Degree Of Investors Research Articles

Market ranking of successful digital business models

The transition from industrial to service economy resulted in a wider adoption of asset-light strategies and technology-enabled business models. In the last ten years, digital companies have ascended to the top of the most valuable firms lists. Various aspects of digital business models have been exhaustively discussed in management literature, but their economic dimension remains under-researched.We analyze how strategy and business model add value and propose using historical volatility of valuation multiples for ranking companies by the degree of investors’ understanding of a firm’s strategy and business model in addition to ratings of companies by market capitalization. This ranking, in our view, is particularly helpful in analyzing business models of digital companies where most of the value is in intangible assets and economic goodwill. We also propose a way of expanding this research topic in the future.

Choice between IEO and ICO: Speed vs. Liquidity vs. Risk

This paper analyzes a financing problem for an innovative firm that is considering launching a web-based platform. The model developed in the paper is the first one that analyzes an entrepreneur’s choice between initial exchange offering (IEO) and initial coin offering (ICO). Compared to ICO, under IEO the firm is subject to screening by an exchange that reduces the risk of investment in tokens; also the firm receives access to a larger set of potential investors; finally tokens become listed on an exchange faster. The paper argues that IEO is a better option for the firm if: (1) the investment size is relatively large; (2) the extent of moral hazard problems faced by the firm is relatively large; (3) the degree of investors’ impatience is relatively small. Furthermore, a non-linear relationship between firm quality and its financing choice is found. Most of these predictions are new and have not been tested so far.

코인 몰입 척도 개발 및 타당화 연구

The purpose of this study was to develop and validate the Coin Engagement Scale (CES) which measures the degree of investors' engagement to coin investment. For this purpose, the concept was set based on open-ended question responses for 219 people with coin investment experience and the literature review of behavioral addiction and stock investment, and a total of 58 preliminary questions were selected through content validation. 191 coin investors were collected to select the final items, and exploratory factor analysis and reliability analysis were conducted. As a result, a total of 23 items were extracted with the structure of 4 factors (Dysfunctional aspects: Negative emotions, Daily living disturbance, Over engagement; Functional aspects: Positive function). In addition, confirmatory factor analysis and validity analysis were conducted, as a result, it was supported that the CES was a reliable and valid scale. These results support that the CES can serve as a tool for multidimensional measurements of the coin investor's engagement. This study could be provided as an important foundation for the study of coin investment and behavioral addiction.

A novel fuzzy dominant goal programming for portfolio selection with systematic risk and non-systematic risk.

In this paper, we consider a fuzzy portfolio selection problem with systematic risk and non-systematic risk simultaneously. These two kinds of risks are measured by beta coefficient and random error variance obtained by Sharp Single Index Model. The total risk as the objective of portfolio decision is obtained by weighting the two kinds of risk. Among them, the weight of systematic risk lambda is regarded as the degree of investors' attention to system risk in economic sense. In addition, the fuzzy return and the degree of diversification are measured by triangular fuzzy number and entropy, respectively. And they are also considered the goal of investment decisions. Hence, a tri-objective portfolio is proposed in this paper. For the fuzzy objectives in the model, a goal programming method based on fuzzy dominance is proposed, which can help investors better capture the ideal point of fuzzy returns according to their risk preference. Finally, combined with the systematic impact of COVID-19 on the financial market, we make an empirical analysis based on our proposed model. The results show that the total risk will be on the high side when lambda value is too large or too small. That means paying too much or little attention to the systematic risk will lead investors to bear more risk. In addition, when investors ignore the systematic risk; that is, the lambda value is low, and investors will concentrate their funds in the same industry.

Research on the Influence of Heterogeneous Beliefs and Media Reports on Investor Behavior from the Perspective of Informationization

Based on the background of informatization, the development and progress of science and technology has brought an immeasurable impact on the securities market. Therefore, this article uses python technology to crawl the number of individual stock news and study the influence of media attention on investor behavior under the constraint of heterogeneous beliefs. It is found that there is a significant positive correlation between the degree of media attention and the degree of investors’ heterogeneous beliefs, and investors are more likely to choose stocks that are frequently reported by the media; further research has found that media reports intensify the degree of investor’s heterogeneous beliefs, leading to stock transactions. The amount has risen abnormally. Therefore, while enjoying informatization, it is necessary to guard against the risks it bring.

FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, S&P 500, HSI, SSE 50, and CSI 300; (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link https://github.com/AI4Finance-LLC/FinRL-Library.

Hedging Demand in Long-Term Asset Allocation with an Application to Carry Trade Strategies

Abstract We derive a closed-form expression for the mean and marginal hedging demand on risky assets in long-term asset allocation problems for individuals with constant relative risk aversion preferences. Our parametric portfolio policy rule accommodates an arbitrarily large number of state variables for predicting the state of nature and number of assets in the portfolio. The closed-form expression for the hedging demand is exact under polynomial specifications of the portfolio policy rule and a suitable approximation for unknown smooth parametric portfolio policy rules using Taylor expansions. The hedging demand on risky assets depends positively on the predictability of the risky asset and the persistence of the predictors, and negatively on the degree of investor’s relative risk aversion. We illustrate these insights empirically for a basket of currencies by showing the outperformance of rebalancing carry trade strategies over different investment horizons against a short-term (myopic) portfolio.

R2 and the corporate signaling effect

AbstractIf corporate announcements provide additional signals about firms' future prospects, the degree of investors' dependency on these news should vary with the relative importance of firm‐specific information on such publicly traded firms. We show that price, volume and volatility reactions to dividend change announcements are significantly stronger for less synchronized firms (e.g., low R2 stocks). This indicates that lower R2 stocks are less informative and thus more surprises on firm‐specific news are experienced. These findings are particularly strong for dividend decrease announcements. We also show that signals about firms' earnings prospects from dividend decrease announcements are more reliable among these companies.

Trust in Financial Markets: the Role of the Human Element

Purpose – This paper suggests that the human element is key when it comes to investors’ trust in financial markets. Ignoring it may jeopardise the effectiveness of the recent regulatory growth. The study takes a demand-based perspective, assuming the relationship between financial advisors and investors is based upon trust, and it analyses the conditions that may lead to the existence (or not) of trust. Design/methodology/approach – Using a fuzzy-set qualitative comparative analysis (fsQCA) of data collected from 366 questionnaires, we are able to test, in a qualitative non-parametric way, the conditional arguments that may lead investors to trust (or not) their financial advisor. Findings – The results show that trust conditions differ, depending on the degree of investors’ participation in financial markets. The way investors with a basic relationship with financial markets perceive the behaviour of their financial advisors is key in establishing (or not) their trust. For investors with a more advanced relationship with the market, trust (or its absence) also depends on investors’ characteristics. In particular, their financial literacy plays a more-than-negligible role. The joint analysis of the conditions leading to trust and its absence highlights the robustness of our findings. Originality/value – By understanding the conditions that establish trust, financial institutions can design strategies to strengthen the level of investors’ confidence in their services, improving the relationship between market players, and increasing business. From the supervisory authorities’ point of view, the approval of a code of conduct for financial advisors, taking into account our results, can help improve the overall trust in financial markets. To the best of our knowledge, this study is the first to apply Butler’s (1991) psychometric scale and the fsQCA methodology to study investors’ trust in financial advisors.

Assessment of Asymmetric Information Cost in Indian Stock Market: A Sectoral Approach

Bid-ask spread, along with profit, also encompass the impact of asymmetric information cost and order processing cost. Asymmetric information influences stock prices with varying degree of investors’ perception. Estimation of asymmetric information cost and its determinants have been explored significantly under low-frequency trading. The literature hardly attempts to study asymmetric information cost under high-frequency trading (HFT). Asymmetric information cost significantly influences bid-ask spread, and hence the nature of its impact under different market conditions needs to be analyzed under HFT. The study attempts to estimate asymmetric information cost in HFT and analyze its determinants under different industry sectors and market conditions. The study followed Affleck-Graves et al. (1994 , The Journal of Finance, 49(4), 1471–1488) model to estimate the asymmetric information cost using 5 minutes interval data for a period of 82 trading days. Information gets reflected in equity through the movement in price, variation in trading volume, and return volatility. The study has found share price, traded volume, return volatility and trading frequency as the major determinants of asymmetric information cost in different market conditions. The findings of the study have significant implications for market microstructure for trading, lowering information asymmetry in market and enhancing market quality.

Choice Between IEO and ICO: Speed vs. Liquidity vs. Risk

This paper analyzes a financing problem for an innovative firm that is considering launching a web-based platform. Our model is the first one that analyzes an entrepreneur's choice between initial exchange offering (IEO) and initial coin offering (ICO). Compared to ICO, under IEO the firm is subject to screening by an exchange that reduces the risk of investment in tokens; also the firm gets access to a larger set of potential investors; finally tokens become listed on an exchange faster. We argue that IEO is a better option for the firm if: 1) the investment size is relatively large; 2) the extent of moral hazard problems faced by the firm is relatively large; 3) the degree of investors' impatience is relatively small. We aslo find a non-linear relationship between firm quality and its financing choice. Most of these predictions are new and have not been tested sofar.

FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, SP (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link this https URL.

Culture and the disposition effect

We study the relationship between national culture and the disposition effect by investigating international differences in the degree of investors’ disposition effect. We utilize brokerage data of 387,993 traders from 83 countries and find great variation in the degree of the disposition effect across the world. We find that the cultural dimensions of long-term orientation and indulgence help to explain why certain nationalities are more prone to the disposition effect. We also find support on an international level for the role of age and gender in explaining the disposition effect.

Hedging Demand in Long-Term Asset Allocation With an Application to Carry Trade Strategies

We derive a closed-form expression for the mean and marginal hedging demand on risky assets in long-term asset allocation problems for individuals with CRRA preferences. Our parametric portfolio policy rule accommodates an arbitrarily large number of state variables for predicting the state of nature, and number of assets in the portfolio. The closed-form expression for the hedging demand is exact under polynomial specifications of the portfolio policy rule and a suitable approximation for unknown smooth parametric portfolio policy rules using Taylor expansions. The hedging demand on risky assets depends positively on the predictability of the risky asset and the persistence of the predictors, and negatively on the degree of investor's relative risk aversion. We illustrate these insights empirically for a basket of currencies by showing the outperformance of rebalancing carry trade strategies over different investment horizons against a short-term (myopic) portfolio.

Asset allocation with multiple analysts’ views: a robust approach

Retail investors often make decisions based on professional analysts’ investment recommendations. Although these recommendations contain up-to-date financial information, they are usually expressed in sophisticated but vague forms. In addition, the quality differs from analyst to analyst and recommendations may even be mutually conflicting. This paper addresses these issues by extending the Black–Litterman (BL) method and developing a multi-analyst portfolio selection method, balanced against any over-optimistic forecasts. Our methods accommodate analysts’ ambiguous investment recommendations and the heterogeneity of data from disparate sources. We prove the validity of our model, using an empirical analysis of around 1000 daily financial newsletters collected from two top 10 Taiwanese brokerage firms over a 2-year period. We conclude that analysts’ views contribute to the investment allocation process and enhance the portfolio performance. We confirm that the degree of investors’ confidence in these views influences the portfolio outcome, thus extending the idea of the BL model and improving the practicality of robust optimisation.

Speculative trading of electricity contracts in interconnected locations

We derive an investor's optimal trading strategy of electricity contracts traded in two locations joined by an interconnector. The investor employs a price model which includes the impact of her own trades. The investor's trades have a permanent impact on prices because her trading activity affects the demand of contracts in both locations. Additionally, the investor receives prices which are worse than the quoted prices as a result of the elasticity of liquidity provision of contracts. Furthermore, the investor is ambiguity averse, so she acknowledges that her model of prices may be misspecified and considers other models when devising her trading strategy. We show that as the investor's degree of ambiguity aversion increases, her trading activity decreases in both locations, and thus her inventory exposure also decreases. Finally, we show that there is a range of ambiguity aversion parameters where the Sharpe ratio of the trading strategy increases when ambiguity aversion increases.

Convex risk measures based on generalized lower deviation and their applications

Considering the implementability and the properties that a reasonable and realistic risk measure should satisfy, we propose a new class of risk measures based on generalized lower deviation with respect to a chosen benchmark. Besides convexity and monotonicity, our new risk measure can reflect the investor's degree of risk aversion as well as the fat-tail phenomenon of the loss distribution with the help of different benchmarks and weighted functions. Based on the new risk measure, we establish a realistic portfolio selection model taking market frictions into account. To examine the influence of the benchmarks and weighted functions on the optimal portfolio and its performance, we carry out a series of empirical studies in Chinese stock markets. Our in-sample and out-of-sample results show that the new risk measure and the corresponding portfolio selection model can reflect the investor's risk averse attitude and the impact of different trading constraints. Most importantly, with the new risk measure we can obtain an optimal portfolio which is more robust and superior to the optimal portfolios obtained with the traditional expected shortfall risk measures.

IPO Price, Heterogeneous Priors and Gradual Information Flows

This paper attempts to develop a theoretical framework that builds on heterogeneous beliefs to explain the financial anomalies related to IPO stocks. In particular, we develop a dynamic analysis framework to study the valuation of IPO price and the short-term probability of falling below IPO price based on perspectives of investors' heterogeneous priors and gradual information flow. Our study shows that the valuation of IPO price increases as the degree of heterogeneity due to investors' heterogeneous priors increases. Moreover, the short-term probability of falling below IPO price increases as the degree of investors' cognitive biases caused by gradual information flow increases.

Economic benefits of using realized covariance forecasts in risk-based portfolios

ABSTRACTThis article examines the economic benefit of using the realized covariance matrix forecasts, for constructing the risk-based portfolios. We use the two-scale realized covariance estimator (TSC), the jump robust two-scale realized covariance estimator (RTSC) and the realized bipower covariance estimator (BPC), to forecast the daily realized covariance matrix. Using these covariance matrix forecasts, we implement three risk-based portfolios: the global minimum variance portfolio, the equal risk contribution portfolio and the most diversified portfolio. There is evidence that the portfolio performance improves by using TSC or RTSC estimators as compared to the daily-returns-based estimator. The performance gains are robust to the choice of risk-based portfolio strategy, the degree of investor’s relative risk-aversion, the market conditions and the choice of time intervals.

Sovereign Credit Ratings and the Transnationalization of Finance – Evidence from a Gravity Model of Portfolio Investment

Abstract It is a matter of debate in how far credit ratings contribute to allocative efficiency or to excessive volatility of asset prices and cross-border capital flows. Yet it is generally taken for granted that ratings play a significant role in the transnationalization of financial relations. This paper tests that hypothesis with data on sovereign credit ratings and foreign portfolio investment. A rating-related gravity model of finance is derived from the choicetheoretical framework of Okawa and van Wincoop (Gravity in International Finance, 2012) and estimated in three stages. At the first stage, the authors find that the introduction and evolution of sovereign ratings since the 1970s has affected inward portfolio investment in host countries. At the second stage, they examine to which extent sovereign ratings help to predict the degree of investors’ home bias, and whether they can account for the divergent dynamics before and after the global financial crisis. At the third stage, the authors look at the explanatory content of ratings for the determination of the size of bilateral portfolio investment. Evidence for a significant role of sovereign ratings is found at all three stages.