The six short-sales constraints (SSCs) regime changes from 2002 to 2009 in the Taiwan stock market provide “natural social” experiments to examine how different SSC intensities affect price adjustment efficiency and market stability. There are three main findings. Firstly, we derive the theoretical price with put–call parity from seven series index options. Using a “threshold error correction model” (TECM), we find a more efficient price adjustment to new equilibria for upward adjustments than for downward adjustments. The SSCs impede the price adjustment downward, especially during the financial crisis of 2008. Therefore, relaxing the short-sales constraints essentially improves price efficiency. Secondly, our findings also refute the claim that tighter SSCs can help stabilize the market since the tightening of the short-sales restriction leads to increases in both market volatility and downside risk even after controlling the investor fear gauge of the Taiwan volatility index (TVIX). These results hold even when market conditions and liquidity are controlled. Finally, the evidence from our counterfactual policy analysis suggests that tighter constraints help restore market confidence even though prices may fall more sharply without short-sales bans. As a result, policymakers may practically optimize to strike a balance between the benefits of restored emerging market order and the cost of elevated market volatility.

Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.

Stock price prediction has always been challenging due to its volatility and unpredictability. This paper performs a preliminary exploratory comparison that utilizes Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms to forecast the stock market in Hong Kong. It considers a public dataset publicly available and uses feature engineering to extract relevant features. Then, LSTM and SVM algorithms are applied to predict stock prices. Our results show that the proposed machine learning techniques can predict stock prices in Hong Kong's share market with the error metrics presented, and, for this purpose, LSTM achieved better results than SVM, with MSE = 0.0026, RMSE = 0.0508, MAE = 0.0406, and MAPE = 1.325.

We examine how short-sales policy regimes affect the efficiency of price adjustments and market stability. Using a ‘threshold error correction model’ (TECM), we find a more rapid convergence rate to new equilibria for upward adjustments than for downward adjustments. Moreover, relaxing the short sales constraints essentially improves price efficiency. Our findings refute the claim that tighter constraints can help stabilize the market since the tightening of the short-sales restriction leads to increases in both market volatility and downside risk. These results hold even when market conditions and liquidity are controlled. Even though prices may fall more sharply without short-sales bans, rather interestingly, the evidence from our counterfactual policy analysis suggests that tighter constraints help restore market confidence. As a result, policymakers may practically optimize to strike a balance between the benefits of restored market order and the cost of elevated market volatility.