arXiv subjects

Sidharth Mallik

Publications and source records attributed to Sidharth Mallik.

4 recordsLinked to original sources

arXiv2026

Knowledge-Optimising Investment Decisions with Informative Datasets

The enormous growth in datasets, both in number and size, has prompted investors to adapt to new ways for assimilating information. Normatively, the approach has been to integrate such datasets into pricing formulations and assess the performance of portfolios created thereafter. However, such approaches underestimate their influence in portfolio investments by limiting their impact to pricing only. While being theoretically valid, this results in a potential sub-optimal performance in the presence of real-life decision constraints, and a blind spot for performance attribution. We start by analysing investment decisions from a knowledge perspective, which unfurls a new structure. We then propose a FinTech process termed Knowledge Optimisation that aims to integrate the influence of knowledge components that could be related to data, models, or business units that extract information. A 3-stage process, namely, decision structure, portfolio selection, and performance assessment is designed. We present an alternative to the ex-ante Sharpe Ratio, integrating a term for knowledge units. Through scenario analysis involving portfolio investment situations, we illustrate the utility. By design, the process improves the importance of knowledge in investment decisions.

q-fin.PM↗

arXiv2026

Open Information: A Defining Perspective on Web Datasets for Carbon Pricing

The impact of web datasets on market prices has suggested the development of new sources of information, such as social media and web portals, indicating the possibility of an emergent phenomenon. We propose a defining perspective, termed open information, that adds to the existing types of public and private information. We demonstrate their existence and justify material significance for pricing. In this respect, we present statistical hypotheses to test for a web dataset, GDELT, integrated for carbon pricing, that is represented by EU Allowance spot prices. Tests are designed with VAR and GARCH-X formulations, and return forecasting. The outcomes cannot rule out the material existence of open information. The result is significant for providing a conceptual basis to integrate a vast number of web datasets as alternative data in investment decisions.

q-fin.PR↗

arXiv2022

A mean-variance optimized portfolio constructed for investment in a reference security, for an investor with a preference towards an accepted set of securities

We consider a reference security, understood to be an attractive investment, with the caveat that an investor is not willing to directly invest in the security, for presence of constraints, either investor specific or pertaining to the security itself. The investor, however, is open to a portfolio constructed with an accepted set of securities, where returns could be considered similar to the reference security. We demonstrate, under a measure of similarity, such a portfolio could be selected with a mean-variance characterization, as defined by Markowitz. Furthermore, we consider the performance relative to the reference security, with the Sharpe Ratio. The objective of the paper is to derive an optimal portfolio to address an investor preference for the accepted set of securities.

q-fin.PM↗

arXiv2022

Pricing cryptocurrencies : Modelling the ETHBTC spot-quotient variation as a diffusion process

This research proposes a model for the intraday variation between the ETHBTC spot and the quotient of ETHUSDT and BTCUSDT traded on Binance. Under conditions of no-arbitrage, perfect accuracy and no microstructure effects, the variation must be equal to its theoretically computed value of 0. We conduct our research on 4 years of data. We find that the variation is not constantly 0. The variation shows a fluctuating behaviour on either side of 0. Furthermore, the deviations tend to be larger in the first year than the rest of the years. We test the sample for the nature of diffusion where we find evidence of mean-reversion. We model the variation using an Ornstein-Uhlenbeck process. A maximum likelihood estimation procedure is used. From the accuracy of the sampling distribution of the parameters obtained, we conclude that the variation may be accurately modelled as an Ornstein-Uhlenbeck process. From the parameters obtained, the long-term mean is shown to have a negative sign and differs from the theoretical value of 0 at 1e-05 precision. We take note of the results in light of efficiency of the markets to price publicly known information.

q-fin.PR↗