arXiv · arXiv q-fin · 2018
The objective of this paper is to measure the degree of home bias (HB) within holdings portfolio and to identify their determining factors. By following literature and an international capital asset pricing model, we have chosen quite a number of susceptible factors that impact HB. This model is, hence, estimated for 20 countries, with cross-section econometrics, between 2008 and 2013. Our results show that all count…
Mounira Chniguir, Mohamed Kefi, Jamel Henchiri
arXiv · arXiv q-fin · 2008
Performance of investment managers are evaluated in comparison with benchmarks, such as financial indices. Due to the operational constraint that most professional databases do not track the change of constitution of benchmark portfolios, standard tests of performance suffer from the "look-ahead benchmark bias," when they use the assets constituting the benchmarks of reference at the end of the testing period, rather…
Gilles Daniel, Didier Sornette, Peter Wohrmann
arXiv · arXiv q-fin · 2026
This study quantifies survivorship bias in India's NIFTY Smallcap 250 index using a dataset of 1,437 stocks over nine years (2016-2025). By reconstructing historical index composition through market capitalization ranking and comparing equal-weight portfolios of current constituents versus all historical members, I show that survivor-only backtesting overstates annual returns by 4.94 percentage points (23.3%) and Sha…
Harjot Singh Ranse
arXiv · arXiv q-fin · 2025
Backtests on historical data are the basis for practical evaluations of portfolio selection rules, but their reliability is often limited by reliance on a single sample path. This can lead to high estimation variance. Resampling techniques offer a potential solution by increasing the effective sample size, but can disrupt the temporal ordering inherent in financial data and introduce significant bias. This paper inve…
Andrew Paskaramoorthy, Terence van Zyl, Tim Gebbie
arXiv · arXiv q-fin · 2022
In recent years, many practitioners in quantitative finance have attempted to use Deep Reinforcement Learning (DRL) to build better quantitative trading (QT) strategies. Nevertheless, many existing studies fail to address several serious challenges, such as the non-stationary financial environment and the bias and variance trade-off when applying DRL in the real financial market. In this work, we proposed Safe-FinRL,…
Zitao Song, Xuyang Jin, Chenliang Li
arXiv · arXiv q-fin · 2010
We study the informational efficiency of a market with a single traded asset. The price initially differs from the fundamental value, about which the agents have noisy private information (which is, on average, correct). A fraction of traders revise their price expectations in each period. The price at which the asset is traded is public information. The agents' expectations have an adaptive component and a social-in…
Gani Aldashev, Timoteo Carletti, Simone Righi
arXiv · arXiv q-fin · 2025
Rolling-window factor pipelines for Chinese A-share markets contain a subtle but costly flaw: daily price-move limits (+/-10% main-board, +/-20% STAR/ChiNext) render a fraction of closing prices non-executable, yet standard implementations ingest these values before any row-filtering runs. The contaminated aggregates propagate silently through moving averages, correlations, and ranks--a failure mode we term "upstream…
Yimin Du
arXiv · arXiv q-fin · 2026
Agent-based models of markets readily produce emergent instabilities, but telling a genuine collective effect apart from a parameter artefact takes discipline. We apply Bouchaud's phase-diagram method to a continuous-double-auction order-book model. The method is to map the full phase diagram, test its robustness to rule changes, and rule out degenerate and numerical origins before we call any feature a tipping point…
Jan Novotny
arXiv · arXiv q-fin · 2025
We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothesis-driven signal generation with reinforcement learning and strict out-of-sample testing. The framework enforces strict information set discipline, employs rolling window validation across 34 independent test periods, maintains complete int…
Gagan Deep, Akash Deep, William Lamptey
arXiv · arXiv q-fin · 2022
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…
M. Reza Bradrania, Maurice Peat, Stephen Satchell
arXiv · arXiv q-fin · 2023
Mean-variance analysis is widely used in portfolio management to identify the best portfolio that makes an optimal trade-off between expected return and volatility. Yet, this method has its limitations, notably its vulnerability to estimation errors and its reliance on historical data. While shrinkage estimators and factor models have been introduced to improve estimation accuracy through bias-variance trade-offs, an…
Kwong Yu Chong
arXiv · arXiv q-fin · 2026
Portfolio optimization under cardinality constraints transforms the classical Markowitz mean-variance problem from a convex quadratic problem into an NP-hard combinatorial optimization problem. This paper introduces a novel approach using THRML (Thermodynamic HypergRaphical Model Library), a JAX-based library for building and sampling probabilistic graphical models that reformulates index tracking as probabilistic in…
Javier Mancilla, Theodoros D. Bouloumis, Frederic Goguikian
arXiv · arXiv q-fin · 2025
Robust optimization provides a principled framework for decision-making under uncertainty, with broad applications in finance, engineering, and operations research. In portfolio optimization, uncertainty in expected returns and covariances demands methods that mitigate estimation error, parameter instability, and model misspecification. Traditional approaches, including parametric, bootstrap-based, and Bayesian metho…
Daniel Cunha Oliveira, Grover Guzman, Nick Firoozye
arXiv · arXiv q-fin · 2023
This work seeks to answer key research questions regarding the viability of reinforcement learning over the S&P 500 index. The on-policy techniques of Value Iteration (VI) and State-action-reward-state-action (SARSA) are implemented along with the off-policy technique of Q-Learning. The models are trained and tested on a dataset comprising multiple years of stock market data from 2000-2023. The analysis presents the …
Ishan S. Khare, Tarun K. Martheswaran, Akshana Dassanaike-Perera
arXiv · arXiv q-fin · 2022
Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage. In this paper, we present an openly accessible FinR…
Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang
arXiv · arXiv q-fin · 2018
Siegel's paradox is a fundamental question in international finance about exchange rates for futures contracts and has puzzled many scholars for over forty years. The unorthodox approach presented in this article leads to an arbitrage-free solution which is invariant under currency re-denominations and is symmetric, as explained. We will also give a complete classification of all such aggregators in the general case.…
Keivan Mallahi-Karai, Pedram Safari
arXiv · arXiv q-fin · 2014
Using a proprietary dataset of meta-orders and prediction signals, and assuming a quasi-linear impact model, we deconvolve market impact from past correlated trades and a predictable return component to elicit the temporal dependence of the market impact of a single daily meta-order, over a ten day horizon in various equity markets. We find that the impact of single meta-orders is to a first approximation universal a…
X. Brokmann, E. Serie, J. Kockelkoren, J. -P. Bouchaud
arXiv · arXiv q-fin · 2013
We analyze realized volatilities constructed using high-frequency stock data on the Tokyo Stock Exchange. In order to avoid non-trading hours issue in volatility calculations we define two realized volatilities calculated separately in the two trading sessions of the Tokyo Stock Exchange, i.e. morning and afternoon sessions. After calculating the realized volatilities at various sampling frequencies we evaluate the b…
Tetsuya Takaishi, Ting Ting Chen, Zeyu Zheng