An Improved BPNN Algorithm Based on Deep Learning Technology to analyze the Market Risks of A+H Shares

The backpropagation neural network (BPNN) algorithm of artificial intelligence (AI) is utilized to predict A+H shares price for helping investors reduce the risk of stock investment. First, the genetic algorithm (GA) is used to optimize BPNN, and a model that can predict multi-day stock prices is established. Then, the Principal Component Analysis (PCA) algorithm is introduced to improve the GA-BP model, aiming to provide a practical approach for analyzing the market risks of the A+H shares. The experimental results show that for A shares, the model has the best prediction effect on the price of Bank of China (BC), and the average prediction errors of opening price, maximum price, minimum price, as well as closing price are 0.0236, 0.0262, 0.0294 and 0.0339, respectively. For H shares, the model constructed has the best effect on the price prediction of China Merchants Bank (CMB). The average prediction errors of opening price, maximum price, minimum price and closing price are 0.0276, 0.0422, 0.0194 and 0.0619, respectively.

Systematic illiquidity, characteristic illiquidity, and stock returns: Timeseries analysis

The objective of this research is to investigate the relationship between illiquidity and stock prices on the Tunisian stock exchange. While previous researches tended to focus on one form of illiquidity to examine this relationship, our study unifies three forms of illiquidity at the same time. Indeed, we simultaneously consider illiquidity as systematic risk, as a characteristic of the market, and as a characteristic of the stock. The aggregate illiquidity of the market is the average of individual stock illiquidity. The illiquidity risk is the sensitivity of the stock price to illiquidity shocks. Shocks of market illiquidity are estimated by the innovations in the expected market illiquidity. Results show that investors on the Tunisian stock exchange do not require higher returns when they expect a rise of market illiquidity, whereas investors on U.S markets are compensated for higher expected market illiquidity. In addition, shocks of market illiquidity provoke a fall in stock prices of small caps, while large caps are not sensitive to market illiquidity shocks. This differs slightly from results based on U.S. data where illiquidity shocks reduce all stock prices but most notably those of small caps. Robustness tests validate our findings. Our results are consistent with previous studies which reported that the “zero-return” ratio predicts significantly the return-illiquidity relationship on emerging markets.

Stock Price Prediction using Machine Learning Algorithms

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

Are companies using Twitter to greenwash and hide bad environmental performance?

Companies and related consumer behaviours contribute significantly to global carbon emissions. However, consumer behaviour is shifting, with the public now recognising the real and immediate impact of climate change. Many companies are aware and seemingly eager to align to consumer’s increasing environmental consciousness, yet there is a risk that some companies could be presenting themselves as environmentally friendly without implementing environmentally beneficial processes and products (i.e. greenwashing). Here, using longitudinal climate leadership, environmental messaging (Twitter) and stock price data, we explore how climate leadership (a relative climate change mitigation metric) and environmental messaging have changed for hundreds of UK companies. Using the environmental messaging, we also assess whether companies are simply greenwashing their true climate change performance. Finally, we explore how climate leadership and environmental messaging influence companies’ stock prices. We found that companies (on average) have increased their climate leadership (coef: 0.14, CI 0.12–0.16) and environmental messaging (coef: 0.35, CI 0.19–0.50) between 2010 and 2019. We also found an association where companies with more environmental messaging had a higher climate leadership (coef: 0.16, CI 0.07–0.26), suggesting messaging was proportionate to environmental performance, and so there was no clear pattern of using Twitter for greenwashing across UK companies. In fact, some companies may be under-advertising their pro-environmental performance. Finally, we found no evidence that climate leadership, environmental messaging or greenwashing impacts a company’s stock price.

Correlation Analysis between Stock Price and Accounting Profit Based on a Vector Autoregressive Model

The study of accounting profitability was initiated by the famous American scholars Ball and Brown in the 1960s. In recent years, with the continuous development of market economy, the continuous improvement of the accounting legal system and accounting standards for enterprises has promoted the research on accounting profit in capital market in China. Due to the restriction of some objective conditions, there are not many valuable research results on the relationship between accounting earnings and stock price changes, and the research methods suitable for the study of accounting earnings still need to be explored and summarized. The China Securities Regulatory Commission (CSRC) has required listed companies to publish quarterly financial and accounting reports since 2002, and the condition of using the regression analysis method to study the accounting profit of listed companies is available. In this context, this paper designs a vector autoregressive model to study the correlation between stock price and accounting profit. First, combining the literature and the research results of accounting profit at home and abroad, this paper expounds the statistical analysis of accounting profit. Then, this paper analyzes the accounting profitability of listed companies in China from static and dynamic perspectives. Finally, according to the accounting profit status and profitability statistical analysis of accounting information, accounting profit and growth relationship, and accounting profit information and the relationship between stock prices, this paper is concluded. Also, this paper shows how to improve the profitability of listed companies and how can investors effectively use the accounting earnings information of listed companies for stock investment and put forward corresponding policy suggestions.

Is ESG Relevant to Electricity Companies during Pandemics? A Case Study on European Firms during COVID-19

The electricity sector was negatively impacted by the coronavirus disease (COVID-19), with considerable declines in consumption in the initial phase. Investors were in turmoil, and stock prices for these companies plummeted. The aim of this paper is to demonstrate the significant negative influence of the pandemic on abnormal returns for the electricity sector, specifically for traditional and renewable companies and the influence of ESG scores, using the event study approach and multi-variate regressions. Our results show that the pandemic indeed had a negative impact on the electricity sector, with renewable electricity companies suffering a sharper decline than traditional ones. Moreover, we find that ESG pillar scores affected electricity companies differently and are sector-specific. For renewable electricity companies, the returns were positively influenced by the environmental ESG scores and negatively by governance ESG scores.

Energy crypto currencies and leading U.S. energy stock prices: are Fibonacci retracements profitable?

This paper investigates the role of Fibonacci retracements levels, a popular technical analysis indicator, in predicting stock prices of leading U.S. energy companies and energy cryptocurrencies. The study methodology focuses on applying Fibonacci retracements as a system compared with the buy-and-hold strategy. Daily crypto and stock prices were obtained from the Standard & Poor's composite 1500 energy index and CoinMarketCap between November 2017 and January 2020. This study also examined if the combined Fibonacci retracements and the price crossover strategy result in a higher return per unit of risk. Our findings revealed that Fibonacci retracement captures energy stock price changes better than cryptos. Furthermore, most price violations were frequent during price falls compared to price increases, supporting that the Fibonacci instrument does not capture price movements during up and downtrends, respectively. Also, fewer consecutive retracement breaks were observed when the price violations were examined 3 days before the current break. Furthermore, the Fibonacci-based strategy resulted in higher returns relative to the naïve buy-and-hold model. Finally, complementing Fibonacci with the price cross strategy did not improve the results and led to fewer or no trades for some constituents. This study’s overall findings elucidate that, despite significant drops in oil prices, speculators (traders) can implement profitable strategies when using technical analysis indicators, like the Fibonacci retracement tool, with or without price crossover rules.

PENGARUH PROFITABILITAS DAN SOLVABILITAS TERHADAP HARGA SAHAM PERUSAHAAN TERINDEKS LQ45 DI BEI

The purpose of this study was to analyze the effect of independent variables, profitability proxied by Return On Equity (ROE) and solvency proxied by Debt to Assets Ratio (DAR) on the dependent variable, namely stock prices. The population in this study were 45 companies in the LQ45 category listed on the Indonesia Stock Exchange. Sampling in this study using purposive sampling method, namely as many as 31 companies that are consistently indexed LQ45 on the Indonesia Stock Exchange (IDX) during the 2015-2019 period with the amount of data processed after the outlier process as many as 129 samples. The analysis test model used in this hypothesis is multiple linear regression analysis. The results showed that profitability (ROE) had no effect on stock prices, solvency (DAR) had no effect on stock prices, and simultaneously (ROE) and solvency (DAR) had no effect on company stock prices. Keywords: Stock Price, Return on Equity (ROE), Debt to Assets Ratio (DAR)

Early Detection of Determination Factors Stock Price Increase

The purpose of this study was to examine the effect of CR, ROE, DER, ROA on stock prices of pharmaceutical sub-sector companies listed on IDX) 2013-2019. The population of this research is all pharmaceutical sub-sector companies listed on IDX during 2013-2019, which amount to 8 companies. The technique of determining the sample is by purposive sampling. The test instruments used in this research are: normality test, heteroscedasticity test, multicollinearity test, coefficient of determination, model test (F test) and hypothesis test (t test). The data analysis method used multiple linear regression. The results show that there is no influence between CR, DER and ROE on stock prices in pharmaceutical sub-sector companies listed on IDX in 2013-2019. . There is a positive and significant influence between ROA on the company's stock price, in the pharmaceutical sub-sector companies listed on IDX in 2013-2019).