returns — momentum at the index, not the stock, layer.
Allocate to the mutual funds (or share classes) with the strongest trailing returns — momentum on the manager wrapper.
Long high R&D (scaled by assets or market) names and short low-R&D — a capitalized-intangibles / innovation sort.
Rank on residual (idiosyncratic) past returns after taking out market/factor beta — momentum with less factor crash.
Hold the equity sectors with the strongest trailing returns and drop the laggards — industry momentum as a monthly rotation.
Be long the index around month-end / month-start and lighter mid-month — a calendar clustering of returns.