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.

Analisis Pengaruh Economic Value Added, Profitabilitas dan Market Value Added Terhadap Harga Saham Perusahaan

The purpose of this study is to find out whether the effect of Market Value Added, Profitability, and Market Value Added on stock price. Sampling in this study is a purposive sampling method. Then the data is tested using multiple regression analysis. The results of the t test showed that the Economic Value Added has a signification value of 0,018 which means smaller than 0,05 and the calculated value of -2.441<t tablel 2.00758 then H1 is accepted. Profitability has a signification value of 0,034 greater than 0,05 and a calculated value of 2.182>t table 2.00758 then H2 is accepted, Market Value Added has significant value of 0,223 greater than 0,05 and the value of t calculated -1.235<t table 2.00758 then H3 is rejected. The results of the F test showed that Economic Value Added, Profitability, Market Value Added have a calculated F value of 2,933 and sig. 0,042. Because the value F calculated 2.933>F table 2.773 and sig. value 0,042<0,05. It can be concluded that partially Economic Value Added has a significant negative effect on stock price, Profitability has a significant effect on stock price, Market Value Added has no significant effect on stock price and simultaneously Economic Value Added, Profitability, Market Value Added has a significant effect on stock price Keywords: Economic Value Added, Profitabilitas, Market Value Added, Stock Price

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.

Option Pricing under the Subordinated Market Models

This paper aims to study option pricing problem under the subordinated Brownian motion. Firstly, we prove that the subordinated Brownian motion controlled by the fractional diffusion equation has many financial properties, such as self-similarity, leptokurtic, and long memory, which indicate that the fractional calculus can describe the financial data well. Then, we investigate the option pricing under the assumption that the stock price is driven by the subordinated Brownian motion. The closed-form pricing formula for European options is derived. In the comparison with the classic Black–Sholes model, we find the option prices become higher, and the “volatility smiles” phenomenon happens in the proposed model. Finally, an empirical analysis is performed to show the validity of these results.

COPULA FRANK UNTUK PERHITUNGAN VALUE AT RISK PORTOFOLIO BIVARIAT PADA MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

Stocks are one type of investment that promises return for investors but often carries a high risk. Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst loss that occurs in a stock portfolio with a certain level of confidence and within a certain time period. In general, financial data have a high volatility value, which causes the residuals are not normally distributed. ARCH/GARCH modoel is used to solve the heteroscedasticity problem. If the data also have an asymmetric effect, it is modelled with Exponential GARCH model. Copula-Frank is part of the Archimedian copula which is used to solve empirical cases. The data on this study were BBCA and KLBF stock price return data in the observation period 30 December 2011 – 6 December 2019. Furthermore, to test the validity of the VaR model, a backtesting test will be carried out using the Kupiec Test. The results showed that the best model used for BBCA stocks was ARIMA (1,0,1) EGARCH (1,1) and for KLBF stocks was ARIMA (1,0,1) EGARCH (1,2). The amount of risk with a 95% confidence level used a combination of the EGARCH and Copula-Frank models was 2.233% of today's investment. Based on the backtesting test used the Kupiec Test, the VaR model of the portfolio obtained was declared valid.

PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ARIMAX-TARCH

The Composite Stock Price Index (IHSG) is a value that describes the combined performance of all shares listed on the Indonesia Stock Exchange. JCI serves as a benchmark for investors in investing. The method used to predict future conditions based on past data is forecasting . Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) is amodel time series that can be used for forecasting. Financial data has high volatility which causes the variance of the residual model which is not constant (heteroscedasticity). ARCH / GARCH model is used to solve the heteroscedasticity problem in the model. If the data is heteroscedastic and asymmetric, then the model can be used Threshold Autoregressive Conditional Heteroskedasticity (TARCH). The data used are the Composite Stock Price Index (IHSG) for the January 2000 - April 2020 period and the dollar exchange rate data for the January 2000 - April 2020 period asvariables independent from the ARIMAX model. The best model used to predict the JCI from the results of this study is the ARIMAX (1,1,0) -TARCH (1,2) model with an AIC value of -0.819074.

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.

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.

Effects of Government's Policy in Stock Price: A Case of Nepse

The purpose of this study is to find out the relationship of government's policy on the price movement of Nepal stock exchange (NEPSE). This study followed a case study research design, because it offers a deeper perspective and clearer understanding of the stock price movement of Nepalese joint venture banks. The sample size of this study consists of five joint venture commercial Banks, economic analysis and survey reports conducted by central bank of Nepal (Nepal Rastra Bank).The judgmental sampling method has been applied for selection of joint venture banks. The study was totally based on secondary data. in order to make proper analysis descriptive and inferential statistics were used using SPSS software version 26. The finding of this study revealed that the GDP and import are inversely associated with stock price movement and CRR, export, interest rate and inflation are positively associated with stock price movement. Further, it is found that the macroeconomic variables are key factors to determine the Nepalese stock price movement. More importantly, stock market has been found to respond significantly to changes in the government policy. It is recommended that CRR, EXPORT, INTEREST RATE and INFLATION are major factors which largely affect the stock price movement of NEPSE. GDP and IMPORT are not compliance with the stock price movement as they produce negative association with the stocks volatility.