Study shows risk-averse investors have consistent ranking of risky assets.
problem Ranking of risky assets in short-term investments.
method Analyzes various decision problems regarding risky assets with continuous returns.
result Risk-averse decision makers have the same ranking over risky assets.
The paper analyzes Indian stock sectors using multifractal analysis for long and short-term investment.
problem Investment risk and stability in Indian stock sectors.
method Sector-wise multifractal analysis of Bombay Stock Exchange, India, over short and long time scales.
result Long-term investment in stable sectors is more profitable, while sectors with large fluctuations may lead to downturns.
Cryptocurrency prices predicted using LSTM, SVM, and polynomial regression.
problem Uncertainty in crypto coin values.
method Long Short Term Memory, Support Vector Machine, Polynomial Regression models.
result Support Vector Machine with linear kernel had the smallest mean square error.
Different investment strategies are adopted in short-term and long-term depending on the time scales, even though time scales are adhoc in nature. Empirical mode decomposition based Hurst exponent analysis and variance technique have been applied to identify the time scales for short-term and long-term investment from …
Optimizes investment model using LSTM for better risk control.
problem Enhancing risk control in multi-factor investment models.
method Combines LSTM with multi-factor investment model for factor selection and weight determination.
result LSTM model outperforms benchmark in risk control metrics.
This study improves stock investment strategies using advanced neural networks.
problem Improving stock investment strategies for better performance.
method Used LSTM-GRU neural networks combined with SVM for stock prediction.
result LSTM-GRU outperformed benchmarks in stock predictions.
The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time ser…
Paper proposes novel hedging strategies using LSTM models for diversified investment portfolios.
problem Hedging risky asset portfolios in turbulent financial markets.
method Four diverse models (LSTM, ARIMA-GARCH, momentum, contrarian) generate price forecasts for diversified AIS.
result LSTM-based strategies outperform other models, with Bitcoin being the best diversifier for S&P 500 index.
We apply the Zipf power law to financial time series of WIG20 index daily changes (open-close). Thanks to the mapping of time series signal into the sequence of 2k+1 'spin-like' states, where k=0, 1/2, 1, 3/2, ..., we are able to describe any time series increments, with almost arbitrary accuracy, as the one of such 's…
ChatGPT scores corporate investment plans, predicting future spending and returns.
problem Measuring and predicting corporate investment plans.
method Created a firm-level ChatGPT investment score based on conference calls.
result The investment score predicts future capital expenditures and returns.
Behavioral theories posit that investor sentiment exhibits predictive power for stock returns, whereas there is little study have investigated the relationship between the time horizon of the predictive effect of investor sentiment and the firm characteristics. To this end, by using a Granger causality analysis in the …
Digital transformation boosts corporate financial asset allocation, especially short-term.
problem Understanding how digital transformation affects corporate financial decisions.
method Fixed-effects models and staggered DID design using A-share listed companies data.
result Digital transformation significantly promotes corporate financial asset allocation, more pronounced in short-term.
The paper fits cash management models to data using stochastic and linear programming.
problem Cash flow probability distribution assumptions in cash management models are relaxed.
method Stochastic and linear programming to fit models to data.
result A small random sample of data is sufficient to fit bound-based models.
This paper proposes a novel adaptive algorithm for the automated short-term trading of financial instrument. The algorithm adopts a semantic sentiment analysis technique to inspect the Twitter posts and to use them to predict the behaviour of the stock market. Indeed, the algorithm is specifically developed to take adv…
Model A outperforms passive investment in stock index prediction with less exposure.
problem Predicting short-term stock index movements with high accuracy.
method Dynamic Deep Neural Networks (DNN) for trading decisions.
result Model A outperforms passive investment and conventional ML methods.
Paper optimizes stock option forecasting using ML models and improved trading strategies.
problem Improving accuracy of stock option predictions and trading decisions.
method Application of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Quasi-Reversibility Method (QRM).
result Optimized stock option investment results through improved trading strategies and model combination.
We propose a stylized model of production and exchange in which long-term investors set their production decision over a horizon τ , the "time to produce", and are liquidity constrained, while financial investors trade over a much shorter horizon δ (<< τ ) and are therefore more duly informed on the exogenous shocks af…
Study finds short-term trading signals can enhance alpha in U.S. S&P 500 portfolios.
problem Traditional factor investing misses real-time market dislocations.
method Double-selection LASSO framework to control for fundamental factors and isolate trading signals.
result 17 distinct trading signals capture significant risk premiums and enhance portfolio diversification.
The paper revisits the investment simulation based on strategies exhibited by Generalized (m,2)-Zipf law to present an interesting characterization of the wildness in financial time series. The investigations of dominant strategies on each specific time series shows that longer words dominant in larger time scale exhib…
Long-term investors, different from short-term traders, focus on examining the underlying forces that affect the well-being of a company. They rely on fundamental analysis which attempts to measure the intrinsic value an equity. Quantitative investment researchers have identified some value factors to determine the cos…
Study examines short-term stress of COVID-19 on major global stock indices.
problem Short-term impact of COVID-19 on global stock markets.
method Secondary data from 41 stock exchanges in 32 countries, focusing on first reported cases.
result Volatility in stock markets increases with the rise of COVID-19 cases, and there is a significant negative correlation.
Study uses deep learning to predict stock trends with superior performance.
problem Predicting short-term equity trends with high accuracy.
method Dual-task multilayer perceptron (MLP) integrating technical signals and deep learning.
result Deep learning model outperforms linear baselines in multi-factor stock selection.
Investment strategies derived from commodity futures curves exploit dynamics in price movements.
problem Modeling and predicting the term structure of commodity futures prices.
method Employed the Nelson-Siegel framework to model term structure, and developed investment strategies based on changes in slope and curvature parameters.
result Significant profits generated from systematic strategies based on the change in slope, unrelated to risk factors and robust to transaction costs.
Paper presents LSTM models for short-term stock price prediction.
problem Accurately predicting short-term stock prices is challenging.
method Univariate and multivariate LSTM models using historical data.
result Multivariate LSTM model with technical indicators outperforms univariate model.
The paper solves a consumption-investment problem with state-dependent lower bounds.
problem A life-time consumption-investment problem with a state-dependent lower bound on consumption.
method Transformed the problem into a state-independent control problem to apply standard theory.
result Explicit optimal strategies provided for both homogeneous and non-homogeneous constraints.
Study introduces a new investment strategy model using lazy factor and probability weights.
problem Optimizing investment strategies in volatile markets with transaction costs.
method Combines Price Portfolio Forecasting and Mean-Variance Models with Transaction Costs, using probability weights as laziness factor coefficients.
result Model demonstrates adaptability and generalizability in transforming investment strategies.
Investigates geometric mean reversion process using Lie symmetry method.
problem Describes dynamics of short-term interest rates.
method Lie symmetry method and optimal system of invariant solutions.
result Constructs an optimal system of invariant solutions.
Interpretable AI model boosts investment confidence and profitability.
problem Challenges in financial forecasting and interpretability in decision-making models.
method SHAP-based explainability technique for interpretable AI models.
result Notable enhancement in investor's portfolio value.
We analyse a multiplex of networks between OECD countries during the decade 2002-2010, which consists of five financial layers, given by foreign direct investment, equity securities, short-term, long-term and total debt securities, and five environmental layers, given by emissions of N O x, P M 10 SO 2, CO 2 equivalent…
This paper investigates how two important sources of risk -- market tail risk and extreme market volatility risk -- are priced into the cross-section of asset returns across various investment horizons. To identify such risks, we propose a quantile spectral beta representation of risk based on the decomposition of cova…
Proposes a stochastic model for South African actuarial use.
problem Long-term forecasting for South African institutions.
method Modeling economic series, estimating parameters, testing stability.
result Validated model for long-term forecasts.
Predicts S&P 500 trends using machine learning models.
problem Market trend prediction for S&P 500 index.
method Feature engineering, machine learning models (Logistic Regression, Decision Trees, Random Forests, Neural Networks, KNN, XGBoost), data preprocessing, hyperparameter tuning, SMOTE.
result KNN for short-term predictions, XGBoost for long-term forecasts.
Study assesses short-term debt's impact on non-financial firms' financial growth.
problem Declining financial performance and reluctance to lend to non-financial firms listed at Nairobi Securities Exchange.
method Explanatory research design, descriptive statistics, and panel data analysis.
result Short-term debt positively and significantly influences financial growth.
Growth-optimal portfolios are guaranteed to accumulate higher wealth than any other investment strategy in the long run. However, they tend to be risky in the short term. For serially uncorrelated markets, similar portfolios with more robust guarantees have been recently proposed. This paper extends these robust portfo…
Predict stock prices using financial news sentiment analysis.
problem Predicting stock market trends for better investment returns.
method Deep Learning (MLP, LSTM, FinBERT-LSTM) integrating news sentiment.
result FinBERT-LSTM model predicts stock prices more accurately.
WSB's investment advice significantly outperformed the S&P500 over 3 years, but not consistently.
problem Reliability of investment advice from WSB community.
method Data analysis of WSB posts and stock performance from 2019-2021.
result A WSB portfolio grew 200% over 3 years and 480% over 1 year, outperforming S&P500.
A simple quantum model explains the Levy-unstable distributions for individual stock returns observed by ref.[1]. The probability density function of the returns is written as the squared modulus of an amplitude. For short time intervals this amplitude is proportional to a Cauchy-distribution and satisfies the Schroedi…
Estimates long-term effects of new treatments using historical and short-term data.
problem Estimating long-term effects of novel treatments with limited historical data.
method Surrogate indices, dynamic treatment effect estimation, and double machine learning combined in a unified pipeline.
result Consistent and asymptotically normal estimates of long-term effects under Markovian assumption.
Models predict stock returns from high-frequency data for better investment.
problem Training effective models for stock selection using high-frequency price-volume data.
method Developed two models: CNN and LSTM, trained on past high-frequency price data.
result Annualized net rate of return of 62.27% for CNN model and 50.31% for LSTM model.
Study investor attention using search volume data before and after mobile device popularity.
problem Accurately measure investor attention in a fast-paced market.
method Compare investor attention using search volume data before and after mobile device popularization.
result Investor attention measured using search volume data is more accurate and faster after mobile device popularization.
This paper uses Gaussian processes to forecast short-term stock price volatility.
problem Inaccurate short-term volatility forecasts for high-frequency trades.
method Combines numerical and probabilistic models, specifically Gaussian Processes (GPs), to correct and forecast stock price data.
result Effective short-term volatility forecasts for high-frequency trades using Gaussian Processes.
Cryptocurrency forecasting model considers macro, sentiment, and technical indicators.
problem High price volatility in cryptocurrency markets.
method Dual-prediction mechanism incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes.
result The proposed model outperforms ten comparison methods in short-term cryptocurrency forecasting.
Paper optimizes trading profits by predicting price direction using ensemble models.
problem Predicting short-term price movements in financial markets.
method Developed a two-layer ensemble architecture optimized through grid search.
result Improvement of 20% over benchmark strategies.
In the seminal work [9], several macroscopic market observables have been introduced, in an attempt to find characteristics capturing the diversity of a financial market. Despite the crucial importance of such observables for investment decisions, a concise mathematical description of their dynamics has been missing. W…
Modeling financial time series with LSTM and trainable initial states.
problem Extracting patterns and information from financial time series.
method Long Short-Term Memory (LSTM) network with trainable initial hidden states.
result Model captures relative similarity and predicts future stock trends.
In the IEEE Investment ranking challenge 2018, participants were asked to build a model which would identify the best performing stocks based on their returns over a forward six months window. Anonymized financial predictors and semi-annual returns were provided for a group of anonymized stocks from 1996 to 2017, which…
Hybrid model combines SV and LSTM for S&P 500 volatility forecasting.
problem Accurate forecasting of S&P 500 index volatility.
method Integrates Stochastic Volatility with LSTM networks.
result Hybrid model outperforms standalone SV and LSTM models.
Paper proposes deep learning model for dynamic stock repurchase forecasting.
problem Complex temporal dependencies in corporate financial conditions.
method Hybrid Temporal Convolutional Network (TCN) and Attention-based LSTM.
result Model significantly outperforms static baselines in stock repurchase forecasting.