Testing An Aggressive Investment Strategy Using Value Line Ranks: A Comment

The purpose of this study is to see if there are differences in stock prices, stock returns, and abnormal returns before and after a stock split in high-profile and low-profile companies. This study's research period was 2020-2022. The research was analyzed quantitatively using a purposive sampling method. Based on the sampling criteria, 39 companies were selected as research samples. Kolmogorov Smirnov The normality test was performed using a single sample test. Following the normality test, the data was processed using the two-paired-sample difference test. If the data were normally distributed, the t-test (paired sample t-test) was used; otherwise, the Wilcoxon Signed Rank test was used. The results of hypothesis testing revealed that (1) there are differences in stock prices before and after a stock split in high-profile companies. (2) In low-profile companies, stock prices differ before and after the stock split. (3) There are differences in stock returns in high-profile companies before and after a stock split. (4) There is no difference in stock returns for low-profile companies before and after the stock split. (5) There is no difference in abnormal returns in high-profile companies before and after the stock split. (6) There is no difference in abnormal returns in low-profile companies before and after the stock split. (7) There are differences in stock prices following a stock split between high-profile and low-profile companies. (8) There is no difference in stock returns in high and low-profile companies before and after the stock split. (9) There is no difference in abnormal stock returns before and after a stock split at high and low-profile companies.

The purpose of this paper is to check whether liquidity plays an important role in asset pricing, i.e., it intends to investigate whether the security prices are also determined by their liquidity. Thus, it examines whether the stock returns are empirically related to the turnover ratio (number of stocks traded as a fraction of the number of stocks outstanding), which was the proxy for liquidity. The statistical test accounted for the effects of beta, size and book-to-market ratio, which are considered as significant in explaining the variation in stock returns. The methodological base consisted of a multiple regression of panel data of the stocks traded at the Bovespa between March 1995 and December 2004, employing the method of seemingly unrelated regressions (SUR). This paper provides evidence of a linear and positive relationship between stock returns and liquidity, measured by the turnover, the number of stocks traded (volume) and the number of trades (frequency). Although this result is not in line with the hypothesis of a liquidity premium, which was proposed by Amihud and Mendelson (1986), it is consistent with the study of Jun et al . (2003) in emerging markets, including Brazil. Key words: liquidity, stock returns, CAPM, capital markets.

Objective of this study is to analyze the impact of policy rate changes on bank stock returns in Pakistan by using daily stock returns from 1998 to 2011. We used event study approach by constructing the estimation window of 250 days and an event window of 31 days (15 pre-event days, event day and 15 post event days. The daily stock returns from 1998 to 2011 have been used to analyze the impact of policy rate changes by State Bank of Pakistan (SBP) on banking stock returns. The study used ARIMA model to estimate the normal returns by using estimation window of 250 days. Since Monetary Policy committee decides changes in policy rate, we have used date of MP Committee meeting as an event. Reportedly, 35 meetings were conducted during study period from Jan 1998 to Dec 2011. Abnormal returns are calculated by taking the difference of actual daily stock returns and estimated daily stock returns. Abnormal daily stock returns are aggregated as cumulative abnormal returns (CAR). The CAR at 0.6340 showed a significant impact of policy rate changes on banks stock returns. The study finds 31 out of all 35 events have significant impact on banks stock returns and returns were normal at 4th day of MP announcement. Further, we analyzed the impact with respect to expansionary and contractionary monetary policy and observed that the highest positive impact on banks stock returns was due to expansionary monetary policy.

This research aims to examine differences in returns, abnormal returns and stock trading volume before and after the event of determining COVID-2019 as an Indonesian National disaster in mining sub-sector companies that are members of the Indonesian Sharia Stock Index (ISSI). This research uses a quantitative approach with secondary data collection. Data analysis was carried out using Statistical Product and Service Solutions (SPSS) software version 26, using the paired sample t-test analysis technique. If the data is not normally distributed then use the Wilcoxon test. The results of the research show that there is a significant difference in stock returns before and after the event that declared Covid a national disaster in Indonesia, with a significance value (0.015) lower than the probability value (0.05). This can be seen from the stock return value which decreased over the period of time. observation. Besides that, there is also a significant difference in abnormal returns before and after the event that declared Covid a national disaster in Indonesia, with a significance value (0.000) lower than the probability value (0.05). This can be seen from the abnormal stock return value which decreased over the period of time. observation. However, there is no significant difference in stock trading volume before and after the event that declared Covid a national disaster in Indonesia, with a significance value (0.012) higher than the probability value (0.05). This can be seen from the absence of changes in trading volume during observation period. Keywords: Abnormal Return, Return, Trading Volume Activity

This paper conducts a comparative analysis on the variability of firm-level abnormal returns in Egypt and the United States (US) amidst three different recent global hazardous events (political, economic, and health). Moreover, it determines the impact of firm-level characteristics in reducing firms’ abnormal returns and identifies the most vulnerable industries during crises in both countries. The paper methodology is conducted in two stages on the selected sample for US and Egypt stock markets, DJIA and EGX30 indices, respectively. The first stage is an event study approach to determine the amplitude and direction of the response of the US and Egypt abnormal returns to each of the studied crises. The second stage encompasses cross-sectional regressions to study the impact of firm-level characteristics and vulnerable industries on the highest significant abnormal stock returns for each event in each country. This study has theoretical and practical contributions. To our knowledge, this is the first study to demonstrate the higher abnormal stock returns of the developing stock market, Egypt, compared to the developed stock market, US, in both health and economic crises under all different time intervals except the examined political crisis. Moreover, emerging and developed countries’ businesses should provide attentive management of this study’s significant firm-level characteristics to strengthen firms’ exogenous shock absorbency. Furthermore, this study’s identification of the most vulnerable industries in each market paves the way for developing supportive policies for these industries resulting in healthier and safer financial markets amidst crises. Keywords: Abnormal Returns, Stock Markets, Crises, Egypt, United States

A recent article in this journal by Tabak (2006) highlighted a potentially serious source of bias that can arise when multiple statistical tests of a damages theory are performed and even one test rejecting the null hypothesis is regarded as supporting the damages theory. Repeated testing will eventually produce a “false discovery,” that is, rejection of the null hypothesis in favor of the alternative hypothesis when the null hypothesis is true, which statisticians refer to as type I error. Consequently, performing multiple tests without adjusting the critical value can be problematical because it can lead to improperly accepting statistical evidence that apparently supports rejection of the null hypothesis as reliable when it is not. Tabak (2006) recommends making the Sidak (1968, 1971) multiple-comparison adjustment to the standard statistical t-test to correct for the false-discovery bias inherent in multiple-comparison testing. In particular, he recommends making this adjustment when performing 10b-5 securities fraud event studies when more than one corrective disclosure date is involved. This article clarifies the circumstances in which a multiple-comparison adjustment is appropriate and explains why the correction is normally not needed in securities fraud event-study testing. More generally, I explain why it is not required when each of several tests is performed and its results are reported separately, as for example, where the objective is simply to test the statistical significance of the abnormal stock return on each day on which a new and distinct curative disclosure occurred. I show that the Sidak multiple-comparison adjustment is nearly as stringent as the classical Bonferroni procedure (Simes, 1986), which can increase the risk of type II error. This article discusses a more powerful alternative to the Sidak adjustment due to Benjamini and Hochberg (1993), which directly corrects for the false-discovery bias in multiple-comparison testing and reduces the risk of type II error. II. Application of Multiple-Comparison Adjustments to Securities Fraud Event Studies Multiple-comparison-false-discovery bias can arise when (a) several statistical tests are performed on subsets of the same larger data set in an effort to

This study develops a structural equation model linking both abnormal stock returns and abnormal trading volume to unexpected changes in accounting ratios that result from the issuance of annual accounting data. The three contributions of this study are as follows: (a) a simultaneous investigation of both the price and the trading activity stock market reactions to the issuance of annual financial statements; (b) the application of a measurement model to depict the changes in a firm's financial characteristics, which result from the issuance of financial statements; and (c) a causal model structure is developed, estimated, and tested. Through overidentification of the model, the hypothesized model structure is evaluated. Instead of investigating the price and trading activity reactions separately, as was done in previous studies, the two reactions are modeled simultaneously and are linked. A measurement model is hypothesized to aggregate the unexpected changes in 13 financial ratios into the unexpected changes in 4 latent financial dimensions: liquidity, leverage, profitability, and activity. The structural model then links the unexpected changes in the financial dimensions to abnormal stock market returns and trading volume. The 13 equations of the measurement model and the 2 structural equations are estimated simultaneously using LISREL, a full information maximum likelihood estimation procedure. The model is estimated, and the hypothesized model configuration recreates 82% of the generalized variance in the observed data. The individual parameter estimates show that the major source of variation in abnormal returns is the unexpected changes in profitability. None of the unexpected changes in the financial dimensions are found to be significantly linked directly to abnormal trading volume. Instead, the link is indirect through the relationship between abnormal returns and abnormal volume.

This study presents data on abnormal stock return movements with simple calculations. The result of deep analysis using event study method from major stocks in each sector, prove that There is negative response by the market to the student demonstration during the end of September 2019 that create abnormal return condition until it gets back to be normal in the middle of October. The sectors studied include the banking sector, construction, infrastructure, consumer needs, media, industrial materials, machinery, mining, and health care. Stock returns in Indonesia with inefficient market conditions are vulnerable to issues. Capital markets tend to be easily influenced by political and macroeconomic conditions, but even though the condition of return is abnormal, investors still have the potential to get favourable results.
 Keywords: Stock market, Market efficiency, Event study, Stock Price, Stock Returns

Purpose: This study seeks to investigate any significant changes in the stock returns of companies listed on the BIST Sustainability Index from 2014 to 2023. Within this framework, the research analyses the impact of inclusion in the index on investor behaviour, focusing on potential increases or decreases in stock returns to evaluate the market's reception of sustainability-oriented indices. Method: This study employs an event study methodology to assess the impact of a specific event—the inclusion of companies in an index—on stock returns. The analysis evaluates investor reactions by calculating abnormal returns during both pre- and post-event periods. To determine if there is a significant difference between the average abnormal returns in these periods, a paired sample t-test is utilized. This analysis will indicate whether inclusion in the index results in a change in stock performance. Findings: The analysis results indicate that the cumulative average abnormal returns (CAAR) of 18 companies exhibit a negative trend in the period from 10 to 5 days before and 10 to 5 days after their inclusion in the BIST sustainability index. The findings from the Paired Samples t-test reveal no significant change in stock returns when comparing the CARs values before and after the event. Conclusion: This indicates that there is no noticeable change in stock returns following the inclusion of companies in the BIST Sustainability Index. This scenario may also imply that investors do not react to the inclusion of these companies or that they are unable to achieve abnormal returns from these stocks.

The Leverage Effect on Stock Returns

The aim of this paper is to find optimal portfolio strategies for an n-stock extension of the stochastic volatility model proposed in Ref. [2]. It is a modification of [19], and characterizes the dependence by the use of a factor structure. The idea of a factor structure is that the diffusion components of the stocks contain one Brownian motion that is unique for each stock, and a few Brownian motions that all stocks share. Our model can obtain strong correlations between the returns for different stocks without affecting their marginal distributions. This was not possible in Ref. [19]. Further, the number of model parameters does not grow too fast as the number of stocks n grows. We use dynamic programming to solve Merton’s optimization problem for power utility, with utility drawn from terminal wealth. Optimal strategies for n stocks are obtained. We also discuss how to estimate the model from data. This is illustrated with an example.

Index tracking has long been of interest for both industry of fund management and academia. Various methods have been proposed and tested and various issues are discussed throughout the past 30 years. Yet one issue remains unresolved is how to perform stock selection optimally. In this article, I propose to use an artificial intelligent method—particle swarm optimization (or PSO) to select the most effective stocks to track a target index most closely. I track the S&P 500 index using a small number of its constituents from 1990 till 2019. Practical constraints such as liquidity (in a form of bid-ask spread), transaction costs (commission), and capital requirement are considered. The overall out-of-sample error is consistent with the literature and shown to be greatly reduced if the rebalancing horizon is shorter and the number of stocks is increased. Also, turnovers are lower if rebalancing is more frequent and if more stocks are chosen. Hence, there is a clear tradeoff between rebalancing cost and tracking accuracy.

IT Applications, Financial Analyst Recommendations, and Firm Stock Market Value

Stock split is a corporate decision in which company divides face value of the equity share into more than one unit. Theoretically stock splits should not have any effect on share prices and stock returns. The present study has attempted to examine the behaviour of share prices around the ex-split day of the stock splits announcement. This effect of stock splits announcements on the share prices is studied by analysing the behaviour of abnormal returns around stock splits of 304 companies listed on BSE, during the period 1999 to 2018. The analysis stresses on finding whether stock splits have an impact on share prices. This impact of stock splits is studied using abnormal returns. The event study is a methodology used to study share price behaviour around specific events and share price reaction to such events as stated by Binder (1998). This effect has been analysed around the event - ex-split day. To test the impact of stock splits - AAR (Average Abnormal Returns) and ACAR (Average Cumulative Abnormal Returns) are computed. The presence of positive AARs with significant Zs -values on t-1 day in pre- ex-split day window implies that informed traders anticipating a price increase on ex-split day buy shares before the ex-split day to make fast profit. In post-ex-split day window positive AARs with significant Zs-values are present on t+1 day probably because of lower share prices after ex-split day. The AARs are negative with significant Zs -values in post event window till t+17 . It implies that in the Indian stock market there is presence of significant negative wealth effect in post ex-split day window not in line with findings of Angel (1997) and Schultz (2000). ACARs are negative and continuously decreasing throughout the event window. The statistical significance of ACARs is tested using Z-statistic. The ACAR do not have significant Z-value in the event window. Significant abnormal returns were found to be present around the ex-split day though for a very short duration of time. The current study suggests that the investors while planning their investment or trading portfolio, must decide a time horizon for a stake in shares subjected to split, so that they take advantage of the fact that share prices do not adjust fully in proportion to split ratio immediately on and after ex-split day. The benefit must be availed quickly since the impact of stock splits is found to be present for a short time duration.

The capital market plays an important role in the life of a country to encourage economic growth. In the capital market, the share price of a company can be used to measure the financial performance of a company, so that under reasonable and normal conditions, the share price of a company can be seen as an increase in its financial performance. A country's stock market is often sensitive to surrounding events, even unusual ones. The aim of the research is to determine the difference in exchange rates on returns before and after the announcement of the first case of the Covid-19 pandemic in automotive and component sub-sector manufacturing companies listed on the Indonesia Stock Exchange. The research approach used is the event study method to analyze the market response to events because it compares differences in exchange rates, market capitalization, trading volume activity on returns and abnormal returns before and after the announcement of the first case of the Covid-19 pandemic in automotive and component subsector companies. listed on the Indonesian Stock Exchange. The research results show that stock trading frequency has no significant effect on stock returns, trading volume has no significant effect on stock returns, market capitalization value has no significant effect on stock returns, stock prices have no significant effect on stock returns, trading days have no significant effect on stock returns.