arXiv · arXiv · 2024
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…
Saumya Kothari, Harsh Shah, Utkarsh Prajapati, Shrinjay Kaushik
arXiv · arXiv · 2023
Introduction: The paper addresses the challenging problem of predicting the short-term realized volatility of the Bitcoin price using order flow information. The inherent stochastic nature and anti-persistence of price pose difficulties in accurate prediction. Methods: To address this, we propose a method that transforms order flow data over a fixed time interval (snapshots) into images. The order flow includes trade…
Artem Lensky, Mingyu Hao
arXiv · arXiv · 2026
We develop a theoretical model that endogenizes the regulator's decision to impose short-selling bans to prevent large stock price declines. Empirically, we test the model's predictions using the cross-sectional variation in short-selling restrictions implemented across European countries in 2020. Consistent with our model, we find that bans had a detrimental effect on liquidity and failed to support the average pric…
Pasquale Della Corte, Robert Kosowski, Dimitris Papadimitriou, Nikolaos P. Rapanos
arXiv · arXiv · 2026
Systematic trend following has, on average, been profitable for at least two centuries; yet since approximately 2009, short-term trends have ceased to deliver reliable returns. Using a cross-section of roughly 100 liquid futures contracts spanning 1995-2025, together with an industry-representative CTA proxy, we document the break and characterise its dependence on signal speed and asset class. We evaluate four candi…
Jutta G. Kurth, Zoltan Eisler, Adam Rej, Jean-Philippe Bouchaud
arXiv · arXiv · 2025
With the reform of interest rate benchmarks, interbank offered rates (IBORs) like LIBOR have been replaced by risk-free rates (RFRs), such as the Secured Overnight Financing Rate (SOFR) in the U.S. and the Euro Short-Term Rate (\euro STR) in Europe. These rates exhibit characteristics like jumps and spikes that correspond to specific market events, driven by regulatory and liquidity constraints. To capture these char…
Alessandro Calvia, Marzia De Donno, Chiara Guardasoni, Simona Sanfelici
arXiv · arXiv · 2024
This paper investigates short-term behaviors of implied volatility of derivatives written on indexes in equity markets when the index processes are constructed by using a ranking procedure. Even in simple market settings where stock prices follow geometric Brownian motion dynamics, the ranking mechanism can produce the observed term structure of at-the-money (ATM) implied volatility skew for equity indexes. Our propo…
Huy N. Chau, Duy Nguyen, Thai Nguyen
arXiv · arXiv · 2024
This study delves into the analysis of financial markets through the lens of Wyckoff Phases, a framework devised by Richard D. Wyckoff in the early 20th century. Focusing on the accumulation pattern within the Wyckoff framework, the research explores the phases of trading range and secondary test, elucidating their significance in understanding market dynamics and identifying potential trading opportunities. By disse…
Jai Pal
arXiv · arXiv · 2021
The Secured Overnight Funding Rate (SOFR) is becoming the main Risk-Free Rate benchmark in US dollars, thus interest rate term structure models need to be updated to reflect the key features exhibited by the dynamics of SOFR and the forward rates implied by SOFR futures. Historically, interest rate term structure modelling has been based on rates of substantially longer time to maturity than overnight, but with SOFR …
Karol Gellert, Erik Schlögl
arXiv · arXiv · 2020
With the rapid development of artificial intelligence, data-driven methods effectively overcome limitations in traditional portfolio optimization. Conventional models primarily employ long-only mechanisms, excluding highly correlated assets to diversify risk. However, incorporating short-selling enables low-risk arbitrage through hedging correlated assets. This paper constructs a Deep Reinforcement Learning (DRL) por…
Gang Huang, Xiaohua Zhou, Qingyang Song
arXiv · arXiv · 2018
It is well known that the Cox-Ingersoll-Ross (CIR) stochastic model to study the term structure of interest rates, as introduced in 1985, is inadequate for modelling the current market environment with negative short interest rates. Moreover, the diffusion term in the rate dynamics goes to zero when short rates are small; both volatility and long-run mean do not change with time; they do not fit with the skewed (fat …
Giuseppe Orlando, Rosa Maria Mininni, Michele Bufalo
arXiv · arXiv · 2016
In this survey paper we discuss recent advances on short interest rate models which can be formulated in terms of a stochastic differential equation for the instantaneous interest rate (also called short rate) or a system of such equations in case the short rate is assumed to depend also on other stochastic factors. Our focus is on convergence models, which explain the evolution of interest rate in connection with th…
Zuzana Buckova, Beata Stehlikova, Daniel Sevcovic
arXiv · arXiv · 2012
We derive explicit recursive formulas for Target Close (TC) and Implementation Shortfall (IS) in the Almgren-Chriss framework. We explain how to compute the optimal starting and stopping times for IS and TC, respectively, given a minimum trading size. We also show how to add a minimum participation rate constraint (Percentage of Volume, PVol) for both TC and IS. We also study an alternative set of risk measures for t…
Mauricio Labadie, Charles-Albert Lehalle
OpenAlex · Applied Sciences · 2020 · cites 191
In quantitative trading, stock prediction plays an important role in developing an effective trading strategy to achieve a substantial return. Prediction outcomes also are the prerequisites for active portfolio construction and optimization. However, the stock prediction is a challenging task because of the diversified factors involved such as uncertainty and instability. Most of the previous research focuses on anal…
Van-Dai Ta, Chuan-Ming Liu, Direselign Addis Tadesse
arXiv · arXiv · 2026
We study whether nuclear and energy-adjacent equity options exhibit a harvestable variance risk premium. Using CRSP and OptionMetrics data for 2000-2024, we construct a systematic cash-secured short-put strategy on a curated universe of nuclear-related firms. The strategy compares at-the-money put implied volatility with GARCH-based realized volatility forecasts, then evaluates unconditional and IV/RV-filtered put-wr…
Jilang Miao, Nonna Sorokina
arXiv · arXiv · 2026
This paper proposes a two-stage decision support system for long-short portfolio optimization under environmental, social, and governance (ESG) considerations. In the first stage, assets are evaluated using a multi-criteria procedure based on TODIMSort, with criterion weights derived using the MEREC (Removal Effects of Criteria) method. This allows assets to be assigned to classes ordered according to preferences tha…
Giacomo di Tollo, Massimiliano Kaucic, Filippo Piccotto
arXiv · arXiv · 2026
In this paper, we investigate a portfolio investment problem under volatility uncertainty and short-sale constraints market via sublinear expectation which is used to model volatility uncertainty. We assume the stocks admit volatility uncertainty. Thus the related portfolio has upper variance (maximum risk) and lower variance (minimum risk). By introducing a risk factor $w$ to conduct coupled modeling of the maximum …
Jing He, Shuzhen Yang
arXiv · arXiv · 2026
We present a study of the short-maturity asymptotics for VIX and European option prices in local-stochastic volatility models with compound Poisson jumps. Both out-of-the-money (OTM) and at-the-money (ATM) asymptotics are considered. The leading-order asymptotics are obtained in closed-form. We apply our results to three examples: the Eraker model, a Kou-type model, and a folded normal model. Numerical illustrations …
Desen Guo, Dan Pirjol, Xiaoyu Wang, Lingjiong Zhu
arXiv · arXiv · 2026
Deep learning models are effective for sequential data modeling, yet commonly used activation functions such as ReLU, LeakyReLU, and PReLU often exhibit gradient instability when applied to noisy, non-stationary financial time series. This study introduces BrownianReLU, a stochastic activation function induced by Brownian motion that enhances gradient propagation and learning stability in Long Short-Term Memory (LSTM…
George Awiakye-Marfo, Elijah Agbosu, Victoria Mawuena Barns, Samuel Asante Gyamerah