quant-finance

Credit card fraud has become a significant threat to individuals, financial institutions, businesses, and governments, causing substantial annual economic losses through increasingly sophisticated fraudulent activities. This research aims to enhance credit card fraud detection by leveraging machine learning algorithms and ensemble learning techniques to improve detection accuracy and model robust…

Financial markets are becoming increasingly complex as global systems grow more interconnected and data-driven. Banks, hedge funds, pension funds, insurers, and asset managers now operate in an environment where interest rates, currencies, commodities, equities, and credit markets can all influence one another simultaneously. As this complexity continues to increase, traditional approaches to fin…

India’s securities market software industry is entering a significant growth phase as financial institutions, brokerages, exchanges, and investment platforms accelerate digital transformation and technology modernisation initiatives. Industry experts note that rising trading activity, regulatory complexity, and increasing demand for real-time Read More ... The post Indian securities market softwa…

A new study presents a neural-network method for building efficient portfolios while keeping the allocation process interpretable. Instead of treating asset correlations as a black box, the model learns how to clean noisy collective market patterns and respects the symmetries of covariance matrices.

Sowilo Investment Manager offers trusted Portfolio Management Services in India designed for investors seeking long-term wealth creation. With expert market research, personalized investment strategies, and disciplined portfolio management, the company helps clients achieve... Read more

A research analyst's perspective on where AI and finance intersect As of 2026, generative AI is used pervasively in investment research. So in this already-crowded market, why does DeepSeek emphasize financial reasoning, and can that translate into genuine excess returns (alpha)? This piece examines the argument not through benchmarks or marketing, but through the model's design architecture and …

My question is simple, consider a European call with payoff max(S_T-K, 0), Let's suppose that the underlying stock follows a binomial tree with up and down factors I know as we take n goes to infinity that the stock is log-normally distributed at time t=T (I know how to derive it). The idea is to derive the B-S-M pricing formula as the expected value of the present value of max(S_t-K, 0) using t…

I've been modelling the relationship between government debt auction calendars and implied volatility surfaces in emerging market contexts — specifically whether φ (sovereign refinancing pressure) and λ (liquidity stress) create predictable, calendar-driven dislocations that standard models like Heston and Bates don't capture. The intuition: retail-dominated EM options markets systematically unde…

The first step in the Black-Litterman method is to find the "implied market returns" (the prior). Usually this is calculated as: $\Pi = \lambda \Sigma w$ , where $\Pi$ is the vector of returns "implied by the market", $w$ is the vector of market weights (each element = security market cap / total market cap), $\Sigma$ is the covariance matrix, $\lambda$ is the market risk aversion (a constant). I…

Cover's universal portfolio maximizes the wealth growth rate Markowitz's mean-variance model minimizes portfolio variance Both allocate assets based on historical returns. How do these two models perform against one another (assuming for Markowitz we use the global minimum variance portfolio by default). How does the universal portfolio compare against the equally-weighted portfolio that is known…

As artificial intelligence becomes embedded in credit scoring, risk assessment, ESG investing and market prediction, explainability is no longer a technical afterthought. It is becoming an essential part of the infrastructure of trustworthy finance.

i'm facing a new and interesting task: We are calculating a time series of (hypothetical) behavioral portfolios, for which i need a few parameters to calculate the portfolio's weights in each asset. I'm using an observed portfolio as starting point, from which i need to extract the implied utility parameters (in the case at hand the CPT utility as seen in my screeenshot). My idea is to find the p…

Abstract This paper discusses how the Belt and Road Initiative (BRI) affect logistics infrastructure and supply chain performance in China and some of the Gulf Cooperation Council (GCC) countries, and specifically how the quality of governance moderate these effects. The analysis utilizes a balanced panel dataset between 1996 and 2023 that employs a fixed effect panel regression model, which comp…

This study aims to analyze the effect of the Coretax System and tax awareness on MSME taxpayer compliance in Pekanbaru, with tax sanctions as a moderating variable. This research uses a quantitative approach with a survey method. The sample consists of 200 MSME respondents registered at KPP Pratama Pekanbaru Senapelan who have implemented the Coretax System. Data analysis was conducted using mult…

Abstract The paper re-examines the principle of the bird-in-hand under the dynamic situation in the emerging spots market in Pakistan. Our sample consisted of 100 KSE—100 companies dating 2009–2024. The abnormal returns are separated out using an event study model and a rolling window version of the CAPM, and we have constructed a new Dividend Announcement Factor (DAF) to capture systematic varia…

Heterodox economics differs from orthodox or mainstream economics. It draws on a multiplicity of ideas, disciplines, methods and voices to present a more radical alternative to the dominant paradigm of neoclassical economics, which is viewed as overly narrow and blind-sided to how economies actually work. Andrew Trigg traces the heterodox tradition from its origins in the anti-capitalism ideas of…