The Journal of Investment Strategies is dedicated to the rigorous treatment of modern investment strategies; going well beyond the “classical” approaches in both its subject instruments and methodologies. In providing a balanced representation of academic, buy-side and sell-side research, the Journal promotes the cross-pollination of ideas amongst researchers and practitioners, achieving a unique nexus of academia and industry on one hand, and theoretical and applied models on the other.
The Journal contains in-depth research papers as well as discussion articles on technical and market subjects, and aims to equip the global investment community with practical and cutting-edge research in order to understand and implement modern investment strategies.
With a focus on important contemporary investment strategies, techniques and management, the journal considers papers on the following areas:
- Fundamental Strategies: including fundamental macro, fundamental equity or credit selection
- Relative Value Strategies: estimation of and investing in the relative valuation of related securities, both vanilla and derivatives
- Tactical Strategies: strategies based on forecasting of, and investing in, patterns of market behavior, such as momentum or mean reversion, and tactical asset allocation strategies.
- Event-Driven Strategies: strategies based on the forecast of likelihood of market-moving events or market reactions to such events
- Algorithmic Trading Strategies: models of market microstructure, liquidity and market impact and algorithmic trade execution and market-making strategies
- Principal Investment Strategies: investment strategies for illiquid securities and principal ownership or funding of real assets and businesses
- Portfolio Management and Asset Allocation: models for portfolio optimization, risk control, performance attribution and asset allocation
- Econometric and Statistical Methods: with applications to investment strategies
The Journal of Investment Strategies has been selected for coverage in the Clarivate Analytics Emerging Sources Citation Index.
An uncertainty quantification framework for the achievability of backtesting results of trading strategies
In this paper, the authors propose a framework for implementing and backtesting trading strategies.
In this paper, the authors provide tools to test the correctness of backtest engines for setups with at most one entry and one exit.
This paper brings Black–Litterman optimization, exotic betas and varying starting portfolios together into one complete, symbiotic framework.
This paper offers a new perspective on portfolio allocation, which avoids any explicit optimization and instead takes the point of view of symmetry.
This paper studies centrality (interconnectedness risk) measures and their added value in an active portfolio optimization framework.
In this paper the authors investigate how fixed-fee transaction costs affect portfolio rebalancing.
The authors propose an analytical framework to measure investment opportunities and allocate risk across time based on the Mahalanobis distance.
In this paper, the authors give complete algorithms and source code for constructing statistical risk models.
This paper proposes using an optimization mechanism in the currency overlay portfolio construction process.
The authors of this paper derive an optimal trading strategy that benchmarks the closing price in a mean–variance optimization framework.
The authors of this paper aim to demystify portfolios selected by robust optimization by looking at limiting portfolios in the cases of both large and small uncertainty in mean returns.
The authors of this paper analyze an equal-weight portfolio of global cross-asset-class risk factor exposures.
The authors of this paper give a complete algorithm and source code for constructing general multifactor risk models via any combination of style factors, principal components and/or industry factors.
This paper uses the fractional Kelly strategies framework to show that optimal portfolios with low-beta stocks generate higher median wealth and lower intra-horizon shortfall risk.
The authors of this paper apply a forward-looking approach to the minimum variance portfolio optimization problem for a selection of 100 stocks.
This paper investigates the causes of the quality anomaly by exploring two potential explanations - the “risk view” and the “behavioral view”.
This paper investigates the optimal design of funds which provide capital protection at a specific maturity.
This paper studies the problem of optimal trading using general alpha predictors with linear costs and temporary impact.
This paper projects an optimal unconstrained factor portfolio onto a set of all feasible portfolios using tracking error as a distance measure.
This paper analyzes empirical data for 4000 real-life trading portfolios with holding periods of about 0.7-19 trading days.