This paper uses SARIMA models to forecast Nifty 50 index.
problem Forecasting the movement of Nifty 50 index.
method Applied Seasonal Auto Regressive Integrated Moving Average (SARIMA) models.
result The most appropriate SARIMA model was selected based on AIC criteria.
Study finds monthly SIPs outperform first-day SIPs in Nifty 50 by 0.5-2.5% annually.
problem Underexplored impact of SIP timing in India's equity market.
method 22-year analysis using multi-layered statistical framework (non-parametric tests, effect size metrics, SSD).
result Monthly SIPs (EXP-SIP) outperform first-day SIPs (FTD-SIP) by 0.5-2.5% annually over short-to-medium-term horizons.
This study predicts stock prices using hybrid machine learning and LSTM models.
problem Accurately predicting stock prices despite the efficient market hypothesis.
method Hybrid modeling combining machine learning and deep learning (LSTM) for NIFTY 50 index prediction.
result LSTM-based univariate model with one-week prior data is most accurate.
Study shows news from various topics impacts Nifty 50 index.
problem Lack of analysis on news impact on Nifty 50 index.
method Analyzed Nifty 50 index movement with sentiments from diverse news topics.
result Sentiment scores from different topics significantly impact Nifty 50 index.
This paper presents deep learning models for NIFTY 50 stock price prediction.
problem Accurately predicting stock prices using historical data.
method Used CNN and LSTM-based deep learning models on NIFTY 50 historical data.
result Univariate encoder-decoder convolutional LSTM model is the most accurate.
CNN improves stock price prediction accuracy.
problem Predicting future stock price movements.
method Hybrid approach combining machine learning and CNN.
result CNN-based model outperforms other models.
Study shows demonetization strengthened Indian currency and stock market.
problem Impact of demonetization on Indian stock market and foreign exchange rate.
method Daily rate of return analysis of foreign exchange rate and Nifty 50 index, use of dummy variable for demonetization period.
result Demonetization led to an upward trend in Indian stock market and strengthened the Indian currency (decreased foreign exchange rate).
This paper optimizes portfolios using HRP and CLA algorithms on NIFTY 50 stocks.
problem Designing an optimal stock portfolio with accurate forecasting of future returns and risks.
method Uses hierarchical risk parity and critical line algorithms on NIFTY 50 stocks.
result Hierarchical risk parity algorithm outperformed the critical line algorithm on test data.
Study analyzes Nifty 50 returns over 34 years, showing P/E ratio predicts long-term gains.
problem Understanding equity return dynamics in the Indian market over various horizons.
method Unified, distribution-aware, complexity-informed framework using 34 years of Nifty 50 data.
result P/E ratio probabilistically maps return distributions across different investment horizons.
The study evaluates various ML models for stock market prediction.
problem Predicting the Nifty 50 Index using machine learning models.
method 8 supervised machine learning models (AdaBoost, kNN, LR, ANN, RF, SGD, SVM, DT) applied to historical Nifty 50 Index data.
result Support Vector Machine performed best, but Stochastic Gradient Descent improved performance with larger datasets.
The paper examines Indian market bubbles using financial ratios.
problem Detecting bubbles in emerging markets like India is challenging.
method Utilizes financial ratios and the Philips et al 2015 right-tailed unit test.
result Identifies various bubble episodes in the Indian market.
Bayesian GPR model predicts extreme stock market losses.
problem Forecasting rare but impactful extreme negative returns in equity markets.
method Developed a Bayesian Generalised Pareto Regression model linking scale parameter to market volatility.
result The Cauchy prior provides the best balance between predictive accuracy and model simplicity.
This study compares two portfolio optimization methods on Indian stocks.
problem Designing an optimal portfolio considering stock returns and risks.
method Hierarchical Risk Parity and Eigen Portfolio approaches on NIFTY 50 sectors.
result Hierarchical Risk Parity portfolio outperforms Eigen portfolio in most sectors tested.
Hybrid model predicts stock prices using ML, DL, and NLP.
problem Improving prediction accuracy of stock price movement.
method Machine learning, deep learning, natural language processing, sentiment analysis.
result LSTM model outperforms traditional machine learning models.
This study proposes an equal-weight portfolio strategy to reduce risk compared to traditional ETFs.
problem Risk of passive ETFs not matching optimal portfolio weights.
method Introduced an equal-weight portfolio strategy to reduce idiosyncratic risk.
result Equal-weight portfolio has lower risk than traditional ETFs, especially during idiosyncratic events.
Novel method prices call options using Pearson diffusion processes.
problem Pricing European call options with skewness and kurtosis.
method Modeling asset returns with Pearson diffusion processes.
result Proposed method outperforms Black-Scholes and Heston models.
NIFTy.re accelerates imaging models and expands Gaussian processes and variational inference.
problem Slow performance and limited inference strategies in NIFTy.
method Rewritten NIFTy with new modeling principles, inference strategies, and JAX integration.
result Dramatic acceleration of models and new inference capabilities.
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.
problem Improving financial market forecasting with LLMs.
method Two datasets for supervised and reinforcement learning.
result Demonstrates applications in stock price prediction.
Study improves stock index prediction accuracy using TPE-GRNN models.
problem Enhancing prediction of stock index prices in volatile markets.
method Gated recurrent neural networks (LSTM, GRU) combined with TPE Bayesian optimization.
result TPE-LSTM method shows lowest MAPE (best accuracy) for NIFTY 50 index prediction.
Study finds dividend policy has no significant effect on IPO stock prices.
problem Impact of dividend policy on IPO price performance.
method Long-run performance statistics and GARCH model, dummy variable used.
result Dividend policy has no significant effect on IPO stock prices.
Study finds physical momentum portfolios in Indian stock market yield higher returns than benchmarks.
problem Determining abnormal returns for physical momentum portfolios in the Indian stock market.
method Constructed physical momentum portfolios for daily, weekly, monthly, and yearly timescales, evaluated historical returns and risk profiles.
result Daily time scale physical momentum portfolios showed the strongest reversal with a 16-fold profit.
Study shows survivorship bias inflates returns in India's small-cap index.
problem Survivorship bias in emerging market small-cap indices.
method Reconstructing historical index composition through market capitalization ranking and comparing equal-weight portfolios of current constituents versus all historical members.
result Survivor-only backtesting overstates returns by 4.94 percentage points and Sharpe ratios by 0.097.
This paper optimizes decarbonized indices for financial tracking, balancing risk and environmental impact.
problem Balancing financial performance with environmental responsibilities in the context of climate risks.
method Develops decarbonized indices using mean-VaR and mean-ES optimization methods.
result Optimized indices reduce financial risk and carbon footprint, providing a balanced investment option.
A new model explains U- and Swoosh-shaped stock price recovery during the COVID-19.
problem Modeling stock price recovery during the COVID-19 with V- and L-shaped recovery.
method Introducing a sentiment variable θ to quantify investor sentiment and simulate U- and Swoosh-shaped recovery. result The model explains U- and Swoosh-shaped recovery of sectoral indices with positive sentiment.
The paper proposes machine learning models for option pricing without using historical or implied volatility.
problem Capturing option pricing without traditional volatility inputs.
method Three supervised machine learning approaches using data from multiple assets.
result Trained models outperform or match Black-Scholes formula for option pricing.
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.
problem Quantum algorithmic resilience for CVaR portfolio optimization
method WS-QAOA vs. HE-VQNN
result WS-QAOA provides exact theoretical mapping but suffers from hardware decoherence, while HE-VQNN preserves hardware coherence but lacks expressibility.
This paper optimizes portfolios of thematic sector stocks using LSTM models.
problem Designing an optimized portfolio of stocks to maximize return and minimize risk.
method Extracted stock prices from Jan 2016 to Dec 2020, used LSTM model for prediction, designed portfolios based on critical stocks.
result LSTM model accurately predicted future stock returns, indicating high accuracy.
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.
problem Noise in empirical correlation matrices from financial returns obscures genuine interactions.
method Spectral decomposition to separate structured and random components.
result Structured networks derived from 10-16 eigenmodes exhibit stronger core-periphery organization and scale-free degree distributions.
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.
problem Inaccurate long-term stock price predictions.
method Adaptive Weighted Genetic Algorithm-Optimized SVR (IGA-SVR).
result Reduction in MAPE by 19.87% compared to LSTM and 50.03% compared to OGA-SVR.
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.
problem Impact of stock market efficiency on investment returns.
method Runs test, Autocorrelation test, Autoregression test on daily stock indices.
result Indian stock market is weak form inefficient and can be outperformed.
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.
problem Optimizing ETF allocation between QQQ and DIA for better risk-adjusted returns.
method Screened relative and macro states, globally screened interactions, fixed position mapping, walk-forward validation.
result RGRR improves Sharpe and CAGR compared to 100% QQQ and 50/50 QQQ-DIA allocations.
Paper predicts cryptocurrency bull and bear phases using Bitcoin's moving averages.
problem Determining cryptocurrency bull and bear phases based on Bitcoin performance.
method Employing predictive algorithms to forecast Bitcoin's 50 Day and 200 Day Moving Averages.
result Predicted data from Bitcoin's moving averages helps identify potential bull and bear phases.
Machine learning reveals inventory effects on VSTOXX futures pricing.
problem Understanding how inventory affects VSTOXX futures pricing.
method Combining stochastic processes and machine learning, we formulate and calibrate a Heston model for VSTOXX futures pricing.
result Machine learning models show that inventory significantly impacts VSTOXX futures prices.
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 …
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.
problem Early detection of ovarian cancer from cysts using TVUS screening.
method Employed Random Forest, KNN, and XGBoost machine learning techniques on PLCO dataset.
result Achieved high accuracy, recall, f1 score, and precision in predicting ovarian cancer.
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…
SWAT reduces training efficiency and speed for CNNs.
problem Efficient training of neural networks on sparse hardware.
method SWAT modifies backpropagation to eliminate small weights and activations.
result SWAT reduces FLOPS by 80% on ResNet-50 for ImageNet, improving training speed by 3.3x.
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…