Paper optimizes portfolios for absolute return funds with constraints.
problem Optimizing portfolios with constraints for absolute return funds.
method Stochastic control framework with numerical solution using kernel-based collocation method.
result Leverage is necessary to achieve the target level.
Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
problem Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
method Decomposed fund returns into price pressure and fundamental components, and identified the impact of price chasing on fund flows.
result Funds' self-inflated returns lead to wealth reallocation and market crashes, and can be predicted by fund illiquidity.
Shorting IG ETFs can hedge bond portfolios during market drawdowns effectively.
problem Managing downside risk in bond portfolios during market crises.
method Constructing three signals (Momentum, Liquidity, Credit) to dynamically hedge short IG positions.
result Dynamic hedge removes when predicted hedged return mean reverts, achieving higher returns and Sortino ratios.
Study examines Indian equity mutual funds' investment style and risk-shifting.
problem Understanding how Indian equity mutual funds' investment styles affect their returns.
method Estimating size and style beta coefficients, identifying breakpoints, analyzing investment styles, and assessing risk-shifting intensity.
result Funds can enhance returns by shifting to high-return styles like Small Value and Small Blend.
Study estimates Medallion's compounded return before fees at 31.8%.
problem Incorrectly using yearly returns for compounding leads to overestimation of fund performance.
method Used fund sizes and trading profits to estimate compounded return; used manager's wealth as proxy for Simons.
result Annualized compounded return of Medallion before fees is likely under 35%
The paper analyzes competition among fund managers using excess logarithmic returns and constructs games to find optimal allocations.
problem Optimal allocation strategies among fund managers considering excess logarithmic returns.
method Constructs both n-player and mean field games to address the competition problem. result The MFE of the MFG represents the limit of n-player game's equilibrium as n approaches infinity. The paper analyzes how open-end fund sales affect prices and returns.
problem How open-end fund sales impact prices and returns.
method Continuous-time market-clearing model to derive expected-return restrictions.
result Forced-sale pressure predicts actual fund selling and positive returns.
Sparse portfolio strategy from mutual funds' favorite stocks in China A share market.
problem Building a sparse portfolio from mutual funds' favorite stocks in a market with limited fund information.
method Analyzed mutual fund favorite stocks, used portfolio optimizer with constraints, and compared different methods.
result Sparse portfolios consistently outperform the benchmark index 930950.CSI.
The proprietary nature of Hedge Fund investing means that it is common practise for managers to release minimal information about their returns. The construction of a Fund of Hedge Funds portfolio requires a correlation matrix which often has to be estimated using a relatively small sample of monthly returns data which…
Study uses ML to analyze how interest rates affect fund returns, finding gradient boosting is effective.
problem Understanding how interest rate changes impact fund returns.
method Combines Machine Learning and causal inference, using Double Machine Learning framework.
result Gradient boosting is useful for predicting fund returns, showing a significant negative effect of interest rate increases.
A concept of martingale-fair index of return, consistent with Arbitrage Free Pricing Theory, is introduced. An explicit formula for the average rate of return of a group of investment/pension funds in a discrete time stochastic model is derived and several properties of this index are shown. In particular, it is proven…
Estimates growth loss in fund models and proposes a shrinkage method.
problem Estimating growth loss in fund models under frequentist and Bayesian estimation.
method Proposes a shrinkage method to target maximal growth with minimal deviation.
result Empirical evidence shows shrinkage gives a stable estimate closer to growth potential.
Investment strategy depends on many factors for venture capital funds.
problem Finding the optimal portfolio size for venture capital funds.
method Analyzes various factors affecting fund returns and optimal portfolio size, starting with basic assumptions and increasing complexity.
result Investment strategy depends on many factors, not a one-size-fits-all formula.
Proposes a comprehensive framework for financial product lead recommendations using graph representation learning and link prediction.
problem Challenges in surface lead recommendations for financial products due to changing market scenarios and difficulty in capturing holder's mindset.
method Bi-partite graph representation of financial holders and funds, GraphSage model for learning representations, link prediction model for ranking recommendations.
result The proposed graph ML solution outperforms baseline by 42%, 22%, and 14% in hit rate for top-k recommendations (50, 100, 200) and 18%, 19%, and 18% on unseen holders.
Exchange Traded Funds (ETFs) have been gaining increasing popularity in the investment community as is evidenced by the high growth both in the number of ETFs and their net assets since 2000. As ETFs are in nature similar to index mutual funds, in this paper we examined if this growing demand for ETFs can be explained …
Analyzes how many people can receive stable income in a pooled annuity fund.
problem Quantifying the trade-off between income stability and the number of members in a pooled annuity fund.
method Investment returns held constant, systematic longevity risk omitted. Derived an analytical expression for income stability.
result The number of fund members who receive stable income is independent of the mortality model.
The study forecasts ETF return direction using machine learning models.
problem Predicting the direction of ETF returns for investment decisions.
method Applied regression and classification models to historical ETF component data.
result Models outperformed naive and buy & hold strategies, especially linear regression and logistic regression.
Study uses machine learning and PolyModel to improve hedge fund performance.
problem Improving hedge fund investment performance with machine learning.
method Integration of machine learning techniques, PolyModel feature selection, and analysis of fund size.
result Machine learning enhances cumulative returns but increases annual volatility.
The use of absolute return volatility has many modelling benefits says John Cotter. An illustration is given for the market risk measure, minimum capital requirements.
Investors in Target Date Funds are automatically switched from high risk to low risk assets as their retirements approach. Such funds have become very popular, but our analysis brings into question the rationale for them. Based on both a model with parameters fitted to historical returns and on bootstrap resampling, we…
Hedge Funds are considered as one of the portfolio management sectors which shows a fastest growing for the past decade. An optimal Hedge Fund management requires an appropriate risk metrics. The classic CAPM theory and its Ratio Sharpe fail to capture some crucial aspects due to the strong non-Gaussian character of He…
Volatility of S&P 500 daily returns increases over 60 years.
problem Why does S&P 500 daily volatility increase over time?
method Hypothetical market forces increasing volatility.
result Long-term volatility of S&P 500 daily returns will continue to increase until a threshold.
This paper is intended as an investigation of the statistical properties of {\it absolute log-returns}, defined as the absolute value of the logarithmic price change, for the Nikkei 225 index in the 28-year period from January 4, 1975 to December 30, 2002. We divided the time series of the Nikkei 225 index into two per…
Combines absolute and relative wealth in portfolio optimization with power utility functions.
problem Optimizing portfolios with both absolute and relative wealth considerations.
method Integrates power utility functions for absolute and relative wealth, considering multiple benchmarks.
result Obtains an explicit solution for portfolio optimization combining absolute and relative wealth.
Study benchmarks mutual funds in India using DEA, finding efficiency metrics.
problem Benchmark mutual funds in India based on efficiency metrics.
method Data Envelopment Analysis (DEA) model incorporating risk, cost, return, and information ratio.
result DEA model identifies efficiency frontier and compares results with traditional metrics.
Study finds Indian mutual funds adjust cash holdings based on inflows, impacting stock purchases.
problem Active liquidity management by mutual funds in India.
method Examined cash holdings and stock purchases of Indian equity mutual funds.
result Funds with active liquidity choices outperform, highlighting the importance of this strategy.
Study examines how COVID-19 affected stock and crypto market efficiency.
problem Impact of COVID-19 on market efficiency of different asset classes.
method Analysis of price returns, absolute returns, and volatility increments in stock and cryptocurrency markets.
result Market efficiency varied by asset class and market, with some time series showing gradual decline over time.
This paper compares different DRO formulations for pension fund management.
problem Navigating uncertainty in asset liability management for pension funds.
method Three DRO formulations: mixture, box, and Wasserstein ambiguity sets.
result Wasserstein and box ambiguity sets outperform traditional approaches in fund performance.
Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may never view the exact composition of portfolio holdings. Therefore, the ability to estimate an informative set of asset weights is highly desirable for analysis. We present a compositional state space model for estimation of an…
Study optimal control strategy for hedge funds managers with PSAHARA utility family.
problem Optimizing risk and reward in incomplete markets with non-monotone risk aversion and convex compensation.
method Introduced PSAHARA utility family to model non-monotone risk aversion and convex compensation. Proved concavification techniques for non-concave utility functions. Derived explicit optimal control strategy.
result PSAHARA utility induces risk-taking behavior even with convex compensation, leading to high returns and volatility.
We suggest an empirical model of investment strategy returns which elucidates the importance of non-Gaussian features, such as time-varying volatility, asymmetry and fat tails, in explaining the level of expected returns. Estimating the model on the (former) Lehman Brothers Hedge Fund Index data, we demonstrate that th…
We present an algorithm for the decomposition of periodic financial return data into orthogonal factors of expected return and "systemic", "productive", and "nonproductive" risk. Generally, when the number of funds does not exceed the number of periods, the expected return of a portfolio is an affine function of its pr…
Deep learning models predict mutual funds' performance better than traditional methods.
problem Predicting mutual funds' performance accurately.
method Deep learning models (LSTM, GRUs) trained with Bayesian optimization and ensemble methods.
result Ensemble method of LSTM and GRUs achieves the highest accuracy in forecasting mutual funds' Sharpe ratios.
We analyze the possibility of reduction of systemic risk in financial markets through Pigouvian taxation of financial institutions which is used to support the rescue fund. We introduce the concept of the cascade risk with a clear operational definition as a subclass and a network related measure of the systemic risk. …
Optimal portfolios are found for a wide range of utility functions under hyperbolic returns.
problem Portfolio optimization under expected utility criterion for large portfolios.
method Analytical expressions for optimal portfolios under hyperbolic return distributions and various utility functions.
result The two-fund separation holds true for a broad class of utility functions.
The paper studies how to protect hedge funds from losses using reinsurance.
problem Identifying and protecting downside risk in hedge fund investments.
method Two approaches: Analytic closed-form solution and numerical simulation.
result The additional reinsurance contract provides full portfolio protection beyond a certain threshold.
New model values equity-linked securities with guaranteed return.
problem Valuation of equity-linked securities with guaranteed return.
method Replicate security price as sum of guaranteed amount and Asian style option price on basket.
result Analytical formulas derived for security price and hedge ratios.
We present a nonlinear stochastic differential equation (SDE) which mimics the probability density function (PDF) of the return and the power spectrum of the absolute return in financial markets. Absolute return as a measure of market volatility is considered in the proposed model as a long-range memory stochastic vari…
The study quantifies the impact of fund miscategorization using machine learning.
problem The impact of fund miscategorization on investment decisions.
method Formulated as a distance-based outlier detection problem, used Random Forest based distance metric learning.
result Identified funds with strong relationship to future returns as outliers.
In a collectivised pension fund, investors agree that any money remaining in the fund when they die can be shared among the survivors. We compute analytically the optimal investment-consumption strategy for a fund of n identical investors with homogeneous Epstein--Zin preferences, investing in the Black--Scholes mark…
A scenario in which regulators take the drastic step of requiring coverage of all venture bank investment loans using interbank borrowed funds is considered. In this scenario, a minimal amount of default insurance is used, such that Tier 1 and 2 capital requirements are still met. To do this, the default insurance perc…
Reinforcement learning crypto agent achieves high returns on Bitcoin derivatives.
problem Maximizing returns on volatile cryptocurrency markets.
method Online transfer learning with an echo state network and recurrent reinforcement learning.
result Achieves a total return of 350%, net of transaction costs, over five years.
GIFsentiment predicts stock market returns and investor sentiment from social media GIFs.
problem Understanding investor sentiment in the stock market.
method Constructing a sentiment index from social media GIFs and analyzing its correlation with market returns and volume.
result GIFsentiment positively predicts stock market returns and negatively predicts returns for up to four weeks.
Optimal portfolios for fat-tailed risks using a new tail risk measure.
problem Optimizing portfolios for pension funds and insurance liabilities with extreme risk sensitivity.
method Developed a new tail risk measure (Extreme Deviation, XD) and optimized portfolios based on this measure.
result Optimal portfolios maximize return per unit of XD, balancing hedging and risk contributions.
Generative model learns investment strategies without explicit utility specification.
problem Challenges in modeling complex, multi-objective fund optimization.
method Generative adversarial network (GAN) framework that learns latent strategy representations.
result Framework captures diverse investment styles and realizations of optimization parameters.
Market timing is an investment technique that tries to continuously switch investment into assets forecast to have better returns. What is the likelihood of having a successful market timing strategy? With an emphasis on modeling simplicity, I calculate the feasible set of market timing portfolios using index mutual fu…
PolyModel theory and iTransformer improve hedge fund portfolio construction.
problem Sparse financial time series data makes portfolio construction challenging.
method Identify asset pool, select risk factors, create quantitative and classical measures, and use iTransformer for trend capture.
result Improved Sharpe ratio and annualized return compared to benchmarks.
We review the dynamics of the returns of Leveraged Exchange Traded Funds (LETFs) and propose a new measure of realized volatility: Shortfall from Maximum Convexity. We show that SMC has a more intuitive interpretation and provides more statistical information compared to the traditionally used sample standard deviation…