Financial Asset Management Using Artificial Neural Networks

Investors typically build portfolios for retirement. Investment portfolios are typically based on four asset classes that are commonly managed by large investment firms. The research presented in this article involves the development of an artificial neural network-based methodology that investors can use to support decisions related to determining how assets are allocated within an investment portfolio. The machine learning-based methodology was applied during a time period that included the stock market crash of 2008. Even though this time period was highly volatile, the methodology produced desirable results. Methodologies such as the one presented in this article should be considered by investors because they have produced promising results, especially within unstable markets.

Stock Return Predictability and Market Efficiency in Pakistan; A Role of Asian Growing Economies of India and Malaysia

The purpose of this study is to determine the predictability of the Pakistani stock market's one-day forward returns by utilizing lagged daily returns for Pakistan, India, and Malaysia from 2006 to 2016. The findings indicate that lagged Pakistani market returns significantly predict Pakistani one-day ahead market returns. However, the other two growing stock markets, India and Malaysia, show no association with one-day ahead market returns. Mostly, stock market behavior in the pre-2008 and post-2008 eras was the same, although industry return behaviour was different due to the economic crisis of 2008. However, the Pakistani stock market one-day ahead returns predict the own Pakistani lag returns due to an inefficient market and prices do not follow a random walk. As a result, investors and financial analysts can foresee and generate anomalous returns by using previous data and information. Key words: Stock Market Returns Predictability, Stock Market crash, Market efficiency

Investment environment, stock market perception and stock investments after stock market crash

PurposeThis study examines how the importance of external investment environment factors affect stock market perception, and how stock market perception affects stock investments after stock market crash witnessed by individual investors in one of the emerging stock markets.Design/methodology/approachA cross-sectional survey was administrated among 223 individual investors who experienced stock market crash in 2010–2011 in Bangladesh, and the proposed model was tested by the partial least squares-structural equation modeling PLS-SEM model.FindingsFindings show that the importance of Bangladesh's stock market performance, government policy, economic issues and neighboring country's stock market performance has effects on investors' stock market perception. This perception, in turn, decreases monthly stock trading and short-term investment horizon. The findings further show the mediating effect of stock market perception.Practical implicationsInvestors need to carefully consider the external investment environment when they form their stock market perception, as this perception drives stock investments. Analogously, regulators should ensure releasing timely and updated statistics on external investment factors.Originality/valueAddressing those investors who encountered stock market crash, a set of external investment environment issues, stock market perception and stock investments are new in the literature.

How Does Deleveraging Affect Funding Market Liquidity?

How does deleveraging affect the market liquidity of high-embedded-leverage securities issued by financial institutions and the funding constraints of these institutions? We use the forced deleveraging of structured mutual funds during the 2015 Chinese stock market crash to study the effects of deleveraging. Our regression-discontinuity analysis shows that deleveraging significantly reduces the market liquidity of the deleveraging funds’ equity units. Moreover, our difference-in-differences analysis shows that deleveraging results in large decreases in subsequent fund flows, stock and cash holdings, and performance, with the impact channeled through the deterioration of the market liquidity of the fund’s equity units. This paper was accepted by Victoria Ivashina, finance.

Understanding Changes in the Topology and Geometry of Financial Market Correlations during a Market Crash

In econophysics, the achievements of information filtering methods over the past 20 years, such as the minimal spanning tree (MST) by Mantegna and the planar maximally filtered graph (PMFG) by Tumminello et al., should be celebrated. Here, we show how one can systematically improve upon this paradigm along two separate directions. First, we used topological data analysis (TDA) to extend the notions of nodes and links in networks to faces, tetrahedrons, or k-simplices in simplicial complexes. Second, we used the Ollivier-Ricci curvature (ORC) to acquire geometric information that cannot be provided by simple information filtering. In this sense, MSTs and PMFGs are but first steps to revealing the topological backbones of financial networks. This is something that TDA can elucidate more fully, following which the ORC can help us flesh out the geometry of financial networks. We applied these two approaches to a recent stock market crash in Taiwan and found that, beyond fusions and fissions, other non-fusion/fission processes such as cavitation, annihilation, rupture, healing, and puncture might also be important. We also successfully identified neck regions that emerged during the crash, based on their negative ORCs, and performed a case study on one such neck region.