The p-index improves investment performance for NYSE stocks but not for SSE stocks.
problem Improving investment performance for stocks using the p-index.
method Comparing different p-ratio strategies and empirical efficient frontiers for SSE and NYSE stocks.
result The p-index enhances investment performance for NYSE stocks but not for SSE stocks.
A new stock index model simplifies high-dimensional stock data.
problem Reflecting the overall stock market activity in high-dimensional data.
method Manifold learning and feature detection on discrete Laplace-Beltrami operator.
result The MF index series approximates the stock market better and has lower risk.
We investigate the strength and the direction of information transfer in the U.S. stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the …
Study reveals the 2020 U.S. stock crash was endogenous, not caused by COVID.
problem Understanding the cause of the 2020 U.S. stock market crash.
method Applied log-periodic power law singularity (LPPLS) methodology to analyze four major U.S. stock market indexes.
result The 2020 U.S. stock market crash was endogenous, stemming from systemic instability, not COVID.
In this paper, we are interested in continuous time models in which the index level induces some feedback on the dynamics of its composing stocks. More precisely, we propose a model in which the log-returns of each stock may be decomposed into a systemic part proportional to the log-returns of the index plus an idiosyn…
Active managers struggle to beat a randomly selected stock subset of a broad market index.
problem Active equity managers underperform a benchmark index.
method Developed a stock selection model to explain underperformance.
result Randomly selecting a subset of securities from the index can dramatically increase the chance of underperforming the index.
Study finds no evidence that stock market indexing affects corporate tax avoidance.
problem Effect of stock market indexing on corporate tax avoidance.
method Regression discontinuity design using annual reconstitution of stock market indices.
result No significant difference in effective tax rates between firms just inside and outside the Russell 2000 index.
This paper is trying to unveil general statistical characteristic of financial; time series data that is subjected to several financial time series data present in Indonesia, e.g. individual index such as stock price of PT. TELKOM, stock price of PT HM SAMPOERNA, and compiled stock price index (Jakarta Stock Exchange I…
p-index approach shows efficient-contrarian strategy outperforms others in low-sentiment periods
problem Evaluating investment strategies in stock markets
method Using p-index risk measure with European put option
result Efficient-contrarian strategy outperforms others in low-sentiment periods
Empirical evidence is given for a significant difference in the collective trend of the share prices during the stock index rising and falling periods. Data on the Dow Jones Industrial Average and its stock components are studied between 1991 and 2008. Pearson-type correlations are computed between the stocks and avera…
Study reveals multifractal nature in Chinese stock markets and predicts future returns.
problem Predicting future stock market returns using multifractal characteristics.
method Multifractal detrended fluctuation analysis (MF-DFA) on high-frequency stock data.
result Spectral width Δα is a significant predictor of future stock returns.
Improved stock index analysis using fuzzy parameters and machine learning.
problem Analyzing the S&P 500 stock index with long-term dependence.
method Combining fuzzy theory and machine learning to modify the Barndorff-Nielsen and Shephard model.
result The new model effectively captures the stochastic dynamics of the stock index time series.
Combines SV and RNN models for stock market volatility.
problem Capturing complex volatility effects in stock markets.
method Statistical Recurrent Stochastic Volatility (SR-SV) model.
result Out-of-sample forecast performance is impressive.
This study compares Markowitz and Single-Index models for Malaysian stocks.
problem Optimizing portfolio selection for Malaysian stocks using different models.
method Applied Markowitz and Single-Index models to 10-year historical data of 10 stocks and a risk-free asset.
result Comparison of minimum variance and maximum Sharpe portfolios for both models under various constraints.
Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation
problem Robust stock index forecasting
method Modified Transformer architecture with Shifted Data Augmentation
result Best performance on benchmark datasets
In this paper we study BSE Index financial time series for fractal and multifractal behaviour. We show that Bombay stock Exchange (BSE)Index time series is mono-fractal and can be represented by a fractional Brownian motion.
The paper proposes a hybrid approach using MODWT and machine learning for stock index prediction.
problem Predicting stock index with improved accuracy for financial markets.
method Hybrid approach combining MODWT decomposition and ANN/SVR machine learning models.
result The MODWT-SVR model outperformed other models in terms of return on investment.
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.
The study introduces a new stickiness parameter for stock prices using a non-linear model.
problem Understanding how closely individual stocks follow a stock index's price movements.
method Developed a non-linear pricing model inspired by tectonic plate movements to measure stickiness.
result Defined a stickiness parameter for stock price returns using a novel model.
Investment horizon approach has been used to analyze indexes of Polish stock market.Optimal time horizon for each return value is evaluated by fitting appropriate function form of the distribution. Strong asymmetry of gain-loss curves is observed for WIG index, whereas gain and loss curves look similar for WIG20 and fo…
We study the temporal evolution of the market efficiency in the stock markets using the complexity, entropy density, standard deviation, autocorrelation function, and probability distribution of the log return for Standard and Poor's 500 (S&P 500), Nikkei stock average index, and Korean composition stock price index (K…
Cubic predicts stock market indices by fusing stock latent embeddings and converting to binary classification.
problem Challenges in predicting stock market indices due to isolated time series treatment and simple regression.
method Fusion of stock latent embeddings, binary encoding classification, and confidence-guided prediction.
result Cubic outperforms state-of-the-art baselines in stock index prediction tasks.
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
problem Impact of oil price volatility on Tehran stock and industry indices.
method Feed-forward neural networks analysis of two periods: sanctions and post-sanctions.
result Neural networks predict stock and industry indices well, showing significant oil price volatility impact.
Multifractal analysis and extensive statistical tests are performed upon intraday minutely data within individual trading days for four stock market indexes (including HSI, SZSC, S&P500, and NASDAQ) to check whether the indexes (instead of the returns) possess multifractality. We find that the mass exponent τ(q) is l…
Deep learning predicts S&P 500 index direction.
problem Accurate stock price prediction remains challenging.
method Convolutional neural network model for S&P 500 index forecasting.
result Model achieves over 55% accuracy in predicting index direction.
We investigate a factor that can affect the number of links of a specific stock in a network between stocks created by the minimal spanning tree (MST) method, by using individual stock data listed on the S&P500 and KOSPI. Among the common factors mentioned in the arbitrage pricing model (APM), widely acknowledged in th…
Sentiment analysis of DAX40 stocks improves performance by 5.38% annually.
problem Creating a more responsive stock market index using sentiment analysis.
method Extract sentiment from news articles, adjust index weights based on sentiment, compare performance to existing indices.
result Sentiment index outperforms DAX40 by 7.51% annually, adjusted for costs.
Scaling properties of the BUX index are similar to those observed in other parts of the world. The main difference is that the traditional quantities like volatility, growth and autocorrelation of returns follows more closely the assumptions of the traditional stock market theory developed by Bachelier and by Black and…
QLSTM outperforms LSTM in predicting KSE 100 index movements.
problem Predicting stock market movement in uncertain economic conditions.
method Used LSTM and QLSTM models on monthly data of economic indicators.
result QLSTM provided more accurate predictions of KSE 100 index values.
New stock market index captures market chaos and volatility.
problem Capturing the chaotic nature of stock market volatility.
method Tensor-based embedding of stock market information, time-dependent dynamical system model.
result Bidirectional causal relation between realized and implied volatility.
Study examines long-term impact of inflation and uncertainty on U.S. stock prices.
problem Long-term relationship between stock prices, inflation, and inflation uncertainty.
method Cointegration analysis with structural break, time-varying unobserved component model.
result Inflation and its uncertainty negatively impact stock prices in the long-run, but not in the short-run.
The Hype Index measures media attention to equities using NLP.
problem Quantifying media attention to equities for volatility analysis.
method Constructs News Count-Based and Capitalization Adjusted Hype Indices using NLP.
result The Hype Index family provides valuable tools for stock volatility analysis.
EXAMM evolves RNNs for stock return prediction and portfolio trading.
problem Predicting stock returns for optimal portfolio trading.
method Evolutionary Neural Architecture Search (EXAMM) for evolving RNNs.
result Evolving RNNs outperform traditional benchmarks in stock trading.
Study shows non-synchronous trading and portfolio effects explain market index behavior.
problem Explaining the difference in market efficiency between NYSE index types.
method Long-term analysis of NYSE indexes, rolling window variance tests, portfolio simulations.
result Joint effects of portfolio and non-synchronous trading explain index behavior.
A model explains stock returns and volatility using multifractal and rough components.
problem Reconciling multifractal stock returns and rough index volatilities.
method Nested factor model with multifractal and rough volatility components.
result The model explains stock index Hurst exponents larger than individual stock exponents.
The study improves stock market valuation using volatility and earnings data.
problem Improving stock market valuation metrics.
method Time series model for asset returns, multivariate kernel density estimation, linear regression.
result The valuation measure is an improvement over Shiller's P/E ratio.
A spring-block chain placed on a running conveyor belt is considered for modeling stylized facts observed in the dynamics of stock indexes. Individual stocks are modeled by the blocks, while the stock-stock correlations are introduced via simple elastic forces acting in the springs. The dragging effect of the moving be…
Volatility, fitting with first order Landau expansion, stationarity, and causality of the Taiwan stock market (TAIEX) are investigated based on daily records. Instead of consensuses that consider stock market index change as a random time series we propose the market change as a dual time series consists of the index a…
The waiting time needed for a stock market index to undergo a given percentage change in its value is found to have an up-down asymmetry, which, surprisingly, is not observed for the individual stocks composing that index. To explain this, we introduce a market model consisting of randomly fluctuating stocks that occas…
Geometric Brownian motion simulates stock prices for Brazilian small caps index.
problem Simulating stock prices for the Brazilian small caps index.
method Used geometric Brownian motion to simulate stock prices of Brazilian small caps index using historical data.
result Simulated prices better for portfolios with higher returns, lower risks, and higher Sharpe Indexes.
Simple, non-optimized portfolios beat capitalization-weighted indexes due to excess growth, not individual stock growth.
problem Simple investment strategies outperform capitalization-weighted indexes over long periods.
method Decomposed portfolio log-returns into average and excess growth components, using rank-based empirical study.
result Excess growth component, not individual stock growth, explains outperformance of naive portfolios.
Study finds power-law tails in order imbalance distributions of Chinese stocks.
problem Analyzing the distribution of order imbalance in Chinese stock markets.
method Examined order imbalance based on order number and size, analyzed distributions at different time scales.
result Order imbalance distributions exhibit power-law tails with varying tail indices across stocks.
Study shows COVID-19 increases stock market crash risk in China.
problem Impact of COVID-19 on stock market crash risk in China.
method Estimated conditional skewness using GARCH-S model and constructed fear index from Baidu Index data.
result Conditional skewness reacts negatively to daily growth in total confirmed cases, indicating increased crash risk.
Based on the tick-by-tick stock prices from the German and American stock markets, we study the statistical properties of the distribution of the individual stocks and the index returns in highly collective and noisy intervals of trading, separately. We show that periods characterized by the strong inter-stock coupling…
Transformer pre-training improves stock return prediction accuracy.
problem Improving stock price prediction accuracy for better investment decisions.
method Pre-trained transformer models on TSX index, fine-tuned for individual stocks, compared to LSTM and XGBoost.
result Transformer model achieved lower mean squared error than benchmarks.
This study presents an agent-based computational cross-market model for Chinese equity market structure, which includes both stocks and CSI 300 index futures. In this model, we design several stocks and one index futures to simulate this structure. This model allows heterogeneous investors to make investment decisions …
Study reveals 2020 stock crashes were mostly endogenous, not exogenous.
problem Identifying the cause of the 2020 global stock market crash.
method Applied log-periodic power law singularity (LPPLS) methodology to analyze stock market indexes.
result The 2020 stock market crashes were mostly endogenous, driven by systemic instability.
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.