arXiv research

Study finds monthly SIPs outperform first-day SIPs in Nifty 50 by 0.5-2.5% annually.

This study predicts stock prices using hybrid machine learning and LSTM models.

Study shows news from various topics impacts Nifty 50 index.

This paper presents deep learning models for NIFTY 50 stock price prediction.

This paper analyses how Time Series Analysis techniques can be applied to capture movement of an exchange traded index in a stock market. Specifically, Seasonal Auto Regressive Integrated Moving Average (SARIMA) class of models is applied to capture the movement of Nifty 50 index which is one of the most actively excha…

Study shows demonetization strengthened Indian currency and stock market.

This paper optimizes portfolios using HRP and CLA algorithms on NIFTY 50 stocks.

Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange of India, over a period of four …

Study analyzes Nifty 50 returns over 34 years, showing P/E ratio predicts long-term gains.

The study evaluates various ML models for stock market prediction.

The paper examines Indian market bubbles using financial ratios.

Bayesian GPR model predicts extreme stock market losses.

This study compares two portfolio optimization methods on Indian stocks.

This study proposes an equal-weight portfolio strategy to reduce risk compared to traditional ETFs.

Novel method prices call options using Pearson diffusion processes.

NIFTy.re accelerates imaging models and expands Gaussian processes and variational inference.

Realization of uncertainty of prices is captured by volatility, that is the tendency of prices to vary along a period of time. This is generally measured as standard deviation of daily returns. In this paper we propose and investigate the application of fuzzy transform and its inverse as an alternative measure of volat…

NIFTY dataset for financial forecasting models.

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most ch…

Study improves stock index prediction accuracy using TPE-GRNN models.

Study finds dividend policy has no significant effect on IPO stock prices.

Study finds physical momentum portfolios in Indian stock market yield higher returns than benchmarks.

Study shows survivorship bias inflates returns in India's small-cap index.

This paper optimizes decarbonized indices for financial tracking, balancing risk and environmental impact.

A new model explains U- and Swoosh-shaped stock price recovery during the COVID-19.

The paper proposes machine learning models for option pricing without using historical or implied volatility.

This non-linear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week's closing exchange rate. Though a good correlation was obtained duri…

Quantum algorithms for CVaR portfolio optimization face trade-offs between hardware coherence and expressibility.

This paper optimizes portfolios of thematic sector stocks using LSTM models.

The inference of correlated signal fields with unknown correlation structures is of high scientific and technological relevance, but poses significant conceptual and numerical challenges. To address these, we develop the correlated signal inference (CSI) algorithm within information field theory (IFT) and discuss its n…

Time series analysis and forecasting of stock market prices has been a very active area of research over the last two decades. Availability of extremely fast and parallel architecture of computing and sophisticated algorithms has made it possible to extract, store, process and analyze high volume stock market time seri…

Spectral denoising recovers meaningful network structure from noisy financial correlations.

Historical daily data for eleven years of the fifty constituent stocks of the NIFTY index traded on the National Stock Exchange have been analyzed to check for the stylized facts in the Indian market. It is observed that while some stylized facts of other markets are also observed in Indian market, there are significan…

Improved genetic algorithm optimizes SVR for robust long-term stock index forecasting.

From the stock markets of six countries with high GDP, we study the stock indices, S&P 500 (NYSE, USA), SSE Composite (SSE, China), Nikkei (TSE, Japan), DAX (FSE, Germany), FTSE 100 (LSE, Britain) and NIFTY (NSE, India). The daily mean growth of the stock values is exponential. The daily price fluctuations about the me…

Study confirms Indian stock market is weak form inefficient.

A method based on wavelet transform and genetic programming is proposed for characterizing and modeling variations at multiple scales in non-stationary time series. The cyclic variations, extracted by wavelets and smoothened by cubic splines, are well captured by genetic programming in the form of dynamical equations. …

RGRR allocates between QQQ and DIA based on relative states, improving Sharpe and CAGR.

Paper predicts cryptocurrency bull and bear phases using Bitcoin's moving averages.

Machine learning reveals inventory effects on VSTOXX futures pricing.

In April 2009, we introduced a model representing the evolution of motor fuel price (a subcategory of the consumer price index of transportation) relative to the overall CPI as a linear function of time. Under our framework, all price deviations from the linear trend are transient and the price must promptly return to …

Neural network training is computationally and memory intensive. Sparse training can reduce the burden on emerging hardware platforms designed to accelerate sparse computations, but it can affect network convergence. In this work, we propose a novel CNN training algorithm Sparse Weight Activation Training (SWAT). SWAT …

In his seminal work, Schapire (1990) proved that weak classifiers could be improved to achieve arbitrarily high accuracy, but he never implied that a simple majority-vote mechanism could always do the trick. By comparing the asymptotic misclassification error of the majority-vote classifier with the average individual …

Over the last decade, dividends have become a standalone asset class instead of a mere side product of an equity investment. We introduce a framework based on polynomial jump-diffusions to jointly price the term structures of dividends and interest rates. Prices for dividend futures, bonds, and the dividend paying stoc…

For the past 5 years, the ILSVRC competition and the ImageNet dataset have attracted a lot of interest from the Computer Vision community, allowing for state-of-the-art accuracy to grow tremendously. This should be credited to the use of deep artificial neural network designs. As these became more complex, the storage,…

This study predicts ovarian cancer from cysts using TVUS and machine learning.

We propose a method to build quantum memristors in quantum photonic platforms. We firstly design an effective beam splitter, which is tunable in real-time, by means of a Mach-Zehnder-type array with two equal 50:50 beam splitters and a tunable retarder, which allows us to control its reflectivity. Then, we show that th…

In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coe…