Financial Practitioners Research Articles

Research on trading strategies based on time series - taking gold and bitcoin as examples

In response to the problem of how to optimally invest in gold and bitcoin, this paper establishes a daily trading strategy model, and uses a large amount of price data to conduct in-depth research on how to conduct optimal transactions to obtain maximum value. Based on the daily prices of gold and bitcoin within 5 years, we first build an ARIMA time series forecast model, and use MATLAB programming to solve the forecast results to get the next day's gold and bitcoin prices. Using this prediction result, we established a daily trading strategy model, and used recursion to find the daily trading strategy and the gains obtained in turn. The application of the model will help financial practitioners to analyze and guide the investment of related products.

Financial product investment decision model based on ultimate benefit

With the vigorous development of the financial market, more and more people enter the financial market for investment, it is very necessary to build a quantitative analysis and decision model based on data for ordinary traders. This paper selects two time series prediction models GM(1,1) and ARIMA for comparison. For GM(1,1), we improve it to obtain unbiased GM(1,1) and unbiased metabolic GM(1,1). Unbiased metabolism GM(1,1) was selected from the three grey prediction, and three combinations were formed by ARIMA and grey prediction respectively.These three results are used separately for the decision model, which prediction model to choose is determined by the final return. The investment decision model established in this paper is helpful for financial practitioners to improve investment profit and has certain application value.

Dynamic dependence and hedging strategies in BRICS stock markets with oil during crises

This paper discusses the asymmetric volatility spillovers between these two markets of BRICS’ countries during different financial and economic circumstances from 2010 until 2021, using the ARFIMA-FIAPARCH to model the dynamics of the marginal distribution. We study the feasibility of hedging stocks with oil, risk measurement functions allow financial practitioners to calculate the optimal hedge ratios and the corresponding to hedge portfolio returns. A dual long memory model is adopted to estimate the conditional mean and the conditional variance. Thereafter, a VaR,CVaR and ΔCVaR are applied to assess oil price exposure. Furthermore, to evaluate the optimal portfolio, we adopted the optimal portfolio weight, the optimal hedge ratio and the hedge effectiveness oil. Our results show that there are distinct economic benefits from hedging stocks with oil, although the effectiveness of hedging is both time-varying and market-state-dependent. We determine that the volatility transmission was time-varying and that influence from the Asian crisis, the bursting of the dot com bubble, the 2008 global financial crisis, the recent oil-price crash, and COVID 19 alternated between negative and positive values, over the entire studied period. During times of global financial uncertainty, investors reduce stock positions more than commodity positions, thus crude oil prices shock negatively affect the portfolio returns of stock-oil hedges. all stock market–oil pairs display the highest hedging effectiveness during the COVID-19 pandemic, which proved that the oil market can assist as a hedge for stock market in a portfolio.

Can artificial intelligence beat the stock market?

PurposeAcademia and financial practitioners have mixed opinions about whether artificial intelligence (AI) can beat the stock market. The purpose of this paper is to investigate theoretically what would happen if AI has further evolved into a superior ability to predict the future more accurately than average investors.Design/methodology/approachA theoretical model in an endowment economy with two types of representative investors (traditional investors and AI investors) is proposed, and based on the model, a long-run survival analysis for both types of investors is implemented.FindingsThe model presented in this paper indicates that being equipped with a superior ability to predict the future more accurately than traditional investors cannot guarantee AI investors to always beat the stock market in the long run. Those investors may be extinct, all depending on the structure/parameters of the stock market.Originality/valueTo the best of the author’s knowledge, they are the first to set up a representative agent equilibrium model to explore the above question seriously.

The Influence of Financial Behaviour on Financial Risk Tolerance In Investment Decision: A Conceptual Paper

There is a growing stream of research to seek for a solution to improve financial risk tolerance (FRT) in investment decisions, which is most likely conducted from financial institutions and was revealed that there are varieties of factors contribute to this aspect. The literature demonstrated that FRT is considered as the powerful factor that encourages investors’ intentions towards investments. Study shows that the FRT can be improved through positive financial behaviour. Hence it is essential to explore financial behaviour and FRT influences in investment decision from studies around the world and Malaysian market. Therefore, this wide discussion will benefit financial practitioners and institutions in financial practices. In addition, this study surely contribute some additional insights towards existing literature.

Intangibles, Intellectual Capital, and the Performance of Listed Non-Financial Services Firms in West Africa: A Cross-Country Analysis

This study aims to examine the impact of intellectual capital and intangible value on the performance of listed non-financial firms in West Africa. The study used the Value Added Intellectual Coefficient (VAIC™) to measure intellectual capital performance (with components as ICE—Intellectual Capital Efficiency an additive measure of the next two metrics, HCE—Human Capital Efficiency, SCE—Structural Capital Efficiency and CEE—Capital Employed Efficiency), financial ratios to measure intangible value and return on assets to measure performance while controlling for firm-level and macroeconomic variables. Using the panel-corrected standard error regression on 59 firms operating from 2007 to 2018, the study found that VAIC, ICE, HCE and SCE measures of intellectual capital are the pièce de résistance that drive the performance of firms. It is found that the relationship is curvilinear taking the shape of an inverted U. CEE does not drive the performance of firms, and asset tangibility inhibits performance but the investment in intangible fixed assets has a positive insignificant effect on performance. Firm size has a positive impact while financial leverage has a negative impact on performance. Human development does not drive performance but foreign direct investment and economic development do. There are country-specific insights where in Ghana intellectual capital and intangible value have a very strong positive effect on performance, followed by a relatively high impact in Cote D’Ivoire while there is a weak effect in the Nigerian context. The study also explores the effect of other variables such as firm size, financial leverage, human development, foreign direct investment and economic development. The findings are useful for policy, accounting, finance, economic and human resource practitioners as well as, for the academic community.

The role of cognitive heuristic-driven biases in investment management activities and market efficiency: a research synthesis

PurposeThis article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management activities and market efficiency. It also includes some of the research work on the origins and foundations of behavioral finance, and how this has grown substantially to become an established and particular subject of study in its own right. The study also aims to provide future direction to the researchers working in this field.Design/methodology/approachFor doing research synthesis, a systematic literature review (SLR) approach was applied considering research studies published within the time period, i.e. 1970–2021. This study attempted to accomplish a critical review of 176 studies out of 256 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioral finance domain-related explicitly to cognitive heuristic-driven biases and their effect on investment management activities and market efficiency as well as on the origins and foundations of behavioral finance.FindingsThis review reveals that investors often use cognitive heuristics to reduce the risk of losses in uncertain situations, but that leads to errors in judgment; as a result, investors make irrational decisions, which may cause the market to overreact or underreact – in both situations, the market becomes inefficient. Overall, the literature demonstrates that there is currently no consensus on the usefulness of cognitive heuristics in the context of investment management activities and market efficiency. Therefore, a lack of consensus about this topic suggests that further studies may bring relevant contributions to the literature. Based on the gaps analysis, three major categories of gaps, namely theoretical and methodological gaps, and contextual gaps, are found, where research is needed.Practical implicationsThe skillful understanding and knowledge of the cognitive heuristic-driven biases will help the investors, financial institutions and policymakers to overcome the adverse effect of these behavioral biases in the stock market. This article provides a detailed explanation of cognitive heuristic-driven biases and their influence on investment management activities and market efficiency, which could be very useful for finance practitioners, such as an investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making their financial management strategies.Originality/valueCurrently, no recent study exists, which reviews and evaluates the empirical research on cognitive heuristic-driven biases displayed by investors. The current study is original in discussing the role of cognitive heuristic-driven biases in investment management activities and market efficiency as well as the history and foundations of behavioral finance by means of research synthesis. This paper is useful to researchers, academicians, policymakers and those working in the area of behavioral finance in understanding the role that cognitive heuristic plays in investment management activities and market efficiency.

Investors Meet Dynamic Strategy

Professor Ghemawat argues that commitment is key to gaining a competitive advantage, but can leave a firm vulnerable to rapid and disruptive changes. He and other leading strategy scholars explore the intricacies of developing and sustaining a dynamic competitive advantage. Yet, economists argues that most firms eventually fail to maintain competitiveness. Functionally efficient financial markets, by capitalizing innovative entrants and culling uncompetitive firms, sustain economy-level prosperity. Ghemawat (1991) highlights this tension: firm-level competitiveness can give investors high returns for years, while returns regress towards the mean. Applying this insight to well-documented historical episodes of rapid innovation in various industries, we show that leading U.S. firms in 1920s acquired durable competitive advantages, as did many in the 1960s, but that later entrants often felled early leaders in the 1990s IT boom, consistent with intensified creative destruction. Still, even these shorter-term winners paid well above average cumulative returns. Strategy research that could predict the durability of leading firms’ competitive advantages through an era of rapid innovation would have tremendous value to practitioners in finance.

Beyond debt and equity

A recurring theme in academic, moralizing, and religious discourses laments the individual and societal perils of debt and praises equity. Contemporary Islamic banking and finance is one conspicuous example. This article recontextualizes this conversation by demonstrating that since the 1980s financial practitioners have been interpreting debt and equity as increasingly illegible cognitive schemas that nonetheless retain their historical and moral connotations. This line of argumentation suggests that normatively contrasting debt and equity is a red herring—a literary device and theoretical construct that misleads and distracts from the fundamental discussion of what constitutes salubrious or odious finance. Little will change in social life if we seek to replace “debt” with “equity.” Rather, since all financial instruments describe social relationships, our conversation should turn to normatively proscribing the kinds of financial instruments that match our normative values for contractual relationships.

Averaging financial ratios

Ratios represent a special kind of relation between two magnitudes, and computing the average of ratios is fairly common among academics and Finance practitioners. How should price-to-earnings (P/E) ratios be aggregated (averaged) at the portfolio level to provide a unified number? The arithmetic mean is the natural alternative. However, in case of financial ratios, it is generally accepted that the much less familiar harmonic mean may be more valuable, because it solves the upward bias encountered when using arithmetic mean. However, and to the best of our knowledge, there is no statistical evidence to show the superiority of the harmonic mean when computing the average of ratios. In this paper, by bootstrapping P/E ratios and earnings yield of companies listed in eight common stock indices, we compare the traditional averages and it is shown that geometric, not the harmonic average, as it is commonly accepted, is more suitable to average the ratios.

Predatory trading: ethics judgments, legality judgments and investment intentions

PurposePredatory trading is a stock market trading technique in which certain market participants exploit information about other market participants' need to trade. Predatory trading often harms others. Hence, this paper examines the determinants and effects of financial practitioners' and lay people's judgments of predatory trading. Specifically, it investigates how the public availability and reliability of the exploited information affect their ethics and legality judgments and how the latter influence their behavioral intentions and regulation support.Design/methodology/approachThe authors conducted two scenario judgment studies. In the first study, participants were financial practitioners, and in the second – lay people.FindingsPractitioners often judge predatory trading to be ethical. Practitioners and lay people incorporate in their ethics and legality judgments the public availability of the exploited information but tend to discount the legal reliability criterion. Lay people justify their ethics judgments using harm, legal or profit maximization principles. Practitioners' intentions to engage in predatory trading and lay people's intentions to let predatory fund managers invest their money depend on their judgments, which influence their regulation support.Originality/valueThis paper is the first to explore people's judgments of predatory trading. It highlights that despite the harm that predatory trading involves, practitioners often judge it to be ethical. Although law tends to lag behind financial innovation, people base their judgments and hence also behavioral intentions on their interpretation of the regulation. Hence, it reveals a dark aspect of the relationship between ethics and legality judgments.

How Executive Functioning and Financial Self-efficacy Predict Subjective Financial Well-Being via Positive Financial Behaviors

Financial well-being is a desirable state as it benefits individuals, families, organizations, and society, and these benefits reach beyond the financial domain. We assessed financial well-being as two components (current financial stress and expected future financial security) and used data from a representative sample of adults in the United Kingdom (n = 411). Our study provides novel insights based on preregistered hypotheses, method, and analysis plan on the Open Science Framework. We hypothesized that both executive functioning and financial self-efficacy are positively related to financial well-being via positive financial behaviors. We also hypothesized that executive functioning moderated the indirect relation of financial self-efficacy with financial well-being, and that financial self-efficacy moderated the indirect relation of executive functioning with financial well-being. As predicted, results showed that financial self-efficacy was strongly positively related to financial well-being via positive financial behaviors. Our results did not show that executive functioning was related to financial well-being via positive financial behaviors, nor that executive functioning or financial self-efficacy operated as moderators. This study provides possible strategies for financial practitioners and service providers, among others, to help individuals and families better their financial behaviors and their financial well-being.

Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation

The proliferation of a digital transformation area is inspiring researchers and practitioners in finance to embrace emerging innovative fintech development (i.e., finance + technology). In this study, we propose a field-aware attentive neural factorization machine (FAFM) model for large-scale data-driven company investment valuation. The proposed FAFM model utilizes the advantage of factorization machine (FM) to efficiently capture nonlinear feature interactions in a sparse dataset. We additionally consider field heterogeneity among features with fuzzy mutual information and develop an attention neural network to learn predictive strengths of pair-wise feature interactions. FAFM contributes to the literature by overcoming the limitation of FM that ignores field heterogeneity by factorizing pair-wise feature interactions with same weight. Further more, FAFM learns the prediction strengths in a stratified manner by using the attention deep learning mechanism, which demonstrates more structured control ability and allows for more leverage in tweaking the interactions in the feature-wise level. Experiments are conducted on a unique real dataset set consisting of 3,500 listed companies in the Chinese market with features from eight fields: demographics, annual reports, stock financial disclosure, land use, intellectual property, tax, bond financing, and certification. Results showed the superiority of FAFM on prediction accuracy and model interpretability over existing baselines. Our study provides a useful tool for company investment valuation that can not only generate accurate investment valuations but also provide interpretations of both individual features and their pair-wise interactions effects, thereby allowing investors better investment decisions.

Solving Parametric Volterra Integral Equation from Distributed-Order Rough Heston Model and Option Pricing

The rough Heston model has recently attracted the attention of many financial practitioners and researchers because it maintains the basic structure of the classic Heston model and has an advantage in describing the microstructure foundation of the market. In this paper, we study the distributed-order rough Heston model with an exponential tempered factor, and from the characteristic function of log-price in this model, we obtain a nonlinear parametric Volterra integral equation. Finally, the Fourier-cosine methods are combined with the Adams methods to price the option.

Internet Financial Data Security and Economic Risk Prevention for Android Application Privacy Leakage Detection.

The rapid development of the Internet has brought great convenience to our lives, but it has also brought many problems. Due to the virtual nature of the Internet, many criminals conduct illegal and criminal activities in the virtual world. In the Internet, ordinary users account for the vast majority of Internet users, but at the same time, the information of ordinary users is also the easiest to steal, and malicious behaviors of stealing information of ordinary users continue to occur. Android system and iOS system are the two most common systems in the current smart phone system market. In the face of the current Internet chaos, both systems have exposed problems to varying degrees, especially the Android system. In order to protect the privacy of users, researchers have also begun to focus on the privacy protection of the Android system. Today, with the rapid development of mobile payments, the privacy of mobile phones is closely integrated with the security of users' property, and the resolution of privacy issues cannot be delayed. Now that the development of the financial industry has developed into the Internet, the Internet has provided a new place for financial development, but it also faces many risks. This requires Internet finance practitioners to formulate corresponding security protection systems based on the characteristics of the Internet. Starting from big data and based on the characteristics of Internet finance, this paper designs a data-centric Internet financial risk early warning system. The existence of this system can analyze the possible risks of Internet finance from the perspective of big data, enabling enterprises to prepare in advance, and effectively reducing the losses in the development of Internet finance.

Private Capital to Improve Nature-Based Solutions for Coastal Protection: Time for a Boost

Coastal protection, an important coral reef ecosystem service, is threatened by increasing coral mortality, exacerbated by global climate change. Nature-based solutions in the form of coral restoration, while not perfect, can assist in rebuilding reef structure and improving the flow of the service for some sites. With a financing gap existing between what is required for conservation and what is being accessed, private investors should be playing a larger role in such restoration activities. Especially so as coastal hoteliers in particular, benefit from stable beaches and also have additional income generating potential with healthy reefs. Blended finance solutions in particular, are especially suited to restoration that incorporates substrate addition, while payments for ecosystem services are more suited to coral gardening. Conservation and finance practitioners must engage further and understand each other’s worlds, in order for these private sources to be effectively sourced and utilized.

Institutional investor attention and stock market volatility and liquidity: international evidence

ABSTRACT In this paper, we examine the influence of the daily institutional investor attention to particular stocks on stock volatility and liquidity. The institutional investor attention is measured from the number of times that users of Bloomberg terminal, who are mostly institutional investors, search for or read articles on a specific stock. Relying on a large international dataset of approximately a million daily observations over the period 2011–2020 from nine countries (Canada, France, Germany, Japan, Russia, South Korea, Switzerland, the UK, and the US), we find that this recent measure of institutional investor attention has a strong positive effect on stock volatility and liquidity. Confirmed by a battery of robustness tests, our findings suggest that this continuous barometer of attention by institutional investors can be used by financial practitioners to predict future stock volatility and liquidity.

THE ESSENCE AND CONTENT OF PUBLIC FINANCIAL CONTROL

The article examines the theoretical and legal basis of public financial control, the concepts and essence of financial control, its content and types, the views of scholars in the field of financial law. The theoretical views and recommendations studied and discussed may be of interest and useful to financial scholars and practitioners.

Poverty Alleviation Strategies Through Sharia Microfinance Institutions Politico-Economics Study With Tawhidi Approach

This research aims to formulate a poverty alleviation strategy in Indonesia by empowering Islamic Microfinance Institutions (IMFI) based on political-economic studies. The Indonesian political-economic system is stipulated in Article 33 of the 1945 Constitution and its Explanatory Paragraph, making the people the center of attention for national economic development. The research uses the Analytic Network Process (ANP) method developed by Saaty. The ANP construction uses a model consisting of 3 main clusters, namely Problems, Solutions, and Strategies. The qualitative-quantitative assessment data is determined based on the Saaty Scale obtained through an in-depth interview of 9 expert respondents and practitioners in Islamic economics and finance. The results show that poverty alleviation strategies need to be carried out through cultural and structural approaches. The cultural approach includes revitalizing the concept of the people's economy as stated in the 1945 Constitution, campaigning for using the domestic product, socialization to increase literacy about waqf and zakat, improving the practices of bureaucratic ethics, and strengthening the values of togetherness and anti-monopoly. The strategy at structural approach is carried out by implementing policies to enhance the role of cooperatives in the national economy; increasing the education budget for the poor and needy; increased coordination between authorities; bureaucratic reform; and honest and fair law enforcement against corruption. For the micro-level at the LKMS institution, the cultural approach is carried out by strengthening literacy on Islamic financial instruments, increasing financial inclusion programs, social inclusion programs, and strengthening methods for obtaining funding sources from Islamic financial instruments. On the structural side, it is necessary to improve the professionalism and capability of supporting LKMS, institutional strengthening, internal control and supervision system, improvement of supporting infrastructure, and enhancing service convenience.

Multiscale Tail Risk Connectedness of Global Stock Markets: A LASSO‐Based Network Topology Approach

Due to the advent of deglobalization and regional integration, this article aims to adopt LASSO‐based network connectedness to estimate the multiscale tail risk spillover effects of global stock markets. The results show that tail risk varies across frequencies and shocks. In static analysis, the risk is centered mostly on the developed European and North American markets at a low frequency (long term), and regionalization is imposed on the moderate frequency (midterm). Moreover, emerging markets could be sources of risk spillover, especially at the highest frequency (short term) where there is no absolute risk center. In dynamic analysis, we use rolling window estimation and find that different frequencies identify distinct episodes of shocks, which provides us with the reason for the diverse risk centers at different time scales in static analysis. Our findings provide heterogeneous financial practitioners, regulators, and investors with diverse characteristics of stock markets under multiple time horizons and help them operate their own trading strategies.