Trading Activities and the Volatility of Return on Malaysian Crude Palm Oil Futures

Trading activities represent the flow of market information to the investors. This paper examines the effect of trading activities, i.e., trading volume and open interest, on the volatility of return for Malaysian Crude Palm Oil Futures. The GARCH model is applied by adding the expected and unexpected elements of trading activities (trading volume and open interest) as the independent variables. The results show that there is a negative contemporaneous relationship between the expected volume and volatility, but that a positive relationship exists between unexpected volume and volatility. On the contrary, the expected and unexpected open interest mitigate the volatility. Therefore, both trading volume and open interest should be considered together when information flows into the market.

Single step estimation of ARMA roots for non-fundamental nonstationary fractional models

We propose a single step estimator for the autoregressive and moving-average roots (without imposing causality or invertibility restrictions) of a nonstationary Fractional ARMA process. These estimators employ an efficient tapering procedure, which allows for a long memory component in the process, but avoid estimating the nonstationarity component, which can be stochastic and/or deterministic. After selecting automatically the order of the model, we robustly estimate the AR and MA roots for trading volume for the thirty stocks in the Dow Jones Industrial Average Index in the last decade. Two empirical results are found. First, there is strong evidence that stock market trading volume exhibits non-fundamentalness. Second, non-causality is more common than non-invertibility.

Research on the Improvement of Big Data Feature Investment Analysis Algorithm for Abnormal Trading in the Financial Securities Market

Traditional investment analysis algorithms usually only analyze the similarity between financial time series and financial data, which leads to inaccurate and inefficient analysis of investment characteristics. In addition, the trading volume of financial securities market is huge, the amount of investment data is also very large, and the detection of abnormal transactions is difficult. The aim of feature extraction is to obtain mathematical features that can be recognized by machine. Different from the traditional methods, this paper studies and improves the big data investment analysis algorithm of abnormal transactions in financial securities market. After processing the captured trading data of financial securities market, the big data feature of abnormal trading is extracted. Combined with the abnormal trading and the financial securities market, the investment strategy is determined. The optimization objective function is set and the genetic algorithm is used to improve the investment analysis algorithm. The simulation experiment verifies the improved investment analysis algorithm, and the average Accuracy of investment analysis is increased by at least 11.24%, the ROI is significantly improved, and the efficiency is higher, which indicates that the proposed algorithm has ideal application performance.

DAMPAK COVID-19 TERHADAP ABNORMAL RETURN DAN VOLUME PERDAGANGAN SAHAM

The COVID-19 pandemic is a non-natural disaster that has a huge impact around the world. This research is a quantitative research with event study method. The purpose of this research is to test the capital market reaction by looking at abnormal returns and trading volume activity before and after the COVID-19 non-natural disaster. The event day in this study was April 13rd, 2020 when the Presidential Decree was issued regarding the designation of COVID-19 as a national disaster. Using purposive sampling method, the sample of this study were 27 companies engaged in the hotel, restaurant, and tourism sub-sectors listed on the Indonesia Stock Exchange. The event period is 11 days, namely 5 days before the event, 1 day at the time of the event and 5 days after the event. Data analysis using t-test and wilxocon signed ranks test. The results of this study are: 1) there is no abnormal return during the event period, 2) there is no difference in the average abnormal return before and after the COVID-19 non-natural disaster event, 3) there is no difference in the average trading volume activity before and after the COVID-19 non-natural disaster event and after the COVID-19 non-natural disaster event. Keywords: Event Study, Abnormal Return, Trading Volume Activity, COVID-19.

Perbedaan Saham Blue Chip dan Non Blue Chip: Analisis Volume Perdagangan dan Return Saham Atas Kebijakan Stock Split

The stock split phenomenon is still challenging to understand the returns to companies and investors. A stock split is a corporate actions to break up more shares so that the price per share changes to a smaller one, which aims to increase stock liquidity. The purpose of this study is to analyze differences in trading volume, and stock returns before and after the company's stock split policy implemented in blue-chip and non blue-chip Indonesian companies in the 2017-2019 period, amounting to 34 companies. This study uses data analysis techniques in the Wilcoxon Signed Ranks Test and the Mann-Whitney T-Test. The results showed a significant difference to the average trading volume, but there was no significant difference to the average stock return before and after the stock split policy. The test results of the average difference between blue chip and non blue-chip companies have no significant differences. The company's market capitalization has no significant effect on stock returns and trading volume in the stock split period. The results of this study can be used as reference material for investors and companies in making decisions.

Expiration-Day Effects of Index Futures in a Frontier Market: The Case of Ho Chi Minh Stock Exchange

This study employs OLS, GARCH and EGARCH regression models to test the expiration-day effects of index stock futures on market returns, volatility and trading volume for the Ho Chi Minh Stock Exchange (HOSE). Data used in this study is from a daily return series of the VN30-Index for the period from 10August 2017 through 30 June 2020. The results derived from GARCH(1,1) and EGARCH(1,1) models consistently confirm that Index futures expiration-day effects on market returns exists in the HOSE. Specifically, the average market return for expiration days is significantly lower than other trading days, by 0.13% at the 5% level of significance. However, the results obtained from the regression models indicate that the expiration-day has no impact on market volatility and trading volume.

Layoffs and stock market performance during the COVID-19 pandemic: evidence from the US

PurposeThe authors examine the short-term stock market reaction surrounding US layoffs during the coronavirus disease 2019 (COVID-19) period. The authors’ specific interest is on any changes that may be observed in US stock markets during the COVID-19 outbreak. This information will help us assess the extent to which policymakers adopted at time revenue and expenditures measures to minimize its negative impact.Design/methodology/approachThe authors study the linkage between layoffs announced by firms and stock markets in US for the COVID-19 period between March 2020 and October 2020. This period shows important economic figures; a huge number of job cuts announced by blue-chip companies listed in the New York Stock Exchange (NYSE) due to widespread economic shutdowns. The authors examine whether and to what extent stock markets in US have reacted to layoff announcements during the COVID-19 pandemic using an event-study methodology.FindingsThe study’s results show that US layoffs during the pandemic did not cause any abnormalities on the stock returns, either positive or negative. Based on the mean-adjusted volume, the authors find that layoffs increase the stocks' trading volume, especially on the event date and the day following the event. US stocks become more volatile on the days following the event. Interestingly, on the event date, the authors find that stocks get the highest abnormal volatility; however, the result is statistically insignificant.Practical implicationsThe authors suggest that layoffs announcements follow the business cycle quite closely in most industries. The study’s results have implications for investors, regulators and policymakers as they permit to examine the effectiveness of the measures adopted.Social implicationsThe study’s results show that policymakers reduced uncertainty implementing intensive measures quickly and should follow similar policy in the future pandemic and/or unexpected events.Originality/valueThis paper contributes to the literature in two directions: First, to the best of the authors’ knowledge this is the first study that provides empirical evidence and assesses the extent to which a major global shock such as the COVID-19 pandemic may have altered the reaction of US stock markets to layoff announcements. Second, this is the first study on this topic that examines volume and volatility abnormalities, while the authors check the robustness of the findings with different methods to calculate abnormal returns.

Automated Algorithmic Trading for Cryptocurrencies

The proposed project work is totally supported and easy yet effective strategy named as Martingale. An automatic system which only requires only some pre-coded instructions to execute trades on variety of market variables starting from asset price to trading volume. The strategy along with each cryptocurrency, the benchmark against which the algorithm is tested is that the market’s performance. Returns are compared with the buying and so multiplying the trade volume at each loss and different scenarios are analysed to work out the chance related to the buying compared with an algorithmic strategy. Results are going to be in love with the market’s actual trends and also with some alternate possible trends to check all market scenarios. An internet interface will accompany the presentation allowing the users to check the strategies by entering their parameters and instantly seeing the results

Overconfidence bias and stock market volatility in Ghana: testing the rationality of investors in the Covid-19 era

PurposeThe paper tests the overconfidence bias and volatility on the Ghana Stock Exchange (GSE) during the pre-Covid-19 pandemic and Covid-19 pandemic period.Design/methodology/approachThe study employs pairwise Granger causality to test the presence of overconfidence bias on the Ghana stock market as well as GARCH (1,1) and GJR-GARCH (1, 1) models to understand whether overconfidence bias contributed to volatility during pre-Covid-19 pandemic and Covid-19 pandemic period. The pre-Covid-19 pandemic period spans from January, 2019 to December, 2019, and Covid-19 pandemic period spans from January, 2020 to December, 2020.FindingsThe paper finds a unidirectional Granger causality running from weekly market returns to weekly trading volume during the Covid-19 pandemic period. These results indicate the presence of overconfidence bias on the Ghana stock market during the Covid-19 pandemic period. Finally, the conditional variance estimation results showed that excessive trading of overconfident market players significantly contributes to the weekly volatility observed during the Covid-19 pandemic period.Research limitations/implicationsThe empirical findings demonstrate that market participants on the GSE exhibit conditional irrationality in their investment decisions during the Covid-19 pandemic period. This implies investors overreact to private information and underreact to available public information and as a result become overconfident in their investment decisions.Practical implicationsFindings from this paper show that there is evidence of overconfidence bias among market players on the GSE. Therefore, investors, financial advisors and other market players should be educated on overconfidence bias and its negative effect on their investment decisions so as to minimize it, especially during the pandemic period.Originality/valueThis study is a maiden one that underscores investors’ overconfidence bias in the wake of a pandemic in the Ghanaian stock market. It is a precursor to the overconfidence bias discourse and encourages the testing of other behavioral biases aside what is understudied during the Covid-19 pandemic period in Ghana.