Liquidity Management

Do Mutual Funds Make Active and Skilled Liquidity Choices in Portfolio Management? Evidence from India ArXiv ID: 2510.02741 “View on arXiv” Authors: Pankaj K Agarwal, H K Pradhan, Konark Saxena Abstract This study examines active liquidity management by Indian open-ended equity mutual funds. We find that fund managers respond to inflows by increasing cash holdings, which are later used to purchase less-liquid stocks at favourable valuations. Funds with less liquid portfolios tend to maintain larger cash reserves to manage flows. Funds that make active liquidity choices yield statistically and economically significant gross and net returns. The performance differences between funds with varying activeness in altering liquidity highlight the importance of active liquidity management in markets with substantial cross-sectional liquidity differences such as India. ...

Turnover of investment portfolio via covariance matrix of returns ArXiv ID: 2412.03305 “View on arXiv” Authors: Unknown Abstract An investment portfolio consists of $n$ algorithmic trading strategies, which generate vectors of positions in trading assets. Sign opposite trades (buy/sell) cross each other as strategies are combined in a portfolio. Then portfolio turnover becomes a non linear function of strategies turnover. It rises a problem of effective (quick and precise) portfolio turnover estimation. Kakushadze and Liew (2014) shows how to estimate turnover via covariance matrix of returns. We build a mathematical model for such estimations; prove a theorem which gives a necessary condition for model applicability; suggest new turnover estimations; check numerically the preciseness of turnover estimations for algorithmic strategies on USA equity market. ...

Fitting random cash management models to data ArXiv ID: 2401.08548 “View on arXiv” Authors: Unknown Abstract Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we relax this strong assumption to fit cash management models to data by means of stochastic and linear programming. We also introduce ensembles of random cash management models which are built by randomly selecting a subsequence of the original cash flow data set. We illustrate our approach by means of a real case study showing that a small random sample of data is enough to fit sufficiently good bound-based models. ...