Predictability Of Stock Market Returns Research Articles

Does the U.S. extreme indicator matter in stock markets? International evidence

We propose a new predictor—the innovation in the daily return minimum in the U.S. stock market (ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document})—for predicting international stock market returns. Using monthly data for a wide range of 17 MSCI international stock markets during the period spanning over half a century from January 1972 to July 2022, we find that ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document} have strong predictive power for returns in most international stock markets:ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document} negatively predicts the next-month stock market returns. The results remain robust after controlling for a number of macroeconomic predictors and conducting subsample and panel data analyses, indicating that ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document} has significant predictive power and it outperforms other variables in international markets. Notably, ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document} demonstrates excellent predictive power even during the periods driven by financial upheavals (e.g., Global Financial Crisis and European Sovereign Debt Crisis). Both panel regressions and out-of-sample tests also support the robust predictive performance of ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document}. The predictive power, however, disappears during the non-financial crisis caused by COVID-19 pandemic, which is originated from the health sector rather than the financial sector. The results provide a new perspective on U.S. extreme indicator in stock market return predictability.

The optimized gate recurrent unit based on improved evolutionary algorithm to predict stock market returns

In order to accelerate the learning ability of neural network structure parameters and improve the prediction accuracy of deep learning algorithms, an evolutionary algorithm, based on a prior Gaussian mutation (PGM) operator, is proposed to optimize the structure parameters of a gated recurrent unit (GRU) neural network. In this algorithm, the sensitivity learning process of GRU model parameters into the Gaussian mutation operator, used the variance of the GRU model parameter training results as the Gaussian mutation variance to generate the optimal individual candidate set. Then, the optimal GRU neural network structure is constructed using the evolutionary algorithm of the prior Gaussian mutation operator. Moreover, the PGM-EA-GRU algorithm is applied to the prediction of stock market returns. Experiments show that the prediction model effectively overcomes the GRU neural network, quickly falling into a local optimum and slowly converging. Compared to the RF, SVR, RNN, LSTM, GRU, and EA-GRU benchmark models, the model significantly improves the searchability and prediction accuracy of the optimal network structure parameters. It also validates the effectiveness and the progressive nature of the PGM-EA-GRU model proposed in this paper with stock market return prediction.

Long-term adjusted volatility: Powerful capability in forecasting stock market returns

We develop the long-term adjusted volatility (LV_ADJ) by removing the interference information of short-term volatility from the simple long-term volatility and examine the role of LV_ADJ in the predictability of stock market returns. Using a sample from January 2000 to December 2019 and considering 19 popular predictors, LV_ADJ positively predicts the next-month returns of S&P 500 and the univariate model with LV_ADJ has the best forecasting performance with adjusted in-sample r-squared of 3.825%, out-of-sample r-squared of 3.356%, return gains of 5.976%, CER gains of 4.708 and Sharpe ratio gains of 0.394. The predictive information of LV_ADJ is independent of that obtained from the 19 popular predictors. Furthermore, we find that LV_ADJ also has predictive power for long-term (3–12 months) stock returns, and can forecast returns of industry portfolios and characteristic portfolios.

PUZZLES INVOLVING THE STOCK MARKET, INFLATION AND THE PREDICTABILITY OF STOCK MARKET RETURNS

The need for stock market is rising in the modern day. Particularly, there is growing interest in companies with high share prices. However, the danger also rises as the interest does. There are several reasons for this. In other words, inflation is one of the largest risk concerns. Inflation control is a national issue, not just a business issue. Research on this subject was done by scientists including Thomas C. Chiang, Amal Essayem Sakir, Gormus Murat Guven, and Pierlauro Lopez. However, they haven't done enough study on how to predict inflation and how much it affects the stock market. The stock market, inflation, and the predictability of stock market returns are all topics that are clarified in this article.

Unintended look-ahead bias in out-of-sample forecasting

ABSTRACT This article shows that out-of-sample tests are susceptible to look-ahead bias not only to multiple testing problem that is emphasized in the literature. A forecaster often constructs a well-performing model without trial and error but with an intuition that is derived from observed empirical patterns in the test sample. Such an intuition, however, is unavailable in the beginning of the test sample. Therefore, the reported forecasting performance in an out-of-sample test is possibly exaggerated, although a forecaster simply utilizes her expertise without any intended p-hacking or fishing. A stylized forecasting model with an example of stock market return predictability quantitatively demonstrates this unintended look-ahead bias in out-of-sample tests.

Belief-based momentum indicator and stock market return predictability

Li et al. (2022) propose a new momentum indicator that combines past returns and consistent belief information, and show that the indicator positively predicts cross-sectional stock returns. Based on the momentum indicator of Li et al. (2022), we further develop a conditional past return (CPR) indicator that additionally adds the direction information for the investors' consistent belief. We examine the effectiveness of CPR as a predictor for stock market returns. Our evidence shows that CPR significantly and positively predicts future one-month market returns. And CPR provides unique predictive information that is not related to the other popular predictors. The abundant out-of-sample evidence further supports CPR’s predictive ability. Additionally, we detect the asymmetric role of CPR in predicting market returns and find that much of the predictive ability of CPR is attributed to the interaction between the positive past returns and the positive consistent belief.

Stock market return predictability: A combination forecast perspective

Based on traditional macroeconomic variables, this paper mainly investigates the predictability of these variables for stock market return. The empirical results show the mean combination forecast model can achieve superior out-of-sample performance than the other forecasting models for forecasting the stock market returns. In addition, the performances of the mean combination forecast model are also robust during different forecasting windows, different market conditions, and multi-step-ahead forecasts. Importantly, the mean combination forecast consistently generates higher CER gains than other models considering different investors' risk aversion coefficients and trading costs. This paper tries to provide more evidence of combination forecast model to predict stock market returns.

Category-specific EPU indices, macroeconomic variables and stock market return predictability

This paper mainly investigates whether the category-specific EPU indices have predictability for stock market returns. Empirical results show that the content of category-specific EPU can significantly predict the stock market return, no matter the individual category-specific EPU index or the principal component of category-specific EPU indices. In addition, the information of category-specific EPU indices can also have higher economic gains than traditional macroeconomic variables, even considering the trading cost and different investor risk aversion coefficients. During different forecasting windows, multi-period forecast horizons and the COVID-19 pandemic, we find the information contained in category-specific EPU indices can have better performances than that of the macroeconomic variables. Our paper tries to provide new evidence for stock market returns based on category-specific EPU indices.

Stock market return predictability revisited: Evidence from a new index constructing the oil market

An increasing number of studies declare that oil volatility information exhibits superior forecasting performance for equity premiums. This study develops a new predictor, namely, RSJV, using the realized semi-variance framework to reflect the proportion of upward (downward) variance on a specific trading day. Our in-sample and out-of-sample results reveal that the RSJV can outperform the 14 popular macroeconomic indicators of Welch and Goyal (2008). Furthermore, our results reconfirm that the economic constraint can help to improve the accuracy of return predictability.

Macroeconomic attention and stock market return predictability

Our investigation evaluates the novel macroeconomic attention indices (MAI) of Fisher et al. (2022) in terms of their ability to predict stock market returns based on dimension reduction methods and shrinkage methods. Our results demonstrate that macroeconomic attention indices can predict stock market returns with a significant degree of accuracy. In addition, the components of MAI indices based on partial least squares (PLS) and the least absolute shrinkage and selection operator (LASSO) methods have a greater capacity to improve the accuracy of the prediction of stock market returns than the components of the traditional macroeconomic variables. Moreover, we find that shrinkage methods can generate performances superior to those of the other models for forecasting stock market returns. We further demonstrate that macroeconomic attention indices embody superior predictive ability during the COVID-19 pandemic and over longer periods of time. Our study sheds new light on stock market returns’ prediction from the perspective of macroeconomic fundamentals.

On the Economic Significance of Stock Return Predictability

AbstractWe study the effects of time-varying volatility and investment horizon on the economic significance of stock market return predictability from the perspective of Bayesian investors. Using a vector autoregression framework with stochastic volatility (SV) in market returns and predictor variables, we assess a broad set of twenty-six predictors with both in-sample and out-of-sample designs. Volatility and horizon are critically important for assessing return predictors, as these factors affect how an investor learns about predictability and how she chooses to invest based on return forecasts. We find that statistically strong predictors can be economically unimportant if they tend to take extreme values in high volatility periods, have low persistence, or follow distributions with fat tails. Several popular predictors exhibit these properties such that their impressive statistical results do not translate into large economic gains. We also demonstrate that incorporating SV leads to substantial utility gains in real-time forecasting.

Stock Price Prediction using Machine Learning Algorithms

Abstract: Machine learning has broad applications in the finance industry. Risk Analytics, Consumer Analytics, Fraud Detection, and Stock Market Predictions are some of the domains where machine learning methods can be implemented. Accurate prediction of stock market returns is extremely difficult due to volatility in the market. The main factor in predicting a stock market is a high level of accuracy and precision. With the introduction of artificial intelligence and high computational capacity, efficiency has increased. In the past few decades, the highly theoretical and speculative nature of the stock market has been examined by capturing and using repetitive patterns. Various machine learning algorithms like Multiple Linear Regression, Polynomial Regression, etc. are used here. The financial data contains factors like Date, Volume, Open, High, Low Close, and Adj Close prices. The models are evaluated using standard strategic indicators RMSE and R2 score. Lower values of these two indicators mean higher efficiency of the trained models. Various companies employ different types of analysis tools for forecasting and the primary aim is the accuracy to obtain the maximum profit. The successful prediction of the stock will be an invaluable asset for the stock market institutions and will provide real-life solutions to the problems of the investors. Keywords: Stock prices, Analysis, Accuracy, Prediction, Machine Learning, Regression, Finance

Asset pricing with return extrapolation

We present a new model of asset prices in which a representative agent has extrapolative beliefs about stock market returns and Epstein-Zin preferences. The model quantitatively explains facts about asset prices, return expectations, and cash-flow expectations. When the agent’s beliefs about stock market returns are calibrated to survey expectations of investors, the model generates excess volatility and predictability of stock market returns, a high equity premium, a low and stable risk-free rate, and a low correlation between stock market returns and consumption growth. Moreover, the model has implications for expectations about future cash flows that are consistent with empirical findings.

Prediction of Stock Market Returns using LSTM Model and Traditional Statistical Model

Prediction of Stock Market Returns using LSTM Model and Traditional Statistical Model

Korean Stock Market Return Predictability in the context of Data-mining effect

Korean Stock Market Return Predictability in the context of Data-mining effect

Lumpy investment and expected stock returns

This study investigates the predictability of stock market returns using a novel corporate investment measure that captures the lumpiness of firm-level investment. We find that the proportion of firms with investment spikes (spike) is a strong predictor of excess stock returns. Specifically, an increase in spike significantly lowers future excess stock returns. The predictive ability of spike is consistently observed in both in-sample and out-of-sample tests. Furthermore, spike shows strong predictive ability at the business cycle frequency, suggesting that its predictive ability is driven by the time-varying risk premium associated with business cycles rather than temporary mispricing.

Hybridization of Bat Algorithm with XGBOOST Model for Precise Prediction of Stock Market Directions

In recent days, prediction of stock market returns is generally treated as a forecasting problem. The implicit volatile nature of stock market across the world makes the prediction process highly challenging. As a result, prediction and diffusion modeling undermine a wide range of issues present in the stock market prediction. The minimization in prediction error will greatly minimize the investment risks. This paper presents a new method to determine the direction of stock market variations indicating gain and loss. A new machine learning ML based model is applied to predict the direction of stock market prices. The presented model undergoes preprocessing, feature extraction and classification. Initially, preprocessing takes place using exponential smoothing. Then, required features are extracted from the preprocessed dataset. Afterwards, an effective Bat algorithm (BA) with the XGBoost model called BA-XGB is applied for forecasting the stock prices in market. The proposed model predicts whether the stock values gets increased or decreased based on the price existing n days in advance. The presented model is experimented using Apple (APPL) and Facebook (FB) stocks. The obtained simulation outcome stated that the BA-XGB model has offered superior outcome by achieving a maximum accuracy of 96.42.

Stock Closing Price Prediction using Machine Learning Techniques

Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. In this work, Artificial Neural Network and Random Forest techniques have been utilized for predicting the next day closing price for five companies belonging to different sectors of operation. The financial data: Open, High, Low and Close prices of stock are used for creating new variables which are used as inputs to the model. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price.

Stock Market Return Predictability Before and After the Dodd-Frank Act

Stock Market Return Predictability Before and After the Dodd-Frank Act

Stock market return predictability: Does network topology matter?

This paper provides new evidence for the predictability of excess market portfolio returns using a network approach. In particular, this article introduces a measure of interconnectedness to capture the interrelationship of returns of 100 largest stocks in S&P500 during 1990–2014. In the financial network literature, the interconnection of a stock network is often regarded as a channel through which an idiosyncratic shock propagates. The idiosyncratic risk propagation is crucial to the debate over the relationship between idiosyncratic risk and market returns because the idiosyncratic risk is not always diversified away. Rather, the network can sometimes amplify the effect of the idiosyncratic risk to cause aggregate fluctuation. In accordance with this theoretical argument, I empirically show that the network topology, measured by diameter, works together with the idiosyncratic risk, measured by average stock variance, to affect the market portfolio returns. This relationship persists after controlling for well-known variables known to forecast the stock market returns.