arXiv subjects

Nikhil Devanathan

Publications and source records attributed to Nikhil Devanathan.

5 recordsLinked to original sources

arXiv2026

Simple Dynamic Stock/Bond/Gold Portfolios

For more than four decades, the 60/40 stock/bond portfolio has served as a benchmark for delivering reasonable returns without excessive risk. More recently, a 50/30/20 stock/bond/alternative portfolio has been suggested. We use gold as the alternative and as an inflation hedge. In this paper we ask: how much improvement over these benchmark fixed-weight portfolios can be obtained using widely available public data and standard methods from quantitative finance? We restrict ourselves to long-only dynamic portfolios of stocks, bonds, and gold, plus cash, rebalancing monthly, using only publicly available data. We evaluate portfolios on the conventional metrics: return, volatility, Sharpe ratio (computed in excess of the federal funds rate), drawdown, and turnover, in addition to consistency of performance over time, judged by the consistency of the realized annual volatility. Over the 20--year period 2006--2026, using a conservative estimate of trading costs, we show that all risk-adjusted and drawdown metrics are improved using simple volatility control, where we dynamically mix the fixed-weight portfolios with cash so as to target a fixed volatility. This method relies on a simple estimate of portfolio volatility derived from past returns. We also demonstrate that more sophisticated portfolios based on convex optimization---similar to those used in quantitative hedge funds---yield further substantial improvement in return and risk-adjusted return. We consider two such portfolios, one that uses a simple estimate of future returns based on past returns, and one that forecasts future returns based on past returns and just a handful of widely available public economic data. These portfolios also outperform a suite of standard risk-based allocation methods, such as risk parity and minimum variance, evaluated on the same assets and data.

q-fin.PM↗

arXiv2026

A Distributed Method for Cooperative Transaction Cost Mitigation

Funds at large portfolio management firms may consist of many portfolio managers (PMs), each managing a portion of the fund and optimizing a distinct objective. Although the PMs determine their trades independently, the trade lists may be netted and executed by the firm. These net trades may be sufficiently large to impact the market prices, so the PMs may realize prices on their trades that are different from the observed midpoint price of the assets before execution. These transaction costs generally reduce the returns of a portfolio over time. We propose a simple protocol, based on methods from distributed convex optimization, by which a firm can communicate estimated transaction costs to its PMs, and the PMs can potentially revise their trades to realize reduced transaction costs. This protocol does not require the PMs to disclose their method of determining trades to the firm or to each other, nor does it require the PMs to communicate their trade lists with each other. As the number of adjustment rounds grows, the trades converge to the ones that are optimal for the firm. As a practical matter we observe that even just a few rounds of adjustment lead to substantial savings for the firm and the PMs.

math.OC↗

arXiv2026

Single-Asset Adaptive Leveraged Volatility Control

This paper introduces a methodology for constructing a market index composed of a liquid risky asset and a liquid risk-free asset that achieves a fixed target volatility. Existing volatility-targeting strategies typically scale portfolio exposure inversely with a variance forecast, but such open-loop approaches suffer from high turnover, leverage spikes, and sensitivity to estimation error -- issues that limit practical adoption in index construction. We propose a proportional-control approach for setting the index weights that explicitly corrects tracking error through feedback. The method requires only a few interpretable parameters, making it transparent and practical for index construction. We demonstrate in simulation that this approach is more effective at consistently achieving the target volatility than the open-loop alternative.

q-fin.PM↗

arXiv2023

Efficient Shapley Performance Attribution for Least-Squares Regression

We consider the performance of a least-squares regression model, as judged by out-of-sample $R^2$. Shapley values give a fair attribution of the performance of a model to its input features, taking into account interdependencies between features. Evaluating the Shapley values exactly requires solving a number of regression problems that is exponential in the number of features, so a Monte Carlo-type approximation is typically used. We focus on the special case of least-squares regression models, where several tricks can be used to compute and evaluate regression models efficiently. These tricks give a substantial speed up, allowing many more Monte Carlo samples to be evaluated, achieving better accuracy. We refer to our method as least-squares Shapley performance attribution (LS-SPA), and describe our open-source implementation.

stat.CO↗

arXiv2023

Polyak Minorant Method for Convex Optimization

In 1963 Boris Polyak suggested a particular step size for gradient descent methods, now known as the Polyak step size, that he later adapted to subgradient methods. The Polyak step size requires knowledge of the optimal value of the minimization problem, which is a strong assumption but one that holds for several important problems. In this paper we extend Polyak's method to handle constraints and, as a generalization of subgradients, general minorants, which are convex functions that tightly lower bound the objective and constraint functions. We refer to this algorithm as the Polyak Minorant Method (PMM). It is closely related to cutting-plane and bundle methods.

math.OC↗