Trading Day Research Articles

IPO Stock Performance amidst the COVID-19 Pandemic: Has It been Undervalued?

The purpose of this study is to examine the underpricing phenomenon from IPO firms listed in Indonesia Stock Exchange during the COVID-19 pandemic. Using purposive sampling method, 34 IPO firms after the announcement of pandemic COVID-19 were selected as research samples. The IPO stock closing prices and returns on days 1, 5, 10, 15, and 20 were analyzed using paired sample t-test. The findings show underpricing phenomenon still occurred during the pandemic period. However, the underpricing only documented statistically significant on the T1 of the trading day. After the first day of trading, the stock’s returns consistently declined and were proved statistically insignificant at T5, T10, T15, and T20.

A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model.

Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.

Stock market index prediction based on reservoir computing models

Prediction of the financial market price is critical for financial decision-making and market policy-making. Recently, various machine learning and deep learning methods have been adopted to predict financial markets’ movements using historical time series of prices. However, accurate prediction of financial prices is still a long-standing challenge that always calls for new approaches. In this study, a novel machine learning model of reservoir computing is developed to predict stock market indices. The performance of the proposed new model is systematically evaluated using the time series of daily closing prices of seven major international stock market indices including S&P500 Index, New York Stock Exchange Composite, Dow Jones Industrial Average, Nasdaq Composite Index, Financial Times Stock Exchange 100 Index, Nikkei 225 Index, and Shanghai Stock Exchange Index between January 4, 2010, and December 31, 2018 covering 2,272 trading days. The results show that our model outperforms the widely used deep learning methods of long short-term memory and recurrent neural network in most cases. To further evaluate the predictive capability of our model, we compare our model to the other two newly reported deep learning methods in recent studies. Comparative results also show that our model is competitive to those deep learning methods in predicting stock market indices. Our study contributes to the literature by developing novel reservoir computing models for financial market predictions. Meanwhile, our results also provide practical implications for financial practitioners of potential financial applications of reservoir computing in financial time series analysis and predictions.

Современные особенности курсообразования валют на примере пары доллар США – евро

The relevance of the research topic is determined by globalization processes, which have a huge impact on our country, as well as on the countries around us and their economies. In modern conditions, it is obvious that the financial difficulties of one country can cause a global crisis. Issues of a qualitative analysis of the monetary system are important for the stability of the economies of countries. The authors of the article emphasize that the monetary system is needed in order to regulate foreign exchange relations. The authors did not choose the EUR/USD pair by chance – it is the most traded currency pair in the Forex market (about 29% of the total daily trading volume). Such popularity is due primarily to the fact that the United States and the European Union are two of the strongest economies in the world. In addition, this pair responds quite predictably to the main economic indicators relating to the United States and the European Union. Based on the analysis, the authors made conclusions, made recommendations on the need to adapt the trading strategy to market volatility. The procedure for forming EUR/USD quotes at various hours, days and months has been studied. EUR/USD is compared with several other currency pairs and their ranges in separate trading sessions. the following conclusions are made: EUR/USD has medium volatility compared to other pairs under consideration, but is clearly inferior to GBPUSD and GBPJPY; the volatility for most couples during the Asian session is low, and in the case of detruding it encourages the use of scalping; during the European and American sessions, volatility is almost doubled.

Disposition effect, demographics and risk taking

Purpose This study aims to investigate the association of demographic characteristics, market conditions and risk taking with the disposition effect using data on Brazilian individual investors. Design/methodology/approach This study uses a unique data set with monthly data from June 2007 to February 2017 provided by one of the largest asset management firms in Brazil. This paper computes the proportion of gains realized and the proportion of losses realized to see if investors incur the disposition effect. This paper then performs logistic regressions to verify the association between investors’ disposition effects and demographic and portfolio characteristics. This paper analyses the prevalence of cognitive biases depending on market conditions (bull or bear markets) and include regressions by asset class as robustness checks. Findings This paper finds evidence that risk averse investors are more prone to the disposition effect, male subjects are less prone to this cognitive bias and age is not associated with the disposition effect. This paper observes that the tendency to incur the disposition effect decreases during bull markets but increases during bear markets. Also, this paper finds that sophisticated investors are more prone to selling winning assets and holding on to losses. Research limitations/implications First, paper gains and losses are based on the highest and lowest prices of the month and not on the price at the moment the sale occurred. Second, this paper had access only to end-of-month information, not to actual daily trading records. Third, because the data set relates to individual investors who trade investment funds, this paper cannot determine whether firm size is associated with the disposition effect. Fourth, age may not necessarily be a proxy for investor experience, so one should interpret the lack of significance for age in terms of generational differences. Practical implications This paper demonstrates that the disposition effect is prevalent even among wealthier and more educated investors with delegated asset classes. This paper also presents evidence on the association between demographic characteristics and cognitive biases considering a liquidity-constrained, highly volatile and developing market. Social implications This paper demonstrates that gender is an important characteristic to understand cognitive biases and that investor sophistication may not necessarily be an attenuation factor for the disposition effect in a liquidity-constrained market. Originality/value This is the first study to analyse the role of demographic characteristics and risk taking to explain the disposition effect using real information at the individual level about Brazilian investors. It is also the first to analyse the intensity of cognitive biases during bull and bear markets in the Brazilian economy.

Market reaction to the COVID-19 pandemic: evidence from emerging markets

PurposeThis study examines the market reaction to the World Health Organization (WHO) announcement of the novel coronavirus disease 2019 (COVID-19) as a global pandemic on the emerging equity markets and compares the reaction with developed markets. This study also compares the market reactions to the COVID-19 pandemic with the market reactions to the 2008 global financial crisis.Design/methodology/approachUsing the Morgan Stanley Capital International daily stock indices data and the Carhart and the GARCH(1,1) models for an event study, the authors examine the cumulative abnormal returns during 30 and 10 trading days and the extended 60 days before and after the WHO pandemic announcement. It also compares the market reactions during the COVID-19 pandemic with the reactions to the Lehman Brothers' bankruptcy announcement during the 2008 global financial crisis.FindingsThis study finds that the COVID-19 pandemic had a significantly greater negative impact to the stock markets in emerging countries than in the developed countries. The negative impact on the emerging markets is more pronounced for firms with small market capitalizations and for growth stocks. The negative impact of the COVID-19 pandemic is stronger in the energy and financial sectors in both emerging and developed markets. The positive impact of the COVID-19 pandemic occurred in healthcare and telecommunications for the emerging markets and information technology for the developed markets. This study also finds that the equity markets in both emerging and developed countries recovered faster from the COVID-19 pandemic relative to the 2008 global financial crisis.Social implicationsInvestors' desire to diversify their risks across different countries and sectors in the emerging markets could bring superior returns. The diversification strategies bring critical financial supports to forestall the contagion of COVID-19, to protect lives, and to save the emerging economies, especially for those financially constrained countries that are facing twin health and economic shocks by channeling their investments to countries with weak healthcare systems.Originality/valueThis study extends the literature that examines market reactions to stock market shocks by examining the market reactions to the COVID-19 outbreak on the emerging and developed equity markets across different market capitalizations, valuation and sectors. This study also finds that the markets recovered quicker from the COVID-19 pandemic announcement than during the 2008 global financial crisis.

Asset-light strategies and stock market reactions to COVID-19’s pandemic announcement: The case of hospitality firms

This research note investigates the stock market reactions of international hospitality firms to COVID-19’s pandemic announcement by the World Health Organization. In line with the behavioral finance literature, the findings indicate that, in the short term, investors overestimated the risks underlying asset-heavy firms because of information uncertainty. Firms pursuing an asset-light (AL) strategy are associated to significantly less negative cumulative abnormal returns in the 4 days following the announcement, especially firms following an AL strategy that reduces significantly the operating leverage. However, this difference vanishes after 5 trading days, meaning that investors revised their expectations. This research note suggests that the cost structure of AL firms matters in reducing information uncertainty and sheds light on the consequences of the COVID-19 crisis on the hospitality industry. It also provides useful information to board members, financial analysts, and companies’ top managers when evaluating whether and how to pursue an AL strategy, and the potential consequences of it.

Tick Size and Price Reversal after Order Imbalance

It is well known that intraday returns tend to reverse the following intraday period, conditional on excess buying pressure on the bid or ask side. This suggests that liquidity providers “overreact” to order imbalance (OIB) by initially altering quotes so much that a negative autocorrelation is seen in mid-price returns. We investigate under which circumstances this behavior is most common. Specifically, it seems the tick size augments “OIB-reversal”. However, if the tick size is binding for much of the trading day, it has the opposite effect of censoring such reversals. In addition, if market liquidity is high, the reversal becomes more frequent.

Analysis the effect of market anomalies and growth options on stock return

در بازارهای مالی اثر هر یک از ناهنجاری‌های سودآوری، درماندگی مالی، بخت‌آزمایی و ریسک غیر سیستماتیک بر بازده سهام به‌تنهایی بررسی شده است. اما ارتباط احتمالی آن‌ها نامشخص است. به‌تازگی این ادعا مطرح‌شده است که فرصت‌های رشد بر تقارن تابع توزیع بازده اثر می‌گذارد و قادر است این ارتباط احتمالی و صرف ریسک ناشی از این ناهنجاری‌ها را توضیح دهد. در این پژوهش با استفاده از خواص آماری حاکم بر گشتاور مرتبه سوم تابع توزیع بازده و با جداسازی چولگی ویژه مورد انتظار ناشی از فرصت‌های رشد این ارتباط بررسی شده است. بدین منظور داده‌های 114 شرکت پذیرفته‌شده در بورس اوراق بهادار تهران با رویکرد حذف سیستماتیک، بین سال‌های 1390 تا 1396 جمع‌آوری شد. فرضیه‌های پژوهش با استفاده از رویکرد سبد بندی و ارزیابی آلفای برخی مدل‌های عاملی آزمون شد. شواهد نشان می‌دهد بین سودآوری، درماندگی مالی، بخت‌آزمایی و ریسک غیر سیستماتیک با بازده آتی رابطه وجود دارد؛ اما مدل‌های مرسوم قیمت‌گذاری دارایی‌های سرمایه‌ای توانایی تبیین صرف ناشی از ناهنجاری‌های مذکور را ندارد. این شواهد به‌صورت غیرمستقیم مؤید وجود معمای سودآوری، بخت‌آزمایی و ریسک غیر سیستماتیک در بازار سرمایه ایران است و نشان می‌دهد سرمایه‌گذاران با گزینش راهبرد سرمایه‌گذاری مبتنی بر این ناهنجاری‌ها می‌توانند بازده متفاوتی داشته باشند.

Wide framing disposition effect: An empirical study

Abstract We estimate the disposition effect for active traders in a large discount brokerage dataset containing US households trading records between 1991 and 1996. We apply a wide framing perspective, focusing on portfolios rather than individual stocks. We find that the disposition effect varies inversely with the proportion of stocks trading at a gain in the portfolio, nearly vanishing when this proportion reaches 50%. This is driven by how the realisation of gains and losses depends on the percentage of gains in the account. The probability to realise a loss increases with the percentage of gains in the account. The relation between the probability of realising a gain and the percentage of gains in the bank account follows a U-shape. We also estimate the change in the disposition effect when an investor realises more than one stock on a trading day. We find when investors sell a stock, they are much more likely to also realise another stock on the same day. In particular, selling a loss increases an investor’s propensity to sell a gain and vice versa. This key finding provides an explanation for the observed dependency of the disposition effect on the portfolio composition. We also propose several psychological explanations for our findings.

When Brokerages Restrict Retail Investors, Does the Game Stop?

We study a set of trading restrictions that limit equity and/or options positions imposed by retail-oriented broker-dealers in 38 stocks, including GameStop. Using brokerages’ capital requirements from the National Securities Clearing Corporation (NSCC) and the exact timing of restrictions, for identification, we find large stock price effects: CARs average -13.54% within two hours following a stock’s first trading restrictions and -51.97% after five trading days. When restrictions are lifted, share prices do not rebound. When traders substitute options for equities, options volume and open interest spike, as do implied volatilities, which rise more than realized volatilities. Options purchasers overpay, creating large transfers to options sellers and market-makers. Margin increases show similarities and differences relative to trading restrictions.

An examination of calendar anomalies: evidence from the Thai stock market

PurposeThe study examines the existence of calendar anomalies, including the day-of-the-week (DOW) effect and the January effect, in the Stock Exchange of Thailand.Design/methodology/approachUsing daily stock returns from March 2014 to March 2019, the study performs regression analysis to examine predictable patterns in stock returns, the DOW effect and the January effect, respectively.FindingsThere is strong evidence of a persistent monthly pattern and weekday seasonality in the Thai stock market. Specifically, Monday returns are negative and significantly lower than the returns on other trading days of the week, and January returns are positive and significantly higher than the returns on other months of the year.Practical implicationsThe findings offer managerial implications for investors seeking trading strategies to maximize the possibility of reaching investment goals and inform policymakers regarding the current state of the Thai stock market.Originality/valueFirst, the study investigates calendar anomalies in the Thai stock market, specifically the DOW effect and the January effect, which have received relatively little attention in the literature. Second, this is the first study to examine calendar anomalies in the Thai stock market across different groups of companies and stock trading characteristics using a range of composite indexes. Furthermore, the study uses data during the period 2014–2019, which should provide up-to-date information on the patterns of stock returns in Thailand.

Effects of strong and weak non-pharmaceutical interventions on stock market returns: A comparative analysis of Norway and Sweden during the initial phase of the COVID-19 pandemic

In this paper, we examine the behaviour of stock market returns in Norway and Sweden during the early days of the COVID-19 pandemic. We test how the different government interventions chosen in Norway and Sweden, including restrictions such as school closures and travel prohibitions along with economic support, affected equity markets in both countries. Our dataset comprises a panel of data for Norway and Sweden over 221 trading days during the period 1 January to 5 November 2020. The result show that while non-pharmaceutical interventions had few or no effects on Norwegian stock market returns, they positively affected the stock market in Sweden, although the strength of this effect weakened with the increasing number of confirmed COVID-19 cases.

Indonesia stock exchange: Abnormal return amid pandemic

This study aims to reveal the behavior of investors on the Indonesia Stock Exchange (IDX) before and during (early) the COVID-19 pandemic. This study is an extension of references to understand market reactions in response to future crises. This study is an event study with a time window of 76 trading days before and after the first case was officially announced by the authorities in Indonesia. Taking a sample of stocks included in the Liquid Index (LQ) 45, this study measures the abnormal return and transaction volume during the pre and post-first official announced cases and test the whole data by t-test. The results of data analysis indicate that there is no difference in abnormal returns, but there is a significant difference in transaction volume. These findings indicate that, in general, the Indonesian market is quite efficient, as evident from the absence of different abnormal returns. On the other hand, the market also appears to be cautious in making investment decisions amid uncertainty.

Santa Claus rally and the Indian stock market: A comprehensive analysis

The study examines the leading Indian stock market indices all of which reflect the “Santa Claus rally” effect where significantly higher daily returns accrue to investors in the last five trading days of December and the first two trading days of the following January. The study also reveals that the BSE SENSEX started exhibiting this effect only during the post-1991 period. Additionally, the results indicate that there is a size effect associated with the Santa Claus rally effect wherein small-cap companies outperform mid-cap and large-cap companies. The evidence also suggests that the effect is prominent in eight industry-specific sub-sectors.

Spot trading strategy considering multiple constraints for electricity retailers

In order to solve the inert trading and irrational trading of electricity retailers in the spot market trading environment, increase the income of electricity retailers and reduce transaction risk, a spot trading strategy considering multiple constraints for electricity retailers is proposed. Similar days are selected by analyzing the date type and the temperature difference of the spot market trading day, on the basis of similar days, the electricity consumption information of electricity retailers’ agent users, day-ahead and real-time electricity price of sport trading market are predicted, combing the user-side excess profit recovery fee, mid-to-long term shortage recovery fee and mid-to-long term curve deviation recovery fee restrictions in Shanxi Province spot trading rules, optimize the mid-to-long term daily 96 points daily electricity purchase curve and day-ahead 96 points daily electricity biding curve, and use the real data of Shanxi spot market transactions to calculate the multi-day income of the electricity retailers to verify the practicability of the spot trading strategies.

Price Movements around Short Covering Trades

Using reported large short position data from Korea stock market, we studied stock price movements around the short covering trades. We found a significant negative relationship between the short covering trades and the stock return on the previous five trading days. trading days. It means that short sellers engage in contrarian trading when they cover short positions. Short coverings are associated with positive returns on the trading day, confirming that there is a positive transient price impact at the coverings of large short positions. We also found that stock prices decline steadily in the 60 trading days following short coverings. This indicates that some short sellers are forced to prematurely close out their positions, thus suffering opportunity losses. Particularly, when a sudden surge in stock prices causes involuntary large-scale short coverings to occur in the short squeeze, the price impact gets larger and short sellers bear greater opportunity losses. This study sheds light on short selling strategies in practice and market stabilizing policies. Key words: Short Covering; Contrarian; Price Impact; Timing Ability; Short Squeeze JEL Classification: G10, G14

Dynamics of Bitcoin trading on the Binance cryptocurrency exchange

Currently, there are a great number of platform-projects and frameworks based on blockchain technology. Consequently, it is necessary to define the most relevant blockchain platforms and analyze them taking into consideration a variety of features. Also, there is a need to investigate the logistics growth and the price of Bitcoin on the Binance cryptocurrency exchange. The authors have examined modern technologies used by manufacturing companies in the field of fintech in the context of the 2019-2024 period. The results show that sensors and automatic identification take the leading position both at present and in 2024. Aartificial intelligence and blockchain are also in demand by manufacturers today, however in the nearest future their ranking positions will increase sixfold from 10% to 60%. In the current paper the authors review the largest companies that effectively use blockchain technology in their businesses. The conducted survey shows that 18% of companies use blockchain technology based on Bitcoin. The authors have analysed a number of Bitcoin transactions for the period from January 2017 to February 2021 and concluded that the COVID-19 pandemic has had a favourable effect on the indicator data. A maximum number of transactions equal to 10.15 million was carried out in July 2020. Using the method of the Ordinary Least Squares (OLS) and statistical estimation methods the authors have revealed an underestimation of the equilibrium state of the empirical distribution of price data and the volume of daily trading of Bitcoin on the Binance cryptocurrency exchange through the channel of the right-hand confidence interval. The blockchain technology based on Bitcoin has positively reacted to the macroeconomic factors such as the COVID-19 pandemics and further growth in Bitcoin transactions is expected. With the help of economic modelling, the authors have defined the predictable volume and the price of Bitcoin on the Binance cryptocurrency exchange.

The cross-section of intraday and overnight returns

I investigate cross-sectional variation in stock returns over the trading day and overnight to shed light on what drives asset pricing anomalies. Margin requirements are higher overnight, and lending fees are typically charged only on positions held overnight. Such institutional constraints and overnight risk incentivize arbitrageurs who trade on mispricing to reduce their positions before the end of the day. Consistent with this intuition, a mispricing factor earns positive returns throughout the day but performs poorly at the end of the day. This pattern strengthens in the second half of the sample and is shared by several well-known anomalies.

Two robust long short-term memory frameworks for trading stocks.

This paper aims to find a superior strategy for the daily trading on a portfolio of stocks for which traditional trading strategies perform poorly due to the low frequency of new information. The experimental work is divided into a set of traditional trading strategies and a set of long short-term memory networks. The networks incorporate general and specific trading patterns, where the former takes into account the universal decision factors for trading across many stocks, while the latter takes into account stock-specific decision factors. Our research shows that both long short-term memory networks, regardless of whether they are based on universal or stock-specific decision factors, significantly outperform traditional trading strategies. Interestingly, however, on average neither has the edge compared to the other, thus remaining ambivalent as to whether universality or specificality is to be preferred when it comes to designing long short-term memory networks for optimal trading.