Price and return predictions are limited by economic complexity, not just volatility.
problem Limited accuracy of price and return probability forecasts by Gaussian distributions.
method Analyzes economic reasons behind limitations in predicting price and return statistical moments.
result Predictions of price and return probabilities by Gaussian distributions are inaccurate due to economic complexity.
Economic factors significantly influence stock returns, as shown by attribution analysis.
problem The influence of economic factors on stock returns.
method Attribution model using five classic factors and new factors like Market Indices, Consumptions, and Oil Prices.
result Stock returns are exposed to economic news and priced based on risk exposure.
Study improves stock return prediction by switching between economic states, outperforming traditional methods.
problem Improving stock return prediction across economic regimes.
method State-switching specification using the slope of the yield curve, with an Aligned Economic Index.
result The Aligned Economic Index outperforms traditional predictors, especially during market turbulence.
Study shows time-varying stock returns across economic states.
problem Equity premium predictability varies by economic state.
method State-switching predictive regression using yield curve slope.
result The Aligned Economic Index improves stock return prediction.
The paper finds stocks with higher dynamic network risk have lower returns.
problem Understanding and pricing short-term and long-term dynamic network risk in stock returns.
method Examined the relationship between stock sensitivities to dynamic network risk and expected returns, using economic theory and empirical analysis.
result A one-standard deviation increase in long-term network risk loadings associates with a 7.66% drop in annualized expected returns.
Paper uses bipartite graph to forecast cross-market returns, revealing asymmetry.
problem Cross-market return predictability and asymmetry between U.S. and Chinese markets.
method Directed bipartite graph capturing time-ordered linkages, hypothesis testing for edge selection, regularized and ensemble machine learning models.
result U.S. returns predict Chinese intraday returns, but not vice versa, revealing asymmetry.
We study association between macroeconomic news and stock market returns using the statistical theory of copulas, and a new comprehensive measure of news based on the indexing of news wires. We find the impact of economic news on equity returns to be nonlinear and asymmetric. In particular, controlling for economic con…
Paper classifies economic states and optimizes portfolios for stagflationary environments.
problem Economic uncertainty and stagflationary conditions.
method Mathematical techniques for analyzing multivariate time series, economic driver analysis, self-similarity identification, and portfolio optimization.
result Constructs economic state classifications and computes economic state integrals.
The study examines how market trade randomness influences price and return volatility.
problem The accuracy of predicting market-based volatilities and macroeconomic variables is limited.
method Analyzes time series of trade values and volumes, and develops econometric methodologies for predicting volatilities.
result Current macroeconomic models underestimate the accuracy of predicting market-based volatilities and macroeconomic variables.
Volatility forecasting and return prediction in high-frequency Chinese equity markets.
problem Improving statistical forecasting performance and economic strategy outcomes in equity markets.
method Developing a sequential two-stage framework combining realized volatility modeling and XGBoost return prediction.
result Regime-aware volatility forecasting outperforms baseline models.
At what level should government or companies support research? This complex multi-faceted question encompasses such qualitative bonus as satisfying natural human curiosity, the quest for knowledge and the impact on education and culture, but one of its most scrutinized component reduces to the assessment of economic pe…
Deep learning models improve stock market portfolio returns.
problem Optimizing portfolio returns using deep learning methods.
method Deep neural networks (feedforward and LSTM) applied to stock market excess returns forecasting.
result Deep learning models deliver significant gains in portfolio certainty equivalent returns and Sharpe ratios.
Inverse statistics in economics is considered. We argue that the natural candidate for such statistics is the investment horizons distribution. This distribution of waiting times needed to achieve a predefined level of return is obtained from (often detrended) historic asset prices. Such a distribution typically goes t…
A macroeconomic model based on the economic variables (i) assets, (ii) leverage (defined as debt over asset) and (iii) trust (defined as the maximum sustainable leverage) is proposed to investigate the role of credit in the dynamics of economic growth, and how credit may be associated with both economic performance and…
Survey finds LLMs match human economic expectations closely.
problem Understanding human economic expectations and their deviations.
method Survey of LLM's expectations based on news articles.
result LLM's expectations closely match existing surveys and exhibit deviations.
This article derives prognostic expressions for the evolution of globally aggregated economic wealth, productivity, inflation, technological change, innovation and growth. The approach is to treat civilization as an open, non-equilibrium thermodynamic system that dissipates energy and diffuses matter in order to sustai…
Analyzes national real estate investment risks and returns.
problem Investors and home buyers face increasing costs and risks.
method Examines economic vulnerabilities and traditional market analysis.
result Ensures positive returns and fair prices for real estate investments.
MarketGAN generates financial returns using GANs to match empirical stylized facts.
problem Generating financial returns under data scarcity and preserving stylized facts.
method Generative adversarial learning with a TCN backbone.
result MarketGAN outperforms conventional methods in portfolio applications.
The paper explores how market trade values and volumes affect price and return statistics.
problem Understanding the statistical properties of market trade, price, and return.
method Introduces secondary averaging procedure to describe statistical moments of market trades, price, and return.
result Predictions of market-based probabilities of price and return are limited by Gaussian distributions.
In this paper we give definitions of matrix rates of return which do not depend on the choice of basis describing baskets. We give their economic interpretation. The matrix rate of return describes baskets of arbitrary type and extends portfolio analysis to the complex variable domain. This allows us for simultaneous a…
This paper studies a continuous-time market {under stochastic environment} where an agent, having specified an investment horizon and a target terminal mean return, seeks to minimize the variance of the return with multiple stocks and a bond. In the considered model firstly proposed by [3], the mean returns of individu…
This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for otherwise unobserved heterogen…
Study finds no consistent return predictability using payout ratios across 16 countries.
problem Return predictability using payout ratios in various markets.
method Analysis of 16 developed countries' bond, equity, and housing markets using payout-price ratios.
result No consistent in-sample and out-of-sample performance with positive utility gain.
Sell-side analysts' reports explain 10% of stock returns, with income statement analyses most impactful.
problem The value of sell-side analysts' information in predicting stock returns.
method Analysis of large language model embeddings and Shapley value decomposition.
result Income statement analyses contribute most to explaining stock returns.
GPR ensemble method predicts stock returns efficiently.
problem Predicting stock returns using machine learning.
method Ensemble Gaussian Process Regression (GPR) for online learning.
result Method outperforms existing models in R-squared and Sharpe ratio. Financial volatility risk and its relation to a business cycle-related intrinsic time is addressed through a multiple round evolutionary quantum game equilibrium leading to turbulence and multifractal signatures in the financial returns and in the risk dynamics. The model is simulated and the results are compared with …
We compare some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on random matrix theory and hierarchical clustering techniques. We apply these methods to a portfolio of s…
Keywords: corporate finance, Wrocław University of Economics, net profit margin lub net sales profitability
Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel framework, in which a robo-advisor interacts with a client to solve an adaptiv…
We provide an economic interpretation of the practice consisting in incorporating risk measures as constraints in a classic expected return maximization problem. For what we call the infimum of expectations class of risk measures, we show that if the decision maker (DM) maximizes the expectation of a random return unde…
The paper uses LSTM to predict stock prices and optimize portfolio weights.
problem Accurate prediction of stock prices and designing optimized portfolios.
method Built sector-wise portfolios and an LSTM model for stock price prediction.
result The LSTM model accurately predicts stock prices with high accuracy.
The paper predicts financial markets using news text and semantic network analysis.
problem Predicting financial markets with news data.
method Semantic network analysis of news text to assess economic keywords' importance.
result The index captures financial market phases and predicts returns and volatilities.
We demonstrate the existence of an empirical linkage between the nominal financial networks and the underlying economic fundamentals across countries. We construct the nominal return correlation networks from daily data to encapsulate sector-level dynamics and figure the relative importance of the sectors in the nomina…
Recent research in economic theory attempts to study optimal economic growth and spatial location of economic activity in a unified framework. So far, the key result of this literature - asymptotic convergence, even in the absence of decreasing returns to capital - relies on specific assumptions about the objective of …
A new method models financial returns by separating sign and magnitude, improving forecasting accuracy.
problem Capturing nonlinear predictability in financial return dynamics.
method Decomposes returns into sign and magnitude components, using a joint distribution model.
result Significantly outperforms traditional linear models in forecasting U.S. stock market returns.
A technique from stochastic portfolio theory [Fernholz, 1998] is applied to analyse equity returns of Small, Mid and Large cap portfolios in an emerging market through periods of growth and regional crises, up to the onset of the global financial crisis. In particular, we factorize portfolios in the South African marke…
With this work we try to analyse the agglomeration process in the Portuguese regions, using the New Economic Geography models. In these models the base idea is that where has increasing returns to scale in the manufactured industry and low transport costs, there is agglomeration. Of referring, as summary conclusion, th…
We review some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on random matrix theory and hierarchical clustering techniques. We apply these methods to a set of stocks t…
Study shows ethanol blends and incentives can significantly reduce transportation carbon emissions.
problem Rapid growth in electric vehicles requires complementary strategies to decarbonize transportation.
method Analysis of ethanol blending, regulatory incentives, and economic assessments.
result Ethanol blending, especially E15 and E85, can substantially reduce carbon emissions and provide economic benefits.
Machine learning improves portfolio allocation between index and risk-free assets.
problem Finding optimal portfolio rules for time-varying returns and volatility.
method Two Random Forest models: one for sign probabilities of excess return, the other for optimized volatility.
result Substantial improvements in utility, risk-adjusted returns, and maximum drawdowns over buy-and-hold.
The prevalent view in the economics literature is that a high level of infrastructure investment is a precursor to economic growth. China is especially held up as a model to emulate. Based on the largest dataset of its kind, this paper punctures the twin myths that, first, infrastructure creates economic value, and, se…
A new model decomposes equity returns and volatilities into memory components.
problem Understanding long-term equity dynamics and volatility patterns.
method Proposes a multivariate generalization of the variance ratio to decompose long-horizon equity dynamics.
result Identifies a five-factor model capturing persistent, antipersistent, and multi-scale memory in returns and volatility.
New methods improve uncertainty in machine learning predictions for asset returns.
problem Uncertainty in machine learning predictions for asset returns.
method Developed new methods to construct forecast confidence intervals for expected returns from neural networks.
result Neural network forecasts of expected returns have the same asymptotic distribution as classic nonparametric methods, enabling standard error calculation.
Customer momentum is a positive relationship between a firm's returns and past returns of its customers.
problem Understanding the relationship between a firm's returns and its customers' past returns.
method Examined customer momentum using a long-short equally-weighted decile portfolio and Fama-French factor models.
result Customer momentum generates significant monthly returns and is statistically significant.
This dissertation reports work where physics methods are applied to financial and economical problems. The first part studies stock market data (chapter 1 to 5). The second part is devoted to personal income in the USA (chapter 6). We first study the probability distribution of stock returns at mesoscopic time lags (re…
MPC outperforms reactive budgeting in non-stationary return environments.
problem Optimizing budget allocation under non-stationary returns.
method Receding-horizon Model Predictive Control (MPC) compared to reactive policies.
result MPC consistently outperforms reactive budgeting when return dynamics are predictable.
Paper introduces a new method for calibrating ESGs to both historical and forward-looking data.
problem Lack of a generally accepted methodology for calibrating ESGs to forward-looking information.
method Conditional Scenario Simulator framework for consistent calibration of economic and financial variables.
result Framework can embed various financial and macroeconomic models and demonstrate practical examples in frequentist and Bayesian settings.
We present a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regression that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributi…