An Analysis of India’s Trade Flow with BIMSTEC nations- A Gravity Model Approach

At present, the Bay of Bengal Initiative for Multi Sectoral Technical and Economic Cooperation (BIMSTEC) represent 22 percent of the global population and carries immense economic promise, with a combined GDP worth $ 3.5 trillion (2018). BIMSTEC have India as the largest economy of the group followed by Bangladesh, Thailand, Sri Lanka, Nepal, Myanmar and Bhutan. The paper tries to examine the impact of determinants of trade, on trading pattern of India with the BIMSTEC nations, by employing an augmented gravity model on panel data, since its formation for the period of 22 years i.e., from 1997-2018. The paper tries to examine the India’s trade flow within BIMSTEC trading bloc by implying augmented gravity model followed by Egger (2000,2002), Baltagi et al. (2003) and Serlenga and Shin (2007). Several checks have been performed to imply the presence of serial correlation, heteroscedasticity and contemporaneous correlation in the panels. Many other preliminary tests have also been performed to know the crosssectional dependency, stationarity, panel co-integration and normality of variables. The simple panel OLS estimation technique has been used conduct Regression. The study finds out that Heckscher-Ohlin- Samuelson theorem explain the India’s pattern of trade with the bloc. The variables GDP, per capita GDP, Trade GDP ratio, common border and belonging to BIMSTEC has positive impact on the trade between the India and country j. While tax and distance are negatively correlated with total trade of the nations as per our expectations.

Do intraday week effect in currencies hourly trading reflect leverage and asymmetric anomalies? Policy implications for traders

Purpose The purpose of this study is to determine if intraday week (IDW) effect of the currencies reflect leverage and asymmetric impact in currencies market. The study data set comprises of intraday patterns of 15 currencies from developed and emerging economies. Design methodology approach The study applies the exponential generalized autoregressive conditional heteroscedasticity (E-GARCH) model technique to observe the IDW leverage and asymmetric effect after introducing hourly dummies variables, namely, IDWmon, IDWwed, IDWfrid and IDWfrid-mon. Findings The study results favor the propositions and confirm that IDW effect do exist in the international forex markets in relation to hourly trading pattern for respective currencies. Mostly, currencies do depreciate on Monday and Wednesday compared to the rest of the days. However, on the last trading day, i.e. Friday currencies observe an appreciation pattern which is for both economies. The results have an evidence of leverage and asymmetric effect confirmed by the E-GARCH model as a result of press releases and influence by micro-factors in the currency markets. Practical implications The study believes to have theoretical connection related to the better understanding of currencies trend for developed and emerging economies, as the IDW effect exists. Moreover, confirmation of both the leverage and asymmetric effect in observed currencies would be able to assist the investors in making rational choices during the trading hours and would confirm considerable profits through profit incentivized strategies. Originality value The study not only add knowledge to the previous study work in relation to the hourly trading pattern of currencies with reference to the IDW effects but also highlights the leverage and asymmetric effect in currencies that will help in formulating future trading strategies particular to emerging economies.

Investors' Trading, Market Timing, and Implementation Shortfall: Evidence from the US Financial Market

In this research, we evaluate the US investors' trading pattern and choice of market timing in the presence of implementation shortfall. Results show that when investors decide to trade, implementation shortfall is being ignored. It is observed that stock performance on Wednesday is positive in the presence of positive and significant implementation shortfall i.e., traders do not seem to manage implementation shortfall during trading on Wednesday. It is also observed that investors seem to ignore the implementation shortfall in April. This behavior seems to persist in other types of market times such as turn-of-the-month, week-of-the-month, and quarter-of-the-year effects on implementation shortfall. We conclude that investors behave aggressively to buy stocks during certain days and times of the year ignoring implementation shortfall.

The trade policy effect in international trade: case of Pakistan

The debate on free trade and protectionism is ravaging in recent years. The industrialized countries are losing more and more market to the benefit of emerging countries. Liberals worry about new tariff barriers, while protectionists fear that unevenly distributed losses and gains will lead to significant economic dislocation of workers in import-competing industries. The economic policy of restricting imports and the economic policy of opening exports remain two critical measures of international trade. This study uses the gravity model to investigate the impacts of trade policy measures on trade flows between Pakistan and its dominant trading pattern for the period 2006 to 2015. The findings revealed the statistically significant correlation of trade policy variables on exports and imports. The study extended the analysis by examining four specificities groups of trade policy and continuing the analysis by estimating different country groups according to geographical or organizational clusters. The findings indicated that the specificities of trade policy have a statistically significant effect on exports and imports. Moreover, the signs of the coefficients are opposite in both models. The main political implication is that the proliferation of free trade agreements can have a positive impact on international trade.

Overconfidence Bias: Empirical Examination of Trading Turnover and Market Returns

Theory of overconfidence states that investors are highly overconfident when valuing the stocks. Self-attribution has been found by the researchers as the root cause for overconfidence bias in investors. Investors attribute the high stock prices and returns with their own art of picking up the stocks, and thus they trade more frequently. In order to test overconfidence and self-attribution Vector Autoregressive (VAR) model has been employed to find out the long-term relationship between endogenous variables: market return and market turnover and exogenous variables: volatility and dispersion. Results revealed that there exists a strong positive relationship between market returns and trading turnover. Also, the crosssectional standard deviation in market prices i-e volatility and the cross-sectional variation in stock returns i-e dispersion has a very strong impact on trading pattern and returns. Since investment decisions made by Pakistani investor largely depend upon psychological factors, giving less weightage to all the fundamentals, the trading pattern exhibited may collectively tend the market behave in an irrational manner.

Earnings management and institutional investor trading prior to earnings announcements

PurposeThe purpose of this paper is to investigate if earnings management affects the trades of different investors prior to earnings announcements.Design/methodology/approachUsing a unique account-level trading data set from the Chinese stock market, the author investigates the different investor trading patterns prior to earnings announcements.FindingsThe author obtains direct evidence to show that: first, institutional investors, particularly active ones, tend to sell (buy) stocks before negative (positive) earnings surprises; second, institutional investors buy stocks intensively with the lowest earnings management and the highest earnings surprises, and the trading patterns are primarily driven by active institutions. No significant trading pattern is observed on the stocks with negative earnings surprises; and third, the author uses a natural experiment in accordance with the Chinese accounting standards reform to address endogeneity, and the causality of the results still holds.Originality/valueThe findings provide clear evidence by emphasizing the importance of earnings management in the formulation of investor decisions.

Predicting Stock Market: An Approach with Artificial Intelligence

Artificial Intelligence (AI) technology has advanced impressively since inventors began tampering with its potential. Many believe that the next great use for AI technology will be in the field of financial market speculation. Technology can be used either to make our lives better or make money. The stock exchange market is the most volatile and most dynamic of all. Special care has to be exercised in buying and selling of stocks from different companies or businesses. The probability of losing the stocks and acquiring benefits through the stocks are fifty-fifty. Volatility of the stock market jumbles up a trader’s nervous system making it difficult to understand or thin rationally. Artificial Intelligence is supposed to be a predictive model that looks at more than technical patterns of trading. It has the ability to identify financial features of companies (e.g. price to earnings ratio, long term (business loans) that will make money in the long run. This requires capabilities from different areas of study and massive computational power which is why it is only prevalent in recent years. This paper tries to attempt of coming up with a basis and prediction using Artificial Intelligence in identifying trading pattern relations which appropriately inter relates with High Frequency Stock Trading based on pre-set criteria