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Artificial Neural Networks (ANNs) has been used as a powerful modeling technique for forecasting. In this study, the relationship between multiples and stock prices has been investigated on the Pakistan Stock Exchange 100 Index by incorporating financial modeling through neural network. The aim is to develop multiple-based valuation model to check whether multiples are viable factor in predicting stock movements. Forecasting model has been developed by using neural network. Prediction accuracy of the developed forecasting model has been evaluated. Findings reveal that neural network outperforms in comparison to linear regression and forecasts stock prices with 98% accuracy.

This study examines how providing peer information for evaluation of progressive firms changes individuals’ evaluations. From the large sample of two experimental surveys, it was found that providing peer information leads to a higher expectation of increasing stock prices and willingness to buy. The effect on willingness to buy is greater than the expected increase in stock prices. The effect on women is greater than on men. Individuals who prefer the environment (women’s empowerment) are more willing to buy pro-environment (gender-balanced) stocks when they have peer information. The effect of peer information is greater for individuals with warm-glow motivations.

After the East Asian crisis in 1997, the issue of whether stock prices and exchange rates are related or not have received much attention. This is due to realization that during the crisis the countries affected saw turmoil in both their currencies and stock markets. This paper studies the non-linear interactions between stock price and exchange rate in Malaysia using a two regimes multivariate Markov switching vector autoregression (MS-VAR) model with regime shifts in both the mean and the variance. In the study, the Kuala Lumpur Composite Index (KLCI) and the exchange rates of Malaysia ringgit against four other countries namely the Singapore dollar, the Japanese yen, the British pound sterling and the Australian dollar between 1990 and 2005 are used. The empirical results show that all the series are not cointegrated but the MS-VAR model with two regimes manage to detect common regime shifts behavior in all the series. The estimated MS-VAR model reveals that as the stock price index falls the exchange rates depreciate and when the stock price index gains the exchange rates appreciate. In addition, the MS-VAR model fitted the data better than the linear vector autoregressive model (VAR).

This study uses the golden cross and death cross formed by the gap between the narrow and broad money growth rates as threshold variables to estimate the threshold model and test the causal relationship between money supply and stock prices in eight emerging market economies (EMEs) in Asia; the sample periods are from January 2000 to December 2020. The results show a high-positive, bi-directional relationship between the money supply and stock prices in the golden cross regime. On the other hand, the money supply has a negative, one-way causality on stock prices in the death cross regime. We also conducted a robustness test during the COVID-19 spread, and the result shows that the mechanism still applies, but the effectiveness is reduced. Thus, our contribution is discovering the golden cross and death cross information formed by narrow and broad money, informing stock market investment.

Adopting the event study method, this paper examines how the stock market reacts to the public release of environmental performance rankings of heavy polluters. Specifically, we explore the impact of the public release of the 100 best and 100 worst companies in terms of environmental performance on the stock prices of companies involved, and the potential moderation of ultimate controllers. We find that the stock prices of the companies on the lists were negatively affected by the event. However, stock price changes are not significantly related to the relative rankings on the list. Furthermore, companies whose ultimate controllers possess a larger control–ownership wedge experienced a less severe fall in stock prices than did their counterparts. This study sheds light on the nuances of the financial implications of negative environmental publicity.

This study uses a cointegration analysis and vector autoregressive models to investigate the transmission of stock price movements among Taiwan and its major trading partners, Hong Kong, Japan and the United States. The results of Johansen cointegration test indicate that four stock markets considered are cointegrated with one cointegrating vector, which violates the semi-strong form of the market efficiency hypothesis. The results from Granger-causality test based on error-correction models suggest the relative leading roles of the U.S. and Japanese markets in driving fluctuations in the other two markets. In order to capture the impacts of the economic shocks, two dummy variables are incorporated into the models taking into account the U.S. stock crash of October 1997 (D97) and the previous spreading Asian finance crises (Dac). The results indicate that D97 significantly affects the U.S. stock market, but shows no significant impact on the others. The Dac, however, shows significant impacts on both the Japanese and the U.S. markets. The robustness of the relative leading roles of the U.S. and Japanese markets are further supported by the variance decompositions and impulsive response functions indicators. The Taiwan and Hong Kong markets are somewhat affected more by regional countries such as Japan than by the U.S.

A series of stock prices typically shows a large trend and smaller fluctuations. These two parts are often studied together, as if parts of a single process; but they appear to be separately caused. In this paper, the two parts are analyzed separately, so that one does not distort the other, and some spurious interaction terms are avoided. This contributes a model, in which a wide range of features of stock price behavior are identified. With logarithms of stock prices, the two parts become of more comparable size. This is found to lead to a simpler additive model. On a logarithmic scale, the stock prices show the trend as a straight line (which can be extrapolated), with added fluctuations filling a narrow band. The trend and fluctuations are thus separated. The trend appears to be largely generated by a positive feedback process, describing investor behavior. The width of the fluctuation band does not grow with time, so positive feedback is not its cause. The movement of stock prices can be understood by analyzing the trend and fluctuations as separate processes; the latter considered as a stationary stochastic process with a scale factor. This analysis is applied to a historical dataset $(S\&P500$ index of daily prices from February 1928). Here, the fluctuations are autocorrelated over short time intervals; there is little structure, except for market crash periods, when variability increases. The slope of the trend showed some jumps, not predictable from price history. This approach to modeling describes many aspects of stock price behavior, which are usually discussed in behavioral finance.

This paper analyzes and compares the effects of the monetary policy on the stock price in China based on SVAR models with two different restriction schemes. As suggested by existing literature, there are four major monetary policy instruments used by the People’s Bank of China. They are the seven-day repo rate, the one-year benchmark lending rate, the M2, and the total loan. We run SVARs with the monetary policy instrument, the stock index, and the macroeconomic variables and show the impulse responses of the stock index to the monetary policy shocks. After comparing two restriction schemes, the short-run Cholesky restrictions and the short-run and long-run combined restrictions for identification, we conclude that the latter restriction method leads to better estimation than the former one. In general, a contractionary monetary policy shock lowers the stock price, appreciates the Chinese currency, reduces the output gap, injects deflation, and shrinks the commodity price gap. We find that the benchmark lending rate is more effective in regulating the Chinese stock market than the other monetary policy instruments. In addition, a combination of price-based and quantity-based monetary policy instruments is suggested for impacting the stock market and stabilizing the economy in China.

The interaction between new energy vehicle (NEV) stock prices and the crude oil market is crucial for resource allocation and risk management. This study employs Multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the multifractal characteristics of the cross-correlation between Tesla stock price (TSLA) and crude oil price (Brent), as well as between TSLA and other NEV stocks (excluding TSLA). The experimental results reveal long-term persistence and multiple fractal characteristics in the cross-correlations. Additionally, multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) demonstrates the asymmetry of the cross-correlation during upward or downward trends between TSLA and Brent, as well as between TSLA and other NEV stocks (excluding TSLA). Furthermore, utilizing the transfer entropy (TE) method, we assess the strength and direction of information flows between TSLA and Brent, and between TSLA and other NEV stocks (excluding TSLA). Interestingly, we observe bidirectional information transmission between TSLA and other NEV stocks, while only unidirectional information transmission from NIO to TSLA is evident. These findings provide valuable insights for resource allocation, supply chain management and sustainable development strategies for decision-makers in the NEV market.

Stochastic system is applied to describe and investigate the fluctuations of stock price changes in a stock market, and a stock price model is developed by the finite-range contact process of the statistical physics systems. In this paper, the scaling behaviors of the return intervals for SSE Composite Index (SSE) and the simulation data of the model are investigated and compared. The database is from the index of SSE in the 6-year period for every 5 minutes, and the simulation data is from the finite-range contact model for different values of the range R. For different values of threshold θ, the statistical analysis shows that the probability density function P_θ(τ) of the return intervals τ for both SSE and the simulation data have similar scaling form, that is ( is the mean return interval), where the scaling function h(x) can be approximately fitted by the function h(x) = ωe^{-a(ln x)γ}, and ω, a, γ are three parameters. Further, with different values of R and θ, the statistical comparison of SSE Composite Index and simulation data are given.

Stock price exhibits distinct features during different time scales due to the effects of complex factors. Analyzing these features can help delineate the mechanisms that determine the stock price and enhance the prediction accuracy of the stock price. By using singular spectrum analysis (SSA), this paper first decomposes the original price series into a trend component, a market fluctuation component and a noise component to analyze the stock price. The economic meanings of the three components are identified as a long-term trend, effects of significant events and short-term fluctuations caused by noise in the market. Then, to take into account the features of the above three components to the stock price prediction, a novel combined model that integrates SSA and support vector machine (SVM) (e.g., SSA–SVM) is proposed. Compared with SVM, adaptive network-based fuzzy inference system (ANFIS), ensemble empirical mode decomposition-ANFIS (EEMD–ANFIS), EEMD–SVM and SSA–ANFIS, SSA–SVM demonstrates the best prediction performance based on four criteria, indicating that the proposed model is a promising approach for stock price prediction.

This study examines how Environmental, Social, and Governance (ESG) performance and state ownership affect firm valuation in Singapore and determines if the effects of ESG on firm valuation are more pronounced in state-owned companies. The data comprises 51 companies listed on the Singapore Stock Exchange with complete ESG and financial information during the five-year period from 2018 to 2022. This study finds that only social practices positively and statistically significantly affect stock prices. Overall ESG values, and the other two dimensions of ESG appear not to be statistically significant. State ownership appears to positively and significantly affect the stock price. The finding suggests that the Singapore government’s substantial influence over corporate practices could accentuate the difference in market perception of ESG efforts between SOEs and companies. The study provides useful and practical implications to policymakers, managers and investors, which affect firm financial and operational performance.

Nowadays, with the rapid growth of information spread, investors involve news and sentiments in their financial decision more than before. This paper investigates the effect of technical and fundamental analysis in the form of technical indicators and sentiments of news on Iranian stocks. Several packages and technologies are developed for English semantic; in this regard, most previous works are done on English, especially Twitter. On the other hand, there are rare attempts about the effect of Persian semantics on Iranian stocks due to the lack of uniform packages and technologies. This study collects news articles in Iran that are related to stocks. After data preprocessing, the polarity of news is discerned by the HESNEGAR lexicon. It is the first to consider a semantic Persian lexicon on Iranian stocks. Three models are proposed based on the deep learning approach-convolutional neural networks; price only, news sentiments and hybrid models. Experimental results showed that hybrid model considering both technical indicators and news sentiments using the HESNEGAR lexicon could significantly improve the prediction accuracy compared to price only and news sentiments models. This study can be the reference model to plan a trading strategy.

Oil price plays a significant role across economies around the globe. As such, there has been an increasing and continued interest for investigating associations between stock market and oil prices over the recent decades. This paper briefly reviews the well established association measures to date and evaluates their performance via an application to stock market and oil price data. 10 oil-importing and 9 oil-exporting countries are included with corresponding stock indices for comparisons. The results provide valuable information relating to the possible existence of linear or nonlinear associations and its corresponding effects on the estimates. Initially, an attempt is made to compare association measures based on the the specific subject of stock market and oil prices relationship. This provides a broad and comprehensive view of the potential associations among all groups of variables whilst taking into account the country and time range differences. The findings provide significant contributions towards helping with the selection of the most suitable model for relationship investigation, and data prediction on stock market and oil prices.