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We address a portfolio selection problem that combines active (outperformance) and passive (tracking) objectives using techniques from convex analysis. We assume a general semimartingale market model where the assets' growth rate processes are driven by a latent factor. Using techniques from convex analysis we obtain a closed-form solution for the optimal portfolio and provide a theorem establishing its uniqueness. T…
This paper studies a type of periodic utility maximization for portfolio management in an incomplete market model, where the underlying price diffusion process depends on some external stochastic factors. The portfolio performance is periodically evaluated on the relative ratio of two adjacent wealth levels over an infinite horizon. For both power and logarithmic utilities, we formulate the auxiliary one-period optim…
We investigate whether the tails of firm-level idiosyncratic return distributions are driven by common shocks. We use quantile factor analysis to extract such common idiosyncratic quantile factors with asymmetric pricing effects and we find a significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year. This premium remains significant even when con…
BACKGROUND: Regularly updated data on stroke and its pathological types, including data on their incidence, prevalence, mortality, disability, risk factors, and epidemiological trends, are important for evidence-based stroke care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) aims to provide a standardised and comprehensive measurement of these metrics at globa…
Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer interpretability but limited flexibility, while deep learning models achieve strong performance yet often underutilize financial priors. We address this gap with PRISM-VQ (PRior-Informed Stock Model with Vector Quantization), a dynamic factor framework that integrates ex…
Estimating the covariance of asset returns, i.e., the risk model, is a key component of financial portfolio construction and evaluation. Most risk modeling approaches produce a factor model that decomposes the asset variability into two components: the first attributed to a small number of factors that are common among the assets and the second attributed to the idiosyncratic behavior of each asset. Third-party provi…
While traditional equity factor investing relies heavily on slow-moving fundamental accounting metrics, these models frequently suffer from factor crowding and miss real-time, sentiment-driven market dislocations. This study explores how institutional investors can leverage a high-dimensional library of 191 short-term, trading-based signals, originally developed for the retail-heavy Chinese A-share market, to enhance…
We document regime-dependent predictive structure between equity factors using 35 years of Fama-French data (1990-2024). We find that Value (HML) Granger-causes Size (SMB) during crisis regimes (p < 1e-4, 9-day lag) but not during normal conditions, validating across 5 of 6 historical stress events (2008, 2011, 2015, 2018, 2020). Regimes are identified via a Student-t HMM, which detects moderate crises such as 2011 (…
Drifts of asset returns are notoriously difficult to model accurately and, yet, trading strategies obtained from portfolio optimization are very sensitive to them. To mitigate this well-known phenomenon we study robust growth-optimization in a high-dimensional incomplete market under drift uncertainty of the asset price process $X$, under an additional ergodicity assumption, which constrains but does not fully specif…
Statistical arbitrage exploits temporal price differences between similar assets. We develop a framework to jointly identify similar assets through factors, identify mispricing and form a trading policy that maximizes risk-adjusted performance after trading costs. Our Attention Factors are conditional latent factors that are the most useful for arbitrage trading. They are learned from firm characteristic embeddings t…
This paper analyses the risk factors around investing in global supply chain infrastructure: the energy market, investor sentiment, and global shipping costs. It presents portfolio strategies associated with dynamic risks. A time-varying parameter vector autoregression (TVP-VAR) model is used to study the spillover and interconnectedness of the risk factors for global supply chain infrastructure portfolios from Janua…
Reinforcement learning (RL) has successfully automated the complex process of mining formulaic alpha factors, for creating interpretable and profitable investment strategies. However, existing methods are hampered by the sparse rewards given the underlying Markov Decision Process. This inefficiency limits the exploration of the vast symbolic search space and destabilizes the training process. To address this, Traject…
Commodity Trading Advisors (CTAs) have historically relied on trend-following rules that operate on vastly different horizons from long-term breakouts that capture major directional moves to short-term momentum signals that thrive in fast-moving markets. Despite a large body of work on trend following, the relative merits and interactions of short-versus long-term trend systems remain controversial. This paper adds t…
Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite advances in large language models and multi-agent systems, current quantitative research pipelines suffer from limited automation, weak interpretability, and fragmented coordination across key components such as factor mining and model innovation. In this pap…
Using introduced concept of the exchange and inflation rates adequacy, the relevance of them to the determining factors is found. We established close positive relation between hryvnia / dollar exchange and inflation rates, fiscal deficit, price level of energy sources, and money supply. On this basis, we give proposals for state macroeconomic policy to stabilize Ukrainian economy.
The fragility of financial systems was starkly demonstrated in early 2023 through a cascade of major bank failures in the United States, including the second, third, and fourth largest collapses in the US history. The highly interdependent financial networks and the associated high systemic risk have been deemed the cause of the crashes. The goal of this paper is to enhance existing systemic risk analysis frameworks …
The optimal portfolio size for a venture capital (VC) fund is a topic often debated, but there is no consensus on the best strategy. This is because it is a function of many factors. It is not easy to find a general formula that can be applied to all situations, and it largely depends on the goal of the fund. In this report, we will go through the different factors step by step, studying how they affect fund returns …
Mining companies to properly manage their operations and be ready to make business decisions, are required to analyze potential scenarios for main market risk factors. The most important risk factors for KGHM, one of the biggest companies active in the metals and mining industry, are the price of copper (Cu), traded in US dollars, and the Polish zloty (PLN) exchange rate (USDPLN). The main scope of the paper is to un…
Vanishing gradients are when backprop multiplies many |Jacobian| < 1 factors so early layers receive ~0 update — the reason plain deep RNNs and tanh stacks died.
Dutch disease is the squeeze on tradable non-resource sectors when a resource boom or capital inflow appreciates the real exchange rate and pulls factors into the booming sector.
An eigenvalue λ of A satisfies A v = λ v. In risk, eigenvalues of the covariance matrix are the variances of the principal components — they tell you how many true factors you have.
PCA finds orthogonal directions of maximum variance in a covariance (or correlation) matrix — the yield-curve level/slope/butterfly and many equity ‘statistical factors’ are PCA.
SVD factors any matrix A = U Σ V' into orthogonal rotations and a diagonal of singular values — the workhorse behind PCA, low-rank approximation, and many recommenders.
Alpha is return not explained by the risk factors you chose — a residual, not a personality.
Factor Momentum — Persistence in relative factor performance exploitable by systematic overlays.
The three-factor model adds size (SMB) and value (HML) to the market — a better cross-section than CAPM, still not the last word.
Idiosyncratic risk is residual variance after the factors — name-specific noise that diversification is supposed to shrink.
Low Volatility Anomaly — Empirical outperformance of low-beta stocks, crowded in risk-off regimes.
Quality Factor — Exposure to profitable, stable balance-sheet companies versus junk quality.
Size Premium — Historical return premium for smaller capitalisation stocks with liquidity caveats.
Smart Beta Strategies — Rules-based factor tilts packaged for institutional asset allocation.
Systematic risk is the part of return that moves with common factors — you get paid for it, and you cannot dilute it by adding names in the same factor.
Value Factor — Cheap versus expensive stocks — cyclical performance tied to rates and inflation.
Rotate smart-beta factors on their own momentum and blend the result with the market — a core-satellite factor timer.
Run value and momentum on the same multi-asset universe and combine them — Asness–Moskowitz–Pedersen ‘everywhere’ as a book.
Alpha is return not explained by the risk factors you chose — a residual, not a personality.
Dutch disease is the squeeze on tradable non-resource sectors when a resource boom or capital inflow appreciates the real exchange rate and pulls factors into the booming sector.
An eigenvalue λ of A satisfies A v = λ v. In risk, eigenvalues of the covariance matrix are the variances of the principal components — they tell you how many true factors you have.
Idiosyncratic risk is residual variance after the factors — name-specific noise that diversification is supposed to shrink.
PCA finds orthogonal directions of maximum variance in a covariance (or correlation) matrix — the yield-curve level/slope/butterfly and many equity ‘statistical factors’ are PCA.
SVD factors any matrix A = U Σ V' into orthogonal rotations and a diagonal of singular values — the workhorse behind PCA, low-rank approximation, and many recommenders.
Rotate smart-beta factors on their own momentum and blend the result with the market — a core-satellite factor timer.
Systematic risk is the part of return that moves with common factors — you get paid for it, and you cannot dilute it by adding names in the same factor.
Run value and momentum on the same multi-asset universe and combine them — Asness–Moskowitz–Pedersen ‘everywhere’ as a book.
Vanishing gradients are when backprop multiplies many |Jacobian| < 1 factors so early layers receive ~0 update — the reason plain deep RNNs and tanh stacks died.