Predicting Stock Market Returns Research Articles

Country-level energy-related uncertainties and stock market returns: Insights from the U.S. and China

Scholars have widely acknowledged the influence of energy on stock market returns, yet the role of energy-related uncertainty in the stock market remains to be explored. This study employs scaled principal component analysis (sPCA), partial least squares (PLS), and principal component analysis (PCA) techniques to investigate the impact of country-level energy-related uncertainty indices (EUIs) on stock market returns. The results indicate that an EUI contains prediction information for U.S. and Chinese stock market returns. Furthermore, the diffusion index derived from all EUIs utilizing supervised dimensionality reduction methods (sPCA and PLS) facilitates predicting stock market returns better and more robustly than country-level EUIs and traditional macroeconomic predictors. The results of the economic value assessment also indicate that models considering the EUI diffusion index tend to yield greater economic value than models considering individual EUIs or traditional predictive factors. Our results emphasize the significance of the EUI diffusion index in predicting stock market returns, providing valuable guidance for portfolio management and decision-making for market stakeholders.

The battle of the factors: Macroeconomic variables or investor sentiment?

AbstractThis paper uses machine learning techniques to investigate whether popular macroeconomic or sentiment factors are better at predicting stock market returns. We find that although either macroeconomic or sentiment variables alone fail to improve the Sharpe ratio of the stock market, combining the factors improves the Sharpe ratio from 0.48 to 0.62 and reduces the investment drawdowns by roughly 30% from 53 percentage points to 36 percentage points. This improvement is significant in both economic and statistical terms. We further evaluate the performance of strategies across business cycle and find that macroeconomic variables tend to outperform sentiment variables during market expansions and underperform during recessions. The combined performance of the macroeconomic and sentiment variables is particularly strong during the late stage of recessions when the stock market is close to its bottom. Our finding is robust to the choice of machine learning technique and indicates that sentiment and macroeconomic information is complementary and, therefore, should be considered jointly by investors.

Predicting stock market returns using aggregate credit risk

We investigate how credit risk predicts stock returns in the time-series at the aggregate level in the Chinese market. We find that the aggregate credit risk, measured by the option-based structural model, is a strong positive predictor of future stock market excess returns at various horizons. The predictive power remains significant even after controlling for a number of widely-researched predictors or under out-of-sample tests. The positive relationship between aggregate credit risk and expected stock market returns accords with the risk-return tradeoff theory. We also find that the predictive power comes from the discount rate channel. A higher level of aggregate credit risk is related to a higher discount rate of future cash flows, and thus generates higher expected returns.

Stock Price Prediction Using Time Series Forecasting

Abstract: Accurately predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and increasing computing power, programmed forecasting methods have proven to be more efficient in predicting stock prices. In this work, artificial neural networks and random forest techniques are used to, predicted the next day's closing prices for five companies in various industries. Financial Data: Use the opening, high, low and closing prices to create new variables that will be used as inputs to the model. The model is evaluated against the standard strategy metrics RMSE and MAPE. The low values of these two indicators indicate that the model is efficient in predicting the closing price of the stock. Accurately predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and increasing computing power, programmed forecasting methods have proven to be more efficient in predicting stock prices. In this work, artificial neural networks and random forest techniques are used to, predicted the next day's closing prices for five companies in various industries.

Implications of war on the food, beverage, and tobacco industry in South Korea

The Food, Beverage & Tobacco (F&B) industry is an essential sector in the competitive economy. Procurement of production factors mainly depends on sales forecasting and the supply chain of raw materials. However, the conflict between Russia and Ukraine has jeopardized the global supply chain. As the conflict worsened, the world faced a food crisis, which was already a significant challenge due to the Covid-19 pandemic. Understanding how conflict-related disruptions in global food markets might affect the stock return of the F&B industry of South Korea, this study forecasts the stock returns on the KOSDAQ F&B sector. This paper highlights that the conflict resulted in immediate and far-reaching consequences on the global food supply chain and future crop harvesting in South Korea. As numerous algorithms have been widely used in predicting stock market returns, we use Autoregressive Integrated Moving Average (ARIMA) model for the prediction. Using daily returns from the KOSDAQ F&B industry from January 1999 to October 2022, the study proposes an ARIMA (2,2,3) model to forecast future movements of the stock returns. With an RMSE of 0.012, the prediction performance holds good using the ARIMA model. The results show a negative trend observed in the F&B sector returns for a few months, implying that sector stock returns decline as the conflict between Russia and Ukraine becomes more pronounced. This study also suggests that South Korea has massive scope to stabilize the demand for healthy, safe food, give more attention to domestic agribusiness, and make itself a self-sufficient agri-economy.

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.

Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models

We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and document a positive correlation between these ChatGPT scores and subsequent daily stock market returns. Further, ChatGPT outperforms traditional sentiment analysis methods. We find that more basic models such as GPT-1, GPT-2, and BERT cannot accurately forecast returns, indicating return predictability is an emerging capacity of complex models. Our results suggest that incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies.

Forecasting US stock market returns by the aggressive stock-selection opportunity

We propose a measurement of aggressive stock-selection opportunity based on positive alphas and idiosyncratic volatilities of cross-section stocks, and examine the role of aggressive stock-selection opportunity in predicting stock market returns. For the US stock market, we find that the change of aggressive stock-selection opportunity has a significant and negative coefficient for predicting future one-month market returns. The out-of-sample results also show the change of aggressive stock-selection opportunity improves the return forecasting performance and increases investors’ economic values. In particular, the predictive information of the change of aggressive stock-selection opportunity is independent of traditional macroeconomic predictors. The economic channel evidence shows that the change of aggressive stock-selection opportunity increases future market volatility and then results in lower market returns.

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.

Pengaruh Pandemi COVID-19 dan Trading Activities terhadap Return Saham

This study aims to determine the effect of COVID-19 pandemic and trading activities on stock market return in healthcare companies and transportation & logistic companies that was listed in Indonesia Stock Exchange during the research period. The study period from March 2, 2020 to December 31, 2020. The research finds that the best model used in predicting stock market returns is random effect model compared to other 2 selected models. Variables related to COVID-19 outbreak which are daily growth in confirmed cases and daily growth in death causes by COVID-19 were found not affect the stock market return significantly. However, this study found that trading activities described by trading volume and trading frequency variables during the research period had positive and significant effect to the stock market returns.

Whether dimensionality reduction techniques can improve the ability of sentiment proxies to predict stock market returns

In this study, we construct China's aggregate sentiment indicator (SsPCA) based on the method of Huang et al. (2021a), which employs a new dimension reduction method of scaled principal component analysis (PCA), to aggregate useful information from individual sentiment proxies, and further examine its return predictability for the Chinese stock market. The empirical evidence suggests that SsPCA significantly improves the prediction accuracy for stock market returns both in and out of the sample, and also obtains considerable economic gain for a mean-variance investor. Additionally, the forecasting effect of SsPCA is superior to that of SPCA and SPLS, evaluated using the traditional PCA and partial least square methods, respectively. Moreover, relative to the period of the bull market, SsPCA exhibits better ability in forecasting stock market returns during the bear market. Finally, special events, such as the outbreak of coronavirus disease 2019 (COVID-19), also affect the predictive performance of the sentiment indicator.

How External Trends and Internal Components Decomposition Method Improve the Predictability of Financial Time Series?

Accurately predicting stock market returns can pay off economically not by yielding significant profit but rather by preventing the loss of a large sum of money. Therefore, stocks with a high degree of predictability gain more attention. This study is intended to investigate the impact of decomposing returns on improving predictability. The decomposition method separates the return’s internal components from their external trend. The approximate entropy technique is then applied to quantify their randomness amounts. The results reveal that the decomposition method improved the predictability of returns from S & P 500, Nasdaq 100, SSE and SZSE 500 stocks. Furthermore, the outcomes show that using the return’s absolute value can further enhance its performance. Moreover, this study shows that S & P 500 intraday data are more predictable than their daily data. These findings propose incorporating the decomposition method in the prediction process to improve the predictability and maximise the investor’s profit and minimise their risk.

ANALYSIS OF MACHINE LEARNING METHODS FOR PREDICTIONS OF STOCK EXCHANGE SHARE PRICES

Since the stock market is one of the most important areas for investors, stock market price trend prediction is still a hot subject for researchers in both financial and technical fields. Lately, a lot of work has been analyzed and done in the field of machine learning algorithms for analyzing price patterns and predicting stock prices and index changes. Currently, machine-learning methods are receiving a lot of attention for predicting prices in financial markets. The main goal of current research is to improve and develop a system for predicting future prices in financial markets with higher accuracy using machine-learning methods. Precise predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and machine learning, forecasting methods have become more effective at predicting stock prices. In this article, we looked at the machine learning techniques that have been used to trade stocks to predict price changes before an actual rise or fall in the stock price occurs. In particular, the article discusses in detail the use of support vector machines, linear regression, and prediction using decision stumps, classification using the nearest neighbor algorithm, and the advantages and disadvantages of each method. The paper introduces parameters and variables that can be used to recognize stock price patterns that might be useful in future stock forecasting, and how the boost can be combined with other learning algorithms to improve the accuracy of such forecasting systems.

Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures

We investigate the median and tail dependence between cryptocurrency and stock market returns of BRICS and Developed countries using a newly developed nonparametric cumulative measure of dependence over the period January 4, 2016 – December 31, 2019 as well as before and after the introduction of Bitcoin futures on December 17, 2017. The new measure is model-free and permits measuring tail risk. The results highlight the leading role of S&P500, Nasdaq and DAX 30 in predicting BRICS and developed countries’ stock market returns. Among BRICS countries, BVSP shows a starring role in predicting stock market returns. BSE 30 is the most predictor of cryptocurrencies, which have a little predictability on stock market returns. Ethereum has the leading role in predicting cryptocurrencies and stock market returns followed by Bitcoin. Tail dependence shows substantial role of S&P500, Nasdaq and BVSP in predicting stock market returns. Subsample analysis show the role of Bitcoin futures in reshaping the mean and tail dependence between cryptocurrency and stock market returns. Our results have important policy implications for portfolio managers, hedge funds and investors.

Predicting stock market returns in the US: evidence from an average correlation approach

PurposeThis study aims to draw on a less explored predictor – the average correlation of pairwise returns on industry portfolios – to predict stock market returns (SMRs) in the USA.Design/methodology/approachThis study uses the average correlation approach of Pollet and Wilson (2010) and predicts the SMRs in the USA. The model is estimated using monthly data for a long time horizon, from July 1963 to December 2018, for the portfolios comprising 48 Fama-French industries. The model is extended to examine the effects of a longer lag structure of one-month to four-month lags and to control for the effects of a number of variables – average variance (AV), cyclically adjusted price-to-earnings ratio (CAPE), term spread (TS), default spread (DS), risk-free rate returns (R_f) and lagged excess market returns (R_s).FindingsThe study finds that the two-month lagged average correlation of returns on individual industry portfolios, used individually and collectively with financial predictors and economic factors, predicts excess returns on the stock market in an effective manner.Research limitations/implicationsThe methodology and results are of interest to academics as they could further explore the use of average correlation to improve the predictive powers of their models.Practical implicationsMarket practitioners could include the average correlation in their asset pricing models to improve the predictions for the future trend in stock market returns. Investors could consider including average correlation in their forecasting models, along with the traditional financial ratios and economic indicators. They could adjust their expected returns to a lower level when the average correlation increases during a recession.Social implicationsThe finding that recession periods have effects on the SMRs would be useful for the policymakers. The understanding of the co-movement of returns on industry portfolios during a recession would be useful for the formulation of policies aimed at ensuring the stability of the financial markets.Originality/valueThe study contributes to the literature on three counts. First, the study uses industry portfolio returns – as compared to individual stock returns used in Pollet and Wilson (2010) – in constructing average correlation. When stock market becomes more volatile on returns, the individual stocks are more diverse on their performance; the comovement between individual stock returns might be dominated by the idiosyncratic component, which may not have any implications for future SMRs. Using the industry portfolio returns can potentially reduce such an effect by a large extent, and thus, can provide more reliable estimates. Second, the effects of business cycles could be better identified in a long sample period and through several sub-sample tests. This study uses a data set, which spans the period from July 1963 to December 2018. This long sample period covers multiple phases of business cycles. The daily data are used to compute the monthly and equally-weighted average correlation of returns on 48 Fama-French industry portfolios. Third, previous studies have often ignored the use of investors’ sentiments in their prediction models, while investors’ irrational decisions could have an important impact on expected returns (Huang et al., 2015). This study extends the analysis and incorporates investors’ sentiments in the model.

Predictive Analytics in Stock Markets with Special Reference to BSE Sensex

Predictive analytics in finance is the art and science of using substantial quantities of data to find arrays. An Array can be termed as pattern or movement. Predictive analytics identifies patterns in large data volumes and helps to minimize future uncertainties. Predicting stock market returns is a puzzling task due to the multifaceted nature of the data. The present study is an applied application of the prediction and random walk theory on SENSEX behavior at an advanced level. Stock Market is the most dynamic element in the financial system and will play a crucial role in the progress of any country. The focus is on how much more on how to improve the forecasting models in terms of the performance of indices. The present model shows some commendable results in the prediction modelling reference to Indian stock market (BSE SENSEX). The designed model is also having utility for traders and investors estimating price movements of stocks at near future. Generally, the Fundamental Analysis comprises of evaluating the company’s profitability on the basis of its current business environment and financial performance in the future. Technical Analysis includes interpreting the charts and using statistical figures to identify the patterns in the stock market. A number of market indicators are believed to offer signals which are beneficial in anticipating future prices. For this purpose data of BSE Sensex data has been taken (January 2010- September 2018) from bseindia.com. The results exhibit that the Sensex would also gain momentum in the year 2019.

Financial Crisis, Inequality, and Capitalist Diversity: A Critique of the Capital as Power Model of the Stock Market

ABSTRACTThe relationship between inequality and financial instability has become a thriving topic of research in heterodox political economy. This article offers the first critical engagement with one framework within this wider literature: the Capital as Power (CasP) model of the stock market developed by Shimshon Bichler and Jonathan Nitzan. Specifically, we extend the CasP model to other advanced capitalist countries, including Germany, France, the United Kingdom, and Japan. Our findings affirm the core prediction of the CasP model, showing that unequal power relations reliably predict future stock market performance. Yet when it comes to the CasP model’s explanation of why power relations predict stock market returns, our findings are more ambiguous. We find little empirical support for the claims that capitalist power is dialectically intertwined with systemic fear, and that systemic fear and capitalised power are mediated through strategic sabotage. The main lesson of our analysis is that any model of the stock market must be attentive to the geographical unevenness and continued national diversity in capitalist development.

Can economic news predict Taiwan stock market returns?

News reports have become an imperative conduit of public information. Several recent studies have used news data from public media to investigate the impact of news on stock market returns. This study analyses the usefulness of news for predicting stock returns in the Taiwan stock market. We employ text mining of economic news, transform documents using a keyword matrix, and then convert the results into news variables. Subsequently, together with other quantitative variables, we construct a fixed effect model to investigate the behaviours of stock market returns in 20 subsectors from January 2008 to December 2014. Empirical analysis reveals that the news variables provide useful information for predicting Taiwan stock market returns, although the out-sample performance is only marginally improved. We also discover an asymmetric effect of economic news for predicting stock market returns. The prediction accuracy is higher when the stock market is booming than when it is glooming.

PREDICTING ANNUAL STOCK MARKET RETURNS WITH MARKET VARIABLES

ABSTRACT This study finds that the earnings and dividend yields are significantly positively related to future annual stock market returns, and they explain 5% and 7%, respectively, of variations in annual stock market returns. Years following high dividend and earnings yields deliver abnormal annual returns of 7.16% and 10.27%, respectively, compared to years following low yields. These results suggest that the two yields indicating stock market valuation have significant predictive power for future stock market returns and can be used to generate abnormally high returns. Dividend yields have greater explanatory power for future stock market returns, but years following high earnings yields produce higher returns. Keywords Predicting Stock Market Returns, Market Variables, Abnormal Returns

Modeling and Predicting Stock Market Returns: A Case Study on Dhaka Stock Exchange of Bangladesh

The available information pertaining to stocks should be entirely reflected in an efficient capital market with a view to aiding policy makers and investors in designing investment strategy. Hence, this paper investigates the time-series behavior of market returns of Dhaka Stock Exchange (DSE) of Bangladesh. This study also aims to find out the parsimonious model for forecasting monthly market returns of DSE more accurately. The monthly data of general index were collected from DSE for the period January 2002 to July 2013. Using Relative Difference method, monthly market returns were calculated. Autoregressive Integrated Moving Average (ARIMA) models were taken into account to model the behavior of stock market returns. Subsequently, based on Akaike Information Criterion and forecast errors, the findings of the study vouchsafe that ARIMA (2, 0, 2) can be employed to model and forecast market returns behavior of DSE efficiently. Finally, the monthly market returns were forecasted using the parsimonious model for the next 24 months and the predicted values fitted the observed values reasonably well.
 Dhaka Univ. J. Sci. 65(2): 97-101, 2017 (July)