Effects of Government's Policy in Stock Price: A Case of Nepse

The purpose of this study is to find out the relationship of government's policy on the price movement of Nepal stock exchange (NEPSE). This study followed a case study research design, because it offers a deeper perspective and clearer understanding of the stock price movement of Nepalese joint venture banks. The sample size of this study consists of five joint venture commercial Banks, economic analysis and survey reports conducted by central bank of Nepal (Nepal Rastra Bank).The judgmental sampling method has been applied for selection of joint venture banks. The study was totally based on secondary data. in order to make proper analysis descriptive and inferential statistics were used using SPSS software version 26. The finding of this study revealed that the GDP and import are inversely associated with stock price movement and CRR, export, interest rate and inflation are positively associated with stock price movement. Further, it is found that the macroeconomic variables are key factors to determine the Nepalese stock price movement. More importantly, stock market has been found to respond significantly to changes in the government policy. It is recommended that CRR, EXPORT, INTEREST RATE and INFLATION are major factors which largely affect the stock price movement of NEPSE. GDP and IMPORT are not compliance with the stock price movement as they produce negative association with the stocks volatility.

Modeling the volatity of cryptocurrency markets

The application of the model of geometric Brownian motion (GBM) for the problem of modeling and forecasting prices for cryptocurrencies is analyzed. For prediction the solution of the stochastic differential equation of the GBM model is used, which has a linear drift and diffusion coefficients. Different scenarios of price movement are considered. Keywords: geometric Brownian motion (GBM), modeling, forecasting, cryptocurrency.

Cluster-Based Mutual Fund Classification and Price Prediction Using Machine Learning for Robo-Advisors

The rise of FinTech has been meteoric in China. Investing in mutual funds through robo-advisor has become a new innovation in the wealth management industry. In recent years, machine learning, especially deep learning, has been widely used in the financial industry to solve financial problems. This paper aims to improve the accuracy and timeliness of fund classification through the use of machine learning algorithms, that is, Gaussian hybrid clustering algorithm. At the same time, a deep learning-based prediction model is implemented to predict the price movement of fund classes based on the classification results. Fund classification carried out using 3,625 Chinese mutual funds shows both accurate and efficient results. The cluster-based spatiotemporal ensemble deep learning module shows better prediction accuracy than baseline models with only access to limited data samples. The main contribution of this paper is to provide a new approach to fund classification and price movement prediction to support the decision-making of the next generation robo-advisor assisted by artificial intelligence.

Impact of Right Share Issue on Share Price of Commercial Banks in Nepal

This study has examined the effect of issue of right share on share price movement in the banking sector using share price and price relative as the predictors of share price movement. Banking sub-index and index relative of different periods were used for analysis. Five different periods of time were selected to observe the share price movement considering the announcement date as the reference point of time. Based on the secondary sources of data, a correctional analysis was administered to examine whether the share price and price relative (banking index) has any relationship with the share price change in case of Nepalese commercial banks. Coefficient of determination and probable error were used to find how much percentage of the variation in the share price could be explained by the occurrence of right share issue and likewise, whether or not the relationship was significant. The results reveal that right share announcements have the signaling effect on share price movement. The share prices and banking indices of selected banks have decreased after the announcement of right share. The results suggest that the information irregularity behavior tempts a negative change in share price after the announcement of rights share. The implication of the results is that investors can forestall the nature of change in share price after rights issue announcement and develop strategic plans to expand the trading activity. Keywords: NCC Bank., Right announcement, Right share issue, Price relative, Index relative.

Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation

Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier. In this study, we proposed a multichannel collaborative network by incorporating candlestick-chart and social-media data for stock trend predictions. We first extracted the social media sentiment features using the Natural Language Toolkit and sentiment analysis data from Twitter. We then transformed the stock’s historical time series data into a candlestick chart to elucidate patterns in the stock’s movement. Finally, we integrated the stock’s sentiment features and its candlestick chart to predict the stock price movement over 4-, 6-, 8-, and 10-day time periods. Our collaborative network consisted of two branches: the first branch contained a one-dimensional convolutional neural network (CNN) performing sentiment classification. The second branch included a two-dimensional (2D) CNN performing image classifications based on 2D candlestick chart data. We evaluated our model for five high-demand stocks (Apple, Tesla, IBM, Amazon, and Google) and determined that our collaborative network achieved promising results and compared favorably against single-network models using either sentiment data or candlestick charts alone. The proposed method obtained the most favorable performance with 75.38% accuracy for Apple stock. We also found that the stock price prediction achieved more favorable performance over longer periods of time compared with shorter periods of time.