Towards a well-functioning stock market in context

Abstract

Purpose The purpose of this paper is to summarise and reflect upon key issues at the interface of prices, information and regulation with a focus upon the stock market in context. Reflecting upon academic research in the area of efficient markets, and regulatory policy, the concern is to discern issues in terms of policy and support for policy. What does the research imply for policy? Is it possible that the research, perhaps given its rhetoric, can be misinterpreted in relation to policy? The study is also concerned to develop avenues for future research based on these considerations. Design/methodology/approach The paper is an analytical and critical review and writing. Findings The reading of the research suggests a pragmatic regulatory policy that should be concerned to improve stock market functioning, including with respect to information, as well as the context of which this is part. At the same time, the literature may be read as promoting anti-regulatory policy. Practical implications On the one hand, these are consistent with the pragmatic policy referred to above. On the other hand, further research is suggested to explore substantively the rhetoric of the research and its interpretation and to explore understandings of the research and its implications amongst key constituencies in practice. Originality/value The concern is to bring key insights from the academic literature together with a view to promoting a pragmatic policy orientation, while cautioning in a critical perspective about how this academic literature and research might be interpreted from a policy perspective.

Predicting the uptrend and downtrend in stock markets for intraday trading using machine learning algorithms

“…In [15] the need for investors to predict the stock market is mentioned, and how this prediction helps retain investors' attention for stocks [14] the paper suggests that an amalgamation of artificial neural networks and any other statistical tool or machine learning algorithm provides better results for financial time series predictions. In [16] the research paper keeps into account factors like pricing, social behaviour, regulations and how it affects the stock market. The paper aims at promoting pragmatic policy orientation.…”

The rise of passive investing: a systematic literature review applying PRISMA framework

“…Since their introduction, various barometers of ETF performance have been explored in depth, including TE (Johnson, 2009;Shin and Soydemir, 2010;Chu, 2011), pricing efficiency (Israeli et al, 2017) and fund flows (Clifford et al, 2014). As ETFs gained prominence, their role in price discovery and information transmission (Tse and Martinez, 2007;Levy and Lieberman, 2013;Haslam et al, 2018), volatility in prices of underlying (Gleason et al, 2004), market liquidity (Boehmer and Boehmer, 2003;Ben-David et al, 2017), market microstructures (Gao et al, 2018), market co-integration and error correction (Chou and Chung, 2006;Delcoure and Zhong, 2007;Broman, 2016), volatility spillover (Krause and Tse, 2013;Inci, 2018) has been investigated in detail. With growing environmental consciousness, ETF research has shifted to evaluating the efficacy of investments in alternative energy (Miralles-Quir os and Miralles-Quir os, 2019).…”

“…, 2014). As ETFs gained prominence, their role in price discovery and information transmission (Tse and Martinez, 2007; Levy and Lieberman, 2013; Haslam et al. , 2018), volatility in prices of underlying (Gleason et al.…”