THE INFLUENCE OF INFORMATION ON TRADING BEHAVIOR WITH PERSONALITY TRAITS AS MODERATION VARIABLE FOR STOCK TRADERS

The development of investment in Indonesia has increased rapidly over the past few years. One of the key factors affecting stock traders' trading behavior is information. Information that was previously difficult to obtain by investors became easily obtained due to technological developments. In addition to information, the characteristics of traders also influence their trading behavior. The population used in this study is the entire citizen of Surabaya, and the sample of this study is stock traders who have a minimum of 1-year trading experience. Data analysis is done by using Partial Least Square with Smart PLS 3.0. The result of this study is that information has a significant effect on trading behavior, while neuroticism, extraversion, openness, agreeableness, and conscientiousness do not moderate the effect of information on trading behavior significantly.

Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market

Volatility is the degree of variation in the stock price over time. The stock price is volatile due to many factors, such as demand, supply, economic policy, and company earnings. Investing in a volatile market is riskier for stock traders. Most of the existing work considered Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) models to capture volatility, but this model fails to capture when the volatility is very high. This paper aims to estimate the stock price volatility using the Markov regime-switching GARCH (MSGARCH) and SETAR model. The model selection was carried out using the Akaike-Informations-Criteria (AIC) and Bayesian-Information Criteria (BIC) metric. The performance of the model is evaluated using the Root mean square error (RMSE) and mean absolute percentage error (MAPE) metric. We have found that volatility estimation using the MSGARCH model performed better than the SETAR model. The experiments considered the Indian stock market data.

Lottery preference and stock market participation: evidence from China

PurposeThis paper studies the effects of lottery preference on stock market participation at the macro level.Design/methodology/approachThe authors use the abnormal search volume intensity for lottery-related keywords from the Baidu search engine to capture retail investors' lottery preference. To measure stock market participation, they use five different macro-level measures from various angles. They perform the time series regression analysis in their empirical study.FindingsFirst, the validation tests show that the lottery preference index in this study is reasonable. Further, the authors find that lottery preference increases people's propensity to enter and trade in the stock market. Besides, they find that the effect on trading behavior is asymmetric, that is, high lottery preference has a more significant impact on trading behavior than low lottery preference. However, lottery preference has no significant effect on the stockholding.Originality/valueThis paper contributes to the growing literature that examines the determinants of stock market participation and the role of lottery/gambling preference in the financial market. It also provides direct and novel evidence for Statman's (2002) conclusions about the similarity of lottery players and stock traders.

The Mediating Effect of E - Satisfaction on E - Service Quality and E - Loyalty Link in Securities Brokerage Industry

The rising popularity and use of the internet - based e in India offers huge potential in the internet connected (e-commerce) market and economy, and in specific for the stock brokerage category, leading to improved electronic service quality (e-SQ), electronic satisfaction (e-Satisfaction), and electronic loyalty (e-Loyalty) and becomes powerful components for investment bankers and stock brokers to attract and keep traders in the online market. To cope and handle with advances in Technology (ICT) and the varying expectations and demands of stock traders, the relationship between e-SQ, e-Satisfaction, and e-Loyalty should be continuously reviewed. Nonetheless, the design of e-SQ for retailers in any industry remains an open question. In this study, E-SERVQUAL was combined with several other e-SQ scales to assess the e-SQ of major share brokers in India. The e-SQ frameworks in particular are Design, Functionality, Privacy, Reliability, and Recovery. An online questionnaire is used in the research and the sampling method used was convenience sampling. From the distributed questionnaire, 50 sets of finished and productive responses were returned by the traders and investors. The findings suggested that the five proposed dimensions of e-SQ for stock brokers in the share market be developed. Every aspect of e-SQ was observed to have significant positive and notable effect on stock dealers' e-satisfaction. The responsiveness of e-SQ had the greatest impact on online shoppers' e-satisfaction. The customer's e-Loyalty to use an online retailer's website on a regular basis was significantly influenced by their e-Satisfaction. The study's findings serve as the foundation for discussion on managerial and theoretical implication.

Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network

Stock performance prediction plays an important role in determining the appropriate timing of buying or selling a stock in the development of a trading system. However, precise stock price prediction is challenging because of the complexity of the internal structure of the stock price system and the diversity of external factors. Although research on forecasting stock prices has been conducted continuously, there are few examples of the successful use of stock price forecasting models to develop effective trading systems. Inspired by the process of human stock traders looking for trading opportunities, we propose a deep learning framework based on a hybrid convolutional recurrent neural network (HCRNN) to predict the important trading points (IPs) that are more likely to be followed by a significant stock price rise to capture potential high-margin opportunities. In the HCRNN model, the convolutional neural network (CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) approach learns the long-term temporal dependencies to improve stock performance prediction. Comprehensive experiments on real stock market data prove the effectiveness of our proposed framework. Our proposed method ITPP-HCRNN achieves an annualized return that is 278.46% more than that of the market.

A Predictive Meta Model for Forecasting Stock Price using Time Series Data

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

The Use of Trusts in Business Structures

This chapter examines the ways in which trusts could be used by stock traders to stabilise business structures and emulate some of the benefits of incorporation, and even limited liability in the testamentary context. One of these ways was settling legal title to the assets of the business on a fixed body of trustees, who acted as its legal personality, and held the assets for the benefit of the business owners. This chapter explores the way that traders could make use of this device to try and emulate some of the benefits of incorporation, despite the business's lack of its own legal personality, and in the face of the perception of the owner-run partnership as the most stable and moral form of trading. In that way, it examines the extent to which the law of trusts met the needs of traders in the Regency era and so facilitated trade. In so doing, it shows that Regency era trusts were not simply part of the law of real property, as has commonly been supposed, but could be used in two forms of business: the deed of settlement company and the testamentary trading trust.

Prediction of Stock Performance Using Deep Neural Networks

Stock performance prediction is one of the most challenging issues in time series data analysis. Machine learning models have been widely used to predict financial time series during the past decades. Even though automatic trading systems that use Artificial Intelligence (AI) have become a commonplace topic, there are few examples that successfully leverage the proven method invented by human stock traders to build automatic trading systems. This study proposes to build an automatic trading system by integrating AI and the proven method invented by human stock traders. In this study, firstly, the knowledge and experience of the successful stock traders are extracted from their related publications. After that, a Long Short-Term Memory-based deep neural network is developed to use the human stock traders’ knowledge in the automatic trading system. In this study, four different strategies are developed for the stock performance prediction and feature selection is performed to achieve the best performance in the classification of good performance stocks. Finally, the proposed deep neural network is trained and evaluated based on the historic data of the Japanese stock market. Experimental results indicate that the proposed ranking-based stock classification considering historical volatility strategy has the best performance in the developed four strategies. This method can achieve about a 20% earning rate per year over the basis of all stocks and has a lower risk than the basis. Comparison experiments also show that the proposed method outperforms conventional methods.