DAY-OF-THE-WEEK EFFECT IN US BIOTECHNOLOGY STOCKS — DO POLICY CHANGES AND ECONOMIC CYCLES MATTER?

2016 ◽  
Vol 11 (02) ◽  
pp. 1650008
Author(s):  
SWARN CHATTERJEE ◽  
AMY HUBBLE
Keyword(s):  
Stock Returns ◽  
Small Firm ◽  
Economic Cycles ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Small Investors ◽  
Garch Processes ◽  
Day Of The Week ◽  
Firm Effect ◽  
Daily Returns

This study examines the presence of the day-of-the-week effect on daily returns of biotechnology stocks over a 16-year period from January 2002 to December 2015. Using daily returns from the NASDAQ Biotechnology Index (NBI), we find that the stock returns were the lowest on Mondays, and compared to the Mondays the stock returns were significantly higher on Wednesdays, Thursdays, and Fridays. The day-of-the-week effect on returns of biotechnology stocks remained significant even after controlling for the Fama–French and Carhart factors. Moreover, the results from using the asymmetric generalized autoregressive conditional heteroskedastic (GARCH) processes reveal that momentum and small-firm effect were positively associated with the market risk-adjusted returns of the biotechnology stocks during this period. The findings of our study suggest that active portfolio managers need to consider the day of the week, momentum, and small-firm effect when making trading decisions for biotechnology stocks. Implications for portfolio managers, small investors, scholars, and policymakers are included.

scholarly journals Day-of-the-Week Effect and Volatility in Stock Returns: Evidence From East Asian Financial Markets

2008 ◽  
Author(s):  
Chiaku Chukwuogor-Ndu
Keyword(s):  
Financial Crisis ◽  
Financial Markets ◽  
Stock Returns ◽  
East Asian ◽  
Asian Financial Crisis ◽  
Return Volatility ◽  
Crisis Period ◽  
Day Of The Week ◽  
Standard Deviations ◽  
Daily Returns

The presence of the day-of-the-week effect has been documented in finance literature. This paper investigates the presence of the day-of-the-week effect and return volatility in ten East-Asian financial markets in the post Asian financial crisis period, after 1998. A set of parametric and non-parametric tests is used to test the equality of mean returns and standard deviations of returns. The results indicate the presence of the day-of-the-week effect and insignificant daily returns volatility in most markets. Some of these results reinforce some previously documented evidence and others are at variance with published results for the same markets. This effect, unlike in devloped markets, is still persistent.  

scholarly journals The Impact of the Introduction of Index Futures on the Daily Returns Anomaly in the Ho Chi Minh Stock Exchange

2021 ◽  
Vol 9 (3) ◽  
pp. 43
Author(s):  
Loc Dong Truong ◽  
H. Swint Friday
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Least Square ◽  
Conditional Heteroskedasticity ◽  
Index Futures ◽  
Futures Contract ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Day Of The Week ◽  
The Impact ◽  
Daily Returns

This study investigated the impact of the introduction of the VN30-Index futures contract on the daily returns anomaly for the Ho Chi Minh Stock Exchange (HOSE). Daily returns of the VN30-Index for the period 6 February 2012 through 31 December 2019 are used in this study to ascertain the new VN30-Index futures contract influence on the day-of-the-week anomaly observed in the HOSE. To test this effect, ordinary least square (OLS), generalized autoregressive conditional heteroskedasticity [GARCH (1,1)] and exponential generalized autoregressive conditional heteroskedasticity [EGARCH (1,1)] regression models were employed. The empirical results obtained from the models support the presence of the day-of-the-week effect for the HOSE during the study period. Specifically, a negative effect was observed for Monday. However, the analysis revealed that the day-of-the-week effect was only present in stock returns for the pre-index futures period, not for the post-index futures period. These findings suggest that the introduction of the VN30-Index futures contract had a significant impact on the daily returns anomaly in Vietnam’s HOSE, providing evidence that the introduction of the index futures contract facilitated market efficiency.

Intraday Stock Returns Patterns Revisited. A Day of the Week and Market Trend Approach

2020 ◽  
Author(s):  
Constantina Kottaridi ◽  
Emmanouil Skarmeas ◽  
Vasileios Pappas
Keyword(s):  
Stock Returns ◽  
Day Of The Week

The Small Firm Effect

1983 ◽  
Vol 39 (3) ◽  
pp. 46-49 ◽  
Author(s):  
Ivan L. Lustig ◽  
Philip A. Leinbach
Keyword(s):  
Small Firm ◽  
Firm Effect

Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data

2021 ◽  
pp. 031289622110102
Author(s):  
Mousumi Bhattacharya ◽  
Sharad Nath Bhattacharya ◽  
Sumit Kumar Jha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
High Frequency ◽  
Jel Classification ◽  
Exogenous Shock ◽  
Intraday Data ◽  
Day Of The Week ◽  
Holiday Effects ◽  
Indian Stock Market ◽  
The Impact

This article examines variations in illiquidity in the Indian stock market, using intraday data. Panel regression reveals prevalent day-of-the-week, month, and holiday effects in illiquidity across industries, especially during exogenous shock periods. Illiquidity fluctuations are higher during the second and third quarters. The ranking of most illiquid stocks varies, depending on whether illiquidity is measured using an adjusted or unadjusted Amihud measure. Using pooled quantile regression, we note that illiquidity plays an important asymmetric role in explaining stock returns under up- and down-market conditions in the presence of open interest and volatility. The impact of illiquidity is more severe during periods of extreme high and low returns. JEL Classification: G10, G12

scholarly journals A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1001 ◽  
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
María de la Cruz del Río-Rama
Keyword(s):  
Markov Switching ◽  
Garch Models ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Arch Models ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Potential Use ◽  
The U.S

In the present paper we tested the use of Markov-switching Generalized AutoRegressive Conditional Heteroscedasticity (MS-GARCH) models and their not generalized (MS-ARCH) version. This, for active trading decisions in the coffee, cocoa, and sugar future markets. With weekly data from 7 January 2000 to 3 April 2020, we simulated the performance that a futures’ trader would have had, had she used the next trading algorithm: To invest in the security if the probability of being in a distress regime is less or equal to 50% or to invest in the U.S. three-month Treasury bill otherwise. Our results suggest that the use of t-student Markov Switching Component ARCH Model (MS-ARCH) models is appropriate for active trading in the cocoa futures and the Gaussian MS-GARCH is appropriate for sugar. For the specific case of the coffee market, we did not find evidence in favor of the use of MS-GARCH models. This is so by the fact that the trading algorithm led to inaccurate trading signs. Our results are of potential use for futures’ position traders or portfolio managers who want a quantitative trading algorithm for active trading in these commodity futures.

Market dynamics, cyclical patterns and market states

2019 ◽  
Vol 37 (4) ◽  
pp. 585-604
Author(s):  
Azza Bejaoui ◽  
Salim Ben Sassi ◽  
Jihed Majdoub
Keyword(s):  
Stock Returns ◽  
Regime Switching ◽  
Empirical Studies ◽  
Arma Model ◽  
Market Dynamics ◽  
Nonlinear Structure ◽  
Content Type ◽  
Portfolio Decisions ◽  
Short Run ◽  
Daily Returns

Purpose In this paper, the authors seek to investigate the dynamics of Bitcoin, Litecoin, Ethereum and Ripple daily returns and volatilities. Design/methodology/approach In this paper, the authors apply the MS-ARMA model on daily returns of Bitcoin (19/04/2013-13/02/2018), Ripple (05/08/2013-14/02/2018), Litcoin (29/04/2013-14/02/2018) and Ethereum (08/02/2015-14/02/2018). This model allows capture of the nonlinear structure in both the conditional mean and the conditional variance of cryptocurrency returns. Findings All the cryptocurrency markets show regime switching in the return-generating process. Market dynamics seem to be governed by two different states which differ from one cryptocurrency market to another in terms of mean return, volatility and interstate dynamics. These findings can be explained by investors’ behavior, i.e. speculative trading and herding behavior. By choosing to participate (or imitating some investors) in some cryptocurrency markets (in particular Bitcoin market), they affect the price movements and therefore the market dynamics in the short run. Practical implications Identifying the different market states provides information for investors to make more accurate portfolio decisions in the virtual market and follow the market timing strategy. Originality/value This paper attempts to analyze potential nonlinear structure in cryptocurrencies returns and analyze if there is a difference between the cryptocurrencies market cycles. So, the search for congruent and adequate specification to reproduce the stock returns dynamics in the virtual market still remains the concern of several empirical studies. This research not only examines the behavior of stock returns in the cryptocurrencies’ market but also highlights the existence of nonlinearity propriety as a stylized fact.

Long-term prior return patterns in stock and sector returns in India

2014 ◽  
Vol 11 (2) ◽  
pp. 192-210
Author(s):  
Sanjay Sehgal ◽  
Sakshi Jain
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Risk Model ◽  
Factor Model ◽  
Size Factor ◽  
Content Type ◽  
Long Run ◽  
Portfolio Managers ◽  
Sector Returns

Purpose – The purpose of this paper is to analyze long-term prior return patterns in stock returns for India. Design/methodology/approach – The methodology involves portfolio generation based on company characteristics and long-term prior return (24-60 months). The characteristic sorted portfolios are then regressed on risk factors using one factor (capital asset pricing model (CAPM)) and multi-factor model (Fama-French (FF) model and four factor model involving three FF factors and an additional sectoral momentum factor). Findings – After controlling for short-term momentum (up to 12 months) as documented by Sehgal and Jain (2011), the authors observe that weak reversals emerge for the sample stocks. The risk model CAPM fails to account for these long-run prior return patterns. FF three-factor model is able to explain long-term prior return patterns in stock returns with the exception of 36-12-12 strategy. The value factor plays an important role while the size factor does not explain cross-section of average returns. Momentum patterns exist in long-term sector returns, which are stronger for long-term portfolio formation periods. Further, the authors construct sector factor and observe that prior returns patterns in stock returns are partially absorbed by this factor. Research limitations/implications – The findings are relevant for investment analysts and portfolio managers who are continuously tracking global markets, including India, in pursuit of extra normal returns. Originality/value – The study contributes to the asset pricing and behavioral literature from emerging markets.

Small firm mutual funds: Additional evidence on the small firm effect

1992 ◽  
Vol 4 (3) ◽  
pp. 211-219 ◽  
Author(s):  
Steven C. Isberg ◽  
Clifford F. Thies
Keyword(s):  
Mutual Funds ◽  
Small Firm ◽  
Firm Effect
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