Monira Aloud

The state of social media engagement in Saudi universities

Purpose The purpose of this paper is to investigate the way that Saudi universities are engaging their audience via social media platforms by means of the five meaningful themes: visibility, branding, authenticity, commitment, and engagement. The study will answer the questions: how do Saudi universities exploit social media platforms to engage their target audience? What are the recommendations for Saudi universities toward maximizing the value of social media engagement? Design/methodology/approach A content analysis approach was used to study all Saudi universities (26 public, 11 private). Facebook, YouTube, LinkedIn and Twitter were the anticipated social media platforms in this study. Findings The results showed that Twitter is the most frequently used platform to communicate with audiences. While visibility in the anticipated social media platforms was high, the engagement was lacking. On the other hand, authenticity and branding in the anticipated social media platforms were medium, while commitment was low except on Twitter. In general, the private universities exceed the public universities in terms of visibility, branding, authenticity, commitment and engagement in the anticipated social media platforms, which indicates their attention on gaining their audience’s satisfaction, a dynamic of trust which will lead to maintaining current relationships or building new ones. Originality/value Since there are few studies in the field regarding social media platforms usage by Saudi universities, this study aims to understand how Saudi universities are utilizing social media platforms to engage their audiences and propose recommendations for how Saudi universities can build value from social media platforms.

Investment opportunities forecasting: a genetic programming-based dynamic portfolio trading system under a directional-change framework

This paper presents an autonomous effective trading system devoted to the support of decision-making processes in the financial market domain. Genetic programming (GP) has been used effectively as an artificial intelligence technique in the financial field, especially for forecasting tasks in financial markets. In this paper, GP is employed as a means of automatically generating short-term trading rules on financial markets using technical indicators and fundamental parameters. The majority of forecasting tools use a fixed physical timescale, which makes the flow of price fluctuations discontinuous. Therefore, using a fixed physical timescale may expose investors to risks, due to their ignorance of some significant activities. Instead of using fixed timescales for this purpose, the trading rules are generated under a directional-change (DC) event framework.We examine the profitability of the trading systems for the Saudi Stock Exchange, and evaluate the GP forecasting performance under a DC framework through agent-based simulation market index trading. The performance of the forecasting model is compared with a number of benchmark forecasts, namely the buy-and-hold and technical analysis trading strategies. Our numerical results show that the proposed GP model under a DC framework significantly outperforms other traditional models based on fixed physical timescales in terms of portfolio return.

Adaptive GP agent-based trading system under intraday seasonality model

The development of computational intelligence based trading strategies for financial markets has been the focus of research over the last few years. To develop efficient and effective automated trading strategies, we need to understand the workings of the market and the patterns emerging as a result of the traders interactions. In this paper, we develop an adaptive Genetic Programming (GP) agent-based trading system under Intraday Seasonality Model (ISM), which is abbreviated as GPISM trading system. ISM is used for creating maps and visualizing the dynamic price evolution of the asset during the day. This new model permits the recognition of periodic patterns and seasonalities in the price time series and hence eliminates any unnecessary data input. We use a high-frequency dataset of historical price data from Saudi Stock Market, which enables us to run multiple market simulation runs and draw comparisons and conclusions for the developed trading strategies. The goal of our work is to develop automated computational intelligence-based strategies for real markets, and this study facilitates a more thorough understanding of a specific market’s workings and constitutes the basis for further exploration into such strategies designed for the stock market. We evaluate the intelligence of the GP-ISM trading system through agent-based simulation market index trading. For comparison, we also include four other types of trading agents in this contest, namely, zero-intelligence agents, Buy-and-Hold agents, fundamental agents and technical analysis agents. As a result, GP-ISM performs the best, which provides a general framework for the further development of automated trading strategies and decision support systems.