A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study shows news from various topics impacts Nifty 50 index.
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…
This paper presents deep learning models for NIFTY 50 stock price prediction.
Study shows demonetization strengthened Indian currency and stock market.
This study predicts stock prices using hybrid machine learning and LSTM models.
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 finds monthly SIPs outperform first-day SIPs in Nifty 50 by 0.5-2.5% annually.
The study evaluates various ML models for stock market prediction.
Study analyzes Nifty 50 returns over 34 years, showing P/E ratio predicts long-term gains.
Bayesian GPR model predicts extreme stock market losses.
This paper optimizes portfolios using HRP and CLA algorithms on NIFTY 50 stocks.
Study improves stock index prediction accuracy using TPE-GRNN models.
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…
Novel method prices call options using Pearson diffusion processes.
This study proposes an equal-weight portfolio strategy to reduce risk compared to traditional ETFs.
The paper examines Indian market bubbles using financial ratios.
Study finds dividend policy has no significant effect on IPO stock prices.
Study shows survivorship bias inflates returns in India's small-cap index.
This study compares two portfolio optimization methods on Indian stocks.
This paper optimizes decarbonized indices for financial tracking, balancing risk and environmental impact.
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…
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…
NIFTy.re accelerates imaging models and expands Gaussian processes and variational inference.
NIFTY dataset for financial forecasting models.
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…
Quantum algorithms for CVaR portfolio optimization face trade-offs between hardware coherence and expressibility.
Improved genetic algorithm optimizes SVR for robust long-term stock index forecasting.
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…
Study finds physical momentum portfolios in Indian stock market yield higher returns than benchmarks.
The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine lear…
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…
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. …
A new model explains U- and Swoosh-shaped stock price recovery during the COVID-19.
This is an expository paper designed to introduce undergraduates to the Atiyah-Singer index theorem 50 years after its announcement. It includes motivation, a statement of the theorem, an outline of the easy part of the heat equation proof. It includes counting lattice points and knot concordance as applications.
The paper proposes machine learning models for option pricing without using historical or implied volatility.
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 …
A new stock index model simplifies high-dimensional stock data.
In terms of the stock exchange returns, we compute the analytic expression of the probability distributions F{DAX,+} and F{DAX,-} of the normalized positive and negative DAX (Germany) index daily returns r(t). Furthermore, we define the alpha re-scaled DAX daily index positive returns r(t)^alpha and negative returns (-…
This paper optimizes portfolios of thematic sector stocks using LSTM models.
AGMMNs improve learning of copula models by adaptively selecting kernels.
Market liquidity plays a vital role in the field of market micro-structure, because it is the vigor of the financial market. This paper uses a variable called convexity to measure the potential liquidity provided by order-book. Based on the high-frequency data of each stock included in the SSE (Shanghai Stock Exchange)…
p-index approach shows efficient-contrarian strategy outperforms others in low-sentiment periods
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…
Adaptive framework improves NB accuracy by fusing two index categories.
We analyze the quarterly average sale prices of new houses sold in the USA as a whole, in the northeast, midwest, south, and west of the USA, in each of the 50 states and the District of Columbia of the USA, to determine whether they have grown faster-than-exponential which we take as the diagnostic of a bubble. We fin…
Spectral denoising recovers meaningful network structure from noisy financial correlations.
This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employe…
This paper presents a methodology to introduce time-dependent parameters for a wide family of models preserving their analytic tractability. This family includes hybrid models with stochastic volatility, stochastic interest-rates, jumps and their non-hybrid counterparts. The methodology is applied to Heston's model. A …